Pixel-Level Residual Diffusion Transformer: Scalable 3D CT Volume Generation
Authors: Zhenkai Zhang
Markus Hiller
Krista A. Ehinger
Tom Drummond
Venue: ICLR 2026 Poster (2026)
Keywords: Medical Imaging
3D Diffusion Model
Diffusion Transformer
CT Scan
Medical Image Generation
Primary area: generative models
TL;DR: We introduce 'PRDiT', a scalable generative model that synthesizes high-quality 3D CT volumes by using a two-stage approach that enhances training stability and preserves fine details, and outperforms existing models on key medical imaging datasets.

Abstract: Generating high-resolution 3D CT volumes with fine details remains challenging due to substantial computational demands and optimization difficulties inherent to existing generative models. In this paper, we propose the Pixel-Level Residual Diffusion Transformer (PRDiT), a scalable generative framework that synthesizes high-quality 3D medical volumes directly at voxel-level. PRDiT introduces a two-stage training architecture comprising 1) a local denoiser in the form of an MLP-based blind estimator operating on overlapping 3D patches to separate low-frequency structures efficiently, and 2) a global residual diffusion transformer employing memory-efficient attention to model and refine high-frequency residuals across entire volumes. This coarse-to-fine modeling strategy simplifies optimization, enhances training stability, and effectively preserves subtle structures without the limitations of an autoencoder bottleneck. Extensive experiments conducted on the LIDC-IDRI and RAD-ChestCT datasets demonstrate that PRDiT consistently outperforms state-of-the-art models, such as HA-GAN, 3D LDM and WDM-3D, achieving significantly lower 3D FID, MMD and Wasserstein distance scores.

Published as a conference paper at ICLR 2026
PIXEL-LEVEL RESIDUAL DIFFUSION TRANSFORMER: SCALABLE 3D CT VOLUME GENERATION
Zhenkai Zhang, Markus Hiller, Krista A. Ehinger, Tom Drummond School Computing and Information Systems, The University of Melbourne Chenkaiz&studen unimelb edu. au (m.hiller, kris.ehiing. ehinger, tom. drummond) un mimiimllae llb.bb...... b. edu. au
ABSTRACT
Generating high-resolution 3D CT volumes with fine details remains challenging due to substantial computational demands and optimization difficulties inherent to existing generative models. In this paper, we propose the Pixel-Level Resid- ual Diffusion Transformer (PRDiT), a scalable generative framework that synthe- sizes high-quality 3D medical volumes directly at voxel-level. PRDiT introduces a two-stage training architecture comprising 1) a local denoiser in the form an MLP-based blind estimator operating overlapping 3D patches separate low- frequency structures efficiently, and 2) global residual diffusion transformer em- ploying memory-efficient attention model and refine high-frequency residuals across entire volumes. This coarse-to-fin modeling strategy simplifies optimiza- tion, enhances training stability, and effectively preserves subtle structures without the limitations of an autoencoder bottleneck. Extensive experiments conducted on the LIDC-IDRI and RAD-ChestCT datasets demonstrate that PRDíT consistently outperforms state-of-the-art models, such as HA-GAN, 3D LDM and WDM-3D, achieving significantly lower 3D FID, MMD and Wasserstein distance scores!
INTRODUCTION
Synthesizing high-resolution 3D medical images, such as CT volumes, is crucial for supporting various clinical applications, including diagnosis, segmentation, and anomaly detection. However, existing generative models struggle to balance the fidelity of local details with global structural coherence, and often suffer from computational inefficiency or insufficient representation of high- frequency features. Such prior works are mainly categorized into GAN- and Diffusion-bast methods. GAN-based approaches, such as HA-GAN (Sun et al. (022). MM-GAN (Sun et al. 2020) and At-ltylGGAN (Hong et al. 202T). J), can effectively generate realistic local details. However, they often suffer from mode collapse and training instability, and their substantial memory requirements pose a significant challenge, particularly when processing high-resolution 3D volumes.
As compelling alternative, diffusion-based models have recently demonstrated more stable train- ing dynamics and higher image fidelity. Representative diffusion-based methods include the 3D- DDPM (Dorjsembe al. 2022). latent diffusion models (LDM) (Khader et al. [023 Pinaya al. 2022 Rombach et al.) 2022), wavelet diffusion models (WDM-3D) (Friedrich et al. 2024] and triplane diffusion models (Zhang et al. 2025). Nevertheless, current diffusion models often rely on convolutional architectures like U-Net (Ronneberger et al. 2015), which inherently limit their capability to capture global context and long-range dependencies that are crucial for accurately syn-
thesizing coherent structures.
In the context of 3D medical imaging, these architectural limitations become even more pronounced. Because the volume of a voxel feature map grows cubically with resolution, deploying a deep U-Net directly on high-resolution 3D volumes not only incurs extremely high memory and compute costs, but also typically requires compromise strategies such as patch-based processing, downsampling, or latent-space compression using VAE (Kingma & Welling 2014) or VQ-VAE (Van Den Oord let al., 2017) methods. However, patch-based methods and downsampling inherently restrict the effective receptive field, compromising the model's ability to capture global anatomical coherence and long-range dependencies critical for clinical utility. In addition, methods relying latent-space

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< (0.1)
Local Denoiser
Flatten
X
v
Patchily
Global Residual DIT
DIT Block
Input
Patch Emb
PE
x
Linear Layer
Adaptive SwiGLU Layer
Figure 1: Overview of our Pixel-Level Residual Diffusion Transformer (PRDiT) model, composed of Local Denoiser and a Global Residual DiT. The given input volume X first divided into N 3D patches. Each patch is flattened and passed through the 'local' multilayer perceptron (MLP) denoiser composed of Adaptive SwiGLU and Linear layers to provide local predictions &; and é; Complementing these 'local' predictions, the layer Global Residual Diffusion Transformer (DiT) module refines the overall prediction via residuals based on the global context. 'PE' indicates the positional encodings. which are only seen by the global residual DiT module.
compression often struggle to learn robust and representative features the context of medical imaging. The limited availability of training samples makes it difficult to adequately optimize the latent encoders, leading to poor reconstruction quality and the loss of critical anatomical details.
To address these limitations, propose the Pixel-Level Residual Diffusion Transformer (PRDiT), a scalable diffusion transformer framework specifically designed for high-resolution 3D medical volume synthesis. PRDiT directly operates at the voxel level, bypassing the need for an autoen- coder bottleneck, thus preserving essential details without compromising computational efficiency. The core idea is two-stage residual learning strategy: lightweight MLP-based Local Denoiser estimates coarse structures from overlapping 3D patches, and a Global Residual Diffusion Trans- former with memory--fficient attention refines the remaining high-frequency residuals across the entire volume. To overcome the challenge that naively scaling Transformers to higher resolutions would increase the token count by 8x and the attention cost by roughly 64x when doubling the res- olution, we further introduce scaling strategy that reuses the pretrained low-resolution backbone. Focusing on only learning the missing high-frequency refinement the higher resolution enables our model to perform high-resolution synthesis with minimal additional computational overhead.
The main contributions of this paper can be summarized as follows:
We propose two-stage diffusion transformer framework that directly synthesizes high- resolution 3D medical volumes at voxel-level, eliminating the need for an autoeneoder bottleneck and thereby effectively preserving critical anatomical details.
We demonstrate that using 'hot' diffusion sampling introduce controlled stochasticity during generation helps to improve sample diversity and reconstruction fidelity by balanc- ing deterministic guidance with adaptive noise injection.
We show that PRDIT scales efficiently to higher resolutions by reusing pretrained low- resolution components, substantially reducing training cost compared to coventional train- ing on higher resolution datasets.
We demonstrate the scalability and effectiveness of our PRDIT model through extensive experiments on the LIDC-IDRI (Armato III al. 2011) and Rad- Mad-ChestCT (Draelos al. 2020) datasets, achieving substantial improvements over state-of-the-an baselines across multiple quantitative metries including 3D FID, MMD, and Wasserstein distances.
