pith. sign in

arxiv: 2503.18836 · v2 · submitted 2025-03-24 · 📡 eess.IV · cs.AI· cs.CV

Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction

Yuxuan Zhang , Jinkui Hao , Bo Zhou This is my paper

Pith reviewed 2026-05-22 22:42 UTC · model grok-4.3

classification 📡 eess.IV cs.AIcs.CV
keywords MRI reconstructiondiffusion modelsself-supervised learningaccelerated imagingdual-domain methodsuncertainty estimationdeep learning
0
0 comments X

The pith

A self-supervised diffusion model reconstructs accelerated MRI without needing fully sampled training data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents DMSM as a framework that trains a diffusion model for MRI reconstruction using only undersampled scans in both image and k-space domains. It adds a lightweight hybrid attention network and a multi-path inference step to produce the final image plus an uncertainty map. This removes the usual requirement for fully sampled reference data during training, which is often unavailable in hospitals. Tests on two human MRI datasets show the approach matches or exceeds several supervised and self-supervised baselines, especially when acceleration is high, while preserving anatomical detail and reducing artifacts.

Core claim

DMSM integrates a self-supervised dual-domain diffusion model training scheme, a lightweight hybrid attention network for the reconstruction diffusion model, and a multi-path inference strategy to enable accurate accelerated MRI reconstruction without relying on fully sampled data during training, while also generating useful uncertainty maps.

What carries the argument

Dual-domain Multi-path Self-supervised Diffusion Model (DMSM) that performs self-supervised training on undersampled data across image and k-space domains, followed by multi-path inference to produce the reconstruction and uncertainty estimate.

If this is right

  • Hospitals can train reconstruction models using only the undersampled scans they already collect in routine practice.
  • Reconstruction quality holds up better than prior self-supervised methods when acceleration factors are large.
  • Generated uncertainty maps align with actual pixel-wise errors and can flag regions needing extra clinical review.
  • The lightweight network reduces compute cost compared with standard diffusion models for the same task.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Deployment becomes possible in scanner sites that never acquire fully sampled reference volumes.
  • Uncertainty output could be used to decide which reconstructed slices warrant immediate radiologist inspection.
  • The same self-supervised dual-domain pattern might transfer to other inverse problems such as CT or ultrasound reconstruction.

Load-bearing premise

The self-supervised dual-domain training scheme can produce accurate reconstructions and useful uncertainty estimates without access to fully sampled reference data during training.

What would settle it

Apply DMSM to a held-out human MRI dataset acquired at high acceleration, compute reconstruction error against a supervised baseline trained on the same full-data pairs, and check whether DMSM error remains comparable or lower.

Figures

Figures reproduced from arXiv: 2503.18836 by Bo Zhou, Jinkui Hao, Yuxuan Zhang.

Figure 1
Figure 1. Figure 1: Overview of the Dual-Domain Multi-Path Self-Supervised Diffusion Model (DMSM). During training, the original under-sampled k-space [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of Light-weighted Hybrid Attention Network (LHAN) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: T1(top) and T2(bottom) MRI reconstruction results on fastMRI dataset across all performed baseline methods. Two different acceleration settings (R=4 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: PD MRI reconstruction results on IXI dataset across all performed [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Uncertainty estimation on the multi-path averaged reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablative study on using different numbers of PAB in LHAN. Please [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Quantitative analysis on the reconstruction improvement from the [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
read the original abstract

