DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
Authors: Jiayang Shi
Daniel Pelt
Joost Batenburg
Venue: ICLR 2026 Poster (2026)
Keywords: benchmark
dataset
inverse problem
computed tomography
diffusion models
reconstruction
Primary area: datasets and benchmarks
TL;DR: The first systematic benchmark for diffusion models in CT

Abstract: Diffusion models have recently emerged as powerful priors for solving inverse problems. While Computed Tomography (CT) is theoretically a linear inverse problem, it poses many practical challenges. These include correlated noise, artifact structures, reliance on system geometry, and misaligned value ranges, which make the direct application of diffusion models more difficult than in domains like natural image generation. To systematically evaluate how diffusion models perform in this context and compare them with established reconstruction methods, we introduce DM4CT, a comprehensive benchmark for CT reconstruction. DM4CT includes datasets from both medical and industrial domains with sparse-view and noisy configurations. To explore the challenges of deploying diffusion models in practice, we additionally acquire a high-resolution CT dataset at a high-energy synchrotron facility and evaluate all methods under real experimental conditions. We benchmark nine recent diffusion-based methods alongside seven strong baselines, including model-based, unsupervised, and supervised approaches. Our analysis provides detailed insights into the behavior, strengths, and limitations of diffusion models for CT reconstruction. The real-world dataset is publicly available at zenodo.org/records/15420527, and the codebase is open-sourced at github.com/DM4CT/DM4CT.

Published as a conference paper at ICLR 2026
DM4CT: BENCHMARKING DIFFUSION MODELS FOR
COMPUTED TOMOGRAPHY RECONSTRUCTION
Jiayang Shi
Daniël M. Pelt',
K. Joost Batenburg'
'LIACS, Leiden University
CCnntrum Wiskunde en Informatica
ABSTRACT
Diffusion models have recently emerged as powerful priors for solving inverse problems. While Computed Tomography (CT) is theoretically a linear inverse problem, it poses many practical challenges. These include correlated noise, arti- fact structures, reliance on system geometry, and misaligned value ranges, which make the direct application of diffusion models more difficult than in domains like natural image generation. perform in this context and compare them with established reconstruction meth- ods, we introduce DM4CT, a comprehensive benchmark for CT reconstruction. DM4CT includes datasets from both medical and industrial domains with sparse- view and noisy configurations. To explore the challenges of deploying diffusion models in practice, we additionally acquire high-resolution CT dataset at high-energy synchrotron facility and evaluate all methods under real experimental conditions. We benchmark ten recent diffusion-basedl methods alongside seven strong baselines, including model-based, unsupervised. and supervised approaches. Our analysis provides detailed insights into the behavior, strengths, and limitations of diffusion models for CT reconstruction. The real-world dataset is publicly available at (we.odo..ogggoeooooo wtt..oo//rrroorr//220/5555. and the codebase is open-sourced githubcoom/DMMCC//D44C7
systematically evaluate how diffusion models
INTRODUCTION
Computed Tomography (CT) is a typical example of an inverse problem, where the goal to reconstruct unknown object from indirect measurements (Sidky et al., 2020; Courdurier et al.) 2008 Purisha et al. 2019). Techniques developed for CT reconstruction often extend to other inverse problems across various imaging applications (Clement et al. 2005 Mistretta et al. 2006 Dines & Lytle 2005| Ladas & Devaney 1993). In many cases, the measurements are sparse or noisy, making the reconstruction problem ill posed. This leads to ambiguity,
multiple solutions may fit the prior knowledge is typically incorporated. Approaches to utilize priors range from heuristic regularizers, such Total Variation (TV) (Sidky & Pan 2008
measurements equally well. To resolve phisr
Goris et al. 2012). to learned priors via supervised deep learning (Jin et al. 2017).
Recently, diffusion models have emerged powerful generative models, achieving remarkable success in text and image generation (Wu et al. 2023c] Li et al. [0022] Nichol et al. 2021 Ho et al.)
2022). Motivated by their expressive modeling capacity, many works have proposed to use diflusion models as learned priors for inverse problems, showing promising results across variety of domains
(Chung et al.) 2023 Song et al. 2024 Zirvi et al., 2025). Theoretically, CT reconstruction is linear inverse problem and thus should benefit from these advances. However, practical CT imaging introduces many additional challenges. Factors such as complex noise characteristics, nonlinear preprocessing steps like log transformation, and various artifacts result in real-world CT pipelines deviating substantially from the idcalized linear model (Hendriksen et al. 2020). Therefore, a comprehensive and realistic benchmark is essential to rigorously evaluate diffusion models for CT reconstruction and to compare them against other established CT reconstruction approaches.
In this work, introduce DM4CT, benchmark designed to evaluate diffusion- based methods for CT reconstruction. We compare diffusion models with each other and against a range of strong, established baselines. As part of DM4CT, also propose unified taxonomy that organizes different

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Published as a conference paper at ICLR 2026
Measurements
Reconstruction pipelines
Output-evaluation
Datasets noise & artifacts
Medical CT. 2016 low dose grand challenge
representative diffusion methods established methods
Industrial LoDolnd
Real dataset sparse-view
Forward model
Real-world datasel scanned at synchrotron faciity
[+) Quantitative metrics
Configurations
Noise
Noise+ring
Qualitative metrics
angles 20 angles w mild noise 80 angles
Visual comparison of reconstructions
Image quality metrics
noise 60 angles w noise ring artliacts 40 limited angles w/o noise
Computation efficiency metrics
[d]
Figure I: Overview the DM4CT benchmark. (a) The reconstruction pipeline, where representative diffusion and baseline methods are applied to measured sinograms using the same forward model. (b) The datasets used the benchmark, including two simulated CT datasets (medical and industrial) and one real-world dataset acquired to evaluate robustness to limited views, noise, and ring artifacts. Two example FBP reconstructions under noise and ring artifact conditions are shown. (d) The evaluation metrics, including both qualitative (visual) and quantitative (image quality and computational efficiency) criteria.
synchrotron facility. (c) The five simulation configurations used
diffusion approaches based on their strategies for incorporating data consistency and prior knowledge (summarized in Table [T. The benchmark includes both medical and industrial CT datasets with controlled levels of noise and artifacts for objective, systematic comparison. addition, we acquire real-world dataset by scanning two rock samples at synchrotron facility, allowing us to examine the limitations of deploying diffusion methods in practice under realistic conditions. overview of our framework is illustrated in Figure
Our contributions: 1) We present DM4CT, the first systematic benchmark of diffusion models for CT reconstruction; 2) We acquire and release high-energy synchrotron CT dataset, offering rare, well-suited resource for benchmarking under realistic conditions; 3) We propose unified taxonomy of diffusion methods based on their strategies for data consistency and prior knowledge (Table D 4) We implement all benchmarked methods in the widely adopted diffusers" framework and open-source the codebase; 5) We perform extensive experiments providing practical insights into the strengths, limitations, and deployment challenges of diffusion models in CT.
We emphasize that our goal is not propose reconstruction algorithm, but provide the first systematic benchmark for diffuuson models CT.
PRELIMINARIES
2.1
COMPUTED TOMOGRAPHY
CT aims recover an unknown object RM from set of projection measurements y R". The measurement process can be mathematically modeled as a linear system
Axx
(I)
where E |nxm is the system matrix determined by the acquisition geometry. practical settings, the measurements can be sparse (i.e., m.) leading to underdetermined and ill-posed inverse problem. Furthermore, the measurements acquired during scanning are typically corrupted by noise. We denote the actual observed measurements ÿ E R The discrepancy between the ideal and observed measurements describes the measurement noise e = ÿ y. The combination of sparsity and measurement noise poses significant challenges for accurate image reconstruction in CT.
To address such challenges, prior knowledge is necessitated for the reconstruction. Classical methods
often utilize heuristie priors such TV regularization (Sidky & Pan 2008 Goris et al. 2012 Liu
https: whw.thhub.... http://gghhbb..ooo//gggggnnaaaee fusers
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et al. 2013 Kazantsev al., 2018). which assume image smoothness but lack domain-speciife
adaptability. Recent approaches leverage data-driven priors by training deep neural networks on paired sparse- and dense-view reconstruction images (Jin et al. [017 Chen et al. 2017: Pelt et al.
2018 /Zhng et al. 2021), capturing more expressive and task -spccffccccccc features. Alternatively, implicit
priors such Deep Image Prior (DIP) (Ulyanov et al.] 2018 [Baguer et al. 2020; Barbano et al.
2022) and Implicit Neural Representations (INRs) (Sitzmann et al. 2020 Shen et al. 2022 Wu et al.
2023b) regularize reconstruction through the neural network itself.
2.2
DIFFUSION MODELS
We briefly review the two types of diffusion models that are used as backbones in this work.
Pixel Diffusion Models. We consider Pixel-space Diffusion Probabilistic Models (DDPMs) (Ho al., 2020, Nichol & Dhariwal, (221) and view the forward diffusion and backward denoising processes as Stochastic Differential Equationns (SDEs) (Song et al., 2020). In the forward process, Gaussian noise is gradually added to data Zo Pdata such that, sufficiently large time T., the perturbed variable 2T approximates a Gaussian distribution IT ~ (O, I). This process can be described by the Variance- Preservvng Stochastic Differential Equation (VP-SDE) (Song et al. 2020):
3r x + v BBdw, 2
dæ
where the Bg is time- dimendependent noise schedule, and dw denotes the standard Wiener process. The backward denoising process attempts to recover samples from the original data distribution by gradually removing noise. This can be described by the corresponding reverse SDE (Anderson, 1982)
3: x - 3. Vn, log d( + B,dwü 2
dæ = -
where dw is the reverse-time Wiener process, and V Væ, log p(x;) is the score function of the interme- diate noisy distribution (Song & Ermon 2019
2020 A neural network is trained to approximate this score function, enabling sample generation by solving the reverse SDE.
Latent Diffusion Models. We also consider Latent Diffusion Models (LDMs) (Rombach et al. 2022), which perform the forward diffusion and reverse denoising processes in a diveer-dmensional latent space instead of the pixel space. The original data is first mapped to latent representation via an encoder E, resulting in = = {xx) The forward SDE is then applied in the latent space dz = = - + VB,dw. After the reverse denoising process in the latent domain, a decoder 'D maps the denoised latent back the data space. In our benchmark, we use a Vector Quantized Variational Autoencoder VAE) (Van Den Oord et al. 2017) as the encoder-decoder pair (E, D) following (Rombach et al. 2022:
Rout et al. 2023
Song et al. 2024).
DM4CT
To apply diffusion models to inverse problems such CT reconstruction, is necessary to incorporate the measurement into the reverse denoising process. From a Bayesian perspective, the posterior distribution over the unknown image given the measurements expressed as p(æ y) o p(z)p(y)) ()) This motivates a modification of the reverse-time SDE (Equation 3) to conditional reverse SDE:
3t x 3, (Va, logp(x;; + V logr(ylz;)) di + /B, dwx,
dx
where Vz, log ((x:) is the score function approximated by the trained diffusion model, and Va, log p( y (z;) introduces a measurement-informee correction. However, this measurement term is generally intractable, since y typically depends on the clean image Bg rather than the noised version Tt. A common strategy is to approximate the conditional term using the clean image estimate Lo(x;).
Ve, logp(y)(x;) xs V log (z))))
(5)
There are two widely used estimators for en (x,), ). both derived from the diffuuson process. The first (E. v1 ā(l)V., n, por,(())))
is based on the DDPM formulation (Ho et al. 2020), 2b
The second uses Tweedie's formula (Song et al. 2020 Kim & Ye 2021 which directly relates
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Table 1: Diffusion-based methods evaluated in DM4CT. Columns under Technique refer implemen- tation choices (e.g., latent-space diffusion DDIM-based sampling). Columns under Reconstruction Strategy denote how measurement conditioning is incorporated, including data consistency gradient steering (DC-grad), separate optimization steps (DC-step), plug-and-play priors, and use of approxi- mate pseudoinverse solutions. A indicates only a single-step update toward the pseudoinverse. / indicates methods that incorporate data fidelity via a conjugate- eereegrrrdnnntttn solve rather than direct pixel-space optimization step.
Technique
Reconstruction Strategy
Method
Latent
DDIM
DC-grad
DC-step
Plug-and-Play
Pscudo Lnv
Variational Bayes
MCG
Chung al.
2022
DPS
Chung et al.
2025
2023
PSLD
Rost et
2023
2023
PGDM
Song et
2025a
2023
DDS Chung et al.
2024
2024
Resample Song et
2024
DMPlug (Wang al.