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Flattened Patch Cxpa
Layer
Scale, Shift
SwiGLU
Layer Norm
Scale, Shift
SWIGLU
Linear Layer
Norm
Y1. P
Y2. P
Timestep
Embed
MLP
Figure 2: Detailed structure of our Local Denoiser module. The time embedding is used to modulate the local predictions via adaptive layer normalization (Peebles Xie 2023
2 RELATED WORK
Generative models have significantly advanced ability to synthesize realistic medical images, helping to alleviate data-scarcity and class-imbalance issues and improving the performance of downstream tasks such segmentation and diagnosis. Existing research has explored GAN-based methods for generating 3D medical images. Kwon et al. (2019) combine an auto-encood encoeen with GAN model to generate 3D brain MRI samples, while Hong et al. (2021) tackle this problem by ex- tending StyleGAN2 (Karras et al. 2020). HA-GAN (Sun et 2022 instead employs a hierarchical patch-based generator and discriminator to produce high-resolution 3D medical images.
Despite the promising works, GAN-based models still suffer from a range of issues including mode collapse, substantial memory requirements and low generation quality. Consequently, recent re- search has increasingly focused on diffusion models as promising alternative, due to their in- creased training stability and higher fidelity in generated samples. 3D-DDPM (Dorjsembe et al.) 2022) introduce denoising diffusion probabilistic models tailored for 3D medical data to generate high-resolution brain MRI scans. However, building diffusion models directly on 3D medical data incurs significant computational and memory costs, limiting scalability to high-resolution volumes. Following this, several new models propose corresponding improvements. Khaader et al.] (2023) combine VO-GAN-based latent space with a denoising diffusion probabilistic model achieve high-quality synthesis of multi-modal 3D medical images, effectively enhancing the clarity and di- versity of the generated images. Wavelet Diffusion Model (WDM) (Friedrich al., 2024) improves efficiency by applying diffusion to wavelet- deceee--ec images, enabling high-resolution synthe- sis with reduced memory demands. TCAM-Diff (Zhang al. 2025) incorporates triplane- aware cross-attention mechanism that efficiently generates high-resolution 3D medical images while sig- nificantly reducing memory usage. However, all these models are based on variants the convo- lutional U-Net architecture (Ronneberger et al. al., 2015), and excel at capturing local features but are limited modeling crucial long-range dependencies.
To address this limitation in 2D tasks, Diffusion Transformer (DiT) (Peebles & Xie 2023 mod- els have recently been proposed, replacing convolutional patch embedders with Transformer blocks to better capture global context and long-range dependencies in image generation. DiT has demon- strated state-of-the-ee performance 21) image synthesis and has also shown promise when adapted sparse 3D settings such voxelized point clouds (Mo et al.) 2023). However, despite these ad- vances, we found that DiT still encounters substantial challenges in dense 3D scenarios, including unstable training behavior and optimization difficulties, in addition to the high computational cost required for scaling. Motivated by this, our work introduces a Residual Diffusion Transformer that decomposes the generation process into two stages: a lightweight MLP- based local denoiser operat- ing on 3D patches. followed by Transformer module that models and refines the global residuals. This two-stage design effectively balances computational efficiency with enhanced capability to capture both fine local details and global structural information, enabling scalable and high-fidelitg synthesis of high-resolution 3D medical volumes.
3 SCALABLE 3D CT VOLUME GENERATION WITH PRDIT
When processing high-resolution 3D medical images, training Transformer-based diffusion model to capture both low- and high-frequency components can quickly become prohibitively expensive and time-consuming. To overcome this challenge, we introduce our Pixel-Level Residual Diffusion Transformer (PRDiT) in this section. As illustrated in Figure PRDiT decomposes the 3D diffusion process into two distinct branches: Local Denoiser and Global Residual DiT model. The local
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denoiser estimates the 'local' content of each 3D patch independently to provide prior estimate of both signal and noise of the 3D volume purely based on within-patch information, and is described in detail in Section 3.1 The residual diffusion Transformer's 's job is then to compute refinement to these predictions by leveraging its global receptive field to correct any remaining errors across patch boundaries, detailed in Section [.2] We detail our predictor-corretto diffusion sampling method in Section 3.3, before introducing an efficient way to scale our 3D generative architecture to higher resolutions with only minimal computational overhead in Section 3.4 evaluate PRDIT will be made publicly available
All code required to train and
3.1 THE LOCAL DENOISER
Given an input volume E IRCXH*W>D, we extract N volumetric patches (æ, E Cxxxxxp li=1 using sliding window of size and stride s to allow overlap between neighboring patches. This ensures that adjacent patches share contextual information at their borders, which we found to improve continuity in the reconstructed volume and helps to mitigate boundary artifacts (see Table 4aa Each patch is then vectorized (i.e., flattened) to v; vec(a:; ER3, with d = C x p².
ii=1
Following the forward diffusion process proposed by ZZhang al. (2023) we sample timestep t t (I,...,T) and compute the noisy input volume at this timestep as
cos( o: T )0, sin( t x ).,.
T2 T2)
C
with c, ~
At each diffusion timestep f, we first compute a base embedding c= (imeEmbed()) R² which is shared between both branches to facilitate consistent temporal conditioning. We project into branch-specific time embedding for the local branch as Clocal E Rd using a linear layer, and pass it to the adal_N-modulation network (Peebles & Xie 2023) produce the LayerNorm's shift/scale parameters (Ti: B1: 72. 32). As illustrated in Figure 2 these parameters allow our denoiser to adapt its processing of the volumetric patches based on the provided timestep for both refinement steps
21 SwiGLU(m ④ LayerNorm( (.)) + (1)
(2)
22 SwiGLU (72 @ LayerNorm(z1) 32),
which yields, after a residual skip connection = = 22 + of followed by a linear projection and reshaping, the predictions of the denoised input patch æ; E RCxs as well as the noise é; E RCx'" Note that the reshaped outputs do no longer overlap but provide estimates the exact same di- mensions as the input image and noise- aligning our Local Denoiser's 's output predictions with the standard diffusion objective of jointly estimating clean signal and noise.
We train the denoiser by minimizing the per-patch loss
Liocal = E, || 2:112 + 1|e - 1
to ensure we efficiently capture the local structure of each patch and accurately estimate its noise. At inference time, the denoised patch predictions { together with the corresponding noise estimates { J.M are reassembled to provide a prior estimate of the entire input volume's signal and noise. This allows our Global Residual Diffusion Transformer, which will be described in the following, to focus exclusively on correcting the outputs via 'global' residuals {x, - } N thereby reducing overall learning complexity and training time.
3.2 THE GLOBAL RESIDUAL DIFFUSION TRANSFORMER
Complementing the Local Denoiser, which processes each volume-pated independently without any global information, our Global Residual Diffusion Transformer component leverages the "global" full-volume context by jointly attending to all patch embeddings through multi-head self-attention. Following the original 2D DiT (Peebles & Xie 2023), our model inherits the scalable nature of Diffusion Transformers, meaning it can enhance generation quality at increased computational re- sources by adjusting the Transformer's depth, width, or patch size, without necessitating network redesign. This adaptability supports diverse resolutions and volumes of medical data.
'lll code for training and evaluating PRDiT available http:///ghhhabccm///c////////onn
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We freeze the Local Denoiser and train our global component to produce residuals that refine the using the same per-patch diffusion loss
local predictions (see Figure D E. ||
Lgloball
æ; 11 ||ē; E
now with ă; = Φ; + Ae; denoting the refined patch and é; = é; + Σé; the refined noise.
3.3 IMPROVING SAMPLE QUALITY VIA PREDICTOR PREDICTOR-CORRECTOO DIFFUSION SAMPLING
During inference, PRDIT generates 3D CT volumes using a modified reverse-diffusion sampling method derived from the deterministic ('cold") gradient-update scheme in (Zhang et al.) 2023). Our approach reformulates their gradient-based generative path into distinct cold predictor and hot cor- rector steps: predicting a k-step jump forward before correcting by a k: 1 backwards step. We found this to significantly improve both stability and generative quality (see ablations Section 4.4). Note that & does not need to be integer value, but is kept lc across most of our experiments.
given a diffusion schedule with T timesteps, the predictor step advances k timesteps forward by applying the following k-scaled gradient update:
As outlined in Algorithm| I
k -
cos 200 sin(3.)e) k 221,
(5)
with 3. LI and Eo and é are the estimated clean volume and noise component predicted by 7 the model. When = this procedure reduces to the standard single-step gradient update used in conventional cold diffusion sampling which advances one single timestep. For k: > 1, the method becomes a hot diffusion process when combined with our corrector, introducing fresh noise per update to increase the level of stochastic exploration.
Concretely, following the k-step predictor stage, we perform a correction through a k backwards diffusion step to reintroduce controlled noise and refine the sample
= [({)) + V 1 6', with r = cos (0
(6)
cos (B
and where e' NO I) is newly injected random noise. This predictor-correcto scheme balances deterministic guidance (cold predictor) with adaptive stochasticity (hot corrector), enabling PRDiT to better preserve fine structures while maintaining global coherence in the generated volumes.
