Are Pixel-Wise Metrics Reliable for Computerized Tomography Reconstruction?
Authors: Tianyu Lin
Xinran Li
Chuntung Zhuang
Qi Chen
Yuanhao Cai
Kai Ding
Alan Yuille
Zongwei Zhou
Venue: NeurIPS 2025 poster ()
Keywords: Reconstruction
Diffusion Model
Primary area: deep_learning
TL;DR: CARE: Completeness Aware Reconstruction Enhancement

Abstract: Widely adopted evaluation metrics for sparse-view CT reconstruction, such as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio, prioritize pixel-wise fidelity but often fail to capture the completeness of critical anatomical structures, particularly small or thin regions that are easily missed. To address this limitation, we propose a suite of novel anatomy-aware evaluation metrics designed to assess structural completeness across anatomical structures, including large organs, small organs, intestines, and vessels. Building on these metrics, we introduce CARE, a Completeness-Aware Reconstruction Enhancement framework that incorporates structural penalties during training to encourage anatomical preservation of significant structures. CARE is model-agnostic and can be seamlessly integrated into analytical, implicit, and generative methods. 
When applied to these methods, CARE substantially improves structural completeness in CT reconstructions, achieving up to **32%** improvement for large organs, **22%** for small organs, **40%** for intestines, and **36%** for vessels.

Are Pixel-Wise Metrics Reliable for Sparse- Oparse-View Computed Tomography Reconstruction?
Tianyu Lin'
Xinran Li
Chuntung Zhuang'
Qi Chen' Zongwei Zhou
Yuanhao Cail
Kai Ding
Alan L. Yuille'
Johns Hopkins University
Yale University
Johns Hopkins Medicine
Code, dataset, and models: https//giihubbccomm/iiiio/////.a
NeRF
NeRF +CARE
Ground Truth
RIGS cAAR
R-GS
Gall bladder
Spleen
Celiac trunk
Aorta
CARE
CARE
Fosticava
Left Kidney
80
large organs
small organs
13.77
80
Intestine
+1222
vessel
60
30.02
+1.00
+35.75
40
40
+20
50
20
20
20-
20
InTome
NeRF
TensoRF
R2-GS
Tomo
NeRF
TonsoRF
RI-GS
InTom
NeRF
RonsoRF
R2-55
Tomoo
NeRF
TensoRF
R4-6
Figure/ CARE Improves Structural Completeness in Sparse- Sparse-View CT Reconstruction. Top: Qualitative comparison of reconstructed CT scans from NeRF and R²-GS, with and without our proposed CARE on clinically important structures. Bottom: Quantitative gains in structural completeness across four reconstruction methods. CARE consistently improves results, confirming its -gnnsstgi and plug-and-pl and-play nature.
Abstract
Widely adopted evaluation metrics for sparse-view CT reconstruction--uchh as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio-prioritte pixel-wise fidelity but often fail to capture the completeness of critical anatomical structures, particularly small or thin regions that are easily missed. To address this limitation, we propose a suite of novel anatomy-aware evaluation metrics designed
structural completeness across anatomical structures, including large organs, small organs, intestines, and vessels. Building on these metrics, we introduce CARE, Completeness-Awazt Reconstruction Enhancement frame- work that incorporates structural penalties during training to encourage anatomical preservation of significant structures. CARE is model-agnostic and can be seam- lessly integrated into analytical, implicit, and generative methods. When applied to these methods, CARE substantially improves structural completeness CT reconstructions, achieving up to +32 % improvement for large organs, +22 % for small organs, +40% for intestines, and +36 for vessels.
*Correspondence Zongwei Zhou (1ZHOU82@JH.ED.)
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Introduction
Reducing radiation dose is a major concern in computed tomography (CT), where standard image acquisition typically requires 800 to 1500 X-ray projections [39, 17]. These high doses pose long- term health risks, especially for vulnerable patients such children, pregnant individuals, and those undergoing frequent imaging [64, 9, 29]. One promising research direction to lower radiation is sparse-view CT, which aims reconstruct high-quality images from significantly reduced number of projection fiews-often fewer than 50 views [63, 50, 59]. While this approach can dramatically lower radiation dose, it introduces significant challenges: reconstructions from limited data often lose fine anatomical details [30]. These details are critical for clinical applications such as surgical planning, radiotherapy, and longitudinal disease monitoring [58, 56]. This raises central research question: how can we ensure the completeness of clinically important anatomical structures in sparse-view CT reconstruction?
Over the past decade, researchers have reported steady improvements in sparse-view CT reconstruc- tion by optimizing pixel-wise metrics- c.g., Peak Signal-to-Noise Ratio (PSNR) [24] and Structural Similarity Index Measure (SSIM) [53]. However; higher PSNR or SSIM scores do not guarantee better clinical utility (analyzed in $3. These metrics compute global averages of per-voxel intensity differences, which means they are largely insensitive to the presence or absence small but clinically important anatomical structures. For example, missing the gall-bladder, adrenal glands, celiac artery. or segmental vein affects only a tiny fraction of the total scan volume-often less than 0.0001 %- and may change PSNR/SSIM by just the third or fourth decimal place. As result, the reconstructed CT scans that appear high-quality under PSNR/SSIM may still miss important anatomical structures and be unsuitable for clinical applications [15]. Our internal reader study at Johns Hopkins University, involving 21 board coard-certifiee radiologists, assessed CT reconstructions produced by tfateoof-the-at CT reconstruction methods. The results raised significant concerns, including missing anatomical structures, implausible ballucinations, and severe artifacts, suggesting growing disconnect between algorithmic novelty and clinical realism. This highlights pressing need for new evaluation metrics that can directly assess whether anatomical structures are preserved in sparse-view CT reconstruction.
To assess the anatomical structures in the reconstructed CT scans, conventional practice requires radiologists to manually inspect the scans-a process that is subjective, time-consuming, and im- practical to scale. Fortunately, recent advances in medical image segmentation, powered by strong model architectures [3, 27, 36, 37, 4] and large-scale annotated datasets [54, 32, 40, 31, 14], now enable AI models automatically segment multiple anatomical structures with high accuracy and consistency. These advances greatly reduce the need for manual inspection and motivate a key hypothesis: automated segmentation can serve as scalable metric to assess whether anatomical structures are preserved in reconstructed scans.
We introduce CARE, Completeness-Awwrt
Reconstruction Enhancement framework that defines new evaluation metrics-derivell from medical image segmentation-ant uses them to directly supervise diffusion models, enhancing the structural completeness of preexisting sparse-view CT reconstruction methods. Our contributions are:
1. Anatomy-Aware CT Reconstruction Metric:
We leverage nnU-Net (under Apache-2.0 license) [26] as an anatomy segmentator, trained on more than 3,000 voxel-wise annotated CT scans [33], to build suite of anatomy- metrics that measure the anatomical completeness of four clinically important categories of anatomical structures (detailed in Figure 2) in reconstructed CT scans.
2. Anatomy-Aware CT Reconstruction Framework: We go beyond evaluation by using the anatomy-aware metries as direct supervision. During training, CARE compares the segmentation output of the reconstructed CT scans with the segmentator's prediction and brokapros penalty whenever any anatomical structure is missing or incomplete. This structural penalty is fully differentiable and agnostic to the underlying forward model, allowing CARE to be applied effectively to a range of preexisting reconstruction methods, including Neural Radiance Fields (NeRF), Gaussian Splatting (GS), and their variants. Instead training a separate reconstruction model for each patient, using diffusion models to enhance the CT scans reconstructed by existing methods enables the model to learn anatomical priors from a large population and generalize across patients, thus making CARE aggnosic.

---

Extensive experiments show that applying CARE to preexisting sparse-viee view CT reconstruction methods produces striking gains (summarized in Figure 1: detailed in $4). Compared with nine preexisting methods, our CARE achieves up to 32% for large organs, +22% for small organs, +40% for intestinal structures, and +36 % for vascular structures. These gains are both statistically significant and clinically important, but worryingly, such improvements are entirely hidden when evaluated using widely adopted pixel-wise metrics like PSNR and SSIM (see Table 2). This highlights critical pitfall in current evaluation practices and raises concerns about their continued usage in clinical reconstruction studies.
2 Related Works
Anatomy-Aware Evaluation. Conventional image quality metrics, such as PSNR [24] and SSIM [53], focus primarily on pixel-wise fidelity but fail to reliably reflect clinical significance, particularly regarding the completeness of fine anatomical structures. This limitation is especially pronounced under challenging imaging conditions, including sparse-view or low-dose acquisitions. Despite recent efforts, research explicitly targeting anatomy-aware evaluation metrics remains scarce [49].
Beyond pixel-wise fidelity, several perceptual image quality metries have been proposed to better align with human visual judgment. The Fréchet Inception Distance (FID) [22] and Kernel Inception Distance (KID) [7] evaluate distributional similarity between sets of real and generated images using features from a pre-trained Inception network. Similarly, LPIPS [65] leverages deep features to measure perceptual dissimilarity between individual image pairs. GMSD [57] assesses quality based on gradient magnitude similarity, capturing structural distortions more effectively than PŠNR or SSIM. More recently, GLIPS [2] combines global and local perceptual cues evaluate photorealism in Al-generated images. While these metrics improve upon traditional pixel-wise measures in natural image domains, they remain agnostic to anatomical semantics and do not explicitly assess the presence, completeness, or geometric fidelity of clinically relevant structures in medical imaging.
CT Reconstruction Methods. Traditional reconstruction methods, such as Filtered Back Projection (FBP) [41] and iterative algorithms like Simultancous Algebraic Reconstruction Technique (SART) [1] and Adaptive Steepest Descent-Projection onto Convex Sets (ASD-POCS) [47], widely employed due to their simplicity and effectiveness. However, these methods exhibit significant quality degradation under sparse-view low-dose scenarios [16]. Early work by Zhang [64] and Chen [12] mitigated this issue by incorporating a prior CT scan as strong regularisation, enabling acceptable image quality with substantially fewer projections. To overcome the remaining limitations, recent advances have leveraged neural rendering methods, including NeRF [38], InTomo [60]. TensoRF [10], Neural Attenuation Fields (NAF) [62], and SAX-NeRF [8], to synthesize views and significantly improve 3D reconstruction fidelity. In parallel, Gaussian splatting methods such as R [61] have also emerged, demonstrating promising results in achieving high-quality reconstructions with enhanced anatomical details. However, their capability to accurately reconstruct anatomical structures remains unverified, primarily due the absence of suitable evaluation metrics.
Generative AI Models. Denoising Diffusion Probabilistic Models [23, 13, 34] have become strong alternatives to GANs [21] and VAEs [28] for high-fidelity image synthesis. In medicine, Stable- Diffusion-style implementations [51, 35] are already used to suppress CT noise and streak artefacts. Very recent work shows that diffusion priors can also tackle sparse-view CT, reporting state-of-the-an PSNR/SSIM with as few projections (50, 59, 48). Yet these methods still optimise only pixel-wise losses and may hallucinate or omit small organs and vessels. Our CARE closes this gap by adding an anatomy-ave aware segmentation loss to the diffusion objective, explicitly safeguarding clinically critical structures while preserving the low-dose advantage of sparse-view acquisition.
3 CARE: Completeness-Awae Reconstruction Enhancement
3.1 Motivation: Pitfalls of Conventional Pixel-Wise Metrics
PSNR and SSIM dominate sparse-view CT reconstruction benchmarks because they are easy to compute and correlate well with global visual fidelity on natural images. In the clinical setting, however, these per-voxel averages are blind to enrors that matter most. Figure 2 shows common situation that even though having reasonable pixel-wise metrics, reconstruction could still fail in anatomical preservation. The reason is straightforward: small organs, intestines and vessels occupy
3

---

A
B
[arge organs)
organs
adrenal gland (R)
Bre
galbbldder
falture success
pixel-wise metrics PSNR SSIM 92%
kdnny
kidney
GundEnum
gland (L)
anatomy-aware metrics
postcava
NSD (lege) 60%
renal vein
NSD (smal)) 20%
cIDice (intestine) 40%
(Rn
<iDice (vesse)) 30%
mesentent
Reconstruction
Ground Truth
[vessel]
iintestine
Figure 2: Pixel-wise Metries Overlook Structural Errors in the Focused Anatomical Structures. A. Example pixel-wise metrics' pitfall. Pixel-wise metrics are insensitive to anatomical preservation failure. B. The four types of organs evaluated by CARE.
far less than 0.01 of the volume, so their absence barely changes the third decimal place of SSIM and PSNR. Clinical evaluation, by contrast, hinges whether these structures are present at all. Our own experiments (detailed in $4.2) confirm the gap: classical analytical methods such as FDK and SART rank mid-pack pixel metrics but top anatomy- scores, whereas several neural renderers achieve the opposite pattern. These observations motivate a shift from intensity similarity towards metrics that directly measure anatomical completeness.
3.2 Anatomy-Aware CT Reconstruction Metries
We propose segmentation-basel anatomy-aware CT reconstruction metrics evaluate CT recon- struction quality. We utilize frozen multi-organ segmentation model to compute metrics for four different kinds of anatomical structures. For large and small organs, we use the NSD metric due to its sensitivity to boundary accuracy and tolerance for minor shape deviations, For ves- sels and intestines, we apply the clDice [46] met- ric, given its effectiveness capturing tubular structures and preserving topological connectiv- ity. Finally, we report these metrics separately for each anatomical category?.
CT-based
large organs
GT-based
small organs
80
40
20
Corr=0.99
20
com
Segmentator based
Segmentaton-based
80
GT-based
intestine
196-
GT-based
vessel
40
Corre0.77
0095
Anatomy Segmentator. great CT reconstruc- tion result should preserve the anatomical struc- tures of the original CT scans, thus having sim- ilar segmentation results from fixed segmen- tator between the original and reconstructed CT scans. Instead of relying on manually annotated ground-truth labels derive our matoomy-aaan metrics, we employ a frozen nnU-Net [26]- termed the anatomy segmentator clinically relevant fidelity. We choose nnU-Net framework for its self-configuring architecture and consistently state-of-the-arn segmentation performance across diverse medical-imaging tasks. was trained internally on the JHH dataset with 3,151 CT scans annotated over three years by team of radiologists, with all annotations independently reviewed by one of three senior radiologists not involved in the initial annotation process to ensure quality [31, 55]. The anatomy segmentator will be publicly released. Leveraging a segmentator to construct our anatomy-aware metrics enables effortless deployment across large-scale CT reconstruction evaluations, climinating the dependency
Segmentator.based
Segmentator-based
Figure 3: Correlation Between Ground Truth (GT) Based and Segmentator Based Anatomy- Aware Metries. High correlation scores indicate that the anatomy segmentator a strong substitute for human expert.
capture
Phhese anatomical categorics are divided based on clinical guidelines (see details $A.1) A private CT segmentation dataset from Johns Hopkins Hospital (see details in 1..).

---

on manual annotations. Figure 3 shows that the anatomy-aae aware metrics building on human annotated ground truth and segmentator's prediction have high correlation, further indicating the robustness of gegmentator-basel metrics. This further allows our anatomy-aware metries to be applied to CT scans in the absence of segmentation labels.
Normalized Surface Dice (NSD). Both large organs and small organs can be viewed as compact, surface-dominated objects. NSD evaluates their geometric agreement by asking what fraction of the predicted surface Sp and reference surface So lie within a tolerance band of width 7:
NSD, |x E Sp d(x, Sc) S r + 1x E Sa d(x,Sp) S |
(1)
SSp| + |Sal
NSD is scale-invariant since every surface element located farther than incurs the same penalty, For example, uniform mm displacement with T = mm therefore drives 2SD2mm to 0 regardless how big the organ is.
On the contrary, the widely used Dice similarity coefficient (DSC) is scale-variant. DSC measures 2|PPG] volumetric overlap via DSC(P, = |P|-|G| As a result, the sensitivity of DSC to the same boundary shift is diluted by organ size. The expected loss satisfies ADSC 6/(2Ret) with Reft = Vg. For instance, with a 50 mm-radius liver, a mm outer-shell error lowers DSC by merely 0.06 far too small to flag what clinicians deem major discrepancy.
By focusing on boundary proximity rather than voxel volume, NSD retains millimetre- sensitivity and offers a more faithful assessment of non-tubular organ segmentations than DSC.
Center-line Dice (eIDice). Vessels and intestine are elongated, branching and hollow. Their clinical relevance stems from connectivity: a single broken segment in the superior- compromise whole-organ perfusion. ciDice operates skeletonised centre-lines and is sensitive to both continuity and topology. penalising even thin discontinuities that DSC or NSD might overlook. However, the ground-truth annotations for intestinal structures in most public datasets are often noisy or incomplete [20], which likely depresses the intestine-metric correlation by about 0.77 shown in Figure 3. Curating higher-quality bowel annotations and re-evaaug cIDice under those labels wabels-will therefore be an important direction for future work.
artery may
Our matoomy-awar formulation thus matches the geometric character of each organ class: surface-sensitive NSD for compact organs, topology-sensitive elDice for slender, branching tubes. Together, they provide an automatic, quantitative, and clinically meaningful view through which to evaluate reconstruction algorithms.
3.3 Anatomy-Aware CT Reconstruction Framework
We propose simple latent diffusion framework named CARE for CT enhancement, as shown in Figure 4. CARE starts by training an autoencoder and a latent diffusion model using a high-quality CT dataset, then integrating anatomy-guides supervision with additional pixel space constraints.
Preliminary. We begin by adapting KL-rrgularized variational autoencoder to CT domain with the JHH dataset. Let X denote an input CT image in pixel space. Note that to consider inter- frame consistency and retain the original architecture of the autoencoder network, we stack three adjacent CT slices to be the three channels of the input image, giving X E # x3 The encoder Eap maps X latent tensor
E (X) E hxx A paired decoder Da,, reconstructs the input as X = Dep (z). The resulting autoencoder model reconstructs scans faithfully yet produces well-behaved latents that remain fixed for all later stages.
Using this frozen autoencoder, every CT image is mapped to the latent space for the diffusion process. During training, a de-noising UNet sees the noisy latent of the CT image concatenated with a deterministic, encodes contrast phase information of the CT scan. Its sole objective is to predict the injected noise, conditioning on the degraded latent steers the learning process toward anatomically consistent de-noising. Therefore, the model acquires an internal ability to transform low-quality latents to high-quality, pructure-preservvie counterparts. The autoencoder and latent diffusion model build the bedrock of our CARE framework.
degradation of the original latent. The text embedding
5