2024
2024
/
/
Reddiff (Mardani et
2024
2024
HlyridReg Dou
2025
2025
1
DiffStateGrad Zirvi ct al
2025
2025
the posterior mean to the score function: to Va(t)
(x. (1- (1-a(t)) Vz, log (ax)();,), where
(t) = 113 By) is the cumulative noise schedule. These approximations enable conditioning on the measurement y during the reverse denoising process in a tractable way, forming the basis for variety of measurement-ang aware diffusion reconstruction methods.
3.1 DIFFUSION MODELS FOR CT RECONSTRUCTION
We compare nine representative diffusion- based methods for CT reconstruction. These methods differ primarily in how they incorporate the prior knowledge encoded by diffusion models into the inverse problem setting. Below, we briefly go through each strategy.
Data Consistency Gradient. Many methods incorporate a gradient- based data consistency steering during the reverse denoising steps (Chung al. 2023 Rout al. 2023 Song et al. 2024 Mardani jtt al. 2024; Dou et al.] 2025) Zirvi et al. [2025] Tewari et al. 2023). At each timestep, after the standard denoising step. the method computes data fidelity gradient gr based on the estimated clean image (2:))
9: = Vn, Va. - y)
(6)
where C(-) is the loss function, e.g., L2 norm. This gradient is then used to steer the current iterate toward data consistency
2 x2 79h
with step size n serving as a tunable hyperparameter balancing prior and data consistency. In the of latent diffusion models, the update must propagate through the decoder D, gt. V=L(AD())) y) 2, - 199t-
Data Consistency Optimization Step. Some methods go beyond gradient steering by inserting full
data consistency optimization steps between reverse denoising iterations (Wang et al. 2024 Song
al. 2024 Zirvi et al. 2025 These steps optimize
x arg min L(Ax; y)
(8)
to directly enforce consistency to the measurements. Since this projection onto the data-consistent manifold may disrupt the reverse diffusion trajectory, some methods include a remapping step to realign x with the reverse trajectory (Song et al. 2024). In the latent setting, the step becomes 2 arg ming, L(AD(z,) y).
Plug-and-Play. houg-and-play method is powerful class of methods that incorporates prior knowl- edge into classical iterative reconstruction method (Venkatakrishnan al. 2013). Unlike data
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consistency gradient-based approaches, they decouple data fidelity and prior enforcement by alternat- ing between solving data consistency subproblem and applying an unconditional denoising step. Specifically, they optimize x" arg min, ((Ax y), and in certain iterations, reverse diffusion (denoising) step is applied to refine the current estimate using the learned prior (Zhu et al. 2023
Wang et al. 2024 Song et al. 2023b Liu et al. 2023).
Pseudo Inverse. Several methods incorporate approximate pseudoinverse information to guide the reverse diffusion process (Chung et al. 2022 Song et al., 2023a] Mardani et al., 2024 Dou et al.) 2025). Instead of relying on the data consistency gradient, these methods compute a residual between pseudoinverse reconstruction of the measurements and that of the forward forward-projected image estimate
9: Va, C(A
A A'y), Dt 22 ?9:-
In addition, the starting noise in the reverse process may be initialized by blending random noise with the pseudoinverse reconstruction already inject data-aware guidance the start.
is important to note that directly computing the Moore-Penrose inverse A is generally infeasible in CT due to the large size and sparse structure of A (Kak & Slaney| 2000 Hansen et al. T02T Sorzano et al. 2017). Instead, we approximate A using Filtered BackProjection (FBP) or algebraic methods such as Simultaneous Iterative Reconstruction Technique (SIRT) (Gilbert 1972). which
serve as practical approximations to the inverse operator.
Variational Bayesian. contrast to direct diffusion sampling methods that iteratively denoise an initialized noise sample, the variational Bayesian approach (Feng et al., 2023 Mardani et al. 2024 Dou et al. 2025 Dou Song 20244] Peng et al. 2024) approximates the posterior distribution p y) using a parameterized family of distributions, typically Gaussian. The parameters of the surrogate distribution are optimized towards both data consistency and in-distribution fit. The optimization is performed using gradient descent or similar methods, and no explicit sampling along the reverse diffusion trajectory is needed.
3.2 DATASETS AND CONFIGURATIONS
Datasets. We perform experiments on three types of CT datasets: medical, industrial, and synchrotron. The medical dataset is the 2016 Low Dose CT Grand Challenge (McCollough et al. (017)). consisting of ten patient volumes ranging from 318 512 x 512 856 512 x 512 voxels, with nine volumes used for training and one for testing. The industrial dataset, LoDolnd (Shi et al. 2024a), contains tube filled with 15 distinct materials (e.g.. coriander, pine nuts, black cumin), yielding diverse structural features and slice-wise variability. We use the central 3,500 slices of the 4000 x 512 x 512 volume, with 3,000 for training and 500 for testing. addition, we include a small case study on sparse-view reconstruction from raw medical projections in Appendix A.8
To highlight, we include high-resolution synchrotron dataset. Two rock samples of similar composi- tion were scanned under identical conditions, resulting in reconstructed volumes of 679 x 768 x 768 voxels after cropping. Compared with the medical and industrial CT datasets, our acquired syn- chrotron CT offers higher spatial resolution, providing fine structural details. Its simple bera, circular- circular-trajectory geometry also enables slice-wise 2D reconstruction, substantially reducing the computational demands of benchmarking diffusion models.
For systematic evaluation, we apply controlled levels of noise and artifacts to the medical and industrial datasets, using four simulation configurations: i) 40 projection angles without noise (noise- free), ii) 20 projection angles with mild noise, iii) 80 projection angles with more noise, iv) 80 projection angles with noise and ring artifacts, v) 40 projection angles in [0, 4 without extra noise. For the synchrotron dataset, we subsample the original 1200 projections to 200/ 10/100/60 and apply minimal preprocessing. Full details of dataset preparation are provided in Appendix |A.6 and and A.7 For consistency across medical, industrial, and synchrotron datasets, we do not use Hounsfield Unit for display, as the benchmark not intended for clinical analysis but for evaluating reconstruction methods across domains.
3.3 IMPLEMENTATION AND COMPARISON METHODS
For each dataset, we train one pixcl-space and one latent-space diffusion model, which serve as shared backbones for all diffusion-based methods ensure fair comparison. Method-speeific
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Table Reconstruction performance (PSNR/ SSIM) of different methods under various configura- tions for medical, industrial and synchrotron CT datasets. The highest score among diffusion- based methods is shown in bold, and the second highest is underlined. A dash (-) indicates that the method exceeded the 40 GB GPU memory limit for single-slice reconstruction and is therefore not executed.
Medical
Industiial
Reel-woehh
Method
coerig1
coolig
cuuii 51
coefig 1r
oosEgi
confg H
coetig
ouiig NV
coelig
200 pess
g00
60gggg
FRBP
30 963164
14 9190111
1S7500 19
1521/018
11 113110
15 15.76113
50
SIRT T
90 100 80
262332-41
94-80 31
75 75.900-0
N 0A00
18,00030 36
164700 30
19 19-18-996
19 17700-00 49
17
17896 16
76 141611 56
7% Deve
27
ADMS PONTEPDTT
25 1150.35
25
16559331
16029
254599445
10255 34
55
01/0.52
21-920155
FISTA- FISTX-5BTV -BTV
90,570882
36,550.70
25.500.57
27844074
27 6600766
17,699.37
153340
30.189
20 20133932
10.204 46
28.080.52
29, 01/052
27.769.50
INX
21255
26 159.76
22.56189
23.300034
20.339357
19449955 52
22 22
21 21.4/060
2033835 56
28,009440
21.22046
32. 12.140155
54,000.7
25453085
25.458074
18388043
153940 22
15. 15.724
185592.9
17.550 A03
-4-
050.51
24
22 1333
MED
50005
28-120044
24.2221134
2000033-0
16610005
21 47440477
2111/8.54
228040.51
23.788050
PSUB
19 00050 75
130
35 7577877
75 06600
16.35 16.360-03
17:3800
300 50
18 08852-00 93
15.14 22
34 10
75/600 16110:46
44
FORM
30 2.360185
22 10
11
2966977777
21.00-955
18.93:0
23.300:53
19
29
77 111
76,7301
Recarpt
23 23653331
26.541775
16.400-11
17-3700
18 160
41
DMtflax
25.720171
25 18-77
25 25.2000E
23.0N0L50
18.119.31
11,530055 55
18. 15.2503:1
18.50031
18.346.00 36
Redail
23,000073
35.890677
23.00033
26-661133
20.528.56
10:11.0.00
21.35-0.99
21-13/8.99
21.4:0.33
28.33
30.441.54 28.060.51
77 31
76 97/86666
97 37
14.9001
18.410
19.1 18.50
30 31.00005 58
19 18000-43
17 15416
hyperparameters are tuned on held-out training subsets. We benchmark diffusion methods against a diverse set of classical and learning- based reconstruction approaches. Classical Reconstruction.
FBP and SIRT (Gilberi 1972). representing traditional baselines. Deep Neural Networks as Priors.
DIP (Ulyanvv et al.] 2018 Baguer et al. [2200] Barbano et al. 2022), INR (Sitzmann et al. 2020
Shen et al. [202]] Wu al. 2023b). both optimized per image without supervised training. Gaussian
Splatting-Base Based Reconstruction. R2Gaussian (Zha et al., 2024), which represents objects using explicit Gaussian primitives and optimizes their parameters directly to fit CT measurements. Model-
Based Iterative Reconstruction (MBIR). Fast Iterative
Algorithm (FISTA)
with Primal-Dual TV (Beck & Teboulle 2009 [hambolle & Pock (00TT) and Alternating Direction Method of Multipliers (ADMM) with Split-Bregman TV (Boyd ct al. 2011 Goldstein & Osher 2009), enforcing structural regularity through iterative updates. Supervised Learning. SwinIR (Liang et 2021). transformer-baste image restoration model trained end-to-end to map sparse- view to dense-view reconstructions (Jin et al.) 2017 Pelt et al. 2018). Full implementation and
training details are provided in Appendix A.
A.16 and A.17
4 RESULTS AND DISCUSSIONS
Reconstruction Performance. As shown in Table 2 diffusion-based methods generally outperform classical and MBIR approaches in terms of PSNR and SSIM, but often fall short of fully supervised SwinIR. The INR-based approach achieves comparable metrics diffusion methods, particularly in the noiseless scenario (config i) and on the real-world dataset. Visual examples in Figure reveal that diffusion models tend recover fine structural details that appear realistic but may diverge from the true reference, thereby reducing metric alignment. In contrast, INR and SwinIR produce smoother reconstructions, resulting in higher quantitative scores despite loss of nigh-freeuency details.
Among diffusion models, no single method or subclass (e.g., pixel vs. latent diffusion) consistently outperforms the others across all datasets and configurations, either visually or quantitatively. Per- formance on the real-worle dataset is generally worse than on simulated data, likely due to factors such as limited training data quality and distribution shift. Perceptual metric LPIPS and full visual comparisons are discussed in Appendix A.
Tradcoff between Prior and Data Consistency. Striking the right balance between prior knowledge and data consistency is crucial for the success of diffusion-based reconstruction methods. Figure Bh illustrates this tradeoff using DPS as an example. Increasing the step size 7 in Equation (DC grad) initially improves both data fit and reconstruction quality. However, when 17 becomes too large, the reverse denoising process is disrupted, leading to model collapse. In this regime, the reconstruction becomes dominated by measurement noise, severely degrading image quality.
"DDS is derived under an additive Gaussian noise model, solving a conjugate- conjugate-gradient gradient system of the form (AT A + +Zf) For Poisson noise, the measurements become biased and heteroscedastic, violating the Gaussian likelihond assumption and resulting in degraded performance. See Appendix A.9 for a detailed discussion and an ablation comparing Gaussian and Poisson noise.