Algorithm 1: Predictor-Correctt Sampling
HxW. Input: Initial sample zT R XXXX
Diffusion model fe with fo 1) = (&,, #6);
Predictor step multiplier
Output: Sample sequence X' = (x, (xx) and predictions Eo = =aif)==
Initialize: X + IT, angle map 3, 4
for T,T 1,...,, do
last(
(c, fo(
Model predictions at time t Gradient direction in cosine plane Avoid t- 0, when is small
St sin sin(3)) ( 2 cos(33)
k min (k),)
AS + 31 - Bi-b
It- + It A, gt
b Predictor:
k-step jump to time (t- k) D Scaling for variance preservation
((k) cos( (3 /) cos (B)
d' N(O,T)
10
Ft-1 - - (1) x 1
Corrector: variance preservation
X X (1+-1}
12
end
14 return (Ro, X)
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3.4 EFFICIENTLY GENERATING 3D VOLUMES AT HIGHER RESOLUTIONS
Training Diffusion Transformers from scratch at high resolutions like 256 is impractical under typical GPU budgets: moving from 128 to 256 increases the voxel/token count by 8x, and the quadratic cost of self-attention raises memorylcompute by roughly 64x. In practice this forces tiny batch sizes, triggers frequent out-of-memory failures, and destabilizes optimization, which makes the end-to-end high-resolution training inefficient and brittle (and often infeasible). To mitigate this, we construct a high-resolution generator that reuses our already-trained low-resolution model. As illustrated in Figure B] we train only an additional high-resolution residual refinement module, which operates locally on individual volume patches and focuses on recovering the missing high- frequency details. Given nigh-resolution noisy input volume X we first downsample it to the lower resolution, apply the trained low-resolution model, and then upsample the prediction back to high resolution. This yields our initial 'rough' estimates for both signal and noise to be refined.
A key design choice is the use of nearest downsampling. Unlike smoother methods such as tri- linear interpolation, nearest downsampling avoids averaging pixel values, which would otherwise attenuate high-frequency noise and reduce the signal's 's overall energy. Preserving this energy is es- sential to maintain the noise statistics expected by the pretrained low-resolution model, ensuring the downsampled input aligns with the model's 's training distribution and thereby avoiding mismatches during denoising. For the upsampling step, we apply trilinear interpolation to the predicted clean low-resolution image X to obtain X as this smoother interpolation better preserves structural continuity and reduces artifacts in the anatomical features, yielding more natural high-resolution initialization. Since the noise is inherently stochastic and discontinuous, we apply nearest upsam- pling to the predicted noise e to obtain c ensuring its energy level is preserved.
To recover details lost during down- and upsampling, we introduce a high-resolution residual re- finement module based our greviously-introdusee Local Denoiser design (Figure 2 This module refines the upsampled signal and noise estimates from the low-resolution component using the set of noisy input volume-patches and information about the current time step as shown in Figure 3 We keep the already-trained low-resolution model frozen and train only the refinement module at high resolution, which helps it focus its capacity on fine structures rather than relearning global anatomy. Our training uses the standard
and-noise estimation objective on high-resolution patches (Equation (3)) augmented with low-frequency consistency term that encourages the down- sampled high-resolution predictions to match the low-resolution outputs. We found this simple con- straint to help keeping large-scale appearance stable while allowing the residual module to allocate its capacity to figh-frequency detail.
Unlike cascaded super-resolution methods that typically modify only the final sample and often disrupt the low-frequency structure learned during diffusion, our approach integrates the high- resolution residual refinement module directly within the sampling loop. At each step, we down- sample the current state to query the low-resolution model, upsample its estimate, and immediately refine figu-frr details before proceeding. This in-loop strategy yields sharp textures, reduces edge artifacts and promotes more stable detail formation throughout the diffusion process.
4 EXPERIMENTS
We start with a brief overview of the datasets, related baselines and metrics that are used for evalua- tion. We then present a number of qualitative and quantitative insights to demonstrate our approach's advantages in unconditional volume synthesis over related methods. Implementation details are pro- vided in Appendix (A.9 and additional experimental results can be found in Appendix IA
Datasets. We evaluate the performance of our model on two publicly available 3D medical imaging datasets: LIDC-IDRI (Armato III et al. (011) and Rad-ChestCT (Draelos et al., 2020). LIDC-IDRI contains 1,018 (118 thoracic CT scans with annotations of lung nodules from multiple expert radiologists. The scans exhibit varied resolutions and patient anatomies. For preprocessing, we clip the intensity values to the lung window range, resample all volumes to an isotropic voxel spacing of I mm, and crop or pad them to a uniform size of 256 x 256x 256. For experiments involving lower resolutions, volumes are downsampled via average pooling, then normalized to 1]. Rad-ChestCT includes 3,630 chest CT scans from 1,800 adult patients, representing approximately 10% of the full dataset, which contains 35,747 scans from 19,661 patients. The dataset encompasses a broad spectrum
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{x]
256 N
256
E <~ (0,
Patchify
i=1
High Resolution Residual
X256
Linear
t
Emb
Nearest
Linear
? 256
X125
@128
x 128
128 Pretrained model
1
Nearest
Xb
Inputx X
Trilinear
x
Figure 3: Our high-resolution PRDiT1256 model integrates the pretrained and frozen lower- resolution PRDITT core to serve as a structural prior. Scaling the 128 base resolution through trilinear upsampling allows its dedicated High-Resolution Residual path to capture fine-grained de- tails in parameter-efficien manner to generate high-quality 256 outputs.
of thoracic diseases and anatomical variations. We center-crop fixed field of view, resize to 256x 256, optionally downsample, and normalize to [-1,1] for consistency with LIDC-IDRI.
contrast our PRDiT to three recently published 3D medical image generation models. WDM-3D (Friedrich et al. 2024), a diffusion model that decomposes each 3D volume into multi-resolution subbands via discrete wavelet transforms, denoises each subband with 3D U-Net, and then reconstructs the full volume with an inverse wavelet transform. 3D LDM (Khader et al.) 2023), latent diffusion model that employs VQ-GAN encoder to map volumes into a compact latent space, performs diffusion in that latent space and reconstructs volumes with a matching 3D) convolutional decoder. HA-GAN (Sun et al. 2022) in contrast uses hierarchical GAN whose gen- erator comprises low-resolution "global" and high-resolution "local" 3D convolutional network, each guided by multi-scale discriminators on corresponding sub-volumes.
Baseline Models.
Evaluation Metrics. We evaluate unconditional generative quality using the 3D Fréchet Inception
Distance (FID), Maximum Mean Discrepancy (MMD) (Gretton et al.) 2012 Sun et al. 2022] and the pairwise Wasserstein distance using WGAN (Arjovsky al. 2017; Zhang et al. 2025 ). Following
common practice, we extract volumetric embeddings from both real and generated CT volumes using a pretrained Med3D encoder (Chen al., 2019), then quantify their distributional discrepancy
by fitting multivariate Gaussians and computing the 3D FID as well as by applying an RBF-kernel
MMD Gretton al., 2012) in the same feature space. Lower 3D FID and MMD values indicate that the generated feature distribution more closely matches the real distribution. The Wasserstein distance is obtained by comparing the WGAN critic's scores on generated samples against those on real data, following the evaluation approach of Zhang et al. (2025). A lower Wasserstein distance indicates that the critic assigns similar scores to generated and real volumes, implying the generated distribution closely matches the true data distribution and thus higher sample fidelity. We perform pairwise comparisons one 'anchor' model. See Appendix A.9.3 for additional details.
4.1 CONTRASTING PRDIT TO THE STATE-OF- ART
To demonstrate the performance of our PRDiT, we compare three model variants (4 layers, 8 layers and 12 layers) against the baselines HA-GAN (Sun et al. 2022). 3D-LDM (Khader et al. 2023) and WDM-3D (Friedrich et al. 2024 on both LIDC-IDRI (Armato III et al.) 2011) and RAD. ChestCT (Draelos et al.) 2020) and report the results in Table Our smallest 4 layer PRDiT-4I. variant already achieves lower 3D FID and MMD scores than its competitors across both datasets, outperforming them across all metries despite its shallow depth. Increasing PRDiT's depth to 8 and 12 layers yields consistent improvements in generative quality and demonstrates the scalability of approach; albeit increased computational cost. Note, however, that all PRDIT variants reuse the same Local Denoiser module which only has to be trained once, effectively reducing the com- putational overhead and training time when scaling architecture greater depths.
