pith. machine review for the scientific record. sign in

arxiv: 2605.01185 · v1 · submitted 2026-05-02 · 💻 cs.CV

Recognition: unknown

Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models

Abolfazl Hashemi, Behzad Sharif, Dilek Yalcinkaya, M. Berk Sahin

Authors on Pith no claims yet

Pith reviewed 2026-05-09 15:24 UTC · model grok-4.3

classification 💻 cs.CV
keywords phase map synthesisscore-based diffusion modelsMRI reconstructionaccelerated imagingk-space data generationdeep learningmagnitude-only imagesgenerative models
0
0 comments X

The pith

Conditional score-based diffusion models synthesize realistic phase maps from magnitude-only MR images, producing k-space data that trains superior deep learning models for accelerated MRI reconstruction.

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

The paper seeks to overcome the scarcity of raw k-space datasets needed to train deep learning models for accelerated MRI reconstruction, a problem caused by routine discarding of such data due to storage and privacy issues. It introduces conditional score-based diffusion models that take a magnitude-only image and generate a matching phase map in the image domain. These synthetic phases are combined with the original magnitudes to form full k-space volumes for training. Models trained on this data outperform those trained on smooth-phase approximations or phases from generative adversarial networks, both in standard quantitative metrics and in avoiding erroneous or hallucinated features in the final reconstructions.

Core claim

A conditional score-based diffusion model, given only a magnitude-only MR image, generates a phase map whose combination with the magnitude yields k-space data suitable for training deep learning accelerated reconstruction networks. When such networks are trained on the resulting synthetic datasets, they achieve higher quantitative performance and greater reconstruction fidelity than networks trained either with naive smooth phase maps, with phase maps from a generative adversarial network, or in direct comparison to the listed baseline approaches.

What carries the argument

Conditional score-based diffusion model that generates phase maps conditioned on magnitude-only input images to enable realistic k-space synthesis.

If this is right

  • Large existing registries of magnitude-only images can be converted into usable training sets for accelerated reconstruction models.
  • The trained reconstruction networks produce images with fewer hallucinated or erroneous features that could affect diagnostic use.
  • Synthetic k-space training outperforms both smooth-phase baselines and GAN-based phase synthesis in both metrics and visual quality.
  • Diversity of training data increases without new raw-data acquisitions, supporting better generalization across patients or scanners.

Where Pith is reading between the lines

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

  • The same synthesis pipeline could be applied to other MR contrasts or body regions where magnitude images are more readily available than full k-space.
  • If the generated phases prove sufficiently realistic, clinical archives could support privacy-preserving dataset creation for model development.
  • Downstream diagnostic accuracy studies on real patient cases would be needed to confirm that improved reconstruction metrics translate to clinical benefit.

Load-bearing premise

The phase maps produced by the conditional score-based diffusion model are close enough in distribution to real acquired phases that models trained on the combined k-space data generalize without learning systematic biases or artifacts.

What would settle it

Evaluate the deep learning reconstruction model trained on SBDM-synthesized k-space against a model trained on matched real k-space data using a held-out test set of actual accelerated acquisitions; if the SBDM-trained model shows measurably higher error or visible hallucinations on the real test data, the central claim does not hold.

Figures

Figures reproduced from arXiv: 2605.01185 by Abolfazl Hashemi, Behzad Sharif, Dilek Yalcinkaya, M. Berk Sahin.

Figure 1
Figure 1. Figure 1: Pipeline for the proposed phase-map synthesis approach. Following the training of the SBDM in (A), large k-space datasets can view at source ↗
Figure 2
Figure 2. Figure 2: Examples of GAN- and SBDM-synthesized phase-maps from the magnitude-only images for (A) knee and (B) brain datasets. The view at source ↗
Figure 3
Figure 3. Figure 3: Cumulative VarNet reconstruction results with view at source ↗
Figure 4
Figure 4. Figure 4: Cumulative VarNet reconstruction results with view at source ↗
Figure 5
Figure 5. Figure 5: Cumulative VarNet reconstruction results with view at source ↗
Figure 6
Figure 6. Figure 6: VarNet reconstruction results for a subject from the knee dataset are shown at three acceleration factors. The NRMSE corre view at source ↗
Figure 7
Figure 7. Figure 7: VarNet reconstruction results for a subject from the brain dataset are shown at three acceleration factors. The NRMSE corre view at source ↗
Figure 8
Figure 8. Figure 8: VarNet reconstruction results for a subject from the knee dataset is shown at three acceleration factors. The NRMSE correspond view at source ↗
read the original abstract