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FBP
ADMM-PDTV
MOG
DPS
PSLD
PGDM
ReSample
0.16
0.76225.3
0.73 0.7328.79
Medical
29.05
0.77 30.97
0.86 25.1 0.8625.12
0.63 27.69
0.7327.25
0.73 26.78
0.75
INR
SwiniR
DMPlug
Reddiff
HybridRag
DifSStateGrad
Reference
FBP
ADMM- PDTV
MCG
DPS
PSLD
PGDM
ReSample
g
Industrial
16.96
SwinlR
DMPlug
Redciff
HybridReg
DiffStateGrad
Reference
FBP
ADMM-PDTV
MCG
DPS
PSLD
PGDM
DDS
25.38
0.28 28.88 80
0.53
28.61
51 25. 81
0, 2.116.644
0. 46 26.9
0.41.28.72
0,50
Synchratron
28.83
4.49
0.71
P8.95
0,53
INR
SwinIR
DMPlug
Reddiff
HybridReg
DifSttteGrad
Reference
Figure 2: Reconstruction results of diffusion- based and other established methods. Top: medical dataset (config iv, 80 angles with noise & ring artifacts); middle: industrial dataset (config ii, 20 angles with mild noise); bottom: real-world synchrotron dataset (60 angles). Red and green boxes show zoom-in regions. PSNR and SSIM appear in the top-left and top-right of each image. A dash (-) indicates that the method exceeded the 40 GB GPU memory limit for single-slice reconstruction and is therefore not executed. Images are consistently linear rescaled across methods to improve contrast.
Reconstruction Uncertainty. Diffusion models for CT reconstruction are inherently probabilistic, enabling uncertainty quantification. Following (Antoran al. 2023 Vasconcelos al.) 2023), we visualize the mean and standard deviation of ten MCG reconstructions from the same measurement in Figure 3p. Uncertainty is highest near structural edges-regions regions that are typically more ambiguous due noise and limited-angle artifacts. Notably, outer object boundaries show high uncertainty, echoing Figure 2 and earlier observations that diffusion models struggle to capture global contours when the learned prior lacks expressiveness.
Prior Contribution and Consistency: A Null Space Perspective. Figure Dilluttrates how different data consistency strategies influence prior contribution, measured by their null space components.

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Increase datn consistency
Sta-Dev
23
6.7
Datla Fit
641
0.000
10-
6,31
Collapse
0.09
643
00
Step
10"h
(b)
Figure (a) Impact of data consistency step size 7) (Equation 7 on PSNR and data fit in DPS. Moderate values improve both, while large 1) disrupts denoising and causes collapse. Visual examples in the plot highlight the transition from prior-dominated to noise-dominated reconstructions. (b) Mean and standard deviation of ten MCG reconstructions conditioned the same real measurement. Note that the real measurement used (b) is different from the one used for (a).
DC-grad (DPS) imposes soft constraints, often allowing more content in the null space. In contrast, DC-step approaches (ReSample) enforce data consistency more strictly, resulting smaller null components. The pseudoinverse-guide method (PGDM) offers middle ground between those two. highlights the trade-off between data consistency and prior-driven contents across strategies.
DPS
PGDM
ReSample
Reference
29.54
70%
29.77
27.38
0.79
Figure 4: Decomposition of reconstructions into range and null space components for different data consistency strategies with config i). For each method, the full reconstruction shown on the left, with zoomed-in in red insets of the range component in the center and the corresponding null component on the right. The top-left of each null component indicates its relative 1.2 energy as a percentage of the total reconstruction, reflecting the extent of content introduced by the prior. Zoom in for details.
Data Consistency for Latent Diffusion: Gradient or Optimization? In latent diffusion models, enforcing data consistency via gradients is more challenging than in pixel-space diffusion (Fabian et al. 2024), as the gradients must propagate through the VQ- VAE decoder. As shown in Figure 5 PSLD (a representative latent diffusion method that relies solely on data consistency gradients) produces discontinuities in the reconstruction,
under noise-free conditions (i.e., 40 projections
without noise). Similar artifacts appear frequently across configurations (see Figure 2 indicating a structural limitation of gradient-based enforcement in latent space.
In contrast, methods that incorporate explicit data consistency optimization steps, such as ReSample, effectively correct these discontinuities and produce more coherent reconstructions in noise- free settings. However, as illustrated in Figure aggressively enforcing data consistency through optimization steps can become detrimental in the presence of measurement noise (c.g., 80 projections with noise). In such cases, the reconstruction may overfit to noisy measurements, leading to degraded image quality and the amplification of noise-like features.
Impact of Measurement Sparsity and Measurement Noise. Figure 6 summarizes how different classes of reconstruction methods respond variations measurement sparsity and measurement noise. Diffusion- based methods, particularly pixel-space models, demonstrate clear advantages under sparse-view and high-noise conditions, where strong learned priors help compensate for limited corrupted measurement information. The performance gap narrows noise decreases views increase, where classical and MBIR methods also become more effective.
Computation Efficiency. Figure 7 shows that pixel diffusion models are generally more memory- and iiee-efficiett than latent ones. An exception is DMPlug, which uses the most memory despite being pixel-based method. SwinIR has the fastest inference but requires substantial memory, whereas INR and DIP are memory-efficiem but slower. Figure 77 details training costs. Latent diffusion involves stages: training the VQ-VAE and then the diffusion model in latent space.
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Config 40 projs noise
Config lit) 80 projs moise
ReSample
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ReSample
Reference
2T 757.05885
Figure 5: Reconstruction results of latent diffusion methods using only data consistency gradients (PSLD) versus additional optimization steps (ReSample) under noise-free (40 projections, noise) and noisy (80 projections) scenarios. ADMM-PDTV serves as a classical model-based baseline that applies data consistency optimization with heuristic prior. Red insets show magnified regions.
25
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(3) increese messurement sparsity
(b) Increase measurement nouse
Figure 6: PSNR comparisons of method categories under (a) varying number projection angles on the medical dataset (sparsity), and (b) different noise levels (average photon count) on the industrial dataset. Pixel diffusion refers to the six diffusion methods operating in image space; latent diffusion includes the other three latent-space methods (see Table I.. Network prior includes DIP and INR. classical methods include FBP and SIRT, and MBIR refers to ADMM-PDTV and FISTA-SBTV. Shaded regions indicate standard deviation of all methods in the category group.
Although it uses only one-third the GPU memory pixel diffusion, the encoder alone takes long to train as the full pixel model, making total training more costly. SwinIR demands the highest memory and training time. Ultimately, the best method depends on resource constraints and dataset size, with diffusion methods offering flexible trade-off between training cost and inference performance.
DIP
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Figure 7: (a) Reconstruction time and GPU memory. The time is counted on medical dataset. (b) Training time and GPU memory of pixel diffusion, latent diffusion and SwinIR.
Challenges in Practice. We identify three main challenges when applying diffusion models to CT reconstruction in practice: 1) Limited data availability: Unlike natural images, CT datasets are often small due to privacy constraints, acquisition costs, or experimental limitations. Our synchrotron dataset narross this gap by providing first step toward higher-quality training data. 2) Mismatched

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value ranges: CT can face inconsistent value ranges. In industrial CT, this arises from uncalibrated machines and heterogeneous materials, while medical CT, calibration information not always accessible, which similarly cause misalignment. Our dataset was acquired under strictly identical conditions to mitigate this issue as much as possible, making it well-suited for benchmarking. An empirical strategy for addressing misalignment is further discussed in Appendix [A.8] 3) Computa- tional overhead from geometry: Diffusion methods are already resource intensive, and sophisticated geometries (e.g., helical and cone-beam) exacerbate this by requiring full 3D reconstructions. Our synchrotron dataset instead uses straightforward parallel-beat circular trajectory, enabling efficient Alce-wise 2D reconstruction and reducing geometric overhead.
CONCLUSION
We present DM4CT, a comprehensive benchmark for evaluating diffusion models for CT reconstruc- tion. Our results demonstrate that diffusion models can serve as strong priors and achieve competitive performance across variety of CT reconstruction scenarios. However, several key challenges remain. These include the difficulty of balancing learned priors with measurement data consistency, especially under realistic conditions involving noise, artifacts and views. While diffusion models show promise, their practical deployment for CT reconstruction is still hindered by factors discussed. The proposed DM4CT can serve as a valuable resource for advancing future research in diffusion inverse problems and close the gap between methodological development and practical applicability.
Future Work. This benchmark highlights several promising directions for further research. First, flow-based generative models such as FlowDPS (Kim et al., 2025) emerging strong priors for inverse problems, and exploring their integration into CT reconstruction is an important next step. Second, combination of INRs with diffusion priors (Du al. 2024) may offer complementary strengths, particularly for structural fidelity in sparse-view regimes. Third, while we include a preliminary downstream segmentation analysis in the appendix, a more systematic evaluation of clinical relevance (e.g., organ-level metrics, radiologist scoring) is essential for assessing practical utility. Fourth, adapting natural-image -mmgee autoencoders CT data and further examining carly-stopping effects in diffusion training remain promising directions for improving training efficiency and understanding how representation quality influences reconstruction performance. Finally, key open question is the generalizability of cettusion-bbssee reconstruction across scanners, geometries, and acquisition protocols. Extending DM4CT with ialliiinntitutinnall or cross possspooooool datasets would enable rigorous testing of how well these models transfer to diverse real-world CT settings.
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Ackownledgement. The authors acknowledge financial support by the European Union H2020- MSCA-ITTN-0200 under grant agreement no. 956172 (xCTing). JS is also supported by grant from Dutch Research Council under grant ENWSS. 2.SS..201..000 (UTOPIA) and no. NWA.1160.18. (CORTEX). The computation in this work supported by SURF Snellius HPC infrastructure under grant EINF-15000. Synchrotron data acquisition financially supported by the Dutch Research Council, project no. 016. Veni. 192.23.
Ethics statement. This work adheres the ICLR Code of Ethics. No experiments directly in- volve human subjects or animals. All datasets used are publicly available. The medical dataset is anonymized and complies with its original IRB protocol.
Reproducibility statement We have taken extensive steps to ensure reproducibility. All datasets, including our newly proposed dataset, are publicly accessible without restrictions. We open-source all code and provide detailed documentation of hyperparameter tuning ranges and procedures.
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Zhicheng Zhang, Lequan Yu, Xiaokun Liang, Wei Zhao, and Lei Xing. Transct: dual-path transformer for low dose computed tomography. In Medical Image Computing and Computer Assisted Intervention-MICCA 2021: 24th International Conference, Strasbourg, France, September 27 27-October 1, 2021, Proceedings, Part 24, pp. 55 64. Springer, 2021.
Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiczhang Cao, Bihan Wen, Radu Timofte, and Luc Van Gool. Denoising diffusion models for plug-and-play image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1219-1229, June 2023.
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A APPENDIX
A.1
LIMITATIONS
This work introduces DM4CT, benchmark for evaluating diffusion-basee based methods in CT reconstruc- tion. While our goal is to provide comprehensive and systematic evaluation, several limitations remain.
First, the benchmark assumes accurate and known forward operators. In practice, system im- perfections such as mechanical misalignments or calibration errors can introduce forward model inaccuracies (Ström et al. 2022; Cho et al.) (205). which are not accounted for in this study and may affect reconstruction quality real deployments.
Second, although all diffusion methods share the same pretrained pixel and latent models back- bones, they still require and additional optimization, the selected hyperparameters may not be optimal for every method or scenario, especially given differing sensitivity to parameter values. This limitation also applies to the other comparison methods.
hyperparameter tuning. Despite performing grid search
Third, while we include datasets from both medical and industrial domains, they still represent only subset of real-world CT applications. As such, conclusions drawn from DM4CT may not fully generalize to other domains or imaging tasks.
Fourth, the evaluation relies primarily PSNR and SSIM, which may not fully capture recon- struction fidelity in practical settings, especially when image intensity ranges vary. For example, consistent structural details with small intensity shifts can yield low scores despite visually accurate reconstructions.
Finally, while we include exploratory downstream segmentation study, it does not guarantee per- formance on clinically meaningful tasks such as anatomical segmentation, organ-volume estimation, or radiologist assessment. The results depend heavily on the choice of segmentation model (SAM) and the instance-level mask-matching strategy, and therefore should be interpreted as preliminary. A more thorough downstream evaluation would required draw firm conclusions about the clinical applicability of diffusion- slffusiin-bbsed reconstructions.
A.2 BROADER IMPACT
This work benchmarks diffusion models in the context of CT reconstruction, a domain where accurate image recovery from incomplete noisy measurements is critical. As generative models, diffusion approaches can effectively leverage prior knowledge to fill information gaps. However, this prior contribution may also introduce content not grounded in the measurement data, raising concerns about potential hallucination. Our benchmark aims to systematically evaluate this trade-off and promote deeper understanding of the behavior and limitations of diffusion-basee reconstruction.
In medical applications, where diagnostic decisions rely on image fidelity, rigorous clinical validation essential before deploying such methods. In industrial settings, although the risks may differ, careful verification and domain-specific assessment are likewise important to ensure reliability and safety.
A.3 USE LARGE LANGUAGE MODELS
We acknowledge the use of the large language model, ChatGPT, to assist in refining the text. The tool was utilized at the sentence level for tasks such as correcting grammar and rephrasing sentences.