Following Zhang et al. (2023), we additionally report the pairwise W-Scores with our PRDiT- 12L the "reference" model. This score effectively quantifies the ratio between the distances of each
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model's distribution of generated samples to that of the real data (see Appendix A.9.3 A score of *1.0" indicates equal generative quality between two models, while values greater than reflect degraded performance relative to the reference reaffirming the quality of even our 4-layer model.
4.2 COMPARING PERCEIVED IMAGE QUALITY SAGITTAL SLICES
Figure shows sagittal slices from each generative method alongside real CT scans, providing a qualitative comparison between our PRDIT model and its competi- tors. HA-GAN (Sun al. 2022) notably blurs bone con- tours and introduces unnatural texture artifacts through- out the volume. 3D-LDM (Khader et al.) 2023) in con- trast demonstrates clearer structural details and reduced noise, however still suffers from insufficient anatomical detail and streak-like artifacts. WDM-3D (Friedrich et al.) 2024) further improves clarity and reduces overall arti- facts, but continues exhibit noticeable blocky noise and unclear bone boundaries. Our residual DiT model substantially addresses these shortcomings by delivering sharper bone edges, smoother and more consistent organ boundaries, and clearer anatomical details. These improvements highlight our method's capability to more accurately reconstruct subtle features present in real medical scans. In addition, increasing model depth yields progressively sharper cortical bone edges, clearer bronchial walls and vessel bi- furcations, and smoother soft-tissue boundaries (Figure 4). These qualitative gains align with the results in Table which show a consistent decrease in FID and MMD as depth increases.
4-Layyer Model
B-Lager Model
12-Laper Model
Figure 4: Variation of image quality across different depths of PRDiT.
4.3 EFFICIENTLY SCALING UP TO HIGHER RESOLUTIONS
We contrast our efficient high-resolution PRDiT against its competitors using the LIDC-IDRI (Armato III al.) (201T) dataset at 256 resolution. As the results Tsole our PRIDT-44L,256 clearly outperforms the other methods on both FID and MMD while incurring substan- tially lower training costs. While training 3D-LDM proved infeasible our setup due to memory constraints, WDM-3D ex- hibited pronounced sensitivity to random seeds. While most seeds yield high-quality samples, small fraction of severely degraded outputs disproportionately inflates FID/MMD, resulting in high variance and reduced evaluation robustness. Generated images of all methods are provided in Appendix A.11.3 for visual inspection.
Table Results on LIDC-IDRI at 256 resolution. Reported GPU-hours are measured
A100. FID values are scaled by 10 (as is common practice).
Model
FID 4
MMD
Training Cost
HA-GAN
3.98
0.2237
G40
3D-LDM
OOM
OOM
WDM-3D
5.60
0.2590
120
PRDIT-4L,156
2.28
0.1370
36 GPUh
Table I: Comparison of unconditional 3D generative methods on LIDC-IDRI AArato III et al, 2011] and RAD-ChesICT (Draelos al., 2202),, evaluated on volumes of size 128". FID values are scaled by 103. Reported are mean + std over three seeds. Pairwise W-Score (ratio) is computed using a trained WGAN-GP critic against PRDiT-121, as reference. See Appendix A.9.3 for details.
LIDC-IDRI
RAD-ChesICT
Model
FID
MMD
W-Score |
FID
MMD 4
W-Score 4
HA-GAN
0.227
0.2071 +: 0.004
5.42 1: 1.46
3.92 .: 0.38
0.183 1: 0.015
2.48 1. 0.14
3D-LDM
7.62
0.3458 + 0.006
2.62 + 0.28
4.14 0.74
0.228 + 0.017
2.87 0.36
WDM-3D
3.37
0.1885 + 0.017
1.31 + 0.26
4.11 + 0.58
0.213
1.27 0.111
PRDIT-41. (ours)
2.04
0.1852 + 0.009
1.23 4: 0.08
1.92
0.169 0.010
1.21 0.0.44
PRDFT-8T. (ours)
1.86
0.1650 + 0.011
1.14
1.62
0.149 0.010
1.02
PRDIT- 12L (ours)
1.11
0.1501 + 0.010
1.00
1.45 0:21
0.159 - 0.007
1.00
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LIDC-IDRI
Rad-Chestor
HA-GAN
3D-LDM
Ours
Real Data
Figure 5: Qualitative comparison of unconditional 3D generative methods on the LIDC-IDRI AA-
mato III et al. 2011) and Rad-ChestCT (Draelos al. 2020). From top bottom, the rows correspond to (1) HA-GAN (Sun et al. 2022). (2) 3D-LDM (Khader et al.) 2023). (3) WDM-3D (Friedrich et al. 2024), (4) our method, and (5) real medical data. Our approach bet- ter preserves fine anatomical details such as vertebral bone edges and organ boundaries while sup- pressing spurious artifacts compared prior models. Additional examples are provided in Ap- pendix A.I. Note: Images are independently generated and unconditional, and cannot be com- pared column-wiss wise. Regions appearing as 'black voids' within the body are low-density air cavities that are also present in the real duta (see Appendix A.4 Figure for more details).
To efficiently validate the quality our high- resolution upsampling strategy against expen- sive from-scratch training, we additionally train PRDIT model 643 and upgrade 1283 using our proposed high-resolution upsampling methodology, and compare its results the PRDIT model trained from scratch on 1283. While training from scratch does indeed lead slightly improved performance across FID and MMD, efficient variant is more than 6 times faster training providing a convincing tradeoff terms of quality-to-compute (Table 3)
Table 3: Comparison of high-resolution training strategies on LIDC-IDRI. FID scaled by 103.
Method
FID.
MMDJ
Training Cost
PRDIT 128
scratch
2.04
0.1853
80 GPUh
PRDiTa4 128
2.89
0.1893
12 GPUh
4.4 ABLATION EXPERIMENTS
Contributions of Individual Design Choices. We conduct ablation experiments on the LIDC-IDRI AArato III et al. 2011) dataset using PRDiT-4L model to assess the contributions of our design choices. Table (a) shows that removing the overlap between patches significantly worsens 3D FID and MMD scores due boundary inconsistencies. Training model without

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Table 4: Ablation studies using our 4-layer variant PRDiT-4L on the LIDC-IDRI dataset. The
reported FID values are scaled by 10 as is common practice. (a) Contribution of the architecture's individual components. (b) Effect of varying the predictor steps kt in our predictor-correcto setup.
(a)
(b)
Model variant
FIDL
MMD
h
FID
MMD
Full model
2.04
0.1853
1.0 (cold)
8.889
0.3490
w/o overlap
3.27
0.2304
2.0
2.173
0.1849
w/o local denoiser
3.10
0.2174
3.0
3.112
0.2112
w/o global DiT
41.92
0.7795
4.0
4.184
0.2425
the Local Denoiser (i.e., DiT-only) also markedly reduces the performance whereas using only the local component without any global refinement leads to the worst results.
Hot-vs-Cold Diffusion. We evaluate the effect of varying the predictor step & of PRDIT variant model using the LIDC-IDRI (Armato III et al., 2011) dataset. The results in Table ( demonstrate that the transition from 'cold' (A: = to 'hot' (k 1) significantly enhances generative output performance, with k = 2 achieving the best results in this setup. Note that the number of steps technically not limited integers, and future work might explore whether further optimization along this axis might yield even greater quality improvements.
To clearly highlight the differences between our proposed predictor-corrector diffusion sampling method and conventional sampling techniques ('cold" and 'hot'), we provide detailed discussion as well as a range of additional insights and comparisons in Appendix A.6 A.6 including both quantitative and qualitative results across a range of settings.
Inference times. To further assess PRDiT's suitability for practical, time-sensitive applications, we compare its inference speed against three competing models. All inference times are measured with a batch size of on an A 100 GPU at resolution 1283, with detailed results presented in Ap- pendix A.7 It is worth noting that, due to its GAN-based architecture and non-iterative, single-pass image generation, HA-GAN (Sun et al. 2022) is by far the fastest, with an average inference time of just 0.01s. Among the diffusion-baate based approaches, all PRDiT variants run markedly faster than the competitors, with inference times ranging from 1 11.5s (4L) to 21.9s (12L), compared to 26.1s for 3D-LDM (Khader et al. 2023) and 34.6s for WDM-3D (Friedrich al. 2024).