---

frozen
trained
concat
encoder
diffusion
reconstruction
phase
segmentator
decoder
de-noising
UNet
Figure 4: CARE Framework. Given the frozen anatomy segmentator, autoencoder, and the pre- trained latent diffusion model, we then adapt the latent diffusion model real reconstructed CT scans. CARE can be integrated into any reconstruction method to perform its enhancement capability. The overall training is supervised by three loss terms: the noise-prediction loss Lns pixel-space reconstruction loss Lp, and matonny-uuidanee loss L..
CARE. With the pretrained latent diffusion model, we then adapt the de-noising UNet to real CT reconstructions by introducing anatomy- -ugdded supervision.
Each CT image X # x3 is first compressed by the frozen autoencoder into latent representa- tion through 2 = Eo, (X)= E Rhxaxe In the forward diffusion process, noisy sequence (= is obtained by
2, Vorz0 v1 Ore,,
C~ T (1,....7),
where T 1, 000, 2g - z and {a: |T= is fixed variance-preserving schedule.
For each training data pair, we take a ground-truth CT image Xgt and its sparse-view reconstruction counterpart Xree (e.g. FDK-50 views) and encode them as 2gt = Ee, (Xgt) and 2ree Eo, (e
The forward diffusion step is applied to Zgt to produce 2,, after which the input of the de-noising UNet is formed by concatenation with the actual reconstruction latent,
2 = Concat (eecat/ze,aa Zree] E R pxxxx2e
The de-noising UNet receives ž, the diffusion timestep I, and fixed text embedding Cphase of the contrast phase information, and predicts the noise residual = c L, Cphaso). Then the first supervision term of the de de-noising UNet is the standard Noise-predictiin loss La
C. = Ex,,.Xgr ||e es (Concatizz, Zree], t, Cphase) 1
(4)
C, focuses on accurate latent de-noising; 2ree supplies anatomy information and provides pro- structural cues during the de-noising process, thereby allowing the diffusion model to anchor the global CT shape and spatial layout while generating fine structural details.
To introduce anatomy-guides supervision, we augment the latent-space objective by incorporating two additional pixel space constraints. Specifically, after getting the predicted noise 0, we then reverse the diffusion process in Equation (2) estimate the input latent via
2p
(ze - VI 11-0-sh dxg6
(5)
Vac
Then, åo is decoded to the pixel space via the autoencoder. The overall fidelity in pixel space is supervised by LI regularization term named Pixel-space reconstruction loss Ep
Cp = Do (26) - Xgell1-
Most importantly, an anatomical completeness supervision is promoted with a segmentation guidance term from the anatomy segmentator 5(.) mentioned in $3.2, named Anatomy-guidance loss Ls
L. Logg (S(Do) S(Xg)))
(7)

---

where leyg denotes cross entropy loss function.
Overall, the training objective of CARE is the weighted of Noise-prediction loss Lu (Equation 4), Pixel-space reconstruction loss Lp (Equation 6), and canttmyygguidaee loss Lu (Equation 7),
LCARE =n Appp Xgh.s
(8)
where Ap = and A 0.001 the weights of Lp and L., respectively. The de-noising UNet receives noisy latents the ground-truth CT image and the concatenated latents of the reconstructed CT image. Meanwhile, adding image and anatomy supervision extends the single-term moise-prediction loss used in the latent diffusion model into a composite objective that jointly optimizes pixel-space reconstruction fidelity and anatomy- consistency.
During inference CARE starts from pure Gaussian noise 2T concatenated with a latent from an unseen reconstruction CT image, iteratively denoises to 20, and decodes it through Do, to deliver an anatomically complete CT image.
Experiments and Results
4.1
Pixel-Wise Metric Pitfalls
33
92
30
88
WISS
27
PSNR
Corr=0.16
Corr=0.30
80
21
anatomy- metrics
anatomy- metrics
76
80
100
20
80
100
Correlation Between Anatomy-Aware Metrics and Pixel-Wise Metrics. The solid red line a linear fit; dashed lines denote #l of the residuals. Pearson correlation coefficients (COrTSSiM = 0.16, CorresNR z 0.30) are low, indicating that better anatomical fidelity does not necessarily yield higher SSIM or PSNR.
Figure 5:
Figure demonstrates that our anatomy-ave aware metrics capture reconstruction quality beyond what pixel-wise criteria can reveal. The horizontal axis shows the average anatomy-aware metrics, com- puted as the mean of the four groups of anatomy-aware metrics (large organs, small organs, vessel, and intestine). The vertical proposed anatomy-aware metrics to possess more faithful organ and vessel geometry show only weak association with SSIM and PSNR, confirming that superior anatomical fidelity does not guarantee higher pixel-similarity scores.
show SSIM (left) and PSNR (right). Reconstructions judged by the
moderate anatomy-aware metric score achieved, additional structural refinement yields progressively smaller gains in SSIM, and even less in PSNR. This saturation effect indicates that pixel-wise metrics quickly reach ceiling, whereas the anatomy-ave aware metrics continue to differentiate models, underscoring the need for structure-centric assessment in clinical utility.
further trend is the flattening of the regression lines:
4.2 Benchmarking Anatomy-Aware Metrics
We evaluated nine state-of-the-an CT reconstruction methods methods-inccluine traditional methods (FDK, SART, ASD-POCS), neural rendering (InTomo, NeRF, TensoRF, SAX-NeRF), and Gaussian-splatting (R2-GS)-aa on our high-quality test set of 61 CT, which is by far the largest CT reconstruction benchmark dataset. Evaluation is conducted on both conventional pixel-wise metries (SSIM, PSNR) and the proposed anatomy-awart metrics. Results are summarized in Table 1.
Table shows that conventional pixel-wise scores can be overly optimistic: neural rendering methods such as In ToTomo, NeRF, and TensoRF report SSIM values above 0.83 and PSNR exceeding 24 dB, yet their anatomy-awer scores collapse- collapse-small saall-mmllllrrrrrr NSD approaches zero and vessel clDice rarely
7

---

Table 1: Benchmarking CT Reconstruction Methods. We evaluate preexisting CT reconstruction methods on high-quality CT scans using both pixel-wise metrics and our anatomy-aware metrics. We report the median and interquartile range (IQR) of these metrics. Cells marked in blue, where deeper color denotes a greater value. Anatomy-awal metrics reveal that poorly performing methods fail in anatomical preservation, especially in small organs, intestines, and vessels.
pixel-wise metric
anafomy-aware metric (ours)
method
SSIM
PSNR
NSD
NSD
elDice 4
elDice
InTomo [60]
82.7 (8.76
24.3 (23.1.52)))
2.5 (1.6,400
(.0,000
(30004)))))
(0.000.))
NeRF [38]
(84.4.89.0)
25.7 (24.2.7711)
(9.33.3)))3)
0.2 (0.0,411)
(96066))
34.5 (20.5.0)))
TensoRF [1]]
88.4 (87.0.9.7)
27.8 (26..28.9)
21.5.22
11.4 (0002887)
(20,433)))
47.2
R²-GS [61]
(91.9.9))))339)
28.6 (27.2.2))))
(55
53.0 (41.8,68.
54,5 (14.9.88.0)
69.7 (37.6.88))
NAF [62]
(9.1.91)
29.4 (28.8.006)
(76.4.87.7)
60.9 (51.3.72.0)
71.3 (61.3,79.0)
76.1 (67.2.828)
FDK [18]
92.4 (9....94)
29.5 (28.8,50.8)
63.96786887.7
63.3 (52.7.001)
70.1 (60.1880.8
77.4 (.9.))
SART [1]
(91.1.11)
29.4 (28 8,306)
84.7
62.3 (53.2.75.3)
71.1 (59.5,79.9) 9)
78.8 (4.448444)
ASD-POCS [47]
(9..941))))
29.5 (26.7.30.7)
87.9..77..88.5)
61.9 (5100,4.8)
72.0 (60.1.000)
75.8 (67.4.888))
SAX-NeRF [8]
93.7 (92.8,95.0)
29.7 (29.0.30.8)
943..8999999
65.8 (53.0,88.2)
71.5 (56.8.82.5)
78.8 (63.085.))
largo organs include liver, kidney, pancreas, and spleen. The resalts are given in NSD
smmall organs include gallbladder, adrenal gland, celinc trunk, and duodenum. The results are given in NSD (1)-
A intestine refers to colon and small intestine. The results are given in eiDice (t).
vessel include aoria, posicava, superior mesenteric artery, veins, and renal vein. The results given ciDice (1).
surpasses 35, revealing poor recovery of clinically critical structures. When evaluation pivots to anatomy fidelity, the methods ranking is effectively reshuffled: the traditional reconstruction baselines (FDK, SART, ASD-POCS) achieve the best or near-best performance across all four anatomical categories despite mediocre SSIM/PSNR, whereas high-SSIM models like R2-GS SAX-NeRF lag on organ and vessel integrity. The results further highlight persistent difficulty in reconstructing fine anatomy: while large-organ NSD reaches the mid-80s for the strongest methods, small-organ surfaces remain challenging- even the top performer attains median NSD below 70, underscoring the need for structure- objectives if CT reconstructions are be clinically reliable. These findings demonstrate that pixel-wise metrics alone are insufficient and anatomy-awar evaluation provides a more discriminative and clinically meaningful assessment of reconstruction quality.
Reader Study. To validate the clinical relevance of our study with 21 board- certified radiologists who independently evaluated subset of reconstructed scans from the nine benchmarked methods. Radiologists were presented with anonymized image pairs (reconstruction vs. ground truth) and asked rate anatomical completeness across the four structure categories defined. All 21 radiologists agreed that higher pixel-wise metrics does not guarantee better anatomy preservation. The rankings derived from their assessments showed strong agreement with our anatomy-aware metrics. This confirms that our proposed metrics align closely with expert clinical judgment, whereas conventional pixel-wise scores do not reflect perceptual or diagnostic fidelity.
aware metrics, we conducted a reader
Discussion. Conventional pixel-wise criteria markedly over-estimate the usefulness of sparse-view reconstructions. For instance, InTomo records SSIM of 0.83 yet recovers virtually none of the small-organ anatomy (median NSD = 0.0) in Table 1. Such disparity underscores the need for task-aligned evaluation: the proposed anatomy-aware metrics reveal structural failures that remain invisible to SSIM and PSNR.
4.3 Diffusion- CT Enhancement
To demonstrate the enhancement performance of CARE, we integrated it into each reconstruction method under sparse- view reconstruction setting, i.c., CT reconstruction with 50 views of X-ray images. Table 2 reports the quantitative gains in both pixel-wise and anatomy-aware metrics. Figure shows the qualitative results of reconstruction with and without CARE. CARE significantly improves reconstruction quality both in pixel-level fidelity and anatomical structures.
Under extreme sparsity in CT reconstruction, anatomical completeness: for example, InTomo+CARE increases large-organ NSD from 2.4% to 30.9%, vessel clDice from 4.7% to 47.9 47.9% and intestine clDice from 6.3% to 33.5%. NeRF+CARE and CAARE show similar uplifts, confirming that our anatomy-guided loss robustly transfers structural priors even in low-data regimes.
reconstructions exhibit remarkable
8

---

Table 2: CARE-enhanced Reconstruction Evaluation. Evaluate preexisting CT reconstruction methods on high-quality CT scans with CARE using both pixel-wise metrics and our anatomy-aware metrics. Note that the results are based on 36 CT scans that CARE has never been trained on. We report the median and interquartile range (IQR) of these metrics and perform the Mann-Whitney Whitney U test for statistical analysis. Cells are marked in color only if CARE shows there is significant difference (p < 0.05) with the original reconstruction, while green if the CARE enhancement results have improvement, and red otherwise. Deeper color represents greater difference.
pixel wise metric
anatomy aware metric (ours)
method
SSIM
PSNR
NSDaage
NSDa
elDiceaussiee
elDicevred
InTomo
50 views
82.7 (80.9.8477)
24.5 (23.3,25.1)
(1636)
(.0,00.
(1..1.2))))
4.7 (00.216
+CARE
76.7 (74.0.99.))
22.0 (20.6.22)))
30.9 (264,40)
7.1 (3.,333))
(2444424)))
(35
NeRF
50 views
87.7 (84.5.89.4) 4)
26.6 (247,2744)
22.1 (8.2,35.8)
0.2 (0.0.-3)
17.2 Oy
(1.0.11)
+CARE
(78.,8800)
23.4 (21.9,244)
549
25.3 (142.303
53.5 (47...99)))
TensoRF
50 views
89.3 (87.3,91.1)
28.0 (27.3.99))))
44.6 8.53.5
12.1 (1.1,2844)
(19.266))))))
46.1 (36..588.5)
+CARE
(5).2.)))))
27.5 (27.2.282)
75.3 (6507.9935)
38.2 (30224664)
67.9 (62.8.5.8)
R*-GS
views
(,9.944))
28.8 (27.4,29.7)
76.3 9.8.6)
(41.1.68.3)
53.9 (45.668
64.5 (59 4.866)
+CARE
(87.9.9))
27.3 (16.2.28.9)
82:5 (74 0.88.5)
55.7 (42.5,67.3)
75.4 (68.0.82.8)
67.6 8,769)
views
(9....0))
29.4 (28.9.30.8)
83.7 (76.2,86.8)
(51.72))))
71.6 (62.2.777))
75.4 170.1.70.1888.
NAF
+CARE
92.8 (91.59933)
29.6 (28.6,30.3)
87.9 (83.8,91 8,913)
68.1 (52.4,76.)
81.8 (24.185.9)
71.8 (63-4,888)
FDK
50 views
(9.1..)))))))))
29.5 (28.9.30.9)
83,8 (77.3,66.
(52.4.0))
(59.8.666)
76.5 (66 (,8.3)
+CARE
(2..501)
29.7 (29.0,50.0
87.1 (82) 3.90.9)
67.0 (55,6,777))
(75.5 7)
71.7 (66 4.8.33
SART
50 views
92.5
29.5 (28.9,31.0)
84.6 (76.9,8811)
61.3 (53.7.75.4)
70.0 (61,0,7.6)
78.7 (68 6,8.66
+CARE
(927.93))
30.3 (29.5,30.9)
88.2 (84 2,90.8)
(56.0.75.5)
83.5 (27.8.87.
75.2 2.83.3)
ASD-POCS
50 views
(91.644)
29.5 (28 (83.7,0.7)
83.8 (76 5,8.22
61.0
71.3 (61.8.77,4))
74.7 (67.4.81.))
+CARE
(9.8.9)))
29.4 (28.4,30.1)
6.9.99
(56.2,75.)
80.7 (74.9.84.))
76.4 (66,6,9)
SAX-NeRF
50 views
93.7 (,2.8950))
29.6 (29.0,51.0)
82.4 (73-9.88.9)
66.5 (55.0,78.0)
68.0 (56.8.71.9)
79.3 (62 4.8.11
+CARE
(2..991))))33)
29.4 (28.8,30.1)
88.7 9.9099
67.1 (545,75.1)
81.5 (26.88888))
(629.44))
These results highlight CARE's transformative impact: by embedding anatomical supervision directly into the diffusion framework, our method consistently elevates structural fidelity across diverse reconstruction backbones and sampling densities, without sacrificing general applicability or requiring additional scanning hardware.
Reader Study. We further evaluated the perceptual and clinical impact of CAREthrough a blinded reader study involving the same cohort of 21 radiologists. Participants assessed side-by-side re- diverse anatomical regions and reconstruction
constructions-willl and without CARE-acrosd backbones. In all cases, radiologists preferred the CARE-enhanced version, citing improved visibility of small organs (e.g.. gallbladder, adrenal glands), vascular continuity (e.g., celiac trunk, renal veins), and intestinal integrity. Notably, even when baseline reconstructions achieved high PSNR/SSIM, radiologists consistently identified missing or distorted anatomy that CAREsuccessfully restored. These findings underscore CARE's ability to translate quantitative gains in anatomy-aware metrics into tangible clinical improvements.
Discussion.
The proposed CARE framework offers a model- and patient-agnostic avenue to inject anatomical priors into existing pipelines. Paired with CARE, diverse backbones-froe classical FDK to NeRF and Gaussian saaaaan--spatinaaaccieee achieve substantial gains across all anatomy-aware metrics.
Discussion and Conclusion
Discussion. Our experiments in $4.2 and $4.3 show that CARE consistently improves the anatomical fidelity of sparse-view CT reconstructions across a wide range of baselines, including analytical, implicit, and generative methods. Further analysis in $E.3 demonstrates that CARE performs similar enhancement ability across arterial and portal venous phase CT scans, revealing CARE's potential in expanding to other CT phases. These improvements are particularly notable in small and complex structures like small organs and vessels, where segmentation-ddrriel metrics such as NSD and ciDice reveal gains of up to 40%, even when pixel-wise metrics remain nearly unchanged. Notably, CARE trained once and applied directly to unseen cases without fine-tuning, demonstrating strong patient- agnostic generalization. This robustness arises from its design: segmentation-guiin -uidedd supervision
9

---

w/o CARE
CARE
Aorta
Adrenal gland
Bila
trunk
Duodinum
W/o CARE
Kidney
O
O
Intestine
Spleen
Renal vein
CARE
Q
0
Q
L
L
LK
E
0
InTomo
NeRF
TensoRF
RT-GS
NAF
FDK
SART
ASD-POCS SAR-NeRF Ground Truth
Figure 6: Visual Comparison. Visualizations of CT reconstruction with or without CARE on two example CT images. Please zoom in for better view. These maps reveal that the most prominent changes occur at organ boundaries and within bone structures, where CAREmore effectively reduces artifacts and enhances contrast. See more detailed comparison in $E.2.
encodes population-leve shape priors, and the diffusion model integrates them without requiring additional annotations or per-case adaptation. Its straightforward design enables seamless integration into existing reconstruction pipelines as a plug-in enhancement, without requiring modifications to or retraining of the underlying models. Moreover, CAREfeatures modular architecture that facilitates the incorporation of future advances in segmentation models, thereby enhancing the reliability of anatomical guidance and enabling the evaluation of an expanding set of anatomical structures. Overall, CARE offers a flexible solution to enforce anatomical plausibility in CT reconstruction, particularly in settings where high-contras structures are represeted in pixel-domain loss functions.
Conclusion, This work reframes CT reconstruction quality through an anatomy-centric viewpoint and introduces CARE, diffusion-based enhancement module that be integrated into any re- construction algorithm. A new suite of segmentation-driee metrics exposes structural deficiencies overlooked by SSIM and PSNR, and extensive experiments on nine baselines under sparse-view settings demonstrate that CARE consistently elevates anatomical fidelity, delivering up to 35% improvements in vessel elDice under 50-view -witnuut additional acquisition burden. limitation of our method is that its improvement becomes marginal when the baseline reconstruction already preserves most anatomical structures. In such high-quality cases, i.e., the reconstructions from SAX-NeRF, CARE offers limited added benefit relative to its computational cost. Our method brings algorithm goals in line with real clinical needs, making easier to produce anatomy-aware CT reconstructions under sparse-view settings and sets new standard for future reconstruction research.
Acknowledgments and Disclosure of Funding
This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research, the Patrick J. McGovern Foundation Award, and the National Institutes of Health (NIH) under Award Number R01EB037669. We would like to thank the Johns Hopkins Research IT team in IT @ JH for their support and infrastructure resources where some of these analyses were conducted; especially DISCOVERY HPC. We thank Jaimie Patterson for writing news article about this project; thank Hamed Hooshangnejad, Heng Li, Wenxuan Li, and Guofeng Zhang for their helpful suggestions throughout the project. Paper content is covered by patents pending.
10