A.4 RANGE NULL SPACE DECOMPOSITION
Given a CT forward operator A 8m it often exist at least pseudoinverse A mx satisfies A I. Since AAAA = A, this implies that A A acts as projection operator onto the range of A. Correspondingly, the operator I A A projects onto the null space of A, it satisfies A(I A A) 0.
Thus, for any signal E R. the following decomposition holds:
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== A' + (I- At Aaa,
(10)
where Al Ax represents the range component, and (I - A) represents the null component.
In the context of CT reconstruction, the range component captures information directly supported by Ax and reflects the data-consistent part of the solution. In contrast, the null
the measurements component contains information not constrained by the measurements and instead arises from the reconstruction method, e-g., by making use of prior knowledge or learned structure. Multiple distinct null components can exist for given measurement, giving rise to multiple feasible solutions that all satisfy the same data consistency condition.
Therefore, decomposing reconstructions into range and null space components provides a valuable perspective for analyzing how much of the reconstruction is supported by data (range) and how much is introduced by priors (null) (Wang et al.) 2023 J. This decomposition enables structured evaluation of data consistency and prior influence diffusion-bais -based CT reconstruction.
Practical Approach for Range Null Space Decomposition. Directly computing the pseudoinverse At generally infeasible in CT due to the high dimensionality and sparse structure of the system matrix A Kak & Slaney 2001; Hansen et al. 2021, Sorzano et al.) (017). Therefore, we adopt more practical approach
computing the null space component without explicitly forming A1
Specifically, we estimate the projection onto the null space, (I A""A)æ using iterative method as in (uoo et al. 2022).
classical algorithm for solving inverse problems, to isolate the null-space component. The procedure outlined in Algorithm Following determine the step size by first estimating the largest eigenvalue μ of the operator A, and then set 0.9op to ensure convergence while accelerating the iteration. After obtaining the null-space component &null, the corresponding range-space component is computed by subtraction: Frange
We employ the Landweber iteration (Landweber) 1951).
(Kuo et al. 2022).
Fmull
Algorithm Landweber method for computing Fuall
Require: forward operator A, object æ, step size a, tolerance
Initialize (0)
2: Set (())) &- Ax
3: Set iteration numbert 0
4: while [|r[(]]| 2 do
5: (t++1)
+ (1) aTT((
6:
(((-1) (+++)
7: 1+1+1
8: end while
9: Taull f- (t)
10: return Fnull
A.5 DIFFUSION MODELS FOR CT RECONSTRUCTION
Algorithm 2 outlines common template followed by many diffusion- based methods for solving inverse problems such CT reconstruction. These approaches typically integrate data consistency into the reverse diffusion process, guiding the sample trajectory toward agreement with the measured data. At each timestep, clean image estimate obtained from the noisy sample, and for example, data consistency gradient is computed and applied to refine the current iterate. Variants of this template differ primarily in how they estimate the clean image, enforce data consistency, or combine prior and measurement information.
A.6 EXPERIMENTAL SETUPS FOR MEDICAL AND INDUSTRIAL DATASETS
Train/Test Dataset Split. For the 2016 Low Dose CT Grand Challenge dataset, the following CT volumes for training: L067, L096, L109, L143, L192, L286, L291, L310, and L333. Volume L506 reserved for testing.
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Algorithm 2 Template for diffusion methods for CT reconstruction
Require: Number of diffusion steps T, measurement forward operator A, pretrained score function estimator s=(;;)
1: 2T ~ IN
2: for t -. do
3:
Estimate noise: εt+1 (2
4:
Estimate clean image: T.g <- through DDPM or Tweedie's formula with 2++1 and ct+1
5: Tt step backwards using dhg and ēt+1 with scheduler, e.g., DDPM, DDIM
6: Ve; - data consistency step using To. e.g., gradient from data consistency
7: 3t Ft V
8: end for
9: return Reconstructed object æ0
For the LoDoInd dataset, which consists of 4,000 slices in total, select slices 501-3500 for experiments. Specifically, slices 501-3000 are used for training and slices 3001-3500 are used for testing.
Normalization. The AAPM 2016 Low-Dose CT Grand Challenge provides HU-calibrated recon- structions. We apply a global linear mapping from the possible HU range [HU min- HU max] of the dataset to 1-1, 1J. ensuring consistent normalization across all volumes. For the industrial CT dataset, training and test slices originate from the same scan. We compute the global minimum and maximum over the full volume and linearly map all slices to [-1, 1] using these values. All mappings are linear and invertible, original attenuation/HU values can be recovered via the inverse transform.
Noise Simulation. According to Beer-Lambert's 's law (Beer 1852). the detected photon count T* related the initial photon count To, the average absorption coefficient y, and the line integral measurement yo. as follows:
I Io exp(-
(11)
where Yo is the clean measurement and 7 controls the attenuation strength. To simulate measurement noise, the detected photon count is modeled as a Poisson-distribute random variable:
~ Poisson(In exp(-796)
(12)
and the corresponding noisy projection is recovered by inverting the Beer-Lambert relationship:
i
y = 7
log
To
(13)
To determine the desired noise level, we first compute the average absorption of the original measure- ment yo by evaluating the mean of exp(-yo) We then adjust the intensity by scaling yo with a constant factor y to match the target average absorption. For Configurations ii), iii), and iv), we set the average absorption to 50% (Table by applying this scaling. The photon count is further varied to simulate different noise levels according to the noise model defined in Equations 12 and 13
Ring Artifact Simulation. Ring artifacts arise from systematic detector defects, such miscalibrated or malfunctioning detector elements (Sijbers & Postnov 20044 Münch et al.] 2009 Boas et al.) 2012)..
These artifacts typically manifest as rings in the reconstructed image due column- wise errors in noise to randomly selected detector
the sinogram. To simulate such effects, we add columns. Let M be a binary mask of the same shape as the measurement y, where fraction Pring of columns are set to (corrupted) and the rest to 0 (clean). Given the clean measurement yo. the corrupted measurement generated as:
y = yo M o(0.
(14)
where a is the standard deviation of the Gaussian noise applied to the defective pixels.
To determine an appropriate noise level a for simulating ring artifacts, we first compute the standard deviation yoo of the clean measurement yoo For Configuration iv) in Table 3 set the ring artifact
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intensity to o² 0.25 -2. which introduces fixed-pattem perturbations to fraction Pring of detector columns. This simulates column-wise inconsistencies the sinogram that manifest as ring artifacts in the reconstruction.
Configurations. The parameter settings for the four simulation configurations used our benchmark summarized in Table Each configuration varies the number of projection angles and the severity of simulated noise and ring artifacts to evaluate reconstruction robustness under different levels of data corruption.
Table 3: Simulation parameters used for generating noisy and artifact-corrupted sinograms. To controls the Poisson noise level based on the Beer-Lambert model (see Equation LTp. while Pring and o² define the severity and intensity of ring artifacts (see Equation 14h. yo is the standard deviation of original clean measurement yo. value of "_" indicates no corruption of that type.
Configuration
absorption (noise)
(moisc)
(ring) of (ring)
40 angles without noise
ii) 20 angles with mild noise
50%
10000
iii) angles
iv) 80 angles with noise and ring artifacts
50%
10000
0.05
0.25
v) 40 angles (0.3))
A.7 REAL-WORLD SYNCHROTRON CT DATASET
We acquire a high-resolution synchrotron CT dataset at a beamline operating at 24 keV with an exposure time of 4 seconds per projection. Two rock samples are scanned using parallel-beam geometry with 1200 projections over 180%, The detector has pixel pitch of 9um and the raw projection size is 679 x 1653 pixels. Example projection images of three different scanning angles To remove the background area, projections cropped to 679 768.
shown in Figure
Table 4: Acquisition parameters of the real-world synchrotron CT dataset used in this benchmark. Both rocks are scanned using the same setup under parallel-beam geometry.
Sample
Size
Exposure
Filter
Energy (keV)
Durk Fields
Flat Fields
Center (pee ctop)
Center crop)
Crop Size
F3.1
1200
None
F3 text)
1200
None
Training Reconstruction. For the training rock, flat-field correction is performed using the median of 10 dark and 10 flat fields. Log-trans transformation and ring artifact reduction are applied. The ring reduction method identifies anomalous detector pixels by computing the difference between the mean and median values of each detector row (Rivers 1998 Boin & Haibell (0066). Full-angle FBP reconstructions are used as the training target. The reconstructions of train and test rocks using full angles, median dark/flat field and ring reduction are given in Figure
Normalization. The two rock samples were scanned under identical settings to align value ranges as much as possible. Nevertheless, slight inter-scan mismatches remain due to noise and mild ring artifacts. After reconstruction of the training images, we prepare the test projections. We linearly rescale its projections so that the resulting reconstructions approximately match the dynamic range of the training rock.
Benchmarking Setup. For benchmarking, the test rock reconstructed using only 60/100/200 evenly subsampled projections (out of 1200). A flat/dark field is randomly chosen for flat field correction and no ring artifact correction applied.
A.8 TOWARDS VALUE RANGE MISALIGNMENT: A SMALL CASE STUDY
practical challenge for diffusion-basse CT reconstruction is the misalignment of value ranges. This arises for several reasons: in industrial CT, complex material characteristics and the absence of standardized calibration make value ranges strongly dependent on scanning conditions; medical CT, although HU standard, the correction factors may be inaccessible, leading inconsistencies:
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Projection nr. 10
Projection né. 400
Projection nr. 800
Figure 8: Example projection images (before cropping) of the synchrotron dataset at different angles.
Reference train rock
Reference test rock
Figure 9: Reference reconstructions of three slices from the training and test rocks using all 1200 angles.
and in synchrotron CT. high photon energies and facility-dependent acquisition settings can likewise cause range shifts.
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Table Reconstruction from raw projections (40 angles) under value range misalignment, corrected
using an empirical linear mapping. Results are approximate and intended to demonstrate feasibility rather than optimal performance. PSNR and SSIM are computed slice-wise in 2D and averaged across slices, using the corresponding physical value range of each reference slice.
Method
FDK
SIRT
INR
MCG
DPS
DMPlug
Reddiff
PSNR/SSIM
19.48 0
23.11 /0.4
24.77 /0.33
29.12 0.64
29.10/0.61
23.65 / /0.48 27.32/0A.
We present small case study demonstrating that diffusion models can still be applied under such misalignment using simple empirical correction. Specifically, we use diffusion models trained on the 2016 Low Dose CT Grand Challenge dataset, where training images were normalized by mapping the minimal and maximal HU values to [-1, For evaluation, we reconstruct from raw helical projections rebinned to fan-beam geometry following Wagner al. (2023). As calibration factors are unavailable, the reconstructed images from raw projections do not align in value range with the
normalized training images.
To address this, we select one training reconstruction and one raw-projection reconstruction at approximately corresponding anatomical locations from different patients. By estimating intensity values for background and bone regions, we establish an approximate linear mapping between the normalized training image Baomm and the target test image Vur. Var a nwem +b. This mapping allows us to sample within the normalized range [-1, during the diffusion reverse process, transform samples into their physical range for data consistency enforcement, and then map them back.
This procedure is purely empirical and yields only approximate reconstructions. Nevertheless, Table 5 shows results for reconstruction from raw projections with 40 angles, suggesting that such linear range alignment may serve as practical workaround for value range misalignment when applying diffusion models in real scenarios. Figure I0 visualizes the approximate reconstructions, Despite the approximate attenuation values, the main structural features are recovered.
FBP
SIRT
INR
MCG
DPS
DMPlug
Reddiff
Reference
16.3
St13a.55
2213
0.55 27.17
0.541211
Dat 24.13
0.5t
1522
2
Figure 10: Reconstruction from raw projections (40 angles) under value range misalignment, corrected using an empirical linear mapping. Results are approximate and intended to demonstrate feasibility rather than optimal performance. PSNR and SSIM are shown in the lower-left and lower-right corners of each reconstruction.
A.9
SENSITIVITY OF DDS NOISE MODEL
DDS
(Chung al. 2024) reconstructs from noisy measurements by solving
((x) = Ax
- doll2.
where ie corresponds to the diffusion model's estimate of the clean signal (denoted as é; in the original paper). Minimizing this objective leads to the linear system
( AT A + DJ tm A y,
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which is solved via conjugate gradient. This formulation implicitly assumes Gaussian likelihood
p(y x)) exp (
1
20 Ax y1(
with noise covariance o² I
More generally, the data- aita-fidelity term can be written as
((x) (y
Ax) Rly Az) + 1 2 2oll2
where R R-E-1p is the inverse noise covariance. The corresponding normal equation becomes
(7 RA I] = Φo + 7 Ry.