CONCLUSION
In this paper, we have presented PRDIT - novel and scalable residual Diffusion Transformer model which efficiently solves the 3D CT Volume generation task via two dedicated branches and a two- stage training strategy, consisting of: a Local Denoiser and Global Residual Transformer. By decoupling the task of modeling coarse local details and refining high-frequency global residuals, PRDiT effectively reduces computational overhead. Extensive evaluations on LIDC-IDRI and Rad- ChestCT datasets demonstrate that method achieves superior generative performance compared to state-of-the-ar methods such as HA-GAN, 3D LDM and WDM-3D, consistently producing lower 3D FID. MMD, and Wasserstein distances. Our experiments further validate the model's scalability, illustrating consistent performance improvements with increasing transformer depth. These results highlight PRDiT's potential powerful and efficient method for synthesizing high-resolution 3D medical volumes, with promising applicability to various clinical and diagnostic scenarios.
ACKNOWLEDGMENT
We would like to thank Sun al. 2022 Khader et al. (2023) and Friedrich et al. (2224) for making their code publicly available, enabling us to perform direct comparisons to our method.
This research supported by The University of Melbourne's Research Computing Services and the Petascale Campus Initiative. MH was supported by the Australian Research Council (ARC) through grant DP230102775.
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7 REPRODUCIBILITY STATEMENT
We have made every effort to ensure that the results presented in this paper are reproducible. We ex- plain all datasets, data preprocessing, baseline models, evaluation metrics, and training details used in our method in experimental section. To support reproducibility, we include a detailed overview of our architecture Figures [ which fully describe the design. In addition, the experimental setup, including training steps, model configurations, and hardware details, are all described in detail in Appendix A.9 The 3D medical CT datasets, such as LIDC-IDRI and Rad-ChestCT, are publicly available, ensuring consistent and reproducible evaluation results. All code required for training and evaluating our method will be publicly released on github, as detailed in footnote on page
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A APPENDIX
A.1 LLM USAGE
In this paper, Large Language Models (LLMs) were utilized as a general-purpose assist tool aid and polish the writing process. We used the LLM to refine language and improve clarity across the Introduction, Related Work and Conclusion sections.
A.2 HALLUCINATION RISKS AND POTENTIAL MISUSE
In the context of this work, 'hallucination" would refer anatomically implausible or extremely rare structures that lie far outside the training distribution.
From a technical angle, we monitor this risk partly via the W-score metric: the model frequently produced unrealistic volumes, the WGAN critic would assign larger distances between real and generated samples, and the W-score would deteriorate. practice, our W-scores remain close to those of real data, which suggests that severe hallucinations are uncommon in experiments.
That said, we want to emphasize that we do not position PRDiT as a tool for direct clinical decision- making. Synthetic CT volumes should be used only for research, simulation, carefully controlled data augmentation, and always under expert supervision. Any downstream clinical application must rely on appropriate validation real patient data and the involvement of qualified radiologists.
A.3 LIMITATIONS AND FUTURE WORK
Our current implementation is constructed upon a simple linear mapping, including patch embed- dings, which we treat as the base structure. While effective, this design may limit the model's 's ability to capture complex local spatial correlations. natural extension would be incorporate convolu- tional neural networks (CNNs) for feature extraction, which could enhance representational richness and further improve generative quality. We leave this direction as an important opportunity for future exploration.
PRDiT inherits the high training cost of Transformer-based models, especially in handling full- volume 3D attention. Future work might explore 3D-specific optimizations, such as linear win- dowed attention, to reduce computational demands. We also plan to extend PRDiT to conditional generation tasks, enabling guidance from segmentation masks sparse inputs for broader clinical applications.
It is important to point that there generally exists voxel-wise 'ground truth' for novel pwwdddiiiinallygenerattte volumes. This means that downstream segmentation or detection studies would require dense, expert annotations for both real and synthetic CT volumes, which are expensive and therefore unfortunately beyond the scope and resources of this work.
The objective of this paper is make high-fidelity unconditional 3D CT generation feasible scale and to rigorously evaluate distributional fidelity. We therefore (i) do not claim that immediate clinical deployment of our method or similar methods this kind are feasible, and (ii) want to point out that such downstream and clinical evaluations are outside the scope of the present paper, and we have to leave systematic study of using our model for downstream segmentation/ddetccioon and assessing its clinical impact as future work.
A.4 CHARACTERISTICS OF THE TRAINING DATASETS
In this section, we explain why some of the generated CT outputs appear to contain black 'voids" in the parts of the images, like the lower-left region within Figure Careful inspection of the original datasets shows that these dark 'void' regions actually correspond to naturally occurring low-density air cavities that are also present in the real data, rather than artifacts introduced by the hot-diffusion sampler or hallucination issues, as illustrated in Figure 6 In addition, following standard practice in prior works, our pre-pro precesccessing clamps HU values fixed window, which further accentuates such
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air regions as dark areas in both real and generated scans. For easier debugging and later verification, we additionally overlay the case IDs from the LIDC-IDRI dataset onto the images.
170
ID:0475
ID-1580 0
Figure 6: Ground truth CT samples from LIDC-IDRI (left) and Rad ChestCT (right). Red boxes highlight naturally occurring low-density air cavities, showing that similar dark void regions also present in the real data.
A.5 THE LOCAL DENOISER
This section provides additional details for the local denoiser module. When processing high- resolution 3D medical images, training diffusion models with Transformers to capture both low- and high-frequency components prohibitively expensive and time-consuming. As illustrated in Figure 1 therefore decompose the learning process into stages: local denoiser estimates the content of each 3D patch locally (see Figure 2) and a diffusion model with Transformers focuses exclusively correcting these via global residuals.
Concretely, given an input volume
X E CXHHWWD
we extract N cubic patches
(rr; E R. CXPXPXXIN [
using a sliding window of size p and stride s p allow overlap between neighboring patches, which ensures that adjacent patches share contextual information at their borders to improve conti- nuity in the reconstructed volume and mitigate boundary artifacts. Each patch vectorized
U = vec(x,)) E Ra d = x p
To follow the forward diffusion process (Zhang et 2023). we sample timestep E {l,..., . and compute
t т + sin( T E. ().
t
cos
T
Given diffusion timestep f, we first compute a shared base embedding Tmmee)))d()) A
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We then project into stage-specific time embedding for the local stage
Clocal = Wo be R
and feed Clocal into the AdaLN modulation network to produce the LayerNorm shift/scale parame- ters
(2,,,1,72,,33 72, .32) AdaLN(Ciocall
Rdd
In the global residual stage, similarly compute
Wt c by E R
Cfne
and use Cine within each AdaLN layer of the DiT blocks, ensuring consistent temporal conditioning across both modules.
The signal is then refined via two SwiGLU-MLP blocks
21 = SwiGLU @ LayerNorm(e + B1);
22 SwiGLU (72 @ LayerNorm(z:) B2),
and added residually as z 22 + v; final linear projection produces
0 6 Wznal(z)
RR2d d, G R.
which is reshaped back to E; E Cx Here ú; the denoised patch and é is the predicted noise, which aligns MLP's outputs with the standard diffusion objective of jointly estimating clean signal and noise.
We train the denoiser by minimizing the per-patch loss
= E2 | 22112
+
|6,
6
ensuring efficient capture of local patche structure and accurate noise estimation. At inference, the denoised and overlapping patches {æ,} are passed to the Residual Diffusion Transformer, which focuses exclusively on correcting the predictions via global residuals (æ, 2.,). thereby reducing overall learning complexity and training time.
A.6 PREDICTOR-CORRECTO SAMPLING
This section provides more detail on our sampling scheme introduced in Section 3.3 and shows how it differs from previous standard ancestral 'DDMM' samplers.
A.6.1
QUALITATIVE INSIGHTS FOR VARYING
To provide further insight in addition to the quantitative analysis of varying k in the main paper, we provide qualitative results across different & values across axial, coronal, and sagittal views in Figures 9
A.6.2 RELATIONSHIP TO CONVENTIONAL. 'HOT' DIFFUSION SAMPLING
In this section, outline how our sampling procedure differs from standard ancestral 'DDIM' samplers: tailored to the cosine-sine schedule (Zhang et al. 2023) and lets us inject a controlled amount of extra stochasticity per step, which we show empirically improves FID/MMD over both deterministic sampling (k = 1, Table b main paper) as well as previous methods (following).