---

References
[1] Anders H Andersen and Avinash Kak. Simultaneous algebraic reconstruction technique (sart): a superior implementation of the art algorithm. Ultrasonic imaging, 6(1):81-90) 1984,
[2] Memoona Aziz, Umair Rehman, Muhammad Umair Danish, and Katarina Grolinger. Global-Iocal image perceptual score (glips): Evaluating photorealistic quality of ai-generated images. IEFF Transactions on Systems, 2025.
[3] Pedro RAS Bassi, Wenxuan Li, Yucheng Tang, Fabian Isensce, Zifu Wang, Jieneng Chen, Yu-Cheng Chou, Yannick Kirchhoff, Maximilian Rokuss, Ziyan Huang. Jin Yc, Junjun He, Tassilo Wald, Constantin Uirich, Michacl Baumgartner, Saikat Roy, Klaus Maier-Hein, Paul Jaeger, Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Yong Xia, Zhaohu Xing. Lei Zhu, Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari, Reza Azad, Dorit Merhof, Pengcheng Shi, Ting Ma, Yuxin Du, Fan Bai, Tiejun Huang. Bo Zhao, Haonan Wang. Xiaomeng Li, Hanxue Gu, Haoyu Dong, Jichen Yang. Maciej A. Mazurowski, Saumya Gupta. Linshan Wu, Jiaxin Zhuang. Hao Chen, Holger Roth. Daguang Xu, Matthew B. Blaschko, Sergio Decherchi. Andrea Cavalli, Alan L. Yuille, and Zongwei Zhou. Touchstone benchmark: Are we on the right way for evaluating ai algorithuss for medical segmentation? Conference on Neural Information Processing Systems, 2024.
[4] Pedro RAS Bassi, Xinze Zhou, Wenxuan Li, Szymon Plotka, Jieneng Chen, Qi Chen, Zheren Zhu, Jakub Przado, Ibrahim E Hamacı, Sezgin Er, et al. Scaling artificial intelligence for multi-tumor early detection with more reports, fewer masks. arXiv preprint arXiv:2510. 10.10333..14403, 2025.
[5] Ander Biguri, Manjit Dosanjh, Steven Hancock, and Manuchehr Soleimani. Tigre: matlab-gu gpu toolbox for chet image reconstruction. Biomedical Physics & Engineering Express, 2(5):055010, 2016.
[6] Ander Biguri, Reuben Lindroos, Robert Bryll. Hossein Towsyfyan, Hans Deyhle, Richard Boardman. Mark Mavrogordato, Manjit Dosanjh. Steven Hancock. and Thomas Blumensath. Arbitrarily large iterative tomographic reconstruction on multiple gpus using the tigre toolbox. arXiv preprint 10XXv:190500374:0
2019.
[7] Mikolaj Binkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton. Demystifying mmnd gans.
arXiv preprint /rXiv:/801 01401, 2018.
[8] Yuanhao Cai, Jiahao Wang, Alan Yuille, Zongwei Zhou, and Angtian Wang. Structure-awe aware sparse- view x-ray 3d reconstruction. In IEEF/CVF Conference Computer Vision and Pottern Recognition (CVPR), pages 11174-11183 2024.
[9] Kai Cao, Yingda Xia, Jiawen Yao, Xu Han, Lukas Lambert, Tingting Zhang, Wei Tang, Gang Jin, Hui Jingg Xu Fang, al. Large-scale pancreatic cancer detection via non-contrast et and deep learning. Nature
Medicine, pages 1-11, 2023.
[10] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, and Tal Hassner, editors, Computer Vision - ECCV 2022, pages 333-350, Cham, 2022. Springer Nature Switzelaand.
[11] Anpei Chen, Zexiang Xu, Andreas Geiger. Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In European conference
computer vision, pages 333-350. Springer, 2022.
[12] Guang-Hong Chen, Jie Tang, and Shuai Leng. Prior image constrained compressed sensing (pices): method to accurately reconstruct dynamic images from highly undersampled projection data sets. Medical physics, (5(2):660 663, 2008.
[13] Qi Chen, Xiaoxi Chen, Haorui Song. Zhiwei Xiong, Alan Yuille, Chen Wei, and Zongwei Zhou. Towards generalizahle tumor synthesis. In /EEE/CVE conference on computer vision and pattern recognition
(CVPR), pages 11147-11158, 2024.
[14] Qi Chen, Xinze Zhou, Chen Liu, Hao Chen, Wenxuan Li, Zekun Jiang, Ziyan Huang, Yuxuan Zhao, Dexin Yu, Junjun He, et al. Scaling tumor segmentation: Best lessons from real and synthetic data, arXiv preprint (xXv:2510.14831/ 2025.
[15] Li Sze Chow and Raveendran Paramesran. Review of medical image quality assessment. Biomedical
signal processing and control, 27:1455154, 2016.
[16] Aanuoluwapo Clement Rnvid-Olawade, David B Olawade, Laura Vanderbloemen, Oluwayomi B Rotifa, Sandra Chinaza Fidelis, Eghosasere Figbon, Akwaowo Owoidighe Akpan, Sola Adeleke, Aruni Ghose, and Stergios Boussios. Ai-driven advances low-dose imaging and enhancement a review, Diagnostics, 15(6):689, 2025.
[17] Aanuoluwapo Clement OlawddO,awade, David B. Olawade, Laura Vanderbloemen, Oluwayomi B. Rotifa, Sandra Chinaza Fidelis, Eghosasere Egbon, Akwaowo Owoidighe Akpan, Sola Adeleke, Aruni Ghose, and Stergios Boussios. Ai-driven advances in low-dose imaging and enhancement review. Diagnostics,
15())6889, 2025.
[18] Lee A Feldkamp, Lloyd C Davis, and James W Kress. Practical cone-beam algorithm. Journal of the Optical Society of Americu A. 1(6):612-61) 229.,, 1984.
[19] EM Geraghty, JM Boone, JP McGahan, and K Jain. Normal organ volume assessment from abdominal ct. 2004.
Abdominal imaging, 29(4):882-49)
[20] Eliot Gibson. Wenqi Li. Carole H. Sudre, Laurent Fidon. Dzhoshkun Shakir, Guotai Wang. Zach Eaton- Rosen, Richard Gray, Tom Doel, Paul Bentley, Ying Hu, Marisa Holden, Chang- Wai Chen, Dean C. Barratt, and Daniel Rueckert. Automatic multi segmentation on abdominal et with fully convolutional networks. IEEE Transactions Medical Imaging, 37(9):1822 1834, 2018
11

---

[21] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing
systems, 27, 2014.
[22] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
[23] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural
Information Processing Systems, 33:6840-6851, 2020.
[24] Aljoscha Hore and Djemel Ziou. Image quality metrics: Psnr vs. ssim. In 2010 20th International Conference on Pattern Recognition, pages 2366-2369. IECE, 2010.
[25] Ziyan Huang. Haoyu Wang. Zhongying Deng. Jin Ye, Yanzhou Su, Hui Sun, Junjun He, Yun Gu, Lixu Gu, Shaoting Zhang. al. Stu-net: Scalable and transferable medical image segmentation models empowered 2023.
by large-scale supervised pre-training. arXiv preprint arXiv:2304.061
[26] Fabian Isensee, Paul F Jaeger, Simon AA Kohl, Jens Petersen, and Klaus I Maier-Hein. nnu-net: self-configuring method for deep learning- based biomedical image segmentation. Nature Methods, 18(2):203-211 2021.
[27] Fabian Isensee, Tassilo Wald, Constantin Ulrich, Michael Baumgartner, Saikat Roy, Klaus Maier-Hein, and Paul F Jaeger. nnu-nel revisited: A call for rigorous validation in 3d medical image segmentation. arXiv preprint arXi::2404. 09556, 2024.
[28] Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2014.
[29] A Koch, T Gruber-Rouh, S Zangos, K Eichler, T Vogl. and L Basten. Radiation protection in cl-guided interventions: Does real-time dose visualisation lead reduction in radiation dose participating radiologists? a single-centre evaluation. Clinical Radiology. 7966)=e785-+790 2024.
[30] Megan Lantz, Emil Y. Sidky, Ingrid S. Reiser, Xiaochuan Pan, and Gregory Ongie. Enhancing signal detectability learning-
ot reconstruction with a model-observer inspired loss function. arXiv
preprint 2r02 10010, 2024.
[31] Wenxuan Li, Pedro RAS Bassi, Tianyu Lin, Yu-Cheng Chou, Xinze Zhou, Yucheng Tang, Fabian Isensee. Kang Wang. Qi Chen, Xiaowci Xu, et al. Scalemai: Accelcrating the development of trusted datascts and ai models. arXiv preprint arXiv:2501.03410 2025.
[32] Wenxuan Li, Chongyu Qu, Xiaoxi Chen, Pedro RAS Bassi, Yijia Shi, Yuxiang Lai, Qian Yu, Huimin Xue, Yixiong Chen, Xiaorui Lin, et al. Abdomenatlas: A large-scale, detaileddannnttaeed, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking. Medical Image Analysis, page 103285, 2024.
[33] Wenxuan Li, Alan Yuille, and Zongwei Zhou. How well do supervised models transfer to 3d image segmentation? In International Conference on Learning Representations, 2024.
[34] Xinran Li, Yi Shuai, Chen Liu, Qi Chen, Qilong Wu, Pengfei Guo, Dong Yang, Can Zhao, Pedro RAS Bassi, Daguang Xu, et al. Text-driven tumor synthesis, arXiv preprint arXiv:2412. 18589, 2024.
[35] Tianyu Lin, Zhiguang Chen, Zhonghao Yan, Weijiang Yu, and Fudan Zheng. Stable diffusion segmentation for biomedical images with single-step reverse process. In Medical Image Computing and Computer Assisted Intervention MICCAI 2024, pages 356-666, Cham, 2024. Springer Nature Switzerland.
[36] Jie Liu, Yixiao Zhang, Jic-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, and Zongwei Zhou. Clip-driven universal model for organ segmentation and tumor detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 21152-21164, 2023.
[37] Jie Liu, Yixiao Zhang, Kang Wang, Mehmet Can Yavuz, Xiaoxi Chen, Yixuan Yuan, Haoliang Li, Yang Yang. Alan Yuille, Yucheng Tang, et al. Universal and extensible ainuage-vision models for organ segmentation and tumor detection from abdominal computed tomography. Medical Image Analysis, page 103226, 2024.
[38] Ben Mildenhall, Pratul Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021.
[39] Samaneh Mostafapour, Marcel Greuter, Johannes H. van Snick, Adrienne H. Brouwers, Rudi A. J. O. Dierckx, Joyce van Sluis, and Adriaan A. Lammertsma. Ultra-low dose ct scanning for pet/ct. Medical
Physics, 51(1) 133-51399 2024.
[40] Chongyu Qu Tiezheng Zhang, Hualin Qiao, Jie Liu, Yucheng Tang. Alan Yuille, and Zongwei Zhou. Abdomenatlas-8k: Annotating 8,000 abdominal et volumes for multi-organ segmentation in three weeks. In Conference on Neural Information Processing Systems, volume 21, 2023.
[41] G. N. Ramachandran and A. V. Lakshminarayanann Three-dimensional reconstruction from radiographs and electron micrographs: application convolutions instead fourier transforms. Proceedings of the National Academy of Sciences, 68(9):2236 2240, 1971.
[42] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björ Ommer. High-resolution
image synthesis with latent diffusion models, 2021.
[43] Saikat Roy. Gregor Koehler, Constantin Ulrich, Michael Baumgartner, Jens Petersen, Fabian Isensee, Paul F Jaeger, and Klaus H Maier-Hein. Mednext: transformer-drivee scaling of convnets for medical image segmentation. In International Conference on Medical Image Computing and Conputer-Asiisted Intervention, pages 405-40 415. Springer, 2023.
12

---

[44] Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. Image super-resolution via iterative refinement. IEEE transactions on pattern analysis and machine intelligence, 45(4):4713-4726, 2022.
[45] Fritz Schick. Automatic segmentation and volumetric assessment of internal organs and fatty tissue: what are the benefits? Magnetic Resonance Materials in Physics, Biology and Medicine, 35(2):187-192, 2022. [46] Suprosanna Shit, Johannes C Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien PW Pluim, Ulrich Bauer, and Bjoem H Menze. eldice-a novel copology-preservee loss function for tubular structure segmentation. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pages 16560-16569, 2021.
[47] Emil Y Sidky and Xiaochuan Pan. Image reconstruction in circular cone-beam computed tomography by constrained. total-variation minimization. Physics in Medicine & Biology, 53(17):4777, 2008. [48] Yiran Sun, Hana Baroudi, Tucker Netherton, Laurence Court, Osama Mawlawi, Ashok Veeraraghavan, and Guha Balakrishnan. Difr3ct: I Latent diffusion for prohabilistic 3d ct reconstruction from few planar x-rays. Xrviv preprint arXiv:2408. 18, 2024.
[49] Yipeng Sun, Yixing Huang. Zeyu Yang, Lindhe Schneider. Mareike Thies, Mingxuan Gu, Siyuan Mci, Siming Bayer, Frank G Zöllner, and Andreas Maier. Eagle: an edge-aware gradient localization enhanced loss for ct image reconstruction. Journal of Medical Imaging, (0(())014000 400110,,0,,,
4001, 2025. [50] Pinhuang Tan, Mengxiao Geng, Jingya Lu, Liu Shi, Bin Huang, and Qiegen Liu. Msdiff: Multi-scale diffusion model for ultra-sparse view et reconstruction. arXiv preprint 2405: 058145 2024.
[51] Patrick von Platen. Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj. Dhruv Nair, Sayak Paul, William Berman, Yiyi Xu, Steven Liu, and Thomas Wolf. Diffusers: thehe-tf diffusion models. https: / //gihubb com/ huggguuggiii nn aee// ffusers, 2022. [52] Yihe Wang. Nan Huang. Taida Li, Yujun Yan, and Xiang Zhang. Medformer: A gramulrrtyy patching transformer for medical time-series classification. Advances in Neural Information Processing Systems.
37:36314 --4441, 2024.
[53] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Fero Simoncelli. Image quality assessment: from error visihility to structural similarity. IEEE transactions on image processing. 1304):600-612 2004.
[54] Jakob Wasserthal, Manfred Meyer, Chhns-Christtan Breit, Joshy Cyriac, Shan Yang. and Martin Segeroth. Totalsegmentator:: robust segmentation of 104 anatomical structures in ct images. arXiv preprint
Sxii::208.008860 2022.
[55] Yingda Xia, Qihang Yu, Linda Chu, Satomi Kawamoto, Seyoun Park, Fengze Liu, Jieneng Chen, Zhuotun
Zhu, Bowen Li, Zongwei Zhou, et al. The felix project: Deep networks to detect panereatic neoplasms.
medRxiv, 2022.
[56] Yao Xu, Jiazhou Wang, and Weigang Hu. Prior-image-based low-dose reconstruction for adaptive radiation therapy. Physics in Medicine & Biology, 69(21), 2024.
[S7] Wufeng Xue. Lei Zhang, Xuanqin Mou, and Alan C Bovik. Gradient magnitude similarity deviation:
highly efficient perceptual image quality index. IEEE transactions on image processing, 23(2):684-66 695, 2013.
[58]
Demin Yang, Haochen Shi, Bolun Zeng, and Xiaojun Chen. 2d/3d registration based on hiplanar x-ray and ct images for surgical navigation. Computer Methods and Programs in Biomedicine, 257: 108444, 2024. Liutao Yang. Jiahao Huang. Guang Yang. and Daoqiang Zhang. Ct-sdm: A sampling diffusion model for sparse-view reconstruction across all sampling rates. arXiv preprint arXiv:2409.013:
[59]
2024.
[60] Guangming Zang, Ramzi Idoughi, Rui Li, Peter Wonka, and Wolfgang Heidrich. Intratomo: self-supervised learning-based tomography via sinogram synthesis and prediction. In Proceedings of the IEEE/CVF
International Conference on Computer Vision, pages 1960-1970, 2021.
[61] Ruyi Zha, Tao Jun Lin, Yuanhao Cai, Jiwen Cao. Yanhao Zhang, and Hongdong L.. -²-gaussian:: Rectifying radiative gaussian splatting for tomographic reconstruction. arXiv preprint arXiv:2-05..066)) 2024. [62] Ruyi Zha, Yanhao Zhang, and Hongdong Li. Naf: neural attenuation fields for sparse-view cbet recon- struction. In International Conference on Medical Image Compating and Compuder-Asiisted Intervention. pages 442-452. Springer, 2022.
[63] Guofeng Zhang, Ruyi Zha, Hao He, Yixun Liang, Alan Yuille, Hongdong Li, and Yuanhao Cai. X-Irm:
X-ray large reconstruction model for extremely sparse-view computed tomography recovery in one second. arXiv preprint 2503 003882 2025.
[64] Hao Zhang, Grace J Gang, Hao Dang, and J Webster Stayman. Regularization analysis and design for prior-image-haset x-ray reconstruction. JEEE transactions on medical imaging, (7(12):275--666, 2018.
[65] Richard Zhang, Phillip Isola, Alexei Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a pereeptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 096669952222 2018.
13