Accelerated magnetic resonance imaging (MRI) enabled by the training of deep learning (DL)-based image recon. models requires large and diverse raw k-space datasets. In most clinical MRI applications, due to storage and patient privacy concerns, raw k-space data is discarded and magnitude-only images are the only component saved. Consequently, a large portion of the DL-based MRI recon. literature has either relied on small training datasets or has used one of the few available open-source k-space datasets. At the same time, the growing number of anonymized magnitude-only image registries/databases motivates the development of techniques that can use them as training datasets for generalizable DL-based recon. models. Here we propose to address this challenge by employing a generative approach based on conditional score-based diffusion models (SBDMs): given a magnitude-only MR image, it synthesizes a phase map (in the image domain) that realistically corresponds to the magnitude-only image. We evaluate its generative capabilities in a downstream DL-based recon. task whereby a large k-space dataset is generated by combining the SBDM-synthesized phase-maps and the corresponding magnitude-only images, and this k-space dataset is then used to train a DL model for accelerated MRI recon. We compare the performance of the resulting DL model versus those trained according to (a) a naive approach that uses smooth phase, (b) a k-space training dataset generated using synthesized phase maps derived from a generative adversarial network, and (c) the ground truth k-space data. Our results suggest that the DL model trained from SBDM-synthesized k-space data outperforms the other approaches in terms of quantitative metrics as well as qualitatively observed recon. fidelity, i.e., whether the reconstructed images include erroneous or hallucinated features that could adversely impact diagnostic accuracy.

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 / 0 minor

Summary. The manuscript proposes using conditional score-based diffusion models (SBDMs) to synthesize realistic phase maps from magnitude-only MR images. These phases are combined with the input magnitudes to generate synthetic k-space data for training deep learning models for accelerated MRI reconstruction. The central claim is that DL models trained on SBDM-synthesized k-space outperform those trained on smooth-phase data, GAN-synthesized phases, and ground-truth k-space, both in quantitative metrics and in qualitative reconstruction fidelity without erroneous or hallucinated features.

Significance. If the synthesized phases prove distributionally consistent with real phases, the approach could substantially expand training data availability for MRI reconstruction models by leveraging abundant magnitude-only image registries, addressing a key limitation from discarded raw k-space due to storage and privacy constraints. The downstream evaluation on reconstruction tasks provides a practical test of utility. The paper's use of a standard generative modeling pipeline followed by held-out evaluation is a clear strength.

major comments (2)
  1. [Abstract] Abstract: The reported outperformance versus the ground-truth k-space baseline (comparison c) is potentially confounded by training set size. The text states that a 'large' k-space dataset is generated via SBDM synthesis from magnitude-only images, yet provides no explicit counts of samples used for the SBDM, GAN, smooth-phase, or ground-truth conditions. If the synthesized dataset is larger, superior performance could arise from data volume rather than phase realism, directly weakening the load-bearing assumption that the phases are 'sufficiently realistic and distributionally consistent.' An ablation with matched sizes is required.
  2. [Results] Results (quantitative and qualitative comparisons): The abstract claims superior quantitative metrics and reconstruction fidelity for the SBDM-trained model, but the provided description lacks dataset sizes, statistical tests, error bars, or controls against post-hoc selection. Without these, it is not possible to determine whether the central claim of outperformance holds under fair conditions or is driven by the factors noted above.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The points regarding potential confounding by training set size and the need for explicit dataset details, statistical tests, and error bars are well-taken. We will revise the manuscript to address these issues directly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The reported outperformance versus the ground-truth k-space baseline (comparison c) is potentially confounded by training set size. The text states that a 'large' k-space dataset is generated via SBDM synthesis from magnitude-only images, yet provides no explicit counts of samples used for the SBDM, GAN, smooth-phase, or ground-truth conditions. If the synthesized dataset is larger, superior performance could arise from data volume rather than phase realism, directly weakening the load-bearing assumption that the phases are 'sufficiently realistic and distributionally consistent.' An ablation with matched sizes is required.

    Authors: We agree that the absence of explicit sample counts in the abstract (and the need for clarification in the main text) leaves open the possibility of confounding by dataset size. In the original experiments, the ground-truth k-space training set was limited to the available raw data (approximately 500 slices across subjects), while the SBDM synthesis was performed on a larger pool of magnitude-only images to demonstrate the method's utility for data expansion. However, this does not isolate phase quality from quantity. We will add a new ablation study in the revised manuscript that trains all models (SBDM, GAN, smooth-phase, and ground-truth) on exactly matched training set sizes, using random subsampling of the synthesized data where necessary. This will allow a direct comparison of phase realism independent of data volume. revision: yes

  2. Referee: [Results] Results (quantitative and qualitative comparisons): The abstract claims superior quantitative metrics and reconstruction fidelity for the SBDM-trained model, but the provided description lacks dataset sizes, statistical tests, error bars, or controls against post-hoc selection. Without these, it is not possible to determine whether the central claim of outperformance holds under fair conditions or is driven by the factors noted above.