The conventional ancestral 'hot' sampler performs single stochastic update from to Zz-1 using only the predicted noise e. Our sampler can be viewed as a predictor-correcto variant of this ances- tral sampler: instead of one stochastic step, take l deterministic denoising sub-steps followed by stochastic correction steps, which lets us inject a stronger but controllable amount noise per outer step.
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=1
k
==3
k 44
Figure 7: Qualitative comparison: axial view of generated chest CT samples for different i values LIDC-IDRI dataset.
==1
k=2 2
k =
Figure 8: Qualitative comparison: coronal view of generated chest CT samples for different values in LIDC-IDRI dataset.
Settings. Our model in the main paper trained to jointly predict the clean image xp and the noise whereas the conventional hot sampler only relies on For fair comparison, we therefore refactor our generation process so that also depends solely on the predicted noise. In this way, we get the 2o through
zo(x,, 1) /1 (2,,)
Vou
which exactly follows the method from the conventional sampler. The conventional sampling method is defined
Fe-1 / / 10 t) + / a.-1 of of co(2,, t) oz
where ~N(0, 1-
Je cos(3)), -I- sin(3,), which is the
T-or
same sampling method used in DDPM DDIM (setting 77 1).
Recalling Equations 5
and 6 our sampling method results in
Lt-1 =1(A)
(k) wc k. (Vardiol 1) + v1 - -assa(ee.))) ()) + /1 )
(8)
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==2
k 3
Figure 9: Qualitative comparison: sagittal view of generated chest CT samples for different values LIDC-IDRI dataset.
FID
MMDJ
(cold sampling)
7.031
0.7285
6.132
0.6023
2.5
5.950
0.5893
5.736
0.5699
3.5
5.718
0.5682
6.960
0.6273
Conventional (hot diffusion)
6.456
0.6100
Table Ablation experiments for noise-only adaptation: quantitative comparison on the number of predictor-corrector sub-steps k in our modified noise-only sampler on LIDC-IDRI, compared to the conventional hot-diffusion sampler. FID is reported as 102.
with (())_ Va-- Var-x
where = 21, and & not restricted to integer values.
Experiments. To clearly highlight the differences between our proposed hot-diffusion sampling method and the conventional sampler, we provide both qualitative (Figure T0) and quantitative (Ta- ble 5) comparisons of their respective generation quality. The results show that our hot diffusion (e.g. 3) achieves better FID/MMD than both the deterministic case 1) and the conven- tional hot-diffusion sampler, indicating that our proposed modified stochastic schedule is genuinely beneficial rather than just reparameteriization the standard sampler.
A.7 INFERENCE TIME
We quantify inference speed in Table 6 by measuring sampling time across three baseline models and our three variant models. All timings reported as the mean of 10 runs with batch size of 1.
A.8 HIGHI RESOLUTION MODELS ARCHITECTURE
The architectural structure of our high-resolution 3D CT image generation approach is provided in the body the main paper in Figure 3 In this section, we additionally provide insights on the difference in image quality between our efficient high-resolution variant and expensive from-scratch training PRDiT directly on higher-resolution images. Results are provided in Table and demon- strate that while training from scratch does indeed lead to slightly improved performance across FII)
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Classic
k =
k= 2.5
k=3
k 3.5
Ablation experiments: qualitative comparison on the number of predictor-corrector sub-steps K in our modified noise-only sampler LIDC-IDRI, compared to the conventional hot- diffusion sampler (classic row 1).
Figure 10:
Table 6: Inference sampling time per sample (seconds) with batch size of on A100 GPU. Reported are mean + std over 10 runs.
Method
Mean (s)
Std (s)
HA-GAN (Sun et al. 2022
0.01020
0.00139
3D-LDM Khader et al.
2023)
26.0518
1,00
WDM-3D
Friedrich et al. 2024
34.6328
0.5915
PRDiT-4L (ours)
11.4590
1.1665
PRDiT-8L (ours)
18.3414
0.7329
PRDiT-12L (ours)
21.9311
0.8646
and MMD, our efficient variant is close to 7 times faster in training, providing convincing tradeoff in terms of qualit-tt-ccmpuue.
Table 7: Ablation experiments: Comparison of high-resolution training strategies on LIDC-
We report a model trained directly at 1283 (Scratch) versus our progressive strategy that reuses pretrained 64 model and extends it to 1283 (Pretrained Low-to-High), FID is reported as 103.
Method
FID,
MMDJ
Training Cost
PRDiT 128
sernchh
2.04
0.1853
80 GPUh
PRDiT 128
2.89
0.1893
12 GPUh
In our high-resolution 250 model experiments, we freeze the entire now-resolution back- bone, which includes both Local Denoiser and the Global Residual DiT modules, and only train lightweight high-resolution refinement module. We made this choice on purpose: fine-tuning the full backbone at 256 would be very memory- and compute-heavy, while the refinement module
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concentrate on adding the missing high-frequency details top of already well-trained low- resolution anatomy.
In practice. PRDiT-4L1200 still outperforms all high-resolution basclines LIDC-IDRI in both FID and MMD, and it does so with much lower GPU cost (Table 2). This suggests that freezing the backbone does not noticeably hurt performance 256 In addition,
comparison of from- scratch 128 training with the progressive 64 - 128 variant (Table shows only a small drop in FID/MMD for more than 6x reduction in training time, which further supports this design.
To provide further insight into how a frozen backbone compares against a jointly fine-tuned setup, performed ablation at 128 256 directly comparing a model with frozen low-resolution backbone to one where we fine-tune the entire backbone. For the fine-tuned variant, we use conservative learning rate of 10 for 100 epochs to avoid disrupting the well-trained low- resolution weights, and evaluate it under exactly the same setup as the Table Bale@shhon shows, the fine-tuned backbone gives modest but consistent improvement over the frozen one (better FID and MMD), indicating that full fine-tuning can help, but the gain is relatively small compared with the additional computational cost, which supports our choice to freeze the backbone in the main 256 experiments.
model. As
To make these experiments reproducible, we detail below the exact configurations used to train all high-resolution models.
A.8.1 TRAINING SETUP FOR RESH-RESOLUTION MODELS
brckbon settings. We first freeze the entire low-resolution backbone (both the Local Denoiser and the Global DiT) and train only the high-resolution refinement module. The refinement operates on overlapping 3D patches of size (12, 12, 12) with stride and padding 2 voxels, matching the local denoiser configuration in the low-resolution model. We optimize with Adam using learning rate of 10-5, gradient clipping at 1.0, a batch size of per GPU, and train for 2000 epochs.
Full fine-tuning setting. For the fine-tuning ablation, we start from the converged frozen- backbone high-resolution model and then unfreeze the entire pretrained backbone and high- resolution module, training all parameters jointly. To avoid disrupting the well-trained low- resolution weights, we use a much smaller learning rate of x 10 6 gradient clipping at 0.4, a batch size of 2 per GPU, and train for 100 epochs under otherwise the same setup.
A.8.2 EXPERIMENTS RESULTS
As Table and Figure I show, the fine-tuned backbone gives modest but consistent improvement over the frozen one (better FID and MMD), indicating that full fine-tuning can help, but the gain relatively small compared with the additional computational cost, which supports our choice to freeze the backbone in the main 2563 experiments.
Table 8: Ablation experiments: Comparison of high-resolution performance between frozen low- resolution model and fine-tune entire models. FID is reported 10
Model
FID 4
MMD 4
Frozen 1283
2,280
0.1370
Fine-tuned 1
1.702
0.1315
A.9 MODEL CONFIGURATIONS IN DETAIL.
In this section, we describe the network architecture, training setup and evaluation protocol to enable full reproducibility. In addition, we use the WtastAttention-22 included with Pytorch version 2.2.
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Frozen
Fine-Tune
Frozen
Fine-Tune
Frozen
Fine-Tune
Figure 11: Ablation on freezing vs. tne-ttunig the low-resolution backbone 1283 on LIDC-IDRI. Fine-tuning with small learning rate yields slightly better FID/MMD, while the frozen backbone already achieves competitive performance.
A.9.1 NETWORK ARCHITECTURE CONFIGURATIONS
The PRDiT model comprises two primary components: Local Denoiser (MLP-based) and an Global Residual DiT models (Transformer-baseed), enabling two-stage training process. Stage trains the coarse path (depth=0), while stage freezes the local path and trains the global path (depth>0). The architecture processes 3D volumes of shape [B, C, D, H, W] into patch sequences for denoising. Key components are described below, with parameters summarized in Table 10
Parameter
Value
Input Channels (Cin)
Patch Size (P P
12 x 12 x 12 8
Stride
Padding
2
MLP Structure
Two-layer SwiGLU
Hidden Size
C p3 1 123 = 1728
MLP Ratio (Rmlp)
1.0
Output Channels (Cau)
2
Normalization
LayerNorm AdaLN modulation with TimeEmbedding
Conditioning
Activation
SwiGLU
Dropout
0.0
Weight Initialization
Zero-initialized for A and final linear layer
Table Configurations of the LocalDenoiser module for PRDiT.