---

Appendix
Table of Contents
A Datasets, Models and Implementation Details
2
A.1 Datasets
2
A.2 Models
3
A.3 Implementation Details
3
B Anatomy Segmentator
5
C Details and Disscussions of Anatomy-Aware Metrics
6
C.I NSD vs. DSC: Example of Sensitivity to Boundary Shifts
6
C.2 High Correlation of GT-based and Segmentator-bess based Anatomy Aware Metrics
7
C.3 Poor Correlation between Pixel-Wis Metrics and Anatomical Preservation Perfor
mance
10
D Technical Details CARE
14
Autoencoder
D.2 Latent Diffusion Model
E Experimental Results of CARE
16
E.1 Benchmarking Other Segmentation Metries
16
E.2 Qualitative Results of CARE
17
E.3 CARE on Arterial and Portal-venous Phase CT Scans
26

---

A Datasets, Models and Implementation Details
A.1 Datasets
We evaluate four different kinds of anatomical structures: large organs, small organs, intestine and vessel. Large organs and small organs divided according to clinical guidelines, showing in Table 3.
Table 3: Grouping strategy for large and small organs. partition organs by typical vivo volumes reported in radiologic volumetry studies. The 100 mL and 10 mm cutoff aligns with standard thresholds for distinguishing major organs from accessory structures abdominal atlases.
Group
Representative mean volume (mL.)
References
Large organs mL)
Liver ==1200-1700; Kidney =150-200; Spleen #=215
Pancrcas
#:71-3
Small organs/ structures 500
Gallbladder 9:30 ml; adrenal gland Celiac trunk diameter #s6-12 mm; Duodenum lumen 2-3 cm
[45]
10 mm diameter)
A.1.1 Large-scale Reconstruction Benchmark Dataset
Table Evaluation dataset size of the CT reconstruction methods. Among all the CT reconstruc- tion methods we evaluate, NeRF [38] and TensoRF [10] uses 20 CT scans but no human body CT included, SAX-NeRF [8] use 15 CT scans with only 9 human body CT scans. Our reconstruction benchmark dataset is by far the largest in the field of CT reconstruction.
Method
Total Scan
Human Body CT Scan
InTomo [60]
7
NeRF [38]
TensoRF [10]
20
0
R2-G8 [61]
NAF [62]
5
FDK [18]
1
SART
ASD-POCS [47]
2
SAR-NeRE [8]
CARE
61
As shown in Table 4, our reconstruction benchmark dataset contains 61 high-quality CT scans, with 36 in arterial phase and 25 in portal venous phase, which is by far the largest CT reconstruction dataset. The real projections these CT scans are not available. Thus, we use TIGRE [5, 6] package to generate synthetic projections following previous works [8, 61]. The detailed meta information about our reconstruction evaluation dataset is listed Table 5.
Table 5: Statistics of the CT reconstruction benchmark dataset.
Dataset
scan"
Age
In-plane spacing"
White"
Training
64.2 4: 8.9 64.8
10 (40.0)
15 (60.0)
0.70 (0.68, 0.79) 0.77 (0.70, 0.98)
(24.0)
Test
16 (44.4)
(55.6)
(
Dataset
Black"
Asian
Other"
Resolution
Height
Voxel spacing
Training
(16.0)
5 (20.0)
(24.0)
512 512
350 +: 302
0.74 4: 0.12
Test
(19.4) 10 (27.8)
(13.9)
512 x 512
427 +: 294
1.20 4: 1.04
Age in years (mean 4: SD),
Spacing in millimeters. Applics to le-plane spacing and Voxel spacing. Values reported as modian (IQR) or mean + SD as shown. A Image resolution and height in pixels. Resolution reported as width x height; height reported as mean d: SD. Counts; columns also include percentages parentheses, e.g., Female, Male, and eategorics. Applies CT Female, Male, White, Black, Asian, Other.
For the anatomy aware CT reconstruction metrics ($3.2), we report the results of all 61 CT scans.
For the anatomy-aware CT reconstruction framework CARE ($3.3), we report the results of a subset of 36 CT scans (23 in arterial phase and 13 in portal venous phase), and the remaining 25 CT scans (13 in arterial phase and 12 in portal venous phase) were used for the training set. The detailed meta information about reconstruction evaluation dataset is listed in Table 6.

---

A.1.2 JHH Dataset for CARE Pretraining
Table Statistics of the JHH dataset. All the CT scans in the dataset have resolution of 512 x 512. This dataset is collected using four major vendors: GE (39%), Siemens (38%), Phillips (12%). and Toshiba (11%).
Dataset
CT scan
Age'
Female"
Male
In-plane spacing"
White
Black'
Training
3151 1958
64.9
(48.4)
1627
0.71 (0.66, 0.77) 0.73 (0.68, 0.79)
619 (19.6)
Test
64.9 = 8.9
1008 (51.5)
950 (48.5)
409 (20.9)
394 (20.1)
Dataset
Asian"
Other"
Height
Voxel spacing"
PDAC"
PNET"
Cyst"
Training
632 (20. I) 389 (19.9)
661 (21.0) 397 (20.3)
678 # 201
0.72 + 0.09
1119
591
429
Test
196
0.74 +
554
327
512
Age in years (mean SD).
Spacing millimeters. Applies In- plane spacing Voxel spacing. reported as median (IQR)
shown.
Height pixels (mean SD).
Counts; some columns also include perceniages in parentheses, egg. Female, Male, and race categories. Applies to CT scan, Female, Male. White, Black, Asian, Other, pancreatic tumor types: PDAC, PNET, Cyst.
The JHH dataset contains 6,212 high-quality artifact-free CT scans of 2,870 patients, where 3,107 are in the arterial phase and 3,105 are in the venous phase. The CT scans in this dataset have spacing of 0.5 mm, providing million CT slices in total. This dataset can be split into a training set of 3,151 CT scans and a testing set of 1,958 CT scans. For all 6,212 CT scans of the dataset, a total of 25 anatomical structures are annotated by a group of 21 radiologists for over three years, including the CT reconstruction metrics. The 25 annotated anatomical
17 structures we use anatomy- structures are: aorta, left adrenal gland, right adrenal gland, common bile duct, celiac artery, colon, duodenum, gall bladder, postcava, left kidney, right kidney, liver, pancreas, pancreatic duet, superior mesenteric artery, intestine, spleen, stomach, veins, left renal vein, right renal vein, common bile duct, pancreatic PDAC, pancreatic cyst, and pancreatic PNET.
This dataset is used both in training the anatomy segmentator, autoencoder, and the latent diffusion model to build the anatomy-awan CT reconstruction metrics and is prepared for the CARE frame- work. Note that the anatomy segmentator is trained on the training set, whereas the autoencoder and latent diffusion model are trained on the entire 6,212 CT scans.
A.2 Models
We provide full access to our model checkpoints to contribute to the open-source community, We release the model checkpoints of our anatomy segmentator, autoencoder, and the CARE models in our codebase.
A.3
Implementation Details
A.3.1 Anatomy Segmentator
The proposed anatomy segmentator used in both the anatomy CT reconstruction metrics and the CARE framework is implemented as a nnU-Net [26]. We extend the training plan of nnU-Net to be trained 48GB NVIDIA RTX 6000 GPU, with all data preprocessing, training, and inference settings set as default. This model is trained on the training set our JHH dataset, with 3,151 expertly annotated CT scans.
A.3.2 Reconstruction Baselines
The experiments are conducted on three traditional reconstruction methods (FDK [18], SART [1].
ASD-POCS [47]), five NeRF-based reconstruction methods (InTomo [60], NeRF [38], TensoRF [10],
NAF [62], SAX-NeRF [8]), and a Gaussian Saaussian-Spalttin--aaan
method R²-GS [61]. We implement
these methods using the CT reconstruction toolbox provided by SAX-NeRF [8] and R²-GS* [61].
For each algorithm, the training setting is set as default. All of these reconstruction experiments are run on an eight NVIDIA RTX 6000 GPU server, each with 48 GB of memory.
hhnps//gittubbco//Ruyii/uzzi gaussian
3

---

A.3.3 Autoencoder Training
The autoencoder model is initialized with the checkpoints provided by Stable Diffusion v1.5 [42]. We set the weight of reconstruction loss and perceptual loss (detailed in $D.1) be Avec Aper The weight of the KL regularization term set to 3 1 10-6 The autoencoder model trained on the JHH CT dataset 1$A.1.2) for 150,000 iterations. We use AdamW optimizer during training.
A.3.4 Latent Difflusion Training
When training the latent diffusion model, we adopted the super resolution training scheme for the model to gain intrinsic enhancement ability. To create low-quality CT images, we followed SR3 [44] use bicubic interpolation with anti-aliasing enabled to downsample the CT image and upsample back to the original resolution. The downsampling factor is set to 4.
During training, the prompt regarding different phases is fixed: the prompt of arterial phase CT image is "An Arterial CT slice.", and the prompt of a portal phase CT image "A Portal-venous CT slice.". The de-noising UNet trained the same JHH dataset used in the autoencoder training, with AdamW optimizer and 50,000 training iterations.
A.3.5 CARE Training
The text prompt of CARE is identical the latent diffusion training stage. The model is finetuned on 25 CT scans (as mentioned in $A.1.1) .) for 50,000 iterations with the Adam optimizer for each given CT reconstruction method. We the weights of the losses to Ap 1 and \ 0.001.
Our diffusion model implemented by the diffusses [51] package with backbone of Stable Diffusion v1.5 [42]. All three training stages of CARE ($A.3.3, $A.3.4 and $A.3.5) done eight RTX 8000 GPUs server, each with 48 GB of memory.

---

B Anatomy Segmentator
As mentioned in $A.3.3, the anatomy segmentator is trained on the training set of our JHH dataset with 3,151 CT scans. In $3.2 of the paper, we showed the high correlation (in Figure 3) of the anatomy-awe CT reconstruction metries with the segmentator pseudolabel and the ground truth label, to support our assertion that employing the anatomy segmentator for these metrics is justifiable.
Here, also provide the segmentation results of our anatomy segmentator the testing set of JHH dataset with 1,958 CT scans. Table shows that our anatomy segmentator achieves excellent performance over the large-scale testing comparing three state-of-the-ar segmentation methods in the Touchstone 1.0 [3] benchmark: MedNeXt [43], MedFormer [52] and STU-Net-B [25].
Table Anatomy Segmentator's Performance on the Testing Set of JHH Dataset. Segmentation performance is reported as the median and interquartile range (IQR) of Dice and ToU (%) for selected anatomical structures across the testing set with 1,958 CT scans. For each structure, the best result bolded and the sencond best underlined.
Anatomical Structures
Segmentator (Ours)
MedNeXt [43]
MedFormer [52]
STU-Net-B [25]
Large Organs
Liver
(96.4.97.))
(95.8.66.)
(96.2.97.1)
(96.8,97.5)
Kidney Left
97.7 (97.4.9)))
97.2 (96.8,977.)
97.1 (96.7.97.5)
96.9 (96.4.97.35
Kidney Right
97.7 (97.5.9)
(92.0,9777)
(96.5.97.4)
(96.3,) 3,97.1
Spleen
96.4 (95.5.97.1)
96.5 (95.7,97.2)
96.8 (96.0.97.3)
96.0 (95.4,9 96.9)
Pancreas
86.5 (81.5,89.3
88.1 (85.3,90.5)
87.2 (84.0,89.6)
(82, 1.88.)
Small Organs
Bladker
(86.1.9344
(85.7928)
(87.4.93.5)
2,9.8)
Adrenal Gland Left
84,6 (75.2.887) 7)
86. (77.3.89
85.4 (76.9.88.3)
83.2 (75 0,87.0
Adrenal Gland Right
80.1 (71.7.8.55
81.4 (73.9.86.5)
81.2 (72.8.854)
79.5 (70.7 7.8.2)
Caliac Artery
65.6 (56.5.334))
66.8 (57.2,744))
67.1 (90.5.75.2)
65.5 (57.073.))
Duodenum
86.4 (82.4.8955)
85.3 (80.5,88. ()
84.8 (80.2,87.4)
85.1 (81.2.88.3)
Intestinal Structures
Colon
84.4 (82.6.9111)
85. (83.0.91.3)
(83.7.92.1)
(82. 1,90.5)
Intestine
75.6 (69.1884))
76.8 (70.4.82.3)
76.9 (71.3.85.0)
(68.9.80.)
Vascular Structures
Aorta
92.1 (87.495.0)
91.3 8,9422
1,93.9)
91 (87.0.95)))
Postcava
(79.3.777)
86.0 (80.4.88.2)
85.2 (78.9.86.9)
84.7 178.1,
Superior Meventeric Artery
(58.2.74.5)
(59.1.5.2)
(60.3.76.5)
(58.4,4488
Since there no ground truth label for renal veins [left or right) in the test set, we don't report the corresooning metries here.
5

---

Details and Disscussions of Anatomy-Aware Metrics
C.1 NSD DSC: an Example of Sensitivity to Boundary Shifts
Consider an organ with volume V and surface area S. When the entire boundary shifts uniformly by distance (e.g., organ boundary displaced by 3 mm), the volume discrepancy can be approximated
as:
AV = S
(9)
The corresponding drop in Dice Similarity Coefficient (DSC) be approximated by:
AV
5
V
ADSC
~
where
Ret
(10)
2V
2V
2
S
For spherical organ with radius R 50mm and 5 33m,, compute the effective radius Rer as:
3 R
R
Reff
16.67mm
(11)
4m R
3
This results in:
8
3
ADSS
0.09
(12)
2Re
2 x 16.67
This demonstrates that even with clinically significant displacement, DSC may only decrease little, reflecting the severe anatomical misalignment. contrast, Normalized Surface Dice (NSD) is independent of organ volume and instead penalizes boundary mismatches directly. For a mismatch tolerance т 2mm, a uniform mm shift would lead NSD to fall zero, reflecting the anatomical eтror more faithfully.
Overall, DSC is volume-biased and may underestimate boundary errors in large structures, while NSD maintains consistent sensitivity across anatomical scales.

---

C.2 High Correlation of GTT-based and Segmentator-basee Anatomy-Aware Metrics
Figure summarizes the high correlation between GT-bbaed and segmentator based anatomy- metrics over all nine CT reconstruction methods, which indicates that the proposed anatomy segmen- tator is a reasonable substitute for ground truth labels to build the anatomy- aware metrics.
Here, we also provide the detailed correlation scatter plots of all nine CT reconstruction methods on anatomy-aware metrics. These correlation plots consistently give the same conclusion. Note that the correlation coefficient of small organs in the In'oomo method is 0.0 (in Figure 7) because all the segmentation metrics are zeros. Moreover, the inadequate correlation coefficient of the intestine highlights an existing issue with the annotation of intestinal structures: the clinical experts concentrate solely on the volumetric precision of the annotation, overlooking the tubular form and connectivity.
GTT-based and segmentator-
large
small
Intestine
vessel
GT-based
GT-based
GT-based
50
GT-based
8-
0.04
40
0.03
30
4-
20
Corr#0.99
0.00=0.00
Corr= 0.64
Corr=
10
Seg-based
Seg-based
Seg-based
Seg Seg-based
0.00
2
10
0.00 0.01 0.02
0.03 0.04 0.05
6
16
24
40
10
20
30
40
50
Figure 7: Correlation Between GT-based and Segmentator- based Anatomy-Aware Metrics InTomo [60]. The solid red line is linear fit and the dashed lines denote 1 of the residuals. High correlation shows that the proposed anatomy segmentator is strong substitute for ground truth labels when building anatomy-avee -wware metrics.
large
small
60
intestine
vessel
60
GT-based
30
GT-based
GT-based
80
GT-based
45
60
10
30
Corr=1.00
Corr=
15
Corr=0.66
20
Corr= 0.97
Seg-based
Seg-based
Seg baseb
Seg basebased
15
30
45
60
0
12
18
24
30
15
30
45
60
15
30
45
60
75
Figure 8: Correlation Between GT-based and Segmentator-basee Anatomy-Aware Metrics
NeRF [38]. The solid red line is a linear fit and the dashed lines denote +1 of the residuals. High correlation shows that the proposed anatomy segmentator is strong substitute for ground truth labels when building anatomy-awas metrics.
75
GT-based
large
50
GT-based
small
60
GT-based
intestine
80
GT- GT-based
vessel
40
45
60
40
30
20
Corr=0.95
1orr=1.00
Corr=0.cm
Corr=0.89
20
based
Seg based
Seg based
Seg based
0
0
15
30
45
60
0
20
40
20
40
80
30
45
75
Figure 9: Correlation Between GT-based and Segmentator-based Anatomy-Aware Metrics TensoRF [11]. The solid red line is linear fit and the dashed lines denote +o of the residuals. High correlation shows that the proposed anatomy segmentator is strong substitute for ground truth labels when building anatomy-award metrics.

---

large
small
75
intestine
vessel
80
GT-based
80
GT-based
60
GT-based
GT-based
60
60
45
40
30
Corr=0.97
Corr=1.00
Corr=
Corr=0.90
20
15
Seg based
Seg based
Seg based
Seg based
0
100
40
60
20
80
20
40
60
80
Figure 10: Correlation Between GT-based and Segmentator- Anatomy-Aware Metrics R [61]. The solid red line is linear and the dashed lines denote =l of the residuals. High correlation shows that the proposed anatomy segmentator is strong substitute for ground truth labels when building anatomy-awant metrics.
large
small
75
intestine
vessel
GT GT-based
80
GT-based
60
GT-based
80
GT GT-based
60
60
40
30
40
Corr=0.94
Corr=0.99
Corr= 0.58
Corr=0.90
20
15
Seg- based
Seg based
Seg based
Seg-based
40
60
80
0
40
60
80
20
40
60
80
100
20
40
60
80
Figure 11: Correlation Between GT-bbaed and Segmentator-based Anatomy-Aaare Metrics NAF [62]. The solid red line is a linear fit and the dashed lines denote #1o the residuals. High correlation shows that the proposed anatomy segmentator is strong substitute for ground truth labels when building anatomy-aware metrics.
large
small
intestine
vessel
GT-based
80
GT-based
GT-based
80
GT-based
60
40
40
Corr=0.95
Corr= 0.99
Corr=0.63
Corr=
20
Seg based
Seg based
Seg based
Seg based
0
0
40
60
80
0
40
80
100
20
40
80
Figure 12: Correlation Between GT-based and Segmentator-basee Anatomy-Aware Metrics on FDK [18]. The solid red line is a linear fit and the dashed lines denote Elo the residuals. High correlation shows that the proposed anatomy segmentator is strong substitute for ground truth labels when building anatomy-ave aware metrics.
large
small
75
intestine
vessel
GT-based
GT-based
60
GT-based
80
GT GT-based
60
60
45
40
30
40
Corr=0.95
Corr=0.99
Corr= 0.63
Corr=0.91
20
15
Seg based
Seg based
Seg based
Seg bas-based
0
40
60
80
40
60
20
60
80
100
20
40
80
Figure 13: Correlation Between GT-based and Segmentator-based Anatomy-Aware Metrics SART [1]. The solid red line is a linear fit and the dashed lines denote Elo the residuals, High correlation shows that the proposed anatomy segmentator is strong substitute for ground truth labels when building matomyyawar metrics.