Magnetic resonance imaging (MRI) is a vital diagnostic tool, but its inherently long acquisition times reduce clinical efficiency and patient comfort. Recent advancements in deep learning, particularly diffusion models, have improved accelerated MRI reconstruction. However, existing diffusion models' training often relies on fully sampled data, models incur high computational costs, and often lack uncertainty estimation, limiting their clinical applicability. To overcome these challenges, we propose a novel framework, called Dual-domain Multi-path Self-supervised Diffusion Model (DMSM), that integrates a self-supervised dual-domain diffusion model training scheme, a lightweight hybrid attention network for the reconstruction diffusion model, and a multi-path inference strategy, to enhance reconstruction accuracy, efficiency, and explainability. Unlike traditional diffusion-based models, DMSM eliminates the dependency on training from fully sampled data, making it more practical for real-world clinical settings. We evaluated DMSM on two human MRI datasets, demonstrating that it achieves favorable performance over several supervised and self-supervised baselines, particularly in preserving fine anatomical structures and suppressing artifacts under high acceleration factors. Additionally, our model generates uncertainty maps that correlate reasonably well with reconstruction errors, offering valuable clinically interpretable guidance and potentially enhancing diagnostic confidence.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes the Dual-domain Multi-path Self-supervised Diffusion Model (DMSM) for accelerated MRI reconstruction. It integrates a self-supervised dual-domain diffusion model training scheme, a lightweight hybrid attention network for the reconstruction diffusion model, and a multi-path inference strategy. The central claim is that this eliminates the dependency on fully sampled training data while achieving favorable performance over supervised and self-supervised baselines on two human MRI datasets, particularly in preserving fine anatomical structures and suppressing artifacts at high acceleration factors, and additionally generates uncertainty maps that correlate with reconstruction errors.

Significance. If the self-supervised training scheme can be shown to produce accurate reconstructions without fully sampled references, the work would address a practical barrier to deploying diffusion models in clinical MRI settings where such data are often unavailable. The addition of uncertainty estimation and the emphasis on efficiency via the lightweight network and multi-path inference would be positive contributions to explainability and usability.

major comments (2)
  1. [Abstract] Abstract: The claim that the 'self-supervised dual-domain diffusion model training scheme' eliminates the dependency on training from fully sampled data is load-bearing for the entire contribution, yet the manuscript provides no loss formulation, masking strategy, forward-model consistency term, or description of how the diffusion reverse process is trained without reference data. This prevents verification that the scheme avoids implicit use of fully sampled or simulated references.
  2. Abstract: No equations, network diagrams, training details, or quantitative results (e.g., PSNR/SSIM values, acceleration factors, or comparison tables) are supplied, making it impossible to assess whether the reported gains in fine-structure preservation and artifact suppression are supported by the experiments.
minor comments (1)
  1. The abstract refers to 'two human MRI datasets' without naming them or specifying the acceleration factors tested.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which helps clarify the presentation of our self-supervised training approach. We address each major comment below and indicate planned revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that the 'self-supervised dual-domain diffusion model training scheme' eliminates the dependency on training from fully sampled data is load-bearing for the entire contribution, yet the manuscript provides no loss formulation, masking strategy, forward-model consistency term, or description of how the diffusion reverse process is trained without reference data. This prevents verification that the scheme avoids implicit use of fully sampled or simulated references.

    Authors: The loss formulation, masking strategy, and k-space consistency term are provided in Section 3.2 of the manuscript, where the dual-domain self-supervised objective is defined using only undersampled measurements and a forward-model consistency loss that does not require fully sampled references. The reverse diffusion process is trained by noise prediction on masked undersampled inputs. To address the concern that these elements are not visible from the abstract alone, we will revise the abstract to include a brief description of the training scheme and a reference to the methods section. revision: partial

  2. Referee: [—] Abstract: No equations, network diagrams, training details, or quantitative results (e.g., PSNR/SSIM values, acceleration factors, or comparison tables) are supplied, making it impossible to assess whether the reported gains in fine-structure preservation and artifact suppression are supported by the experiments.

    Authors: Abstracts are subject to strict length limits and conventionally omit equations and figures, which appear in Sections 3–4 and Tables 1–2 of the manuscript (including PSNR/SSIM at 4× and 8× acceleration). We agree that adding selected quantitative highlights would improve the abstract and will revise it to report representative PSNR/SSIM values and acceleration factors from the two datasets. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical self-supervised method with no derivation chain or self-referential reductions

full rationale

The provided abstract and text describe an empirical modeling approach proposing the DMSM framework, including a self-supervised dual-domain diffusion training scheme evaluated on human MRI datasets. No equations, loss formulations, or derivation steps are present that could reduce any claimed prediction or result to fitted inputs by construction. No self-citations, uniqueness theorems, or ansatzes are invoked in a load-bearing manner. The central claims rest on experimental performance comparisons rather than any mathematical chain that collapses to the inputs, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that self-supervised training on undersampled data alone suffices for high-quality diffusion-based reconstruction and uncertainty estimation; the model itself is the primary invented component with no external validation of its components provided in the abstract.