For Gaussian noise with variance o², 1 I, and DDS reduces tuning single scalar hyperpa- rameter d.
In CT, measurements are typically corrupted by Poisson noise:
ay,)²² = Elys] = Le -Ar));
After log transformation,
e(Ax);
Var Var(-log(y/Io))
1o
so the noise covariance becomes
Σ = diag(of),
R = R-E-== diag
where each a, depends the forward projection (Az). Thus, R data-dependent and cannot be absorbed into a single scalar hyperparameter a. DDS therefore implicitly mis-specifies the likelihood under Poisson noise.
For fairness and consistency, we keep the DDS results in Table 2 using the standard Poisson noise model employed throughout our benchmark. To further illustrate the effect of noise-model mismatch, Figure [ presents a controlled comparison in which Gaussian and Poisson noise are simulated at matched levels (i.e., producing similar FBP PSNR/SSIM). Under Gaussian noise, DDS successfully recovers fine structures, consistent with its Gaussian likelihood assumption. However, under Poisson noise (despite the same overall noise severity) the reconstruction quality degrades substantially, both visually and quantitatively. This experiment supports our observation that DDS is highly sensitive to the assumed noise model and explains its weaker performance when applied to Poisson- corsuttd CT measurements.
A. SEGMENTATION AS DOWNSTREAM TASK
We use segmentation as downstream task for the medical CT reconstructions to provide an initial exploration of how different reconstruction methods affect anatomical structure interpretation. We emphasize that DM4CT is not intended for clinical analysis; the goal of this subsection is to offer preliminary technical discussion of how reconstruction quality may influence downstream tasks.
To obtain segmentation masks, we apply the Segment Anything Model (SAM) (Kirlllvv et al., 2023 the reconstructed images from configuration i). Since SAM is trained on RGB images, each CT slice is duplicated across three channels before inference. The 5AM-generater segmentation of the reference reconstruction treated as the geedoogroung truth for computing Dice (Dice) 1945) and Intersection-over- Union (IoU) (Rezatofighi et al. 2019). Because SAM may produce different discrete label sets for different reconstruction methods, we align instance masks using the commonly adopted Hungarian matching strategy (Lin et al. 2014 Kirillov et al. 2019 before evaluating metrics.
Table 6 reports the average Dice and IoU scores. Overall, diffusion based methods underperform classical and MBIR approaches this task. A likely explanation is that, even when classical and MBIR reconstructions are blurrier and yield lower PSNR/SSIM. they tend to preserve coarse anatom- ical boundaries more faithfully, resulting in greater spatial overlap with the reference segmentation.
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Reference
FBP
DDS
40 angles
26.45
0.68 30.08
0.83
Gaussian
22.11
0.41 28.54
0.76
Poisson
22.27
0.42 25.00
0.84
Figure 11: Effect of noise model mismatch on DDS. Gaussian and Poisson noise are simulated to produce similar FBP baselines (roughly PSNR/SSIM). DDS reconstructs fine details under Gaussian noise but degrades significantly under Poisson noise, both visually and quantitatively.
Table 6: Segmentation performance (Dice IoU) on the medical dataset with 40 projections.
Method
Dice/ IoU
FBP
0.603/0.500 0.19/0.735
SIKT
Conssian
0.551
SwinlK
0.665/0.666
MCG
0
DPS
0.555
PSLD
0.261/0.33 000320
PGDM
0.635 0.566
Resimple
0,633/0.35 548
DMPhig
0.497/0 403
Reddit
0.193/0.414
DintStuetirad
0.717/0665
HlybiidRgg
In contrast, diffusion bbaed reconstructions often introduce subtle hallucinations or structural shifts reflecting traiming-set set statistics rather than exact anatomy, which can reduce overlap despite producing visually cleaner images. Figure 12 visualizes the segmentation overlays and illustrates this behavior.
We stress again that this benchmark is not designed for clinical evaluation. The segmentation results here are influenced by the choice of SAM and the mask-matching protocol, and therefore serve only as an early investigation of how diffusion-basee reconstructions may impact downstream tasks. More carefully controlled studies are required before drawing conclusions about clinical applicability.
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Glossionl DEP
Grussien
MBIA
2I2
0.33
0.44
28.33
02
2ATFER)
C21 2038
Reccom tructions
PSP
SIRT
ALRMM PDTV
-13TA-91VV
Mor
DC
PGDM
DMPls
Redsat
24 1M
B.
21.00
22
215:
GoT 28:0
DIP
INR
SWEER
Supervised
Reference
E1001
Segmentations
LIfS:
PGDM
LIMPPa
2.43
INR
MybrldPeg
HeGaamp w
Figure 12: Segmentation masks obtained using SAM for different reconstruction methods. Dice and IoU scores are shown in the upper-left and upper-right corners of each mask, respectively. These visualizations highlight how variations in reconstruction quality influence the resulting anatomical segmentation.
A.11 FINE TUNING EXISTING NATURAL IMAGE ENCODERS
We investigate whether natural-image autoencoders, specifically the KL-regularized variational au- toencoder (AutoencoderKL) used in SDXL (Podell et al. 2023), can be adapted to CT reconstruction through fine tuning. This contrasts with the VQ-VAE used throughout our benchmark. We begin with the pretrained AutoencoderKL released by stabilitya I and evaluate two strategies: (i) using it directly without modification, and (ii) fine tuning it on CT images. We also train an AutoencoderKL (input/output channel set to 1) from scratch for comparison.
Since SDXL's AutoencoderKL is trained on RGB images, we duplicate each CT slice across three channels for both input and target. Both fine tuning and training from scratch use MSE loss with a KL regularization weight of 10-4, Fine tuning proved unstable and benefited from very small learning rates (10-6), whereas training from scratch uses a learning rate of 10-4, After obtaining the autoencoders, we train separate latent diffusion models for each encoder for 200 epochs under identical settings.
Table 7 summarizes the representational and reconstruction capabilities of these autoencoders. The pretrained AutocncoderKL already provides reasonable representations of CT images, but fine tuning improves its autoencoder PSNR/SSIM slightly. Training AutoencoderKL. from scratch unexpectedly results in lower representation quality despite using the same architecture. In contrast, the VQ-VAE used in our benchmark achieves the strongest representation quality (highest PSNR/SSIM).
These differences stem from architectural properties. KL-regularized VAEs enforce latents to follow standard Gaussian prior, which works well for large and diverse datasets but can degrade
in low-data low-diversity regimes (Xiao et al. 2025 Wang et al. 2021). CT datasets typically
contain far fewer samples and exhibit lower structural diversity compared to natural images.
For CT reconstruction using PSLD (Rout et al. 2023), fine tuning improves the performance of AutoencoderKL compared to using it directly. The KutoencoderKL trained from scratch achieves the highest SSIM among AutoencoderKL variants, while the fine-tuned version attains slightly better PSNR. The VQ- VQE again produces the best CT reconstruction scores overall.
Figure visualizes autoencoder reconstructions, unconditional diffusion samples, and CT recon- structions. The pretrained AutoencoderKI, preserves fine details well in autoencoder mode but fails in conditional generation and CT reconstruction. Fine-tuned and tranch models produce
https://huggingfaaae co/ stabi http://////a1llya//////111/
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Table Representation (autoencoder reconstruction) and CT reconstruction quality for fine-tuned natural-image encoders. Values are PSNR/SSIM.
Autoencoder type
config
training epochs
autoencoder reconstruction
CT reconstruction
AutocncoderKL (SDXL)
w/o fine tuning
37.12/0.88
21.34/0-42
Kutoencoder (SDXL)
fine tuning
37.13/0.90
23.97/0.66
AutoencoderKL (SDXL)
from scratch
200
34.70/0.88
23.79/0.72
VQ- VAB (this benchmark)
from scratch
200
39.30/0.92
25.52/0.70
smoother representations and more stable reconstructions, though still lower quality than those produced by the VQ-VAE.
We emphasize that this section provides only a preliminary evaluation fine tuning -aturel-image autoencoders for scientific imaging. Performance depends strongly the specific autoencoder architecture, data scale, and diversity. CT images differ substantially from natural images in structure and statistics, and more sophisticated adaptation strategies may be required. A comprehensive investigation is therefore needed before such models can be reliably applied in CT reconstruction.
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Original
w/o fine tuning
w fine tuning
Scratch
VQ-VAE
36.76
0.88/37.63
0.91 34.20
0.87 39.14
0.92
Autoencoder reconstruction
35.01
0.81 26.29
0.8633.42
0.82 37.38
0.88
w/o fine tuning
w fine tuning
Scratch
VQ-VAE
Unconditional generation
Reference
w/o fine tuning
fine tuning
Scratch
VQ-VAE
17:49
0.3527.07
0.77
0.80 27.04
0.75
CT reconstruction
21.97
0.4625.75
0.7422.12
0.7025.23
0.71
26.33
0.54
0.57
26.70
0.71
26.59
0.68
Figure 13: Comparison of autoencoder reconstruction, unconditional diffusion generation, and CT reconstruction across different autoencoders. The VQ-VAE used in our benchmark produces consistently superior representations and reconstructions, while SDXL AutoencoderKL. variants exhibit reduced stability and quality.
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A.12 ABALATION FOR DATA CONSISTENCY OPTIMIZATION
We perform an ablation study to examine how the number of pixel-space and latent-space data consis- tency optimization iterations affects reconstruction quality under different noise conditions. In main benchmark, these iterations are treated as fixed, and learning rates are tuned as hyperparameters. For this ablation, we instead fix both learning rates 10 and vary only the number of optimization iterations.
We use the Resample method (Song et al. 2024 on the medical dataset (config iii). To study the effect of noise, we simulate measurements with identical numbers of projections but different photon statistics: high-photon setting (10000 photons) yielding lower noise, and low-photon setting (2500 photons) yielding higher noise.
Table X reports average PSNR/SSIM over ten random slices. We see that in the high-noise regime, increasing latent optimization iterations improves stability and yields higher PSNR/SSIM than increasing pixel iterations alone. While the low-noise regime, increasing pixel iterations continues to improve reconstruction accuracy, whereas additional latent iterations provide marginal benefit.
Figure 14 visualizes selected reconstructions. When the measurement noise high, too few iterations lead to overly smooth reconstructions lacking fine structures; conversely, too many iterations cause overfitting to noise. Interestingly, the highest PSNR/SSIM values often occur at the point where reconstructions begin to slightly overfit. This illustrates the balance between detail recovery and noise overfitting when performing optimization- data consistency steps in diffusion pipelines.
Table &: Average PSNR/SSIM for different combinations pixel and latent optimization iterations under three noise conditions.
coanfig lil)
Pixel lters
25
50
100
200
Latest
200
100
27.91/0.78
77.9000.76
28.79/0.79
29.23/0.79
20.2000
28.8700.77
28.9000.77
config
27.64/0.77
4010.77
200
277750777
38/0.78
28.400.78
27.650.72
contig lii less nobse)
Latesz ieer
200
100
27, 97-9500.78
27, 97999.8777
29. 06/0666080
29.04/0.30 80
70 821088
29.78A081
29.8440.1
29.82/0
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25
50
100
200
0.78
28.92
28.28..9
0.77
100
Config ii) Pixel |terations
27.75
0.7728.54
0.78.2901
0.78.28.00 .0
0.78
200
Latent Iterations
100
27.34
0.7628.32
0.78 28,
0.7627.74
0.72
100
Pixee Ieerations
0.77228.4
Contig
200
25
50
100
200
27.77
0.77
83.83
0.80 29.73
0.81 29.78
0.81
Config less noise
100
Pixel Rterations
27.93
0.7828.88 99
0.8022.81
0.61299.4
0.80
200
Figure 14: Effect of pixel and latent optimization iterations on reconstruction quality. Fewer iterations lead to oversmoothing, while excessive iterations cause overfitting to noise. The best-performing settings often occur near the transition between these two regimes.
13 COMPARISON BETWEEN TRAINING STAGES ON RECONSTRUCTION PERFORMANCE
We investigate whether the training stage of a diffusion model affects CT reconstruction quality. Specifically, we train a standard pixel-space diffusion model for 200 epochs on the medical training dataset and save checkpoints at three stages: an early stage (25 epochs), a mid stage (100 epochs), and the final stage (200 epochs). The final-stage model is the version used throughout the benchmark.
To isolate the effect of the diffusion model itself, we evaluate these checkpoints using the DDS method (Chung et al.) 2024), which does not rely on hyperparameter choices in the noiseless setting. Table reports the average PSNR and SSIM on 100 randomly selected test slices.