---

large
small
intestine
vessel
80
GT-based
80
GT-based
GT-based
80
GT-based
60
60
40
40
Corr=0.94
Corr=0.99
Corr=0.57
Corr=0.88
20
20
20
bag-based
Seg- Seg-based
Seg based
leg-based
60
80
40
60
40
a0
Figure 14: Correlation Between GTT-based and Segmentator-based Anatomy-Aware Metrics on ASD-POCS [47]. The solid red line is linear fit and the dashed lines denote =10 of the residuals. High correlation shows that the proposed anatomy segmentator a strong substitute for ground truth labels when building anatomy-aware metrics.
large
small
intestine
vessel
80
GT-based
80
GT-based
GT-based
80
GT-based
60
60
40
Corr*0.95
Corr
Corr=#0.60
Corr= 0.93
Seg- based
Seg Reg-based
Seg based
Seg based
60
80
20
40
60
80
100
20
60
80
Figure 15: Correlation Between GT-based and Segmentator- Anatomy-Aware Metrics on SAX-NeRF [8]. The solid red line is linear fit and the dashed lines denote #lo of the residuals. High correlation shows that the proposed anatomy segmentator a strong substitute for ground truth labels when building anatomy-award metrics.

---

C.3 Poor Correlation between Fixel-Wise Metrics and Anatomical Preservation Performance
Figure 5 summarizes the poor correlation between pixel-wiss metrics and anatomy- aware metrics over six CT reconstruction methods: FDK [18], SART [1]. ASD-POCS [47], NAF [62], LAX-NeRF [8], R--GS [61]. where NeRF [38], TensoRF [10], and Tomo [60] are eliminated due large number of zero metric samples. This indicates that better anatomical preservation does not ensure higher pixel-wise metrics.
Here, we also provide the detailed correlation scatter plots of all nine CT reconstruction methods on all pixel-wise and anatomy-aware metrics. These correlation plots consistently give the same conclusion.
WISS
Corre-Di22
Comtoan
Come00002
Come--0.0 0.02
large organs
small organs
intestine
vessel
80
100
60
100
60
100
20
60
100
20
PSNS
PNSd
15
Corr-nan
Corn--D.00
Cor+D.007
10
large organs
20.0
small organs
20.0
Intestine
vessel
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
0
20
40
60
80
100
Figure 16: Correlation Between Anatomy The solid red line is a linear fit and the dashed lines denote =1 of the residuals. Low correlation indicates that better anatomical preservation does not guarantee higher pixel-wise metrics.
Metries and Pixel- Wise Metrics of InTomo [60].
90
90
85
/WISS
85
85
85
0.19
Corlxs0000
Connes-D.33
large organs
small organs.
intestine
vessel
20
80
100
40
60
100
20
80
100
40
60
100
27.5
27.5
25.0
25.0
26
Sd
22.5
Co.3-
Corbe-0-24
22.5
Corn--0.01
24
Come--0-2
large organs
small organs.
Intestine
vessel
22
20
80
100
60
100
20
80
100
10
100
Figure 17: Correlation Between Anatomy-Aware Metries and Pixel-Wise Metries of NeRF [38]. The solid red line is a linear fit and the dashed lines denote #lo of the residuals. Low correlation indicates that better anatomical preservation does not guarantee higher pixel-wise metrics.
10

---

85
WISS
85
WISS
85
WISS
85
Corr=0.00
Corr=-0.04
Corm--0.04
Comm-00.02
80
80
large organs
organs
intestine
vessel
30
30
BSNR
25
PNSR
BNSR
25
Corr=0.00 03
Corr=0.00 0.
Corn--0.03
Comm-00.00
large organs
small organs
Intestine
vessel
60
100
20
40
80
100
20
60
80
100
20
40
80
100
Correlation Between Anatomy-Aware Metrics and Pixel-Wise Metrics of Ten- soRF [11]. The solid red line is linear fit and the dashed lines denote #lo of the residuals. Low correlation indicates that better anatomical preservation does not guarantee higher pixel-wise metries.
Figure 18:
WISS
WiSS
WISS
3-
Corr=0
%
Corn=0 1212
Corr=0007
Corr#0.24
85
85
large organs
srhalll organs
intestine
yessel
20
40
60
100
20
40
80
100
20
40
60
80
100
20
40
80
100
30
30
30
30
BSSR
PSNR
Corr=0000 42
Corn=0:14
Co
Com=0.27
20
large organs
20
small organs
20
intestine
20
vessel
0
20
40
60
100
20
40
80
100
20
60
80
100
20
40
80
100
Figure 19: Correlation Between Anatomy-Aware Metries and Pixel-Wise Metries of R-GGS [61]. The solid red line is a linear fit and the dashed lines denote +: 1o of the residuals. Low correlation indicates that better anatomical preservation does not guarantee higher pixel-wise wise metrics.
95
95
95
95
90
90
WI5S
WISS
SSIM
Corr=0.13
Corr=0.0
Cor--0.0 07
Cor++022 0.21
Jarge organs
small organs
intestine
vessel
20
40
60
100
20
40
80
100
20
40
60
80
100
20
40
80
100
PSSR
25
JNSd
BNSd
25
JNNSa
Corr=0.27
Corr=0.20
Corr-D.16
Cor-
20
large organs
20
small organs
20
Intestine
20
60
100
80
60
Figure 20: Correlation Between Anatomy-Aware Metrics and Pixel-Wise Metrics of NAF [62]. The solid red line a linear fit and the dashed lines denote =1 of the residuals. Low correlation indicates that better anatomical preservation does not guarantee higher pixel-wise metrics.

---

90
90
90
90
WISS
WISS
WI59
WNS
Corr=0.15
85
Corr=0.04
Corm=-0.03
85
Corn=0.14
Jarge organs
small organs
intestine
vessel
80
80
0
20
40
60
80
100
20
40
60
80
100
20
40
60
80
100
20
40
60
80
100
30
25
CSNR
25
PNNR
25
8NSR
25
Carr-0.22
Cor--0.17
Carn 08
Com=0.22
Jarge organs
20
small organs
intestine
20
vessel
40
60
100
20
40
80
100
60
100
20
40
80
100
Correlation Between Anatomy-Aware Metries and Pixel-Wi Wise Metries of FDK [18]. The solid red line is a linear fit and the dashed lines denote =1 of the residuals. Low correlation indicates that better anatomical preservation does not guarantee higher pixel-wise metrics.
Figure 21:
95
95
95
95
90
Wi55
85
wiSs
Wi55
05
Corr=0.19
Corr=
CorreI
Corr=0.10
80
80
80
80
large orgams
small organs
intestine
vessel
40
60
100
20
80
100
60
100
20
40
80
100
25
JivSa
BSNR
25
NIVSd
Corr=00 25
Corr=0.11
Corr=0 09
Cor=00117
20
Jarge organs
20
small organs
20
intestine
20
vpssel
60
100
40
80
100
60
100
40
80
100
Figure 22: Correlation Between Anatomy-Aware Metrics and Pixel-Wise Metries of SART [1]. The solid red line a linear fit and the dashed lines denote lo of the residuals. Low correlation indicates that better anatomical preservation does not guarantee higher pixel-wise metrics.
95
95
95
95
90
90
90
WISS
WISS
SSIM
Corr 15
85
Corr=0.00 0606
Corr=0.00
85
Corr=0.05
large organs
small organs
intestine
vgssel
60
100
80
100
60
100
80
100
30
30
HNSd
PSNR
Corre0 26
Corr=0.15
Corr=0.15 1.18
Cor-00114
large organs
small organs
intestine
vessel
20
40
60
80
100
20
40
60
80
100
20
40
60
80
100
0
20
40
60
80
100
Figure 23: Correlation Between Anatomy-Aware Metrics and Pixel-Wise Metries of ASD-
POCS [47]. The solid red line is linear fit and the dashed lines denote +o of the residuals. Low correlation indicates that better anatomical preservation does not guarantee higher pixel-wise metrics.
12

---

95
95
95
95
90
90
WISS
wiSs
WISS
wISs
large orgams
small organs
intestine
vessel
40
60
100
20
80
60
100
20
80
100
000
NIVSa
UNSa
NIVSd
25
25
Corr=0.33 32
Corr=0.22
Corr=0 20
Corn= 34
large organs
20
small organs
20
intestine
20
vessel
60
100
20
40
80
100
60
100
40
80
100
Figure 24: Correlation Between Anatomy-Aware Metrics and Pixel-Wise Metrics of SAX-
NeRF [8]. The solid red line is a linear fit and the dashed lines denote #1 of the residuals. Low correlation indicates that better anatomical preservation does not guarantee higher pixel-wise metrics.
13

---

D Technical Details of CARE
In $3.3, discuss the Preliminary stage of the CARE framework: Autoencoder and Latent Diffusion Model. In this section, we provide more technical details of the autoencoder in $D.1, and provide more technical specifications of the latent diffusion model in $D.2.
D.1 Autoencoder
In this initial stage, we finetuned a -regullrred Variational Autoencoder (KI.-VAE), using the JHH CT scans. The encoder of the VAE compresses input CT images into latent representations (denoted as 2), while the decoder reconstructs the original images from these latent variables. The training objective aims to optimize both image reconstruction quality and the regularization imposed by the Kullback-Leibhe divergence to ensure meaningful latent representations. These trained autoencoder serve as the foundational components for subsequent diffusion model training.
Specifically, let denote an input CT image in pixel space. Note that to consider inter-frame consistency and retain the original architecture of the autoencoder network, stack three adjacent CT slices to be the three channels of the input image, giving X E HR x3 The encoder Eng maps X to latent representation z Egg (X) E gxxwxe A paired decoder Dep reconstructs the input as X = Dup (z). Training adapts the KL-VAE pretrained on natural images (checkpoint provided by Stable Diffusion v1.5 [42]) to the target CT domain by minimising weighted sum of three complementary criteria:
LAE = Aree ||X - 1111 1 +Aper Σ
Eurlloi(X) oz xx
reconstruction
perceptual
(13)
B (To (= ()II(O,
regularisation
where:
Reconstruction term Cree directly penalises pixel-wise errors, preserving low-frequency fidelity.
Perceptual term Lper computes the & distance between intermediate feature maps Φx(-) of fixed vision backbone (here VGG-16 pretrained ImageNet). sharpens textures and maintains semantic consistency that pure pixel metrics miss.
KL term CKL pulls the encoder's 's approximate posterior q (2 Gaussian prior, preventing latent collapse and ensuring the aggregated latent distribution remains close to (0, 7). This normalization is crucial for the subsequent diffusion prior, whose noise schedule assumes unit-variance latents.
X) towards the unit
The weights Aree Aper. and 3 control the fidelity-regularity trade-off between generative capacity and roughly standard-normal latents. Unlike the addersarially-augeenne auto-encoders used in Stable Diffusion, we fintuned on the three penalizations above directly, simplifying the optimization process and eliminating adversary-induced instabilities without sacrificing downstream sample quality. After fintuning the KL-VAE model on Stable Diffusion's 's checkpoint, frozen latents 2 serve as training data for the latent-space diffusion model described below.
14

---

D.2 Latent Diffusson Model
This stage pretrains a latent diffusion model with the JHH dataset enhance low-quality CT scans while preserving anatomical structures. Each high- CT image X E IR xx W x3 is first compressed by the frozen KL- VAE encoder Egg into a latent representation 2 Eeg (X) E IR hxwxe In the forward diffusion process a noisy sequence {=+] is obtained by
21 20 vI aic,
c~ C~N(0,,), t T).
(14)
where 20 - 2 and fār fixed prreeerriii schedule. To expose structural cues during de-noising we degrade with deterministic operator H(-) and concatenate it with along the channel dimension,
f Co, H())] E hxxx2x
(15)
where H(--) is a downsampling process with a factor of 4 and followed by an upsampling process back the original resolution.
de-noising UNet ep receives ε, the diffusion timestep i, and a fixed text embedding Cphase of the contrast phase information, and predicts the noise residual é c0 ly Cphase)- Then, the network parameters are optimized with the standard noise-prediction loss
LDM E,,,, ||c - co (Concat|z: H())), Cphase) 11
(16)
Because only LLDM is used, learning focuses on accurate latent de-noising; H(2) supplies anatomy the de-noising UNet acquires intrinsic enhancement capability without extra image-space supervision.
15

---

E Experimental Results of CARE
E.1
Benchmarking on Other Segmentation Metries
Table Benchmarking CT Reconstruction Methods on NSD. We evaluate preexisting CT recon- struction methods high-quality CT scans using both pixel-wise metrics and our anatomy- metrics. We report the median and interquartile range (IQR) of these metrics. Cells are marked in blue, where a deeper color denotes greater value.
pixel-wise metric
anatomy-aware (ours)
method
SSIM
PSNR
NSDnag
NSD
NSDanssie
NSD
InTomo [60]
82.7 (80.6.84.7)
(2....23)))
(1.4.))
(000000.0
2.0
(.0
NeRF [38]
86.8 (84.4.89.0
25.7 (24.7.27.1)
21.7 (9.37.6))
0.1 (0.0.3.9
5.1 (34.66)
(.8(1.9.166)
TensoRF [11]
88.4 (87.0,90.7)
27.8 (26.9.29))
(213..00))
(4.8.143)
(1.5..10.140.2)
R²-GS GS [61]
93.2 (9.9.399)
(27...2)))
(57.1.2)))))))
542.
15.1 (10.5.198)
62.1 (47.9.55.6)
[62]
(2.. (21...1)))
(28.8,300))
(6..2.2)))))))))
(47.0.68.6)
(13.22246)
(64.8.8)))))))
FDK [18]
29.5 (28.8.0.8)
(6...69.)
62.2 (50.4.9.))
18.1 (14.1.24.6)
72.3(62..811)
SART [1]
(91..91))))
29.4 (28.8.30.6)
76.2 (67.2.888)))
61.8 (49.3,72.5)
18.0 (12.9224.)
(61.661))
ASD-POCS [47]
29.5
76.3 (67.4.87..88877
63.0
18.6
72.2 (64.622.))
SAX-NeRF [8]
93.7 (92.8.950)
29.7 (29.0.30.8)
(67.0.67))22
64.4 (49.8.76.3)
17.5 (13.5,24.2)
70.3 (56.2.85))
Table 9: Benchmarking CT Reconstruction Methods on DSC. We evaluate preexisting CT recon- struction methods high-quality CT scans using both pixel-wise metrics and anatomy- metrics. We report the median and interquartile range (IQR) of these metrics. Cells are marked in blue, where deeper color denotes greater value.
pixel-wise merric
anatomy-aware (ours)
method
SSIM
PSNR
DSCare
DSCamal
DSC
DSC
ToTomo [60]
82.7 (80.6.84.7)
24.3 (23.1.5)
7.9 (0.6.10.6)
0.0 (0.00.00.))
(0.1.)
(.0
NeRF [38]
86.8 (84.4.89.))
25.7 (24.7.27.1)
32.1 (15.7.49.8)
(.0.2.0220)
(3.1.144))
(1.7.00))
TensoRF [1]]
88.4 (87.0.90.7)
27.8 (26...8))
(32.3.8)))
(.0.2.0))))
(49.020
(1299,32)))
R [61]
93.2 (91.9.99)))
28.6 (270.29.)
79.3
46.9 (35.0.61
2167.335
57.1 (40.5.659)
[62]
(9....1)))
29.4 (28.8.3.))
84.1 (76.5.87.5)
52.9 (39.6.63.9)
29.7 (21.2,40.0)
63.3 (56.6.33))
FDK [18]
(9..199.
29.5 (28.8.30))
84.2 (77.38888)
53.5 (42.0,64.0)
29.6 (20.6,20.3)
63.3.26.))222))
SART [1]
92.5 (9L.1.19)))
29.4 (28.8,30.6)
83.8 (37.5577.5. (77.5.8888
52.3 (40 (,64.)))
28.5 (30.4,20.5)
63.6 (55.2,743)
ASD-POCS [47]
92.5
29.5
84.2
52.6
29.2 (34.5,444)
62.4
SAX-NeRF [8]
93.7 (92.8.95.0)
29.7 (29.0.30.8)
83.4 (75..86)))
54.2 (39. 1.706)
29.7 (20.5.38.8)
63.5 (50.6.75.3)
16

---

E.2 Qualitative Results of CARE
Figure 25: Qualitative results of InTomo [60] with and without CARE. Every two rows showcase the results of eight CT slices of single CT scan. CARE performs great anatomical preservation during enhancement. The images are center-cropped to eliminate the background. soft-tissue window (300/50 HU) used enhance abdominal organ visibility.
17

---

Figure 26: Qualitative results of NeRF [38] with and without CARE. Every two rows showcase the results of eight CT slices of single CT sean. CARE performs great anatomical preservation during enhancement. The images are center-cropped to eliminate the background. A soft-tissue window (300/50 HU) was used enhance abdominal organ visibility.
18

---

Figure 27: Qualitative results of TensoRF [11] with and without CARE. Every two rows showcase the results of eight CT slices of single CT sean. CARE performs great anatomical preservation during enhancement. The images are center-cropped to eliminate the background. A soft-tissue window (300/50 HU) was used enhance abdominal organ visibilityy
19

---

Figure 28: Qualitative results of R [61] with and without CARE. Every two rows showcase the results of eight CT slices of single CT sean. CARE performs great anatomical preservation during enhancement. The images are center-cropped to eliminate the background. A soft-tissue window (300/50 HU) was used enhance abdominal organ visibility.
20