free parameters (2)
  • hybrid attention network weights
    Parameters of the lightweight hybrid attention network are learned during the self-supervised training process and are required for the reconstruction performance.
  • multi-path inference parameters
    Settings controlling the number and combination of inference paths are chosen to achieve the reported performance.
axioms (1)
  • domain assumption Self-supervised learning on undersampled MRI data can substitute for supervised training on fully sampled references in diffusion models.
    This premise is required for the claim that the method eliminates dependency on fully sampled data.
invented entities (1)
  • DMSM framework no independent evidence
    purpose: Integrates self-supervised dual-domain diffusion training, lightweight hybrid attention network, and multi-path inference for MRI reconstruction.
    The framework is introduced by the paper as the core contribution.

pith-pipeline@v0.9.0 · 5738 in / 1433 out tokens · 30986 ms · 2026-05-22T22:42:13.340706+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages · 2 internal anchors

  1. [1]

    M. T. Vlaardingerbroek and J. A. Boer, Magnetic resonance imaging: theory and practice . Springer Science & Business Media, 2013

    work page 2013

  2. [2]

    Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review,

    J. Bernal, K. Kushibar, D. S. Asfaw, S. Valverde, A. Oliver, R. Mart ´ı, and X. Llad ´o, “Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review,”Artificial intelligence in medicine, vol. 95, pp. 64–81, 2019

    work page 2019

  3. [3]

    ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI

    Y . Yang, J. Sun, H. Li, and Z. Xu, “Admm-net: A deep learning approach for compressive sensing mri,” arXiv preprint arXiv:1705.06869 , 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  4. [4]

    A deep cascade of convolutional neural networks for dynamic mr image reconstruction,

    J. Schlemper, J. Caballero, J. V . Hajnal, A. N. Price, and D. Rueckert, “A deep cascade of convolutional neural networks for dynamic mr image reconstruction,” IEEE transactions on Medical Imaging , vol. 37, no. 2, pp. 491–503, 2017

    work page 2017

  5. [5]

    Dagan: deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction,

    G. Yang, S. Yu, H. Dong, G. Slabaugh, P. L. Dragotti, X. Ye, F. Liu, S. Arridge, J. Keegan, Y . Guoet al., “Dagan: deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction,” IEEE transactions on medical imaging , vol. 37, no. 6, pp. 1310–1321, 2017

    work page 2017

  6. [6]

    Deep generative adversarial neural networks for compressive sensing mri,

    M. Mardani, E. Gong, J. Y . Cheng, S. S. Vasanawala, G. Zaharchuk, L. Xing, and J. M. Pauly, “Deep generative adversarial neural networks for compressive sensing mri,” IEEE transactions on medical imaging , vol. 38, no. 1, pp. 167–179, 2018

    work page 2018

  7. [7]

    Compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss,

    T. M. Quan, T. Nguyen-Duc, and W.-K. Jeong, “Compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss,” IEEE transactions on medical imaging , vol. 37, no. 6, pp. 1488– 1497, 2018

    work page 2018

  8. [8]

    Unsupervised mri reconstruction with generative adversarial networks,

    E. K. Cole, J. M. Pauly, S. S. Vasanawala, and F. Ong, “Unsupervised mri reconstruction with generative adversarial networks,” arXiv preprint arXiv:2008.13065, 2020

    work page arXiv 2008

  9. [9]

    Swin transformer for fast mri,

    J. Huang, Y . Fang, Y . Wu, H. Wu, Z. Gao, Y . Li, J. Del Ser, J. Xia, and G. Yang, “Swin transformer for fast mri,” Neurocomputing, vol. 493, pp. 281–304, 2022

    work page 2022

  10. [10]

    Dsformer: A dual-domain self-supervised transformer for accelerated multi-contrast mri reconstruction,

    B. Zhou, N. Dey, J. Schlemper, S. S. M. Salehi, C. Liu, J. S. Duncan, and M. Sofka, “Dsformer: A dual-domain self-supervised transformer for accelerated multi-contrast mri reconstruction,” in Proceedings of the IEEE/CVF winter conference on applications of computer vision , 2023, pp. 4966–4975

    work page 2023

  11. [11]