Surprisingly, the early-stage diffusion model achieves the highest CT reconstruction accuracy by large margin, followed by the final-stage and mid-stage models. Figure [5 visualizes unconditional samples and reconstructed CT slices for the three checkpoints. The early-stage model produces noisy unconditional generations, indicating that it has not yet learned the full data distribution. Nevertheless, when used for CT reconstruction. it yields the sharpest structures and the best fine-detail recovery. The mid-stage and final-stage models produce similar unconditional samples and reconstruutions, with the final-stage model producing slightly smoother image features.
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This phenomenon is intriguing and requires further investigation. Future work may explore whether this behavior generalizes to other datasets, other diffusion architectures, or other types of inverse problems. Beyond its practical implications, this finding suggests that (in some cases) early-stage diffusion models may already contain sufficiently strong structural priors for reconstruction tasks, potentially reducing diffusion training cost by up 87.5% in inverse problem settings.
Table Comparison of CT reconstruction using early stage, mid stage and final stage trained pixel diffusion models.
Stage
Early
Mid
Final
PSNR/SSIM
30.68/0.75
28. 28.46/ 0.72
28.71/00 00.73
Early
Mid
Final
Unconditional generation
32,88 22.9
0.86.29.7
0.80
51.23
0.84 83
0.80
31.47
0.8428.76
0.80
CT reconstruction
28.87
0.69331.5 55
0,70
27
0.6727661
0.88
27.57
0.87
0.67
Figure 15: Visualization of unconditional generation and CT reconstruction using different stages of the trained diffusion models.
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A.14 ADDITIONAL RESULTS
Figures 17 19 and T8 present the full visual comparison of all evaluated methods across the three benchmark datasets. As observed, diffusion-based methods generally perform worse on the real-world dataset compared the simulated datasets. This degradation may be attributed limited high-quality training data and out-of-distribution effects. While supervised learning with SwinIR often achieves higher PSNR and SSIM scores, its reconstructions tend to be overly smooth and lack fine structural details. In contrast, diffusion models often recover more distinct structural features; however, these details can be hallucinatory and misaligned with the ground truth.
Real- World Dataset. Figure 17 shows that ADMM-PDTV and INR produce visually smooth reconstructions, which explains their high metrics, but they often miss finer details.
Among diffusion- based methods, DPS and PGDM reconstruct plausible object contours but introduce slight shape distortions, likely due to the out-of-distribution nature of the task, i.e., differences in material composition between training and test rocks. MCG better captures the overall structure but introduces unnatural porosities, hinting at a prior mismatch. PSLD fails to recover the object's correct geometry, while Reddiff struggles to preserve fine textures, likely due to the limited amount of high-quality training data. These results emphasize the increased difficulty of applying diffusion models in real CT reconstruction, where practical challenges such noise characteristics, data scarcity, and acquisition inconsistencies must be addressed.
Simulated Datasets. Overall, diffusion models substantially outperform traditional methods like FBP and SIRT, particularly under realistic conditions involving noise and limited projection angles. End-to-end supervised learning with SwinlR achieves the highest scores in most configurations. In the idealized setting (configuration i: sparse views without noise), the INR method slightly outperforms diffusion-basse bassdd approaches in terms of PSNR and SSIM. However, as noise and artifacts increase, diffusion methods demonstrate greater robustness and begin to outperform INR.
No single diffusion method dominates across all metries or datasets. On the medical dataset, ReSample achieves the highest PSNR in several configurations, but its performance drops significantly on the industrial dataset, which contains more complex structures. This suggests that ReSample's 'ss strict enforcement of data consistency may work well for smooth, low-frequency features but struggles with sharper textures. Across the board, latent diffusion methods do not consistently outperform pixel-space diffusion methods.
Figure 19 and Figure 18 provide visual comparisons under challenging conditions. Classical methods like FBP and ADMM-PDTV fail to recover fine structures, while SwinIR produces smooth outputs but may lose local continuity. Diffusion models capture the global shapes well, but their reconstructions vary in detail fidelity. Interestingly, methods based purely on data consistency gradients (e.g., DPS) often yield more visually faithful results than those incorporating explicit data consistency steps (e.g.. ReSample), particularly when measurements are Suggesting that hard data consistency can degrade results under such conditions.
Data Fit. To quantify data fidelity, we compute the 1.2 norm between the simulated measurement y and the forward projection of each reconstruction Ø:
Litata = A y 1112.
(15)
Results across all methods and configurations are shown in Table 10 In the noise-free settings (config i). pixel diffusion methods (e.g.. DPS, MCG) consistently achieve lower data fit loss compared to latent diffusion methods (e.g., PSL.D, DMPlug), reinforcing our earlier observation that enforcing data consistency is more challenging in latent space. Pseudo-inverse guided methods such as PGDM and MCG generally achieve better data fit (lower in numbers) than purely gradient-based plug-and- play approaches.
In noisy configurations, however, lower data fit does not necessarily indicate better performance: small residuals may result from overfitting to noise, while higher residuals may reflect denoising behavior. For example, although SwinIR often yields larger data fit values, its reconstructions perceptually smoother. Thus, in noisy regimes, data should be interpreted alongside visual quality and robustness overfitting.
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Table 10: L2 data fit between reconstructed projections and noisy measurements (I : 2)) for all benchmarked methods under different CT configurations. Lower values in noise-free settings indicate better consistency with the measurement, while in noisy cases, overly low values may reflect overfitting rather than accurate reconstruction.
Medical
Industrial
Real-worll
Method
contig 1
coollig
contig II
costiig
config
cootig
contig
coatig III
Consig
ccalii
200 projs
100 projs
50 poojs
FRP
12799.3
1996.67
7191.11
70,90
5604.93
534.30
614.73
508.00
SIRT
207.28
917.56
878.33
344.56
131,53
ADMM-PDI PDTV
1074.95
3025.34
1196.18
54.25
42.94
196.32
749.30
148661
161.64
304.55
226.74
185.25
PISTA SBTV
1650,66
218.40
365.39
1515.28
589.53
307.44
204,25
145.94
DIP
639 69
985 68
2726.00
1802,99
565.00
677.67
439.45
1228. 74
1214.36
531.00
1023.01
559, 77
195 69
INR
213.40
119019
2818.56
1700.74
122.04
686 08
549.0 45
740.66 66
1265. 16
271
341.66
226.68
165.99
R2 Ganseian
330.45
6347.24
1991323
1768.97
460.30 30
5002 29
553577 71
5074
5131.43
2091.11 12
-/-
+/-
SwintR
355.65
862 64
2344.76
1790.27
353.78
944.09
1313.88
961.95
1538.45
2125.84
5656.37
4161.74
3188.40
MCG
196.0 07
834 16
2228.57
1662.77 72
78.18
477.35
70
$68. 10
1354.64
532.25
410.44
225. 70
159.05
DPS
261.35 35
848 95
2259.96
1792.25
268.79 79
55
364.61
1159.35
1468.00
1755.81
1280.35
4803 68
2878.75 757875
PSLD
757. 37
63
2476 15
2058.60 66
892.5 50
2822.79
1789 62
4158. 79
4409 87
4312.15
5791.18
1906. 16
1187 63
PGDM
209.96
$26.91
2307.33
1654,57
$3.20
338.59
311.40
774.78
1247.13
1143.05
466.36
1051.43
547. .48
DDS
14.91
3979:25
$168.71
15382.82
18.06
15.36
2455 27
4919.95
2428.38
26.76
189.56
91,42
4100.51
Reample
736.59
846 29
2272.55
1775.00
976.97
4367.25
1998.44
6962.81
7276.90
6036.66
---
DMPlug
941.97
1620.57
2547.00 04
2169.60
2497.76
1705 36
1113.62
2525 21
2541.57
1748.81
..-
Reddit
360.21
857.07
2270.13
1695.80
196.03
681.36 36
388
1287 98
1630.75
8091 12
188.48
95.12
52.21
HybridReg
419.04
862 45
2286 82
1722. 13
230,88
781 52
439 16
1410.00
1706 94
79.27
---
DifiStonGGrad
951.74
979.61
2503.64
2110.76
1089.06
5289 29
1734 75
4765 78
4848.37
50.76
-4
Table 11: LPIPS for all benchmarked methods under different CT configurations. Lower values mean better perceptual alignment.
Medical
Industrial
Real-world
Method
contigi
config ii
config iii
contig iv
config
config
config ii
config iii
config iv
config v
200 projs
100 projs
50 projs
0.65
0.76
0.61
0.78
SURT
0.41
1.16
0.54
0.57
0.35
0.64
0.73
0.53
0.56
0.63
0.42
0.40
0.41
ADMM-PDITV
0:38
0.91
0.62
0.70
0.51
0.56
0.49
0.50
PISTA SBTV
0.40
0.52
0.52
0.46
0:50
0.72
0.75
0.56
0.66
0.72
0.56
0.56
0.57
0:38
0.50
0.59
0.41
0.46
INR
0.30
0.52
0,46
0.43
0.29
0.63
0.59
0.41
0.45
0.48
0.55
0.55
0.56
RE Gaussian
0.25
0.32
0.37
0.42
0.32
01.67
0.67
0.55
0.55
0.60
SwinlR
0.20
0.33
0.28
0.26
0.19
0.24
0.35
0.20
0.24
0.22
0.27
0.30
0.27
0.29
0.24
DPS
0.13
0.21
0.19
0.18
0.13
0.24
0.28
0.18
0.21
0.32
0.24
0.39
0.38
PSLD
0.29
0.31
0.29
0.28
0.24
0.34
0.40
0.36
0.36
0.46
0.42
0.39
0.38
PGDM
0.16
0.29
0.36
0.37
0.12
0.24
0.34
0.18
0.24
0.34
0.26
0.37
0.38
DDS
0.09
0.61
0.91
0.84
0.10
0.21
0.25
0.26
0.39
0.28
0L16
0,16
0.17
Resamgic
01:2
0.34
0.34
0.31
0.26
0.42
01.8
0.37
0.41
0.44
DMPlug
0.30
0.30
0.30
0.32
0.42
0.41
0.41
0.42
0.41
0.40
Rediiff
0.28
0.32
0.29
0.26
0.27
0.31
0.34
0.29
0.28
0.34
0.29
0,33
0.36
Hybridkeg
0.33
0.31
0.29
DifSSueeGrod
Perceptual Metrics. In addition to PSNR and SSIM, we evaluate reconstructions using the perceptual metric LPIPS (Zhang et al.) 2018), reported in Table While reconstruction accuracy is typically the primary goal in CT, perceptual metrics can provide complementary insights into how well reconstructions align with human perception. To compute LPIPS, we duplicate each grayscale CT reconstruction across three channels and use AlexNet as the backbone, following standard practice. As Table II shows, LPIPS scores for individual methods do not always correlate with PSNR/SSIM (Table 2 highlighting that perceptual similarity may capture different aspects of reconstruction quality. Nevertheless, the overall trends are consistent with our earlier findings: diffusion- methods generally achieve comparable or slightly worse perceptual scores than the supervised SwinIR baseline, but clearly outperform conventional approaches such as FBP and SIRT.
Data Consistency Strategies in Null Space Perspective. Figure [6] presents range-nut null space decompositions for DPS, PGDM, MCG, and ReSample under configuration iv) of the industrial dataset, which includes both noise and ring artifacts. These methods implement distinct strategies for enforcing data consistency.
Consistent with findings in Section 4 DPS (which uses data consistency gradients) imposes only soft constraints, resulting in a substantial null space component indicative of prior contributed features. PGDM, which incorporates a pseudoinverse guidance, shows better alignment with the measurement, yielding a lower null energy. Interestingly, MCG, which applies only single step toward pseudoinverse, exhibits higher null energy than DPS, suggesting that a single-step update may not sufficiently enforce consistency.
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ReSample, which uses explicit data consistency optimization steps, achieves the lowest null space energy, reflecting strong enforcement. However, unlike the noiseless case, such strict enforcement in this noisy and artifact-prone scenario leads to degraded visual quality. Despite suppressing ring artifacts, ReSample introduces new structured distortions, possibly due to the optimization process redistributing ring-related inconsistencies across the reconstruction in an effort to maintain global consistency.
DPS
PGDM
Reconstruction
Range space
Null space
Reconstruction
Range space
Null space
28%
73%
0.59
0.57
Reconstruction
Range space
space
Reconstruction
Range space
Null space
70%
26
74%
MCG
ReSample
Figure 16: Range-null space decompositions of four reconstruction methods with different data consistency strategies, evaluated on the industrial dataset (config iv: projections with noise and ring artifacts). PSNR and SSIM are shown in the top-left and bottom- left corners of the reconstruction images. Energy percentages the range and null space components are shown in their respective top-left corners.