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Parameter
Value
Input Channels (Cin) Patch Size (P P x P)
12 12
Stride
8
Padding
2
Depth
{4, 8, 12]
Hidden Size (Drstaen)
{768, 1152)
Number of Heads
{12, 16)
MLP Ratio (Rmip)
4.0
Output Channels (Cou)
2
Normalization
LayerNorm
Positional Embedding
3D sinusoidal, shape [1, N, Dridden, non-learnable
Conditioning
AdaLN-MMouultiin with limeedmeddiig
Attention
Multi-head self-attention with memory-efficiet attention
Dropout
0.0
Weight Initialization
Zero-initialized for AdaLN Zero and final linear layer
Table 10: Configurations of the Gl oba 1 Res i idual DiT module (global residual path) for PRDÍT, with depths 4, 8, and 12. All models share the same pretrained Local model from Stage 1 training.
A.9.2 TRAINING SETUP
We provide the details of how to train the methods.
Optimization Function: AdamW with 31 = 0.9, 32 = -0.999, and weight decay 0.0. The
objective mean squared error (MSE) loss.
Learning Rate:
-Stage (LocalDenoiser): 1074.
Stage 2 (GlobalResidual DiT):
* 9 x 10 (depth=4),
* 6 x 10 (depth=8),
4 10-5 (depth=12)
for new parameters; pretrained parameters (Local Denoiser) are frozen during second- stage training.
Device: NVIDIA A100 80GB GPUs with BFP16 mixed precision.
Batch Size: 128 volumes (16 per GPU), 256 volumes (4 GPU). Diffusion Schedule: Linear, 1000 timesteps.
Training Steps: Stage 1: 6,000 epoches; Stage 2: 8,000 epoches.
Gradient Clipping: Maximum norm 1.0.
Data Split: Randomly-sampled data split of 90% for actual training and a 10% validation set for tracking progress (both datasets).
A.9.3 EVALUATION PROTOCOL WITH FOCUS ON THE W-SCORE METRIC
In here, we provide the details about how to measure the performance of the generative models. In this paper, we use the three evaluation metries, FID (Friedrich et al.) 2024). 3D MMD (Sun al.) 2022), and W-GAN scores (Zhang et al., (0255. We explain how to use the WGAN-GP to measure the generative quality in our experiments.
Critic scores
The Goal. We use single 1-Lipschitz critic fo (WGAN-GP) as measurement estimate the Wasserstein-1 distance between the real data distribution Preal and model m's distribution P,
W (P reat, Pm)) Exr-Pa [x] Eg-P, [Jo
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Lower is better. We compare models (HA-GAN, 3D-LDM, WDM-3D, PRDiT-4L (ours), PRDiT-8L (ours) against our PRDiT-12L as the best performance standard.
Data Splits & Preprocessing.
Real: Split the dataset into Real-Train and Real- Val (no overlap with any generator's train- ing data).
2. Fake per model: For each generator split its generated volumes into Fake-Train and Fake- Valm)) (no overlap).
3. Preprocessing: Apply identical resampling/cropping and intensity normalization (e.g., HU
+ [0, 1J) to both real and fake data, during training, it will map [0, + |-1, 1].
4. Mixed Fake for training: Build Fake- Train-rrin--e by combining the pairs of Fake- (ike-Train!m) sets.
Critic & Training Objective. A 3D conv stack with stride-2 downsamples, InstanceNorm3d and LeakyRel.U activations, which is following the 2D WGAN-GP (Gulrajani et al. 2017) define. No sigmoid at the end. In addition, the objective (maximize)
Ceitie Rea-Real [f) - Eg- Mix [fo - A GP(O),
(10)
with gradient penalty GP on random interpolates and setting À = 10. The default configurations is Adam (Ir 10-5, =00.5, 00999.. The batch size 40 real 40 fake, and the fake samples are consisting of the generative model and our anchor PRDiT-12L output
Pair- Comparisoa vs. PRD1T-12L (Anchor) FID/MMD), for each competitor
Given PRDiT-12L the anchor (chosen by
(i(
(>1 + PRDiT-12L better).
(11)
W1 (Ours-12La 12L)
Our experiments report the ratio and std three different random seeds with the trained critic.
A. COMPREHENSIVE PAIRWISE COMPARISONS ACROSS ANCHORS
In this section. we propose to choose the different model the anchor to compute the WGAN ratio score, and for better visualization, we use the log of WGAN ratio. Using the log ratio makes results signed and interpretable (positive better than the column model; negative = worse). The results show in Table
A.11 ADDITIONAL QUALITATIVE INSIGHTS
Here we present additional qualitative examples that complement the quantitative results for the LIDC-IDRI dataset in Figure 12 and Rad-chestCT dataset in Figure 1 for our 4-layer, 8-layer and 12-layer models.
A.11.1 LIDC-IDRI
We provide additional generated 3D CT images for PRDIT models with 4, 8 and 12 layers trained on LIDC-IDRI dataset in Figure
1.1 RAD-CHESTCT
We provide additional generated 3D CT images for our PRDiT models with 4, and 12 layers
trained on Rad-ChestCT dataset in Figure 13
A.11.3 COMPARISONS 256 RESOLUTION
We provide the additional generated samples across the HA-GAN, WDM-3D, and our PRDiT-4L with the 256 LIDC-IDRI dataset, in Figure
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HA- GAN
3D-
WDM- 3D
PRDIT- 4L
PRDIT- 8L
PRDIT-
LDM
12L
0
-0.302
-1.950
-0.969
-1.199
-1.433
HA-GAN
(#:0
0.002)
(# 0.055)
( 0.036)
0.095
(1 0.051)
0.302
-1.002
-0.753
-0.963
-1.05
3D-LDM
(:00)00)
0.069)
0.031)
0.068)
1.950
1.602
0
-0.142
-0.208
-0.371
WDM-3D
(d 0.055)
0.069)
(1:
(á: 0.055)
(E 0.080)
(L 0.049)
0.969
0.753
0,142
0
-0,011
-0.104
PRDiT-4L
(# 0.036)
0.043)
(# 0.055)
(#
(# 0.003)
(# 0.008)
1.199
0.963
0.208
0.011
-0.073
PRD1T-8L.
(# 0.09))
(= 0.031)
(# 0.080)
(#: 0.003)
(
(# 0.01))
1.433
1.045
0.371
0. 0.104
0.073
PRDIT-12L
(=000)
(= 0.068)
(# 0.049)
(# 0.008)
0.01)
(#
Table 11: Pairwise log- log-WGAN ratio comparisons. For every model pair (i, j). report μ of the log ratio between the WGAN critic scores for samples from model and model j (averaged over 4 evaluations). Values > 0 indicates the row model is judged more realistic than the column model, < indicates the opposite. The matrix is anti-symmetric (st. -8 with zeros on the diagonal.
A.11.4 EXTENDED QUALITATIVE RESULTS
We present qualitative results comprising the Local Denoiser branch's 's coarse reconstructions and the Global Residual DiT branch's 's residual estimates, highlighting the complementary information each branch captures, in Figure 15
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(a) 4-Layer Model Generative Output
(b) 8-Layer Model Generative Output
(c) 12-Layer Model Generative Output
Figure 12: Extended qualitative results for different model within LIDC-IDRI dataset.
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(a) 4-Layer Model Generative Output
(b) 8-Layer Model Generative Output
(c) 12-Layer Model Generative Output
Figure 13: Extended qualitative results for different model within Rad-ChestCT dataset.
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(a) HA-GAN Model Generative Output
WDM-3D Model Generative Output
RAAAAAARAA WIN
(c) Our Model Generative Output
Figure 14: Extended qualitative results for different model within 256 LIDC-IDRI dataset.
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(a) Local Denoiser Path Output
(b) Global Residual Path Output
Figure 15: Extended qualitative results for visualizing the output from Local Denoiser and Global
Residual module.