    Diffusion models: A comprehensive survey of methods and applications,

    L. Yang, Z. Zhang, Y . Song, S. Hong, R. Xu, Y . Zhao, W. Zhang, B. Cui, and M.-H. Yang, “Diffusion models: A comprehensive survey of methods and applications,” ACM Computing Surveys , vol. 56, no. 4, pp. 1–39, 2023

    work page 2023

  12. [12]

    Adaptive diffusion priors for accelerated mri reconstruction,

    A. G ¨ung¨or, S. U. Dar, S ¸. ¨Ozt¨urk, Y . Korkmaz, H. A. Bedel, G. Elmas, M. Ozbey, and T. C ¸ ukur, “Adaptive diffusion priors for accelerated mri reconstruction,” Medical image analysis , vol. 88, p. 102872, 2023

    work page 2023

  13. [13]

    Correlated and multi- frequency diffusion modeling for highly under-sampled mri reconstruc- tion,

    Y . Guan, C. Yu, Z. Cui, H. Zhou, and Q. Liu, “Correlated and multi- frequency diffusion modeling for highly under-sampled mri reconstruc- tion,” IEEE Transactions on Medical Imaging , 2024

    work page 2024

  14. [14]

    Smrd: Sure-based robust mri reconstruction with diffusion models,

    B. Ozturkler, C. Liu, B. Eckart, M. Mardani, J. Song, and J. Kautz, “Smrd: Sure-based robust mri reconstruction with diffusion models,” in International Conference on Medical Image Computing and Computer- Assisted Intervention. Springer, 2023, pp. 199–209

    work page 2023

  15. [15]

    Dp-mdm: Detail-preserving mr reconstruction via multiple diffusion models,

    M. Geng, J. Zhu, X. Zhu, Q. Liu, D. Liang, and Q. Liu, “Dp-mdm: Detail-preserving mr reconstruction via multiple diffusion models,” arXiv preprint arXiv:2405.05763 , 2024

    work page arXiv 2024

  16. [16]

    Op- timizing sampling patterns for compressed sensing mri with diffusion generative models,

    S. Ravula, B. Levac, A. Jalal, J. I. Tamir, and A. G. Dimakis, “Op- timizing sampling patterns for compressed sensing mri with diffusion generative models,” arXiv preprint arXiv:2306.03284 , 2023

    work page arXiv 2023

  17. [17]

    Deep learning for undersampled mri reconstruction,

    C. M. Hyun, H. P. Kim, S. M. Lee, S. Lee, and J. K. Seo, “Deep learning for undersampled mri reconstruction,” Physics in Medicine & Biology , vol. 63, no. 13, p. 135007, 2018

    work page 2018

  18. [18]

    Complex fully convolutional neural networks for mr image reconstruction,

    M. A. Dedmari, S. Conjeti, S. Estrada, P. Ehses, T. St ¨ocker, and M. Reuter, “Complex fully convolutional neural networks for mr image reconstruction,” in International Workshop on Machine Learning for Medical Image Reconstruction . Springer, 2018, pp. 30–38

    work page 2018

  19. [19]

    Accelerating magnetic resonance imaging via deep learning,

    S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” in 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, 2016, pp. 514–517

    work page 2016

  20. [20]

    Deep admm-net for compressive sensing mri,

    J. Sun, H. Li, Z. Xu et al. , “Deep admm-net for compressive sensing mri,” Advances in neural information processing systems , vol. 29, 2016

    work page 2016

  21. [21]

    Convolutional recurrent neural networks for dynamic mr image reconstruction,

    C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V . Hajnal, and D. Rueckert, “Convolutional recurrent neural networks for dynamic mr image reconstruction,” IEEE transactions on medical imaging , vol. 38, no. 1, pp. 280–290, 2018

    work page 2018

  22. [22]

    Dudornet: learning a dual-domain recurrent network for fast mri reconstruction with deep t1 prior,

    B. Zhou and S. K. Zhou, “Dudornet: learning a dual-domain recurrent network for fast mri reconstruction with deep t1 prior,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 4273–4282

    work page 2020

  23. [23]

    Neural network- based reconstruction in compressed sensing mri without fully-sampled training data,

    A. Q. Wang, A. V . Dalca, and M. R. Sabuncu, “Neural network- based reconstruction in compressed sensing mri without fully-sampled training data,” in Machine Learning for Medical Image Reconstruction: Third International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings 3 . Springer, 2020, pp. 27–37

    work page 2020

  24. [24]