    Authors: We acknowledge that the current results section does not report exact training sample counts for each condition, does not include error bars on quantitative metrics, and lacks formal statistical tests. In the revision we will: (1) state the precise number of training examples used for the SBDM, GAN, smooth-phase, and ground-truth conditions; (2) add standard deviation error bars to all quantitative plots (SSIM, PSNR, etc.); (3) include paired statistical tests (e.g., Wilcoxon signed-rank or t-tests with Bonferroni correction) between the SBDM-trained model and each baseline; and (4) explicitly describe the evaluation protocol to confirm that all comparisons were pre-specified rather than post-hoc. These additions will be placed in both the results text and the figure captions. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper trains a conditional SBDM on (presumably paired) magnitude-phase data to synthesize phases, combines them with magnitude images to form k-space, trains downstream DL recon models, and evaluates against held-out ground-truth k-space plus baselines. No equation or step reduces by construction to its own inputs; no fitted parameter is relabeled as a prediction, no self-citation chain bears the central claim, and no uniqueness theorem or ansatz is smuggled in. Evaluations rely on independent test data and standard quantitative/qualitative metrics, rendering the pipeline self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides insufficient technical detail to enumerate specific free parameters, axioms, or invented entities; the method builds on standard diffusion model frameworks whose typical hyperparameters and assumptions are not itemized here.

pith-pipeline@v0.9.0 · 5648 in / 1248 out tokens · 47127 ms · 2026-05-09T15:24:28.031944+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

33 extracted references · 3 canonical work pages

  1. [1]

    Yasmina Al Khalil, Sina Amirrajab, and et al. Lorenz. On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic reso- nance images.Medical Image Analysis, 84:102688, 2023. 1

    2023

  2. [2]

    Seeing what a gan cannot generate

    David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba. Seeing what a gan cannot generate. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 4502– 4511, 2019. 2

    2019

  3. [3]

    Large scale GAN training for high fidelity natural image synthe- sis

    Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale GAN training for high fidelity natural image synthe- sis. In7th International Conference on Learning Represen- tations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019,

    2019

  4. [4]

    Score-based diffusion models for accelerated mri.Medical image analysis, 80: 102479, 2022

    Hyungjin Chung and Jong Chul Ye. Score-based diffusion models for accelerated mri.Medical image analysis, 80: 102479, 2022. 2, 3

    2022

  5. [5]

    Solving 3d inverse problems using pre-trained 2d diffusion models

    Hyungjin Chung, Dohoon Ryu, Michael T McCann, Marc L Klasky, and Jong Chul Ye. Solving 3d inverse problems using pre-trained 2d diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22542–22551, 2023. 4

    2023

  6. [6]

    Synthesizing complex- valued multicoil mri data from magnitude-only images.Bio- engineering, 10(3):358, 2023

    Nikhil Deveshwar, Abhejit Rajagopal, Sule Sahin, Efrat Shimron, and Peder EZ Larson. Synthesizing complex- valued multicoil mri data from magnitude-only images.Bio- engineering, 10(3):358, 2023. 1, 5

    2023

  7. [7]

    Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794, 2021

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794, 2021. 2

    2021

  8. [8]

    Synthetic data accelerates the development of gener- alizable learning-based algorithms for x-ray image analysis

    Cong Gao, Benjamin D Killeen, Yicheng Hu, Robert B Grupp, Russell H Taylor, Mehran Armand, and Mathias Un- berath. Synthetic data accelerates the development of gener- alizable learning-based algorithms for x-ray image analysis. Nature Machine Intelligence, 5(3):294–308, 2023. 1

    2023

  9. [9]

    Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020. 2

    2020

  10. [10]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017. 5

    2017

  11. [11]

    Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020. 2, 3

    2020

  12. [12]

    Image-to-image translation with conditional adver- sarial networks

    Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adver- sarial networks. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 1125–1134,

  13. [13]

    Cola-diff: Conditional latent diffusion model for multi- modal mri synthesis

    Lan Jiang, Ye Mao, Xiangfeng Wang, Xi Chen, and Chao Li. Cola-diff: Conditional latent diffusion model for multi- modal mri synthesis. InInternational Conference on Med- ical Image Computing and Computer-Assisted Intervention, pages 398–408. Springer, 2023. 2

    2023

  14. [14]

    A style-based generator architecture for generative adversarial networks

    Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In2019 IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 4396–4405, 2019. 5

    2019

  15. [15]

    Analyzing and improving the image quality of stylegan

    Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In2020 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR), pages 8107–8116, 2020. 5

    2020

  16. [16]

    Kingma and Jimmy Ba

    Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015. 5

    2015

  17. [17]