---

Figure 29: Qualitative results of NAF [62] with and without CARE. Every two rows showcase the results of eight CT slices of single CT scan. CARE performs great anatomical preservation during enhancement. The images are center-cropped to eliminate the background. A soft-tissue window (300/50 HU) was used enhance abdominal organ visibility.
21

---

Figure 30: Qualitative results of FDK [18] with and without CARE. Every two rows showcase the results of eight CT slices of single CT scan. CARE performs great anatomical preservation during enhancement. The images are center-cropped to eliminate the background. A soft-tissue window (300/50 HU) was used enhance abdominal organ visibility.
22

---

Figure 31: Qualitative results of SART [1] with and without CARE. Every two rows showcase the results of eight CT slices of single CT scan. CARE performs great anatomical preservation during enhancement. The images are center-cropped to eliminate the background. A soft-tissue window (300/50 HU) was used enhance abdominal organ visibility.
23

---

Figure 32: Qualitative results of ASD-POCS [47] with and without CARE. Every two rows showcase the results of eight CT slices of single CT scan. CARE performs great anatomical preservation during enhancement. The images are senter-cropped to eliminate the background. A soft-tissue window (300/50 HU) was used enhance abdominal organ visibility.
24

---

Figure 33: Qualitative results of SAX-NeRF [8] with and without CARE. Every two rows showcase the results of eight CT slices of single CT scan. CARE performs great anatomical preservation during enhancement. The images are center-cropped to eliminate the background. A soft-tissue window (300/50 HU) was used enhance abdominal organ visibility.
25

---

E.3 CARE on Arterial and Portal-venous Phase CT Scans
Table 10: CARE-enhaneed Reconstruction Evaluation on Arterial Phase CT Scans. Evaluate preexisting CT reconstruction methods on high-quality CT scans with CARE using both pixel-wise metrics and our anatomy-awes metrics. Note that the results are based on 23 arterial CT seans that CARE has never been trained on. We report the median and interquartile range (IQR) of these metrics and perform the Whin-Wy U test for statistical analysis. Cells are marked color only if CARE brings significant difference (p 0.05), while green if the CARE enhancement results have improvement, and red otherwise. Deeper color represents greater difference.
pixel-wise metric
anatomy-aware metric (ours)
method
SSIM
PSNR
NSD
NSD
eiDicenesste
eIDictveud
InTomo
50 views
(81.889))
24.7 (23.5.55))
(1.5..))
0.0
(1.2277))
9.3 (00.186)
+CARE
76.3 (74.5.00.1)
22.2 (21.1220)
(20,3.0))
(1,,01.))
30.2 (23.7.88.
(31.66)))))))))
NeRF
50 views
88.1 (85.3.0.3)
(24.222)))))))
(1.333)))))
0,1 (0.0228)
17.1 (9..259))
(2.9.24)1.467)
+CARE
82.1 (796688.1)
24.0 (22.52447)
58.2 (16.2)))))
(138.23)
50.4 (39.3,5611)
57.7 (.5.4688))
50 views
(87.8,22)))
28.7 (27.6,30.2)
41.3 (2) 6.53.)
13.9 (2.5,27.8)
31.1 (20.9.35.)
44.7 (33.2.574)))
TensoRF
+CARE
(57.4.91))
(27.3,28.7)
(60.2.73.4)
(48.76008)
R"-GS
50 views
93.5 (92.
29.3 (28.0,29.7)
75.8 (63, 5.84.)
60.5 (41.3.68.)
53.4 (42.9/66.6)
(58...57)
+CARE
(88.46)))))
27.8 (26.3.9.0)
80.8 (73. (73.8,66.))
(42.0667))))
72.0
(6.3,11))
NAF
views
(91.4,9)
29.9 (29.0,31.3)
85.2 (74.5,854)
60.9 (49.2.69.0)
71.3 (61.5.76.7)
(6.00..))
+CARE
(913,90)
29.9 (28.5,30.4)
87.0 (80.9.0))
63.0 (48.7.74.3)
80.8 (23.1.84))
68.2 (605,74.3)
FDK
(91.3.9)))
29.8 (29.2,31.3)
83.0 (.8.6)))))
(48.6,71.2)
64.4 (58.2.76.5)
77.2 (.778.977
+CARE
92.8 (91.5.777))
30.3 (29.0.31.0)
85.7 (79.1.8877)
65.8 (54.1,75.5
81.4 (72.6,84.))
(65 8.73.)
50 views
(91.6.6))
30.2 (29.4,314)
83,4 (15.3.155.8
60.9 (50.0,70.0
66.4 (58.4.5.5)
78.8 8.8266
+CARE
(9.79.3)
30.6 (29.8,31.2)
87.1 (81 6.90.1)
68.1 (54.0,75.6)
83.4 (75.8,86.) 1)
66.5 (55.67533)
ASD-POCS
50 views
(9.894)))
99.9 (29.2.31.3)
(72.9.9...
66.0 (49.7,6999)
70.5 (57.6.77.00
74.9 8,7.9.99.)
+CARE
(9.9,.)))))355))
29.9 (28.8,30.6
86.1 (8) 5,89.7
67.00 (52.3,73.8)
80.3 (72.6.84.6)
70.0 (59.7.7.6))
SAR-NeRF
50 views
93.9 (93.0,95.))
30.5 (29.5.31.5)
82.8 (78.9)88.))
65.0 (54.3,77.3)
66.2 (54.4.77.5)
79.3 (66-3,84.3)
(90.9.95))
(28.8.30.3)
86.2 2,9000)
(54.2.73.4)
(72.2,86.3)
(56.3,75.)
26

---

Table 11: CARE-enhanced Reconstruction Evaluation on Portal Venous Phase CT Scans. Evalu- ate CT reconstruction methods on high-quality CT scans with CARE using both pixel-wise metrics and our anatomy-awart metrics. Note that the results are based on 13 portal venous CT scans that CARE has never been trained on. We report the median and interquartile range (IQR) of these metrics and perform the Mann-Whitnes U test for statistical analysis. Cells are marked in color only if CARE brings significant difference (p < 0.05), while green if the CARE enhancement results have improvement, and red otherwise. Deeper color represents greater difference.
pixel-wise metric
anatomy- metric
method
NSDrag
NSD
elDicenesite
elDicesred
InTomo
50 views
81.7 (79...9)))))
(22.8,22,444))
2.3 (1.3.)))
(00,00.)0))
(2.....)))
0.0 (10.156)
+CARE
(733..8))
21.5
42.0(279
(59.5.))))2)
(3..57.)))
48.6 7.6.7.3)))
NeRF
views
334..3)))
25.3 (24.7,677)
30.7 1.4.8)
(0...5))
(7.,32 m
29.2
+CARE
(7844811)
22.2 (21.1.22))
$7.7 (56.0.6.5)
27.3 (143,31.5)
56.6 (52.6611)
$2.9 (25
TensoRF
views
(.6.0.))))
27.6 (26.8.28.0)
47.9 (23.7.533)
8.6
(14.7.3))))))
(445.68)
+CARE
88.7 (867,89.7)
27.3 (26.9,27.5)
78.1 (.8200
a0.6 (39.2,403)
71.3 (65.8.77.4)
63.0 (51.,,5))
R²-GS
50 views
(80.7.5))
27.7 (26.32287)
80.8 4.847)
(41.8.3)))
54.5 (50.9.73.888
71.0 (.0.18810)
+CARE
(84
27.3 (24.9,07.8)
83.7 (74-4.977)
55.1 (45.6.60.2)
80.7 (76.1.84.9)
76.3 (56.8,86.3)
NAF
50 views
(9004.9)
29.3 (28.0.29.6)
84.7 (81.187.))
61.1 (52.274.9)
71.9 (63.6.85.6)
79.2 (73.0.855))
+CARE
(92.0,8))
29.5 (29.0,30.0)
91.0 (86.4.918
72.3 (99.8.77.))
82.9 (78.4.0.5)
83.8 (77.9.844)
50 views
(9.0..)))))33)
29.1 (28.3.29.5)
85.7 (81.1,86.9)
63.3 (57.5.69.1) 3)
70.1 (66.5.338)
FDK
(91.,99)))))
(29.0,29.7)
(86.1.914)
(62.0,79.0)
(16.3.66.5)
(14.8888))
SART
views
(109696))
29.2 (28.2,29.6)
87.5 (77.1.902))
67.2 (55.4,77.2)
71.1 (63.5.00.0)
77.8 (.478.847)
+CARE
7.2..92)
30.0
89.8 5.91
70.3
85.7 (79.7,88.2
83.6
ASD-POCS
views
(90...99
29.2 (28.2,29.7)
86.4 6,8888
(59.7,78.4)
72.0 (67. 380,3)
74.5 4.8.5)
+CARE
(9.6668)
28.8 (28.3.29.3)
88.8 (84.2.91.4)
71.0 (60.3,75.7)
81.5 (18.7.8655)
(78.7.8)))
SAX-NeRI
(9233,9)))
29.3 (28.8,29.6)
79.3
(56.2.79.))
71.1 (618.79
79.3
+CARE
92.3 (91.5.9.3)
29.7 (28.9.29.9)
90.8 (86
70.5 (63.7,78.9)
80.5 (77.2.900))
83.8 (76.5.88))
27

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The abstract and introduction accurately reflect the paper's contributions by clearly framing the limitations of pixel-wise metrics, introducing anatomy- aware evaluation framework and CARE enhancement pipeline, and previewing the empirical gains multiple reconstruction backbones.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
The paper discusses the limitations of the work performed by the authors in
$ 5.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged
the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
28

---

Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
The paper does not include theoretical results. Its contributions lie in proposing suite of anatomy-awar evaluation metrics, introducing diffusion-basee CT enhancement framework CARE, and providing thorough empirical validation, rather than in formal assumptions or mathematical proofs.
Justification:
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proof' can either appear the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Thcorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Yes]
Justification: We provide the experimental details both in $ 3.3 and in the supplementary materials.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution a specific model and empirical evaluation, may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often good way to accomplish this, but reproducibility instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, other means that appropriate to the research performed.
also be provided via detailed
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm.
(b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
29

---

(d)
We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, described in supplemental material?
Answer: [Yes]
Justification: Note that only provide open access code and model checkpoints; data would be kept private. The code is attached in the supplementary materials with a link the model checkpoint attached.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please the NeurIPS code and data submission guidelines thttps://nigg cc/
public/ guides/ Code ss ssisssninn assooPoolin Poylil for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (c.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https: /nips. cc/ public/ guggguiieesssee seuiid///// Code S Submi s s oonooooolll onnolicyy for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data,
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible supplemental material (appended the paper) is recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (c.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: The experimental setting/details of training and testing provided in 3.3 and the supplementary materials.
Guidelines:
The answer NA means that the paper does include experiments.
The experimental setting should be presented the core the paper to a level detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: The paper performs the Mann- Sain-Whitney U test at Table mark significance difference between results with baselines.
30

---

Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the enror bars capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall
run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
is OK to report I-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables figures symmetric error bars that would yield results that are out range (c.g. negative error rates).
If error bars reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: The paper provides sufficient information on the computer resources in the supplementary materials.
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics https: /nwrrieurpppp tttpppplcc/tttiicoooooococcccccc www.iiiecccieeelleccccc line?
Answer: [Yes]
Justification: We preserve anonymity in all submitted materials and strictly conform the NeurIPS Code of Ethics in every respect. All patient-identifibble information was anonymized during preprocessing to ensure privacy protection, and the released dataset contains identifiable information.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No. they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws regulations in their jurisdiction).
10. Broader impacts
31

---

Question:
Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: See and $ 5.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (c.g.. disinformation, generating fake profiles, surveillance), fairness considerations (e.g.. deploymeett of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility ML).
Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data or models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
The paper poses such risks.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: The paper has cited, credited and listed the licenses of all the papers of the code bases used in 1 and supplementary materials.
Guidelines:
32

---

The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g.. website). the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, papers thcode com/ dat asets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and the documentation provided alongside the assets?
Answer: [Yes]
The paper introduce new code and models and they are all well documented in the supplementary materials.
Justification:
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the detaset/code/model part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: The paper only uses pre-acquired CT scans, and does not involve crowdsoure- ing nor research with human subjects.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurlPS Code of Ethies, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
33

---

Answer: [NA]
Justification: The paper only uses pre-aequired CT scans, and does not involve crowdsoure- ing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with
human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology. scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification:
The core method development in this research does involve LLMs.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non noa-standard components.
Please refer to our LLM policy (https: /tee://neeeee //neurrripp..../ /c/ coc/Cofereecees 2025/ L.LM) for what should should not be described.
34