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Classical
Diffusion methods
25.00
0.31 27.43
0.39
27.29
0.36.7 25
0.36
26.99
FBP
SIRT
ADMM-PDTV
FISTA-SBTV
MCG
PGDM
200 projs
24.47
0.31 27
0.34
28.
0.52
25.26
0.28 27.53
0.40 27.15
0.37
DIP
SwinlR
PSLD
Reddif
DDS
Network prior
Supervised
Reference
Classical
MBIR
Diffusion methods
22.63
0.1827.22 26
0.37
27.
0.36 27 22
0.35
26.90
0.35 26.95
0.32 26.
0.32
100 projs
FEP
SIRT
ADMM-PDTV
FISTA-SBTV
MOG
DPS
PGDM
28 28.44
0.28 27.16
0.32
29.34
0.48
25. 94
0.34 27.30
0.37 26.72
0.33
DIP
INR
SwiniR
PSLD
Reddiff
DDS
Network prior
Supervised
Reference
Classical
Diffusion methods
20.67 67
0.12 27.08
0.35
27.15
0.3527.7 18
88
0.36
0.17
0.53
FISTA-SBTV
MCG
DPS
PGDM
60 proja
0.29
INE
SwiniR
PSLD
Reddiff
Network prior
Supervised
Reference
Visual comparison of all benchmarked methods on the real-world synchrotron CT dataset under three different sparse-view scenarios (200, 100, and 60 projections). PSNR and SSIM values are shown in the top-left and top-right corners of each image, respectively. Red and green boxes mark zoom-in regions for structural detail comparison. The reference image is reconstructed using all 1200 projections, median flat-field correction, and additional ring artifact suppression.
Figure 17:
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Gloosiaal
Gaussien
MelA
Difusion medlonds
Cont 40 proje wif noloe
VONBA
RB 2B1
17
Supervied
Clansical
Goussian
MBI
Diftuaion mathods
Contlg 5) 20 projo nodoo
Supervised
Clocoioal
Ditusion
Contin lift so preje noiee
Network: prior
Superviedd
Reference
Latent
Latart
Latert
Clansical
MBIA
Difusion methods
Contie Lo proje w noise & tbe
E917
Reference
Contip 1) 40 proja frrsted amglcca
Network price
Supervited
Refercntce
Latent
Latent
Latent
Figure 18: Visual comparison of all benchmarked methods on the simulated industrial CT dataset under four different configurations: (i) 40 projections without noise, (ii) 20 projections with mild noise, (iii) 80 projections with stronger noise, and (iv) 80 projections with noise and ring artifacts. PSNR and SSIM are shown in the left and top-right corners. Red and green insets highlight zoomed-in regions.
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Classioal
Geussian
MBB
0.51
$7.52
01.00
0.44
2E1
GEN 20.9
proje wet nolee
PEP
SPR
AGVM-POTV
F1STA-38TV
Moo
DC%
PCTM
DMPPu
26.03
64.98
BMM
27.21
21.00
12.30
2761
0.00 23.0r
DUT
INR
DettlntsGred
Reference
Classical
Gaunsian
Diffusion mathode
22.09
A022
0
02222 Kc
0.44
20.32
0.462449
FEP
EPAT
ADVAM-PTTV
RBTA-B8TV
MDG
DPS
POOM
DMPPa
10) proje
17.03
2.002
24.4
22.54
3 55
Bde 92.37
0.50
chlape
ReBomble
DinsutGred
prior
Supenteed
Peference
mathods
EMPPLIT
MDB
DPS
DMP
Teskal
80 proje
BuBemple
Network prior
Superised
Reforence
Latent
Latent
Latent
Classical
Gaussian
MERR
Difusioo metheds
11.00
21032
0.77
217
051
CLET
02221
24
0,5
Conelig Ni proja w nolsa ring
FEP
SIFT
R2 GLLseEe
RETA-SBTV
MDD
DPS
PCDM
DMPa4
Fasdii
25.02..0
0d1
3.22
21.02
0.421.52
0:3024544
03022'11
457
INRT
HaondRogg
2073
Helomrele
BDS
Superieed
Claspiool
Gaussion
MBIR
Difhusion methods
14
06100
246
26.6
UASS4
0.5 27.16
3822E2
36
proja anggles
AUKMAPPITY
P1S A-BA-B
MD3
DPS
KGLMM
Redc3t
10:00
0.000.0 14
NG47
5700
$3.01
2400
25-42
2.73222
D.96.34733
544
CiP
NR
SWINRR
Maz Neg
FLED
Factample
OyrSuutrrradd
G08
Netroark
SuperWied
Referrnce
Latent
Latant
Latent
Figure 19; Visual comparison of all benchmarked methods the simulated medical CT dataset under four different configurations: (i) 40 projections without noise, (ii) 20 projections with mild noise, (iii) 80 projections with stronger noise, and (iv) 80 projections with noise and ring artifacts. PSNR and SSIM are shown in the left and top-right corners. Red and green insets highlight zoomed-in regions.
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A.15 IMPLEMENTATION DETAILS
Diffusion Models. We implement all diffusion methods using the diffusers library Training follows
(Song et al., 2020: Ho et al.) 2020), using a UNet-based architecture described in (Ronneberger et al. 2015). The detailed UNet configurations used for different datasets are summarized in Table
and the (Q-VAE configurations for latent diffusion models are provided in Table 13
Table 12: UNet configurations used for different CT datasets.
Parameter
Medical CT
Indemtrial
Synchrutrom
Exput claanels
Onipur chornels
Sample
51 S121piezl (8(bbtent silu Q.U
7680piszl
finction
Dropout
Nomalication groups
32
Block curput
[128, 128, 256, 512)
128, 256, 256 512,
(192, 192, 356, 288, 208)
Layers back
2
Dows backk types
[Dowa, Down, Dows, Down, AmnDonn Down]
[Down, Dowa, Down, Dowa, AmnDown, Down]
[Dowa, Down, Dowa, Down, AmaDown, Down] [Up, AmsUp. Up. Up. Up. Up)
Up block types
[Up, AmmUp. Up. Up. Up. Up)
[Up. AmuU, Up. Up. Up. Upl
Altention hezale
Table 13: VQ-VAE configurations used in the benchmark for different CT datasets.
Purumeter
Medical CT
Industrial CT
Synchrotron CT
Input channels
Ouiput channels
1
Sumple
512 [125, 256, 512]
function
Block ouput
512)
Layers black
block types
[DownEac, DownEie, Dowelind] [UpDec, UpDec, LpDec]
[Downfoc, DownEns. Dewelfne) [UpDec, UpDec, UpDes) Yos group 32
[DownEnc, Downfine, Dewellnd) [UpDDee UpDec. LpDes)
Up block types
Mid-block atteation
Nonmilization type
group
group
Nommalization groups
Laneni chumnel
Num VQ smbedddings
512
512
512
Scallnn fictor
SwinIR. We implement the SwinIR method using the Hugging Face t transformes library"! The detailed configurations used for different datasets are summarized in Table 14
Table 14: SwinIR configurations used in the benchmark for different CT datasets.
Parameter
Medical CT
Industrial CT
Synchrotron CT
size
512x512
512x512
768x7668
Embed
126
128
128
Depchs
[2, 21
[2,
[I.
Num haads
12, 2, 21
2,, 21
II, L,
MI P ratio
2.0
2.0
1.0 8 GELU
Window size
8
$
Activation
GELU
GELU
Input channels
Chiput channels
INR. We implement an implicit neural representation (INR) using a SIREN network (Sitzmann al. 2020). which employs a multilayer perceptron (MLP) with sinusoidal activation functions to model the image as a continuous function over spatial coordinates. The network has a depth of 8 and a hidden layer width of 256. To encode spatial information, we use Fourier feature mapping (Tancik et al. 2020), where a random matrix of shape 3xx256 projects the 3D input coordinates to a higher-frequenc space. The resulting features are then passed as input to the MLP, allowing the network to represent high-frequency image content effectively. This framework allows direct reconstruction of CT volumes by optimizing the network weights to fit the projection data, without relying on a fixed image grid. Further details the application of INR to CT reconstruction can be found in (Shen et al. 2022 Wu et al. 2023b at Shi et al. 2024b).
DIP. For the DIP (Deep Image Prior) method, adopt UNet architecture (Ronneberger et al. 2015) as the backbone network. The UNet consists of an encoder with channel sizes 8, 16, 32, and
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64, and symmetric decoder with channel sizes 64, 32, 16, and 8. Skip connections with channels are added at each resolution level. A fixed random noise imput, matching the shape of the image to be reconstructed, is fed into the network. The output of the UNet is forward- forward-projecter and compared with the actual measurement data. The network parameters are then optimized to minimize this data consisteney loss. Despite the absence of external training data, the network's structure alone serves as strong prior that guides the reconstruction. Further applications of DIP for CT reconstruction can
be found in (Baguer al. 2020 Gong et al. 2018 Barbano et al. 2022 Alkhouri al. 2024
MBIR. For Model-Based Iterative Reconstruction (MBIR), we use the open-source TomoBAR library We include two representative total variation (TV) regglaaalr algorithms: Fast Iterative Taakk-oo-nTree Algorithm with Primal-Dual TV (HISTA-PTV) (Beck & Teboulle 2009) Chambolle & Pock 201T), and Alternating Direction Method of Multipliers with Split-Bregman
TV (ADMM-SBTV (Boyd et al. 2011 Goldstein & Osher 2009). These algorithms iteratively minimize a data fidelity term combined with a TV prior reconstruct the object from projection data.
The regularization weight that balances data fidelity and prior is tuned via grid search using held-out validation set. All reconstructions are performed using the same geometry and projection operators as in the diffusion-basee methods to ensure comparability. We use 200 iterations for both methods and set the regularization iterations at 100.
A.16 TRAINING DETAILS
Diffusion Models. We train both pixel-space and latent-space diffusion models separately for each dataset and use the resulting models shared backbones across all diffusion- methods to ensure fair comparisons. Pixel diffusion models are trained for 200 epochs using a batch size of and the AdamW optimizer @oshchilov & Hutter), with initial learning rate of 10-4.
For latent diffusion models, we first train a VQ-VAE for 100 epochs using the same optimizer and batch size. Early stopping is applied: training halts if the validation loss does not improve for 10 consecutive epochs. Once the VQ-VAE trained, we train the UNet in the latent space for 200 epochs using A with the same initial learning rate.
For the real-world dataset, which contains fewer training samples than the simulated datasets, we reduce the learning rate 10 for both pixel and latent diffusion models prevent overfitting. We apply data augmentation during training by randomly flipping images (horizontally and vertically) and performing random crops between 90% and 100% of the original area, followed by resizing to the original size.
The supervised SwinlR model is trained for 200 epochs using AdamW with an initial learning rate of 1 x 10-4 Early stopping is employed in the same way as for VQ-VAE. Training typically stops between 120 and 180 epochs based on validation performance.
SwinlR.
INR and DIP. For INR and DIP, we optimize the network to fit the measured projections for 10,000 iterations using the Adam optimizer (Kingma & Bal 2014). The learning rate is treated as tunable hyperparameter, selected separately for each dataset and configuration on held-out subset of the training data.
A.17 HYPERPARAMETER SELECTION
To ensure a fair comparison across methods in the DM4CT benchmark, we determine all method- specific hyperparameters through grid search. For each method, we define search range based on commonly used values in prior work and empirical performance. Ten images are randomly selected from the training dataset of each domain for hyperparameter tuning. Reconstructions are evaluated against corresponding reference images using the mean squared error (MSE), and the hyperparameters yielding the lowest average MSE across the selected images are chosen. The search ranges and selected values for each method are summarized in Table 15
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Published as a conference paper at ICLR 2026
Table 15: Search ranges and selected hyperparameters for each reconstruction method in the DM4CT benchmark. Parameters are optimized via grid search on ten randomly selected training images by minimizing average mean squared error with respect to reference reconstructions.
Rengr
MED
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0.3
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DPS
lake one
0.33
PG2M
du dai
TX 100
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pasi opiesiation varning
II 10
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0.009
INR
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1N
DNP
Traning ras
5% bi 1/5 50 1/5 423 530.0
1JEx 93 1/2 3/3
3
FIST-SRIV
10
18
A.18 DIFFERENTIABLE CT FORWARD OPERATOR
To enable CT' reconstruction with diffusion models, the forward operator A must be differentiable so that it can be used to enforce data consistency by backpropagating gradients with respect to either the noisy variable Hg or the latent variable 2, Several open-source tomographic toolkits support this functionality, including the ASTRA Toolbox (Van Aarle al., 2016). the Operator Discretization Library (ODL) (Adler et al. 2017), and the TIGRE toolbox (Biguri et al. (016), as well as many other CT libraries (Kim & Champley [023] [Jörgensen al. 2021). Some of these libraries offer native integration with PyTorch allowing automatic gradient propagation through the projection operator.