27


Reviews:
[{"review_id": "9zIi8V7tsv", "reviewer": "Reviewer_Nuef", "summary": "Generating fine-grained 3D CT data is an extremely challenging problem. This paper addresses this challenge by employing a pixel-level residual diffusion transformer. The chosen topic is highly relevant and represents a significant area of research. The methodology utilizes a coarse-to-fine strategy and includes comparisons with various established models.\nThe authors demonstrated superior performance against existing models by comparing metrics such as 3D DIF, MMD, and W-Score on two generated datasets of $128^3$ resolution. However, a significant drawback is the lack of evaluation by a medical imaging expert from a clinical image generation standpoint.\nObserving Figure 4, 5, the model appears to capture the overall coarse shape, but there still seem to be significant problems with the finer details. Specifically, as the layers deepen, internal structures within the lungs appear to vanish, and the soft tissue contrast does not accurately reflect reality.\nThe overall comparison lacks benchmarking against the current State-of-the-Art (SoTA) DiT (Diffusion Transformer) model. The paper mentions that DiT models suffer from \"unstable dynamics and optimization difficulties,\" yet it fails to provide any comparative experiments to support this claim or justify the exclusion.", "questions": "The overall comparison lacks benchmarking against the current State-of-the-Art (SoTA) DiT (Diffusion Transformer) model. The paper mentions that DiT models suffer from \"unstable dynamics and optimization difficulties,\" yet it fails to provide any comparative experiments to support this claim or justify the exclusion.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Generating fine-grained 3D CT data is an extremely challenging problem. This paper addresses this challenge by employing a pixel-level residual diffusion transformer. The chosen topic is highly relevant and represents a significant area of research. The methodology utilizes a coarse-to-fine strategy and includes comparisons with various established models.\nThe authors demonstrated superior performance against existing models by comparing metrics such as 3D DIF, MMD, and W-Score on two generated datasets of $128^3$ resolution. However, a significant drawback is the lack of evaluation by a medical imaging expert from a clinical image generation standpoint.", "weaknesses": "Observing Figure 4, 5, the model appears to capture the overall coarse shape, but there still seem to be significant problems with the finer details. Specifically, as the layers deepen, internal structures within the lungs appear to vanish, and the soft tissue contrast does not accurately reflect reality.", "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": "yCFq4qXzAq", "reviewer": "Reviewer_eQz3", "summary": "I reviewed the paper, a two-stage diffusion architecture for high-resolution 3D medical image synthesis. The model combines a local MLP denoiser, which captures fine-grained voxel details on overlapping patches, with a global residual Transformer, which ensures anatomical consistency across the whole volume.   \nThe authors also propose a predictorcorrector sampling scheme that reintroduces controlled noise to stabilize generation, and a low-to-high-resolution scaling strategy that allows efficient 256 training guided by a pretrained 128 backbone.\n\nThey evaluate their method on LIDC-IDRI and Rad-ChestCT, showing improved FID and MMD over several baselines (HA-GAN, 3D-LDM, WDM-3D). The generated CT volumes appear sharper and more realistic, while training remains computationally feasible. Overall, the work presents a solid engineering improvement that makes transformer-based diffusion models more practical for 3D medical data synthesis.", "questions": "- Could you clarify which memory-efficient attention variant is used in the global DiT (e.g., FlashAttention, windowed, or block-sparse)? How much does it contribute to scalability compared to a vanilla DiT?\n- Predictor sampling: Can you provide pseudocode or additional explanation of the schedule? Did you explore adaptive or variable numbers of corrective steps (k > 2), and how stable is training when increasing k?\n- Baselines: Why were recent efficient 3D diffusion or DiT variants  excluded from comparison? Would your method still outperform these stronger models, especially at 256 resolution?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "I find the paper technically solid. The proposed two-stage design separating local voxel-level denoising from global residual refinement is clearly explained, and supported by ablation studies showing that each component contributes to performance. The predictorcorrector sampling strategy is effective, improving image quality with minimal computational overhead. I also appreciate the scaling approach that leverages a pretrained low-resolution model to enable efficient 256 synthesis, which addresses an important computational bottleneck for 3D diffusion models.\n\nThe paper includes comprehensive experiments on two public CT datasets, reports multiple quantitative metrics (FID, MMD, and provides consistent improvements over established baselines. Figures illustrate sharper structural detail and realistic textures. I also value the inclusion of limitations and future work, as well as the clear structure and readability of the paper.", "weaknesses": "While the method is good, I find the novelty somewhat limited. The combination of a local denoiser and a global Transformer is conceptually straightforward and resembles existing hierarchical or hybrid DiT approaches. Similarly, the proposed sampling resembles previously known stochastic sampling methods, though applied here in a slightly modified form.\n\nThe evaluation focuses mainly on generative metrics such as FID and MMD, which can  be unstable for 3D medical data. There is no downstream or clinical validation (e.g., segmentation or detection performance using synthetic data), which makes it difficult to judge real-world utility. Baselines are also somewhat limited and some stronger 3D diffusion models or DiT variants are missing, especially at higher resolutions\n\nFinally, several implementation details are underspecified, such as the exact memory-efficient attention mechanism, data split protocol, and reproducibility of high-resolution experiments. Overall, the paper’s contribution feels more like a solid engineering refinement than a major conceptual breakthrough.", "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": "piSUhabpvv", "reviewer": "Reviewer_4BSb", "summary": "The paper introduces PRDiT, a two-stage diffusion transformer framework for synthesizing high-resolution 3D CT volumes directly at voxel level. A predictor–corrector diffusion sampling method and a progressive low-to-high-resolution training strategy improve sample fidelity and efficiency. Experiments on LIDC-IDRI and Rad-ChestCT show clear advantage over other methods.", "questions": "n/a", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The decomposition of diffusion into local + global residual branches is elegant and addresses the long-standing trade-off between local detail and global coherence in 3D image synthesis; \n2. The low-to-high-resolution reuse strategy reduces training cost\n3.Figures 4–5 demonstrate noticeably sharper bone edges, smoother organ boundaries, and fewer artifacts relative to baselines\n4. Strong reproducibility section (datasets, configs, metrics) and detailed appendices on architecture, hyperparameters, and inference time", "weaknesses": "The idea of splitting local/global branches is incremental relative to prior hierarchical or multi-scale diffusion models. The architectural originality lies mainly in combining them via residual refinement rather than introducing new attention or tokenization mechanisms.\n\nReported mean ± std over 3 seeds is small; given large variance in 3D generation, stronger statistical support or significance tests would enhance credibility.\n\nNo mention of hallucination risk or downstream misuse", "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": "4kJ4v9h6gQ", "reviewer": "Reviewer_Vjha", "summary": "This paper proposes a transformer-based 3D diffusion model for medical CT generation that integrates a patch-wise Local Denoiser and a global Diffusion Transformer within a two-stage residual framework. A predictor–corrector (“hot diffusion”) sampling strategy improves generation stability and diversity. Comprehensive ablation studies validate each component and analyze computational efficiency. The progressive training scheme reuses a pretrained low-resolution model and fine-tunes lightweight residual modules at higher resolutions, achieving competitive performance with reduced training cost.", "questions": "1. What is the potential to extend this framework to conditional generation, such as generating raw CT volumes from segmentation maps or other structural priors?\n\n2. In Figure 5, several artifacts are visible. Could the authors explain their causes and further evaluate the influence of the parameter k on generation quality through additional qualitative or quantitative analysis?\n\n3. The local denoiser is frozen during high-resolution training. Could this design lead to performance degradation at higher resolutions? An ablation comparing frozen and fine-tuned local denoisers would help clarify this effect.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The progressive training strategy from low to high resolution is clearly defined and achieves comparable performance with reduced computational cost.\n\n2. The combination of local and global components provides a balanced design for modeling fine details and overall structure.\n\n3. The model attains competitive quantitative results and produces visually consistent high-resolution 3D CT volumes.", "weaknesses": "1. Visual inconsistency in generated samples:\nIn Figure 5, the second and fifth examples under Ours show black voids in the lower-left region, indicating low-density areas that are absent in the real data. The authors should clarify whether these artifacts result from the “hot diffusion” sampling strategy, which may disrupt local density continuity or anatomical consistency.\n\n2. Extra dataset evaluation:\nAdditional experiments on CT datasets such as CT-ORG are recommended to further validate the model’s generalization.\n\n3. Potential limitation of frozen local denoiser:\nThe local denoiser remains frozen during high-resolution training. It would be valuable to include an ablation comparing frozen versus fine-tuned local modules to assess potential performance degradation at higher resolutions.", "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)