    Self-supervised physics-based deep learning mri recon- struction without fully-sampled data,

    B. Yaman, S. A. H. Hosseini, S. Moeller, J. Ellermann, K. U ˘gurbil, and M. Akc ¸akaya, “Self-supervised physics-based deep learning mri recon- struction without fully-sampled data,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2020, pp. 921–925

    work page 2020

  25. [25]

    Zero-shot self-supervised learning for mri reconstruction,

    B. Yaman, S. A. H. Hosseini, and M. Akc ¸akaya, “Zero-shot self-supervised learning for mri reconstruction,” arXiv preprint arXiv:2102.07737, 2021

    work page arXiv 2021

  26. [26]

    Dual-domain self-supervised learning for accelerated non-cartesian mri reconstruction,

    B. Zhou, J. Schlemper, N. Dey, S. S. M. Salehi, K. Sheth, C. Liu, J. S. Duncan, and M. Sofka, “Dual-domain self-supervised learning for accelerated non-cartesian mri reconstruction,” Medical Image Analysis , vol. 81, p. 102538, 2022

    work page 2022

  27. [27]

    fastmri: An open dataset and benchmarks for accelerated mri,

    F. A. R. (FAIR) and N. L. Health, “fastmri: An open dataset and benchmarks for accelerated mri,” https://github.com/maroomir/fastMRI, 2024, accessed: 2025-02-12. [Online]. Available: https://fastmri.med. nyu.edu/

    work page 2024

  28. [28]

    Coil com- pression for accelerated imaging with cartesian sampling,

    T. Zhang, J. M. Pauly, S. S. Vasanawala, and M. Lustig, “Coil com- pression for accelerated imaging with cartesian sampling,” Magnetic resonance in medicine , vol. 69, no. 2, pp. 571–582, 2013

    work page 2013

  29. [29]

    Information extraction from images (ixi) dataset,

    I. C. London and K. C. L. Guy’s Hospital, “Information extraction from images (ixi) dataset,” 2010, accessed: 2025-02-12. [Online]. Available: http://brain-development.org/ixi-dataset/

    work page 2010

  30. [30]

    A parallel mr imaging method using multilayer perceptron,

    K. Kwon, D. Kim, and H. Park, “A parallel mr imaging method using multilayer perceptron,” Medical physics, vol. 44, no. 12, pp. 6209–6224, 2017

    work page 2017

  31. [31]

    Prior-guided image reconstruction for accelerated multi-contrast mri via generative adversarial networks,

    S. U. Dar, M. Yurt, M. Shahdloo, M. E. Ildız, B. Tınaz, and T. C ¸ ukur, “Prior-guided image reconstruction for accelerated multi-contrast mri via generative adversarial networks,” IEEE Journal of Selected Topics in Signal Processing , vol. 14, no. 6, pp. 1072–1087, 2020

    work page 2020

  32. [32]

    Self-supervised mri recon- struction with unrolled diffusion models,

    Y . Korkmaz, T. Cukur, and V . M. Patel, “Self-supervised mri recon- struction with unrolled diffusion models,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2023, pp. 491–501

    work page 2023

  33. [33]

    Bbdm: Image-to-image trans- lation with brownian bridge diffusion models,

    B. Li, K. Xue, B. Liu, and Y .-K. Lai, “Bbdm: Image-to-image trans- lation with brownian bridge diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern Recognition , 2023, pp. 1952–1961

    work page 2023

  34. [34]

    Dual diffusion implicit bridges for image-to-image translation,

    X. Su, J. Song, C. Meng, and S. Ermon, “Dual diffusion implicit bridges for image-to-image translation,” arXiv preprint arXiv:2203.08382, 2022

    work page arXiv 2022

  35. [35]

    Progressive Distillation for Fast Sampling of Diffusion Models

    T. Salimans and J. Ho, “Progressive distillation for fast sampling of diffusion models,” arXiv preprint arXiv:2202.00512 , 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  36. [36]

    Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,

    C. Lu, Y . Zhou, F. Bao, J. Chen, C. Li, and J. Zhu, “Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,” Advances in Neural Information Processing Systems , vol. 35, pp. 5775–5787, 2022

    work page 2022