Reviews:
[{"review_id": "3wSbYUbCyB", "reviewer": "Reviewer_Wkf4", "summary": "The paper presents a new evaluation metric to address the reconstruction of abdominal CT. The new metric based on organ-anatomy preservation is more medically relevant than the standard pixel-level metrics PSNR and SSIM. Using the new metric, the authors introduced a new method to improve the sparse-CT reconstruction.", "questions": "see weaknesses 1, 3, 4", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n-\tThe new metric is very relevant for the medical community since it measures the reconstruction fidelity of the organs.\n-\tThe metric allows the development of anatomy-aware methods.\n\nWeaknesses:\n\n-\tBased on my understanding, the metric focuses on abdominal area only. This information should be stated in the abstract and introduction, since there are other regions such as brain and whole-body CT.\n-\tCaption Fig4. Add more information since the losses appear in the figure but not in the caption. Is Lc a typo?\n-\tLine 247-251: The authors claim that the order given SSIM/PSNR changes drastically, when the methods are ordered in terms of the new metric, however, the re-ordering is not as drastic as stated, since the best methods in terms of ssim/psnr are still in the top methods in terms of the new metric.  SAX-NeRF is the best method in terms of ssim/psnr and is the best/second best in terms of the new metric. I do agree with the usefulness of the new metric, but I believe that the statement should be reduced a bit. \n-\tAnother concern is in terms of the quality of the Segmentator which can influence the overall performance negatively. A study testing different segmentation models should be performed.\n\n\n\nThe paper tackles an important problem of anatomy-aware metric for CT reconstruction. The proposed metric is sound and obtains good performance. However the metric is limited to abdominal organs which should be clearly stated in the paper, and experiments modifying the segmentator should be added.", "was_revised": true, "final_justification": "I still believe that this work will benefit the community. The authors answered all my concerns, therefore I keep my initial score.", "rebuttal": "Thank you for giving the **Accept** score and acknowledging the potential of our proposed metric for guiding anatomy-aware methods in CT reconstruction.\n\n---\n\n> **Q1.** Based on my understanding, the metric focuses on abdominal area only. This information should be stated in the abstract and introduction, since there are other regions such as brain and whole-body CT.\n\n- We agree that our current manuscript does not clearly state that the proposed metric is specifically designed for abdominal CT reconstruction. \n\n- We have revised the abstract, introduction, and method sections to explicitly clarify that our work focuses on the abdominal region, distinguishing it from applications in other anatomical areas. For example, the revised abstract now includes:\n\n    *“Our proposed anatomy-aware metric is specifically developed for evaluating abdominal CT reconstruction, leveraging organ-level segmentation within the abdominal region.”*\n\n---\n\n> **Q2.** Caption Fig4. Add more information since the losses appear in the figure but not in the caption. Is Lc a typo?\n\n- “Lc” is a typo and should refer to “Ls” as defined in Equation (7). \n\n- We have now clarified the meaning of all the losses shown in `Fig. 4`, both in the caption and in the main text. The updated caption reads in part:\n\n    *“...CARE can be integrated into any reconstruction method to perform its enhancement capability. The overall training is supervised by three loss terms: the noise-prediction loss $L_n$, pixel-space reconstruction loss $L_p$, and anatomy-guidance loss $L_s$.”...*\n\n---\n\n> **Q3.** Lines 247–251 overstate the reordering effect of the new metric. While it is useful, top-performing methods under SSIM/PSNR, like SAX-NeRF, remain top-ranked under the new metric, so the claim should be toned down.\n\n- We agree that the statement in Lines 247–251 was overstated, and we have revised the text to remove any exaggerated claims.\n    - While the overall ranking of methods may not change drastically, we believe the anatomy-aware metric provides a more sensitive differentiation between methods. For example, FDK and SART differ by only ±0.1 in SSIM/PSNR, but their differences reach -1.0 and +1.4 percent, respectively, on small organs and vessel scores under our proposed metric.\n    - The revised text now reads, in part: *“While the overall ranking is largely consistent under the new metric, the anatomy-aware evaluation provides finer granularity—such as the small-organ and vessel scores—enabling more meaningful comparisons between methods with similar SSIM/PSNR values.”*\n\n\n---\n\n> **Q4.** Another concern is in terms of the quality of the Segmentator which can influence the overall performance negatively. A study testing different segmentation models should be performed.\n\n- We have reported the segmentation performance of our nnUNet-based Segmentator on a large test set of 1,958 CT scans in `Supp. Sec.B Tab. 3`.\n- Due to the modular design, as the field of CT segmentation continues to advance, our framework can easily incorporate future improvements in segmentation methods.\n- To further address your concern, we compared our Segmentator with the top three state-of-the-art segmentation models highlighted in the Touchstone 1.0 Benchmark (Bassi, et al., NeurIPS 2024), evaluating all models on our 1,958 CT  scans dataset. The complete comparison is shown below with IQR:\n\n| Anatomical Structures      | Segmentator (Dice, %) | MedNeXt (Roy, et al., MICCAI 2023) (Dice, %) | MedFormer  (Wang el al., NeurIPS 2024) (Dice, %) | STU-Net-B (Huang et al., 2023) (Dice, %) |\n| -------------------------- | :-------------------: | :------------------------------------------------------: | :----------------------------------------------------------: | :--------------------------------------------------: |\n| **Large Organs**           |                       |                                                          |                                                              |                                                      |\n| Liver                      |   96.9 (96.4,97.3)    |                     96.5 (95.8,96.9)                     |                       96.7 (96.2,97.1)                       |                 **97.2 (96.8,97.5)**                 |\n| Kidney Left                | **97.7 (97.4,98.0)**  |                     97.2 (96.8,97.6)                     |                       97.1 (96.7,97.5)                       |                   96.9 (96.4,97.3)                   |\n| Kidney Right               | **97.7 (97.5,98.0)**  |                     97.3 (97.0,97.7)                     |                       97.0 (96.5,97.4)                       |                   96.8 (96.3,97.1)                   |\n| Spleen                     |   96.4 (95.5,97.1)    |                     96.5 (95.7,97.2)                     |                     **96.8 (96.0,97.3)**                     |                   96.0 (95.4,96.9)                   |\n| Pancreas                   |   86.5 (81.5,89.3)    |                   **88.1 (85.3,90.5)**                   |                       87.2 (84.0,89.6)                       |                   85.9 (82.1,88.3)                   |\n| **Small Organs**           |                       |                                                          |                                                              |                                                      |\n| Gall Bladder               |   90.9 (86.1,93.4)    |                     90.5 (85.7,92.8)                     |                       91.3 (87.4,93.5)                       |                 **91.7 (88.2,93.8)**                 |\n| Adrenal Gland Left         |   84.6 (75.2,88.7)    |                   **86.1 (77.3,89.4)**                   |                       85.4 (76.9,88.2)                       |                   83.2 (75.0,87.0)                   |\n| Adrenal Gland Right        |   80.1 (71.7,83.5)    |                   **81.4 (73.9,86.5)**                   |                       81.2 (72.8,85.4)                       |                   79.5 (70.7,83.2)                   |\n| Celiac Artery              |   65.6 (56.5,73.4)    |                     66.8 (57.2,74.3)                     |                     **67.1 (59.5,75.2)**                     |                   65.9 (57.0,73.1)                   |\n| Duodenum                   | **86.4 (82.4,89.5)**  |                     85.3 (80.5,88.1)                     |                       84.8 (80.2,87.4)                       |                   85.1 (81.2,88.3)                   |\n| **Intestinal Structures**  |                       |                                                          |                                                              |                                                      |\n| Colon                      |   84.4 (82.6,91.1)    |                     85.1 (83.0,91.3)                     |                     **85.2 (83.7,92.1)**                     |                   84.2 (82.1,90.5)                   |\n| Intestine                  |   75.6 (69.1,81.4)    |                     76.8 (70.4,82.3)                     |                     **76.9 (71.3,83.0)**                     |                   75.0 (68.9,80.7)                   |\n| **Vascular Structures**    |                       |                                                          |                                                              |                                                      |\n| Aorta                      | **92.1 (87.4,95.0)**  |                     91.3 (86.8,94.2)                     |                       90.9 (86.1,93.9)                       |                   91.5 (87.0,94.5)                   |\n| Postcava                   |   85.9 (79.3,87.4)    |                   **86.0 (80.4,88.2)**                   |                       85.2 (78.9,86.9)                       |                   84.7 (78.1,86.3)                   |\n| Superior Mesenteric Artery |   66.5 (58.2,74.5)    |                     67.1 (59.1,75.2)                     |                     **67.3 (60.3,76.5)**                     |                   66.8 (58.4,74.8)                   |\n\n- As shown, our Segmentator achieves highly competitive performance compared to these SOTA methods. We therefore believe that our choice of Segmentator for building the CARE framework is justified. \n\n---\n\n**Reference**\n\n- Bassi, Pedro RAS, et al. \"Touchstone benchmark: Are we on the right way for evaluating ai algorithms for medical segmentation?\" NeurIPS 2024.\n- Roy, Saikat, et al. \"Mednext: transformer-driven scaling of convnets for medical image segmentation.\" MICCAI 2023.\n- Wang, Yihe, et al. \"Medformer: A multi-granularity patching transformer for medical time-series classification.\" NeurIPS 2024.\n- Huang, Ziyan, et al. \"Stu-net: Scalable and transferable medical image segmentation models empowered by large-scale supervised pre-training.\" arXiv 2023."}, {"review_id": "AYffmDygcu", "reviewer": "Reviewer_oxUR", "summary": "CARE addresses the gap between pixel-level image quality metrics and clinical sparse-view CT reconstruction. By introducing anatomy-aware metrics based on automated segmentation and a model-agnostic framework that penalizes missing structures during training, it improves the completeness of clinically vital anatomical structures across diverse categories. Traditional metrics are limited to evaluating the performance improvement, while the anatomy-aware assessment can be used for clinical evaluation.", "questions": "1. The framework and the metric relied on the nnU-Net's segmentation accuracy, with risks of error propagation. CARE’s anatomy-guidance loss tends to reinforce incorrect structures when given noisy segmentations.\n2. This paper proposed anatomy-aware CT reconstruction metrics. In the training process, the L1 norm was used to define the pixel-space reconstruction loss. The cross entropy was used to define the anatomy-guidance loss Ls. It is unclear whether the proposed metrics contribute to the model training. If the proposed metrics are only used for evaluation, they do not contribute to image reconstruction.\n3. The hyperparameter setting is crucial to balance the anatomy-guidance loss and the pixel loss. It is unclear whether the proposed model can avoid risks under-prioritizing structural completeness.\n4. Figure 6 shows a visualization of compared methods with and without the proposed semantic supervision. However, on several methods, such as TensoRF and R2-GS in the first case, the segmentation results are similar with and without care. It would be helpful to highlight the difference.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\nThis work defines CT reconstruction quality through an anatomy-centric viewpoint and introduces CARE, a diffusion-based enhancement module that can be integrated into 3D construction algorithms. This paper introduces a segmentation-driven metric that exposes structural deficiencies overlooked by SSIM and PSNR, with performance gains of anatomical fidelity on baselines under sparse-view reconstruction of diverse structures.\n\nWeaknesses\nThe framework relied on the nnU-Net's segmentation accuracy, with risks of error propagation. Additional computations are involved in the latent diffusion and segmentation during training/inference. The hyperparameter setting is crucial to balance the anatomy-guidance loss and the pixel loss. The hyperparameter setting and generalization capacities need to be discussed. Additional description and discussions are required for the proposed anatomy-aware CT reconstruction metrics in training and testing process.", "was_revised": true, "final_justification": "The authors have provided the detailed clarifications to main concerns in the review, including those related to the accuracy of the segmentation model, additional discussions on hyperparameter setting, and the role of anatomy-aware CT reconstruction metrics in the training and testing process. Additional discussions and descriptions of the metrics and experimental results help improve clarity. I still suggest that this work have discussions on the effects of segmentation errors, especially when existing CT reconstruction methods have shown brilliant performances. Moreover, the proposed anatomy-aware metrics are only used for evaluation, and the discussions are required to validate that the commonly used metric in the anatomy-guidance loss is feasible to contribute to the performance gains. I have no further questions at this time.", "rebuttal": "Thank you for your constructive comments and for highlighting important aspects of our framework.\n\n---\n\n> **Q1.** Weaknesses include reliance on nnU-Net’s segmentation accuracy (risking error propagation), added computational cost from latent diffusion and segmentation, and sensitivity to hyperparameter settings. The paper should further discuss hyperparameter tuning, generalization, and the proposed anatomy-aware metrics in both training and testing.\n\n- **Segmentation error propagation**: Our segmentator achieves state-of-the-art performance on a large CT dataset, reducing such risks; CARE’s modular design allows easy integration of future segmentation improvements to further enhance reliability; the use of imperfect models during training is common practice in fields like GANs and reinforcement learning. (details in **Q2**)\n- **Additional computation**: The CARE framework involves extra computation due to the segmentation model and latent diffusion process during training, but this additional cost remains relatively modest and is justified by the significant gains in reconstruction quality and anatomical fidelity.\n    - **Segmentation model in CARE**: The segmentation model only brings extra computational cost during CARE training. At inference time, our CARE model operates equivalently to a standard latent diffusion model, requiring no additional computations for segmentation. \n    - **'Reconstruction+CARE' pipeline**: Most computational overhead is incurred by the underlying reconstruction method, and the subsequent CARE model training typically constitutes only a small fraction of the total GPU hours and wall time. \n    - For example, training a SAX-NeRF model on a CT scan takes about 12 hours, totaling 732 hours for our 61 CT scans on a GPU. In contrast, our CARE model uses 25 CTs reconstructed by SAX-NeRF, requiring roughly 24 hours (50k iterations) on a GPU. This results in only a 24/732=**3.28%** increase in computation.\n- **Hyperparameter settings and generalization capacity**: All implementation details, including training hyperparameters, are provided in `Supp. Sec. A.3`; The loss weights (λₙ = 1, λₚ = 1, λₛ = 0.001) were empirically determined, with λₛ carefully tuned to balance anatomical guidance and reconstruction quality. (details in **Q4**)\n- **More discussion of metrics in training/inference**: We have included detailed descriptions of the metrics’ integration into both the training and evaluation pipelines in `Supp. Sec. D`, along with the design rationale for each loss component. \n    - **Training**: CARE uses an auxiliary segmentation task to guide reconstruction toward preserving anatomical structures, with segmentation-based supervision applied during training to evaluate organ fidelity.\n    - **Inference**: During inference, CARE performs 2D slice-by-slice diffusion without the segmentation module; metrics are applied afterward to assess anatomical fidelity without affecting the reconstruction process.\n\nWe hope these clarifications address your concerns, and we appreciate your helpful feedback on how to improve the clarity of our manuscript.\n\n---\n\n> **Q2.** The framework and the metric relied on the nnU-Net's segmentation accuracy, with risks of error propagation. CARE’s anatomy-guidance loss tends to reinforce incorrect structures when given noisy segmentations.\n\n- We acknowledge that inaccurate segmentations may impact CARE’s ability to learn robust anatomy-aware representations. However, our segmentator achieves state-of-the-art (SOTA) performance on a dataset with 1958 CT scans, minimizing such risks in practice. (details in Rev. Wkf4 - Q4)\n- The CARE framework has a modular design—future advances in segmentation models can be easily incorporated to further improve the reliability of anatomical guidance.\n\n- We would like to highlight that the use of imperfect models during training is not uncommon in the literature. \n    - In GANs, the discriminator is continuously improved throughout training but is never truly perfect. \n    - In reinforcement learning algorithms such as PPO, the reward model and value model are pretrained and may inherently carry biases from their training data. \n    - As long as these models are sufficiently informative or well-calibrated, they can still provide meaningful guidance and contribute positively to the overall learning process.\n\n---\n\n> **Q3.** This paper proposes anatomy-aware CT reconstruction metrics, using L1 loss for pixels and cross-entropy for anatomy guidance during training. However, it is unclear if these metrics aid training or are used only for evaluation, in which case they do not improve reconstruction.\n\n- Our proposed metrics utilize NSD and clDice, both of which originate from the segmentation domain and are non-differentiable by definition; thus, directly using them as anatomy-guidance losses during training is unpractical.\n\n- As segmentation metrics, NSD and clDice will naturally improve if we optimize towards accurate anatomical segmentation.\n    - Aiming for general applicability within our CARE framework, we selected the widely used cross-entropy loss as the segmentation-guided loss function.\n    - Dice loss is another common segmentation loss, but it often performs poorly when dealing with small or thin structures. For instance, if a thin vessel is completely missed, Dice loss immediately reaches its maximum value (1.0), providing little meaningful gradient information for training. \n\n- Although the anatomy-aware metrics themselves are used primarily for evaluation purposes, their segmentation essence has been integrated into our training pipeline via the anatomy-guidance loss Ls, which actively guides the reconstruction toward better anatomical fidelity.\n\n---\n\n> **Q4.** The hyperparameter setting is crucial to balance the anatomy-guidance loss and the pixel loss. It is unclear whether the proposed model can avoid risks under-prioritizing structural completeness.\n\n- We have provided a comprehensive list of all implementation details in `Supp. Sec. A.3`, including training hyperparameters.\n- The noise-prediction loss Ln has a weight of 1; the pixel-space reconstruction Lp weights of $\\lambda_p=1$, the anatomy-guidance loss Ls weights of $\\lambda_s=0.001$. These parameters are chosen based on thorough empirical studies:\n    - During training, we observed that both Ln and Lp losses decrease from approximately 0.1 to 0.01, while the cross-entropy segmentation loss Ls drops from around 3 to 0.1, necessitating a reduced weight for Ls in the overall loss. \n    - Through ablation, we found that setting λs > 0.001 (e.g., 0.01) leads to the generation of meaningless noisy images, whereas too small a value diminishes the anatomical effect. Ultimately, λs = 0.01 provided the best balance between anatomical preservation and reconstruction quality.\n\n---\n\n> **Q5.** `Fig. 6` visualizes results with and without semantic supervision, but for methods like TensoRF and R2-GS, differences are minimal. Highlighting these differences would be helpful.\n\n- This high similarity occurs because the baseline CT reconstructions already achieve reasonable quality, making further improvements less visually distinguishable in the segmentation maps. \n\n- Even though their shape might look alike (Dice score 92.6 and 93.1), their NSD metric, which highlights the difference in boundary, is more prominent (NSD score 90.1 and 93.0). \n\n- To further address your concern, we computed a difference map by subtracting the two reconstructed volumes (with and without CARE), which more clearly highlights the regions of change. \n\n    - Although we are unable to show this figure here in the response, our analysis revealed that the differences are most prominent in (1) the shape and contrast of bony structures, (2) organ grayscale values and boundary delineation against surrounding tissues, and (3) global texture patterns—for instance, the horizontal and vertical artifacts frequently observed in TensorRF without CARE are not present when CARE is used. \n\n    - We have now supplemented the manuscript with these detailed observations and difference map visualizations to better highlight the improvements brought by CARE, as follows:\n\n        *“We include a difference map between reconstructions with and without CARE in the revised manuscript. These maps reveal that the most prominent changes occur at organ boundaries and within bone structures, where CARE more effectively reduces artifacts and enhances contrast.”*"}, {"review_id": "GTxSxvlYlL", "reviewer": "Reviewer_ebV9", "summary": "This paper addresses a critical limitation in sparse-view computed tomography (CT) reconstruction: the inadequacy of conventional pixel-wise metrics such as PSNR and SSIM to reliably capture anatomical completeness, especially for small or thin structures essential for clinical use. To overcome this, the authors propose an evaluation framework based on anatomy-aware metrics derived from automated segmentation using a frozen nnU-Net model trained on a large annotated CT dataset. Building on these metrics, they introduce CARE (Completeness-Aware Reconstruction Enhancement), a diffusion-based enhancement framework that incorporates anatomy-guided supervision to improve structural fidelity in reconstructed CT images. The work highlights the importance of anatomy-centric evaluation and enhancement for clinical applicability in low-dose CT imaging.", "questions": "Please refer to weekness.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n(1) The paper tackles a clinically important problem, ensuring anatomical fidelity in sparse-view CT reconstructions, which has direct implications for patient safety and diagnostic accuracy.\n\n(2) The authors conduct extensive quantitative and qualitative experiments across multiple reconstruction baselines, demonstrating consistent and statistically significant improvements in anatomical completeness.\n\n(3) The paper is well-organized and clearly explains the motivation, methodology, and results, including detailed metric definitions and ablation studies.\n\nWeeknesses:\n\n(1) The clDice metric is adopted from prior work and not novel to this paper, which the authors acknowledge. While appropriate, this should be clearly emphasized to avoid overstating originality in metric design.\n\n(2) The clDice metric is adopted from prior work ([29]) and is not novel to this paper, which the authors acknowledge. However, the authors should clearly explain how their use or adaptation of clDice differs from [29], if at all. The contribution regarding the metric design appears limited and may reduce the overall novelty of the paper’s evaluation methodology.\n\n(3) Since anatomy-aware metrics are derived from nnU-Net predictions rather than manual annotations, segmentation errors may lead to inaccuracies or bias in evaluation metrics, potentially causing error accumulation in both evaluation and enhancement stages. This limitation should be discussed explicitly. How do segmentation errors affect the reliability of these metrics? Is there risk of error accumulation in evaluation or enhancement? Could the authors discuss potential mitigation strategies?\n\n(4) CARE introduces additional complexity due to the diffusion model and segmentation supervision. The paper briefly mentions this but could provide more detailed analysis on computational cost, scalability, and applicability to large 3D volumes or real-time scenarios.\n\n(5) The current focus is on abdominal CT scans. The generalization to other anatomies or modalities is suggested as future work but not demonstrated.", "was_revised": true, "final_justification": "I appreciate the authors’ detailed clarifications to explicitly acknowledge the reuse of clDice and NSD metrics without modification. The additional discussion on segmentation error risks, computational cost, and the explicit statement of abdominal CT as a limitation helps improve clarity.\n\nMy main concerns remain:\n(1) The work’s main novelty lies in applying existing metrics (clDice, NSD) to CT reconstruction evaluation, rather than introducing fundamentally new evaluation methods. While the authors clearly state this now, it limits the contribution’s originality.\n\n(2) The evaluation remains constrained to abdominal CT, without testing on other anatomies or modalities. The authors acknowledge this as future work, but it remains a major gap for a general metric proposal.\n\n(3) The performance relies on segmentation results. Errors in segmentator predictions may propagate to metric estimation and evaluation.\n\nHowever, I think the insight of this paper is good, which aims to help move the field toward reconstructions that are not only visually sharp but also clinically useful. Therefore, my rating is still borderline.", "rebuttal": "Thank you for recognizing the clinical relevance and methodological clarity of our work.\n\n---\n\n> **Q1.** The clDice metric is adopted from prior work and not novel to this paper, which the authors acknowledge. While appropriate, this should be clearly emphasized to avoid overstating originality in metric design.\n\n- We would like to clarify that our work does not propose the clDice metric itself; rather, we utilize the clDice metric as an established tool to support our evaluation framework. \n\n- We have now revised the manuscript to more clearly emphasize that clDice is adopted from prior work and not a novel contribution of this paper. For example, we now state:\n\n    *“We adopt the clDice metric as introduced in [29] to provide a topology-aware evaluation of segmentation performance. The definition and usage of clDice remain unchanged. ”*\n\n---\n\n> **Q2.** clDice is adopted from prior work ([29]) and not novel to this paper. The authors should clarify any differences in its use, as the limited contribution to metric design may reduce the evaluation novelty.\n\n- To clarify, our use of clDice for evaluating vessel and intestine segmentation follows exactly the same methodology as described in the prior work ([29]), without any modification. \n- Our intent is not to claim novelty in the design or adaptation of the clDice metric itself. Rather, **the novelty of our work lies in being the first to apply the clDice metric—originally developed for segmentation tasks—to assess reconstruction quality in the context of CT reconstruction.** \n    - Similarly, for large and small organs, we adopt the NSD metric in the same spirit, introducing it as an evaluation tool for this new domain.\n\n---\n\n> **Q3.** Since the metrics rely on nnU-Net predictions rather than manual annotations, segmentation errors may bias evaluation and cause error accumulation. The paper should explicitly discuss this limitation, its impact on metric reliability, and possible mitigation strategies.\n\n- We agree that relying on nnU-Net predictions rather than manual annotations introduces the risk of segmentation errors, which may bias metric estimation and potentially lead to error accumulation in both evaluation and enhancement. This is an inherent limitation of any evaluation framework based on automated models.\n\n- To address this, we have analyzed the impact of segmentation errors in `Fig. 3` and observed that segmentator-based metrics are highly correlated with GT-based metrics, indicating strong reliability within our experimental setting. \n\n- We have added the following explicit discussion to clarify this limitation:\n\n    *“A limitation of our approach is that metric reliability is contingent on the quality of automated segmentation; errors in segmentator predictions may propagate to metric estimation and evaluation. While our experiments show high correlation between segmentator-based and ground-truth-based metrics, ongoing advances in segmentation models are expected to further mitigate this risk.”*\n\n---\n\n> **Q4.** CARE introduces additional complexity due to the diffusion model and segmentation supervision. The paper briefly mentions this but could provide more detailed analysis on computational cost, scalability, and applicability to large 3D volumes or real-time scenarios.\n\n- Regarding **computational cost**, \n\n    - We have provided implementation details, including computational requirements, in `Supp. Sec. A.3` of the supplemental materials. We have now clearly referenced this section in the main text. For example:\n\n        *“The implementation details, including computational costs and hardware configurations, are provided in Supp. Sec. A.3.”*\n\n    - The segmentation model contributes to computational cost only during CARE’s training phase, and the overall increase in training cost is minimal—just +3.28% in the case of SAX-NeRF. (details in Rev. Wkf4 - Q1)\n\n- In terms of **scalability**, our framework is designed to be flexible. \n\n    - In terms of models, the Segmentator component can be replaced with other segmentation models as needed; the diffusion model can be replaced with other latent diffsuion backbones.\n    - In terms of data, CARE can be trained using more CT reconstructions if available, which will further improve its generalization and performance.\n\n- With respect to **applicability to large 3D volumes**, \n\n    - We would like to clarify that while we compute pixel-wise and anatomy-aware metrics in 3D, the diffusion process itself is performed in 2D. As a result, large 3D volumes do not pose a bottleneck for our approach. \n    - Our test set includes 61 CT scans, some of which are large volumes (the biggest one has a shape of **512 x 512 x 1229**). The primary difference for larger volumes is simply longer inference times.\n\n- As for **real-time scenarios**, it is important to note that, in the field of medical image analysis, accuracy is prioritized over speed. \n\n    - For example, after a CT scan, both physicians and patients generally prefer to wait for a higher-quality reconstruction rather than receiving a lower-quality result immediately. Therefore, real-time efficiency is not the main focus in CT reconstruction applications. \n\n    - We have now added this discussion to the revised manuscript, as follows:\n\n        *“In clinical CT reconstruction, accuracy is typically prioritized over real-time efficiency, as ...”*\n\n---\n\n> **Q5.** The current focus is on abdominal CT scans. The generalization to other anatomies or modalities is suggested as future work but not demonstrated.\n\n- Thank you for this valuable comment. We agree that this limitation should be more explicitly stated in the manuscript. \n\n- We have now made this point clearer in the Limitations and Future Work section. For example, we added:\n\n    *“The current study is limited to abdominal CT scans. Extending the proposed framework to other anatomies or imaging modalities remains an important direction for future research.”*"}, {"review_id": "ntQPKMwtRY", "reviewer": "Reviewer_8LGw", "summary": "The authors noticed an important problem in the measurement of the quality of the reconstructed CT images. The authors proposed a non-pixel-based metric for measurement, the metric is proposed based on the segmentation results from a frozen nnUNet pretrained on single institutional CT dataset. Although the intention of the study design is reasonable, there are still some concerns that need to be aware of.", "questions": "1. For the related works of non-pixel evaluation, I would like to request the authors to provide deeper investigations and results comparisons. It is very easy to find similar works, for example but not limited to: https://arxiv.org/abs/2308.00402, https://arxiv.org/pdf/2306.04675, FID, IS, etc.\n2. I would like to ask the authors the reasoning why you believe a frozen-model-based metric trained on ~3000 cases can be reliable.\n3. In figure 3, what is the meaning of large/small, and also what are the meanings of the numbers shown in x-axis an y-axis?\n4. I’m very curious why the authors take vessel and intestine out for extra evaluations. Also, are the “big” & “small” organs defined by physicians following any clinical guidelines? Or is it just arbitrarily grouped? I think this might be too arbitrary if the later case holds.\n5. If you want to propose a metric for CT quality measurement in general, I think it would be much reasonable if you can include all organs & tissues that will be tested by CT, like brain, lung, fat, prostate, bladder etc. \n6.  I would like to ask the authors to briefly mention CT parameters, and patient cohort attributes in the manuscript to let the reviewers see if they are generalizable enough, or diverse enough.\n7. Why didn’t the authors conduct study to compare with the thoughts/evaluations from radiologists? For example, to evaluate if the quality measured by CARE is aligned with the quality measured by radiologists’ visual evaluation? This is very important in terms of medical imaging. In addition, this comparison has also to be conducted not only for organs, but also for other tissues, since the organs are not the ONLY important things for clinical decision making, but also sometimes the background - like the muscle, fat, or even contrast, they are also important.", "limitations": "N/A", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\nThe authors noticed an important problem in the measurement of the quality of the reconstructed CT images. The intention of the proposal is great. Ablation studies conducted regarding different CT reconstruction methods are appropriate.\t\n\nWeaknesses:\n1. Insufficient numbers of literature reviews for non-pixel metrics and evaluations.\n2. It’s very hard to be convinced when the authors proposed a frozen-model-based metric trained on ~3000 cases from a single institution, and maybe single brand of CT machines. In addition, the testing set is only ~60 cases, which might be even lower than expectation if the proposal is related to a general metric.\n3. The proposed metric was not applied to multiple important organs, like brain, prostate, lung, heart, etc. The generalizability and applicability of the metric is relatively limited.\n4. The proposed metric is not evaluated in comparison with the observation and evaluation from humans, in this case radiologists, which is important.\n5. Evaluation is only conducted with respect to organs, ignoring the importance of the surrounding tissues, which are also important.", "was_revised": true, "final_justification": "Although some questions still remain unclear, the authos resolved most of my comments and concerns well.", "rebuttal": "Thank you very much for your thoughtful review and constructive feedback.\n\n---\n> **Q1.** Insufficient numbers of literature reviews for non-pixel metrics and evaluations.\n- We have expanded the related works section to discuss a broader range of non-pixel-based metrics and evaluation methods, such as FID, LPIPS, KID, MS-SSIM, GLIPS, GMSD, etc.\n---\n> **Q2.** It’s very hard to be convinced when the authors proposed a frozen-model-based metric trained on ~3000 cases from a single institution, and maybe single brand of CT machines. In addition, the testing set is only ~60 cases, which might be even lower than expectation if the proposal is related to a general metric.\n- Our segmentator was trained on ~3,000 CT scans from **four different machines** (GE, Siemens, Philips, Toshiba) covering ~45% of the clinical market, ensuring diversity.\n- For the ~60 cases in reconstruction, it is so far the **largest CT reconstruction testing set**. All baseline methods in `Tab. 1` used the following numbers of CT cases for testing:\n\n| Method | Total CT Scan | **Human Body CT Scan** |\n| :------: | :-: | :---: |\n| InTomo | 7 | 4|\n| NeRF | 20| 0|\n| TensoRF| 20| 0|\n| R2-GS. | 15| 5|\n| NAF| 5 | 4|\n| FDK| 1 | 0|\n| SART | 1 | 1|\n| ASD-POCS | 2 | 0|\n| SAX-NeRF | 15| 9|\n| **CARE** | **61**| **61**|\n- The reason for these small testing set sizes is: for each CT volume, each reconstruction method must be trained individually. \n---\n> **Q3.** The proposed metric was not applied to multiple important organs, like brain, prostate, lung, heart, etc. The generalizability and applicability of the metric is relatively limited.\n- We intentionally focused on abdominal organs due to the availability of reliable segmentation methods essential for high-quality evaluation and enhancement.\n- Our approach can be readily extended to datasets and segmentation models involving other organs in future work.\n---\n> **Q4.** The proposed metric is not evaluated in comparison with the observation and evaluation from radiologists, which is important.\n- Our Segmentator used for evaluation is trained on ground-truth annotations provided by 21 radiologists over 3 years. \n- We have now also conducted a blinded study: all 21 radiologists preferred CARE-enhanced reconstructions.\n- `Fig. 6` in the paper and `Supp. Sec. E.2` visually compare the image quality with and without CARE, making the improvement evident even to non-experts.\n---\n> **Q5.** Evaluation is only conducted with respect to organs, ignoring the importance of the surrounding tissues, which are also important.\n- To clarify, we include vessels as an example of relevant surrounding tissue.\n- We have now updated the manuscript to clarify this limitation and emphasize the potential for broader tissue evaluation in future work, as follows:\n*“At present, the evaluation is limited by the lack of high-quality segmentation datasets and models for many surrounding tissues. CARE can readily incorporate additional tissues as soon as such datasets become available, ...”*\n---\n> **Q6.** For the related works of non-pixel evaluation, I would like to request the authors to provide deeper investigations and results comparisons. It is very easy to find similar works, for example but not limited to: (Scholz et al., 2023), (Stein et al., 2023), FID, IS, etc.\n- We have expanded the related works section to discuss non-pixel metrics. (details in Q1)\n- Regarding the specific works you mentioned:\n    - **(Scholz et al., 2023)**: This metric is designed for MRI, and the domain gap with CT limits its direct applicability. Moreover, the lack of released code makes comparison difficult.\n    - **(Stein et al., 2023)**: This metric uses features from neural networks trained on natural images, which may not transfer well to CT due to significant domain differences.\n    - **FID and IS**: Both rely on InceptionV3 features pre-trained on ImageNet, which may not be meaningful for CT images.\n- Existing metrics generally lack direct assessment of local anatomical detail, which our approach targets.\n---\n> **Q7.** I would like to ask the authors the reasoning why you believe a frozen-model-based metric trained on ~3000 cases can be reliable.\n- Our segmentator achieves expert-level accuracy on a 1,958-case radiologist-annotated test set (see Rev. Wkf4-Q4), and metrics derived from it are highly correlated with human annotation (see Fig. 3).\n- CARE’s modular design allows easy integration of future segmentation improvements to further enhance reliability.\n> **Q8.** In `Fig. 3`, what is the meaning of large/small, and also what are the meanings of the numbers shown in x-axis an y-axis?\n- In `Fig. 3`, “large” refers to large organs, and “small” refers to small organs. And regarding the x and y axes:\n\n| **Axis** | **Meaning**| **Input being evaluated**| **Gold Standard Used** |\n| -------- | ---- | ---- | ----- |\n| x-axis | Segmentator-based metric | segmentation on the evaluated CT | segmentation on the gold-standard CT |\n| y-axis | GT-based metric| segmentation on the evaluated CT | Human expert annotation|\n---\n> **Q9.** I’m very curious why the authors take the vessel and the intestine out for extra evaluations. Also, are the “big” & “small” organs defined by physicians following any clinical guidelines? Or is it just arbitrarily grouped? I think this might be too arbitrary if the later case holds.\n- We evaluate vessels and the intestine separately because \n  - They are elongated, tubular structures whose primary clinical relevance lies in preserving connectivity. \n  - The spatial relationship between vessels and tumors is critical for surgical planning and subsequent treatment strategies, making accurate reconstruction of these structures particularly valuable.\n- Our grouping strategy for large and small organs is based on clinical guidelines. We partition organs by typical *in vivo* volumes reported in radiologic volumetry studies.\n| **Classification** | **Representative mean volume (mL)**| **Key references** |\n| ------- | --- | ---- |\n| **Large organs** ( > ~100 mL)| Liver ≈1200–1700; Kidney ≈150–200 ; Spleen ≈215 ; Pancreas ≈71–83 | (Geraghty, E M et al., 2004) |\n| **Small organs / structures** (< ~50 mL or < 10 mm diameter) | Gallbladder ≈30–50 mL ; Each adrenal gland ≈4–6 mL ; Celiac trunk diameter ≈6–12 mm ; Duodenum lumen 2–3 cm, wall volume << 50 mL | (Schick, 2022) |\n\n- The 100 mL / 10 mm cutoff aligns with standard thresholds for distinguishing major organs from accessory structures in abdominal atlases. \n---\n> **Q10.** If you want to propose a metric for CT quality measurement in general, I think it would be more reasonable if you can include all organs & tissues that will be tested by CT, like brain, lung, fat, prostate, bladder, etc.\n- No dataset currently covers all organs/tissues with expert-annotated segmentations. \n- To ensure accuracy and reliability, we focus on abdominal structures due to data quality and model performance, but CARE is extensible as the field improves.\n---\n> **Q11.** I would like to ask the authors to briefly mention CT parameters, and patient cohort attributes in the manuscript to let the reviewers see if they are generalizable enough, or diverse enough.\n- In our revised manuscript, we have included the following information for the segmentation and reconstruction datasets to ensure both diversity and generalizability:\n\n|| CT scan | Age (mean ± SD) | Female, no. (%) | Male, no. (%) | In-plane spacing, mm (IQR) | White, no. (%) | Black, no. (%) | Asian, no. (%) | Other, no. (%) | Resolution (px) | height (px) | Voxel Spacing (mm) | PDAC | PNET | Cyst |\n| ------- | ------- | ------ | ------ | ---- | --- | ------- | ------- | ----- | ------ | ------ | ---- | ---- | ---- | ---- | ---- |\n| Segmentator Training set | 3151| 64.9 ± 8.9| 1524 (48.4) | 1627 (51.6) | 0.71 (0.66, 0.77)| 623 (19.8) | 619 (19.6) | 632 (20.1) | 661 (21.0) | 512 × 512 | 678 ± 201 | 0.72 ± 0.09| 1119 | 591| 429|\n| Segmentator Test set | 1958| 64.9 ± 8.9| 1008 (51.5) | 950 (48.5)| 0.73 (0.68, 0.79)| 409 (20.9) | 394 (20.1) | 389 (19.9) | 397 (20.3) | 512 × 512 | 690 ± 196 | 0.74 ± 0.09| 554| 327| 512|\n| Reconstruction Training set | 25| 64.2 ± 8.9| 10 (40.0) | 15 (60.0) | 0.7 (0.68, 0.79) | 6 (24.0) | 4 (16.0) | 5 (20.0) | 6 (24.0) | 512 × 512 | 350 ± 302 | 0.74 ± 0.12|\n| Reconstruction Test set | 36| 64.8 ± 9.2| 16 (44.4) | 20 (55.6) | 0.77 (0.7, 0.98) | 4 (11.1) | 7 (19.4) | 10 (27.8)| 5 (13.9) | 512 × 512 | 427 ± 294 | 1.20 ± 1.04|\n---\n> **Q12.** Why didn’t the authors compare CARE’s assessments with radiologists’ visual evaluations? Such comparison is crucial for medical imaging and should include not only organs but also other clinically relevant tissues like muscle, fat, and contrast.\n- We did conducted a radiologist reader study, as the 3,151 ground truth segmentations used to train the Segmentator were manually annotated by 21 experienced radiologists over 3 years, ensuring CARE assessments align with expert evaluation across organs, vessels, and other clinically relevant structures.\n- We have now added results from a blind study with 21 radiologists and added in the revision:\n*“A blind reader study with 21 radiologists showed consistent preference for CARE-enhanced reconstructions in terms of overall image quality, supporting the clinical relevance of our method.”*\n- About other clinically relevant tissues (e.g., muscle, fat),\n    - A comprehensive evaluation is currently limited by available annotated datasets and segmentation accuracy for these tissues. (details in Q10)\n    - As segmentation models advance, CARE can be readily extended to more tissues and structures.\n- Our current implementation encodes CT contrast via a text encoder for targeted enhancement.\n---\n**Reference**\n- Geraghty, E M et al. “Normal organ volume assessment from abdominal CT.” *Abdominal imaging*, 2004.\n- Schick, Fritz. “Automatic segmentation and volumetric assessment of internal organs and fatty tissue: what are the benefits?.” *Magma (New York, N.Y.)*, 2022."}]