In cases where direct integration is not available, a differentiable forward operator can be imple- mented manually by wrapping the underlying projection and backprojection routines in a custom aet.hautoorrr..unnno..
This enables scamless integration of the forward model within modern deep learning pipelines. In our implementation, we use the ASTRA Toolbox and show how to wrap its 3D projection and backprojection functionality in PyTorch--ompaaible operator. A pseudocode-style listing is provided in Listing [ to illustrate the approach. The same principle applies to other CT toolkits that expose low-level projection routines.
class OperaterFunct (torch. autograd. Function) @staticmethed def forward volume, projector, profection_shape, volume_shaper if velume. ndim =- 4:
projection torch. zeros (volume. devices cuda')
sprojection_shape
for i
range (volume, shape [0]]:
[0]]:
astra. eew.raaexpeiinetalllliio
FP3D (projector, vol=volume [1].
detach (), projsprojection
else:
projection torch zercs wttpcccccccccpppooctttooo.oooee
devico= cuda')
astra.
vol=volume, ()
proj=projcction) ctx. stxe.saveffor trre___orbbccwrrrr (volume)
ctx. ctx.projector projecter
ctx. sex.volume_shape volume_shape return projection
@staticmethod
def backward (ctx, grad_output):
https://pytorcch.org
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Published as a conference paper at ICLR 2026
volume, etx. saved tenscrs
projector etx. prx.projector volume_shape
ctx, etx.volume_hhape
if velume ndim == 4:
grad_volume volume = torch... zeros (volume. shape [0], Avolume_shape),
device='
for i range (volume. shape
astra, .
di (projector, vol-grad_volumee
[i], puoj=grad__output dettccc)
else:
grad torch (volu_ee______ee device-' cuda')
astra m projagrad_output.deeee. detacch ())
vol-graa_vollmm,
return volum,, None, None, None
class Operator:
def _init_ vol proj_geom);
sclf colf.projector = attra.crrattorrroccttr (1 c'uddd'', proj_gcom, vel_geom)
self.volume_shape astra. gerra.geomsstti size (vol geom_
shaf astra geom geee
size (proj_geem))
def
volume)
return OperatorFunction applnnapppyycco (volume, selt selt.projector, self. projection shape,
shape)
def (seelf, projection):
volume torch, zeros vollf..vllu__shape,, device=' estraaexperimental. www.nttta.ddd.e.....e..m www.dddrreeeeee BP3D projectr,,,,, vol=volume, proj= projection.detach return volume
Listing Pyloocch-cmpptiile differentiable ASTRA operator (core logic)
41


Reviews:
[{"review_id": "VJwpSIoOYH", "reviewer": "Reviewer_MUjs", "summary": "This paper provides a benchmark on CT reconstruction algorithms on different datasets and settings.", "questions": "I do not have other questions.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. As far of my knowledge, this paper provides a first comprehensive benchmark of current SOTA CT reconstruction methods. This benchmark is very comprehensive, consisting of INR-based methods, pixel-diffusion methods, latent-diffusion methods, MBIR methods, traditional methods, and transformer-based methods.\n2. The codebase is maintained very well and easy to use. The code is easy to follow and I can switch between different methods. The code style is great and different classes of methods are separated clearly. Furthermore, it combines a lot of algorithms very simply in just a couple of python files.\n3. The benchmark on different datasets is comprehensive and easy to interpret. \n4. The analysis is also insightful. I appreciate the discussion on the tradeoff of latent gradient-based approach and external optimization.", "weaknesses": "1. Including some GAN-based methods will strengthen this paper. If authors have time, I would also encourage trying some Gaussian splat based methods as these methods are gaining more popularity recently. \n2. If authors have time, I am also interested in the performance of flow-based methods, such as FlowDPS or so on.\n3. I would encourage the authors to fine tune from some pretrained autoencoder, specifically that from SDXL or SD3, instead of training from scratch, as training from scratch may lose some generalization capability. \n4. For external consistency optimization, there is usually a necessity of using early stopping by noise level adaptively based on diffusion timestep. Also, the choice of latent optimization v.s. pixel optimization can be crucial (more noise level probably more latent optimzation). I understand that this setting can be tricky, but I encourage authors to adopt some hyperparameter tuning to see whether this will solve the artifact problem in the external-consistency relied methods (Especially for latent diffusions).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LvoRgkCUrW", "reviewer": "Reviewer_L1Ta", "summary": "The paper gives a summary of nine diffusion models for CT reconstruction. Three datasets are included in the paper. However, there is not any new method or improvement was proposed and only one dataset was given.", "questions": "Inference time very important for diffusion model needs sampling. Add some analysis of inference is preferred.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper presents a comprehensive and well-organized summary and comparison of nine existing algorithms within the domain. \n\n1 All the nine methods were evaluated and extensive experiments were performed. The paper gives comparison with other method and the quantitive result.\n\n2 Comparison and comprehension of nine diffusion-based methods were given. Every dataset was evaluated with different configuration. \n\n3 A high-resolution synchrotron dataset was provided. The dataset is a new dataset for reconstruction.\n.", "weaknesses": "1 Lack of Novelty and Original Contribution: The paper primarily focuses on summarizing, comparing, and reproducing results of existing algorithms. The paper gives the result and summary of different papers. The main work is the running and comparison of different method. There is not any new design of the model.\n\n2 Limited new understanding for future research of diffusion model in CT reconstruction. Better suited for publication as a survey paper or technical report.\n\n3. More details of the high-resolution synchrotron dataset is needed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9scdWVMsBR", "reviewer": "Reviewer_KS57", "summary": "The manuscript introduces **DM4CT**, a comprehensive benchmark for evaluating diffusion-model–based methods for CT reconstruction across medical, industrial, and synchrotron datasets. The work fills an important gap in benchmarking diffusion priors for CT, offering a unified taxonomy, shared backbones, and multi-regime evaluations, including a newly released real synchrotron dataset. The results are insightful: no single diffusion approach dominates, and the trade-off between data fidelity and prior strength is clearly demonstrated. The taxonomy of conditioning strategies—data-consistency gradients, plug-and-play, pseudoinverse guidance, and variational Bayes—is well-structured, and nine diffusion-based and seven classical baselines are compared under a fair setup. Shared latent and pixel diffusion backbones are trained per dataset to ensure fairness, and evaluations span multiple sparsity and noise regimes. Overall, the findings indicate that diffusion models are competitive with classical or MBIR methods but often fall slightly behind a supervised SwinIR baseline in PSNR/SSIM.", "questions": "1. For the medical CT configuration (Config 1), none of the methods seem to produce reasonable results for 40-view noiseless sparse-view reconstruction. Surprisingly, most DM-based methods almost completely fail, while the self-supervised INR baseline—though blurry—still reconstructs the main anatomical structures. Is such failure expected in this simplest setup?  \n2. The INR-based approach consistently performs well across CT scenarios, especially in low-noise and real-data regimes. In Figure 2 (industrial Config 2), it is the only method that correctly reconstructs the main structural shape, while all others fail. This suggests that INR has a unique advantage in preserving structure, albeit at the cost of sharpness. This naturally raises the question: could combining INR with diffusion priors yield further gains? Recent works, such as **DPER [2]**, have explored this direction, and it may be valuable for the authors to discuss or reflect on this possibility.  \n3. In my own experience training diffusion models on the AAPM 2016 dataset, I observed an interesting phenomenon: with such a small dataset (~ 5k slices), diffusion models overfit easily. Early-epoch models generate poor unconditional samples but produce better reconstructions when used as priors, while later-epoch models generate visually impressive samples yet degrade in reconstruction quality. This effect lessens on larger datasets (~100k slices). I encourage the authors to verify whether this occurs in their experiments, as it could be a useful observation and reinforces the need for larger-scale training.  \n4. There is a typo in line 420 of the manuscript: “Figure 6a” and “Figure 6b” should be corrected to “Figure 7a” and “Figure 7b,” respectively.\n\n> [2] Du, Chenhe, et al. *“DPER: Diffusion Prior Driven Neural Representation for Limited-Angle and Sparse-View CT Reconstruction.”* arXiv:2404.17890 (2024).", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- **Comprehensive and impressive baselines.**  \nThe range of baselines included is remarkably complete, even surprisingly so. The authors compare not only traditional iterative methods but also a large set of mainstream DM-based inverse solvers, including those not originally designed for CT reconstruction. They further include state-of-the-art supervised models and even classical self-supervised methods such as DIP and INR. This level of comprehensiveness greatly enhances the paper’s value and allows readers to truly understand how much diffusion-based solvers advance CT reconstruction over prior paradigms. Such a thorough comparison is rarely seen in previous work.  \n\n- **Unified framework for DM-based inverse solvers.**  \nA major contribution of this work is the unification and implementation of recent state-of-the-art DM-based inverse solvers under a common interface. Many of these methods have never been applied to CT reconstruction but were validated in other imaging domains. Bringing them together under a consistent framework is a significant service to the CT community and will facilitate the development of more advanced inverse solvers in the future.", "weaknesses": "1. **Small-scale training data.**  \nAlthough the authors trained both latent and pixel diffusion models within a unified Diffuser framework, a major concern is the extremely small size of the training datasets. Even the largest dataset, AAPM 2016, contains only about 5,000 slices. In contrast, diffusion models used in natural image inverse problems are typically trained on datasets like ImageNet, which contain millions of images. Models trained on such small data are likely to overfit or memorize training samples. While the benchmark may still provide relative performance comparisons among diffusion-based solvers, it cannot accurately reflect their true potential on large-scale data, nor the absolute advantage of diffusion priors over classical or learned reconstruction methods.  \n\n2. **Missing key baseline DDS [1].**  \nAlthough many DM-based solvers are included, the benchmark omits the crucial **DDS** method. While DDS can loosely be categorized as a pixel-domain data-consistency optimization, it does not strictly follow the plug-and-play paradigm. In fact, DDS empirically finds a strong balance between prior and data consistency through a conjugate-gradient scheme and empirically chosen step size. I strongly recommend including DDS in future comparisons.  \n\n3. **Limited diversity of experimental settings.**  \nExperiments are conducted under only four configurations. Although these capture different regimes, the experimental coverage still feels limited for such an ambitious benchmark.  \n\n4. **Limited real-data evaluation.**  \nThe real-projection evaluation uses only two rock samples. I appreciate that the authors conducted real-data experiments, which indeed demonstrate the method’s applicability to high-resolution CT. However, for broader impact, it would be valuable to include real medical CT data. I noticed that the authors conducted medical CT reconstruction experiments in the appendix. However, these experiments mainly serve to illustrate the potential value-range mismatch issue between HU-normalized training and real-world data. Moreover, only quantitative results are provided, without any qualitative visualizations. I encourage the authors to include more comprehensive experiments on real medical projection data.\n\n5. **Limited evaluation metrics.**  \nThe study mainly reports PSNR and SSIM. Although these are standard metrics, they do not capture diagnostic quality, which is critical in medical CT. For example, in Figure 13, particularly under Config 2 and 3, the supervised method achieves the highest PSNR and SSIM but produces reconstructions that are severely blurred and anatomically meaningless—an instance of “high scores but clinically useless results.” While the appendix includes LPIPS and data-fit metrics, these still fail to reflect whether key anatomical structures are correctly reconstructed. I encourage the authors to include more diverse evaluation strategies, such as downstream segmentation accuracy, organ-volume error, or even radiologist scoring.  \n\n6. **Limited generalization analysis.**  \nThe paper does not address an essential aspect of generalization—cross-dataset robustness. I encourage the authors to evaluate across datasets collected under different protocols or vendor settings. Since AAPM 2016 volumes are relatively homogeneous, this would significantly enhance the completeness of the benchmark.  \n\n> [1] Chung, Hyungjin, Suhyeon Lee, and Jong Chul Ye. *“Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems.”* ICLR, 2024.  \n\n---\nDespite these issues, the paper remains a valuable and well-structured contribution. I encourage the authors to address these concerns in a revised version, which would substantially improve the paper’s quality and impact. If these improvements are made, I would consider increasing my score. I genuinely hope this line of inquiry will continue, as it has strong potential to advance CT reconstruction research.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)