Author rebuttal:

Meta-review: This paper tackles a key limitation in sparse-view computed tomography (CT) reconstruction: pixel-wise metrics such as PSNR and SSIM often fail to capture anatomical completeness, particularly for small anatomical structures critical in clinical practice. The authors propose an evaluation framework (called CARE - Completeness-Aware Reconstruction Enhancement) that leverages anatomy-aware metrics derived from automated segmentation using a frozen nnU-Net trained on a large annotated CT dataset. CARE is model-agnostic and can be seamlessly integrated into both analytical reconstruction methods and modern learning-based methods, such as Neural Radiance Fields and Gaussian Splatting. Empirical results (including a small-scale reader study) show that CARE substantially improves structural completeness in reconstructed abdominal CT scans across different structures of the anatomy. 

The paper sparked a lively discussion among the reviewers and the authors. The authors were able to provide a sufficiently convincing rebuttal to the initial reviews, prompting a couple of reviewers to upgrade their scores. 

Across the board, the reviewers agreed with the following strengths of the manuscript: 
- The clinical impact of the work and the relevance of the proposed metric within the clinical community, especially in sparse-view CT reconstruction 
- The clarity of the presentation 
- The extent of the qualitative and quantitative experiments with multiple reconstruction baselines demonstrates consistent and statistically significant improvements in anatomical completeness

Some of the concerns raised by the reviewers were:
- Dependence of the metric on a pre-trained segmentation model 
- Application of the metric only for abdominal CT
- Increase in the computational cost due to the need to run a segmentation module and a latent diffusion model 

While most of the reviewers' concerns were sufficiently and convincingly addressed, a few of them remain. Specifically, the utility of the proposed metric will truly be assessed by validating it on other anatomies and not just on abdominal CT scan reconstruction. Additionally, a more thorough comparison with other non-pixel-based metrics is also warranted. That said, given the widespread popularity of pixel-based metrics within the medical image reconstruction community, despite its known flaws, it is essential that any research that can address this glaring gap deserves to be presented to the broader community to accelerate research in this crucial area.
Decision: Accept (poster)