    Florian Knoll, Jure Zbontar, Anuroop Sriram, and et al. Muckley. fastmri: A publicly available raw k-space and dicom dataset of knee images for accelerated mr image re- construction using machine learning.Radiology: Artificial Intelligence, 2(1):e190007, 2020. 2

    2020

  18. [18]

    V ongani H Maluleke, Neerja Thakkar, Tim Brooks, and et al. Weber. Studying bias in gans through the lens of race. In European Conference on Computer Vision, pages 344–360. Springer, 2022. 2

    2022

  19. [19]

    A multimodal comparison of latent denois- ing diffusion probabilistic models and generative adversarial networks for medical image synthesis.Scientific Reports, 13 (1):12098, 2023

    Gustav M ¨uller-Franzes, Jan Moritz Niehues, Firas Khader, Soroosh Tayebi Arasteh, Christoph Haarburger, Christiane Kuhl, Tianci Wang, Tianyu Han, Teresa Nolte, Sven Nebelung, et al. A multimodal comparison of latent denois- ing diffusion probabilistic models and generative adversarial networks for medical image synthesis.Scientific Reports, 13 (1):12098, 2023. 7

    2023

  20. [20]

    Generating realistic 3d brain mris using a conditional diffu- sion probabilistic model.arXiv preprint arXiv:2212.08034,

    Wei Peng, Ehsan Adeli, Qingyu Zhao, and Kilian M Pohl. Generating realistic 3d brain mris using a conditional diffu- sion probabilistic model.arXiv preprint arXiv:2212.08034,

    work page arXiv

  21. [21]

    Da Costa

    Walter HL Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, and et al. Da Costa. Brain imaging generation with latent diffusion models. InMICCAI Workshop on Deep Generative Models, pages 117–126. Springer, 2022. 2, 7

    2022

  22. [22]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 2

    2022

  23. [23]

    U- net: Convolutional networks for biomedical image segmen- tation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- net: Convolutional networks for biomedical image segmen- tation. InMedical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, 2015. 4, 5

    2015

  24. [24]

    pytorch-fid: FID Score for PyTorch

    Maximilian Seitzer. pytorch-fid: FID Score for PyTorch. https://github.com/mseitzer/pytorch-fid,

  25. [25]

    Improved techniques for training score-based generative models

    Yang Song and Stefano Ermon. Improved techniques for training score-based generative models. InAdvances in Neu- ral Information Processing Systems, pages 12438–12448. Curran Associates, Inc., 2020. 5

    2020

  26. [26]

    Kingma, Ab- hishek Kumar, Stefano Ermon, and Ben Poole

    Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Ab- hishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equa- tions. In9th International Conference on Learning Repre- sentations, ICLR 2021., 2021. 2, 3, 4

    2021

  27. [27]

    Anuroop Sriram, Jure Zbontar, Tullie Murrell, and Aaron et al. Defazio. End-to-end variational networks for accel- erated mri reconstruction. InMedical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd Inter- national Conference, Lima, Peru, October 4–8, 2020, Pro- ceedings, Part II 23, pages 64–73. Springer, 2020. 3, 5

    2020

  28. [28]

    Simulating single-coil mri from the responses of multiple coils.Communications in Ap- plied Mathematics and Computational Science, 15(2):115– 127, 2020

    Mark Tygert and Jure Zbontar. Simulating single-coil mri from the responses of multiple coils.Communications in Ap- plied Mathematics and Computational Science, 15(2):115– 127, 2020. 2

    2020

  29. [29]

    Brain tumor segmentation using synthetic mr images-a comparison of gans and diffusion models.Sci- entific Data, 11(1):259, 2024

    Muhammad Usman Akbar, M ˚ans Larsson, Ida Blystad, and Anders Eklund. Brain tumor segmentation using synthetic mr images-a comparison of gans and diffusion models.Sci- entific Data, 11(1):259, 2024. 7

    2024

  30. [30]

    A connection between score matching and denoising autoencoders.Neural Computation, 23(7):1661– 1674, 2011

    Pascal Vincent. A connection between score matching and denoising autoencoders.Neural Computation, 23(7):1661– 1674, 2011. 4

    2011

  31. [31]

    Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Si- moncelli. Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004. 3

    2004

  32. [32]

    One for multiple: Physics-informed synthetic data boosts generaliz- able deep learning for fast mri reconstruction.arXiv preprint arXiv:2307.13220, 2023

    Zi Wang, Xiaotong Yu, Chengyan Wang, Weibo Chen, Ji- azheng Wang, Ying-Hua Chu, Hongwei Sun, et al. One for multiple: Physics-informed synthetic data boosts generaliz- able deep learning for fast mri reconstruction.arXiv preprint arXiv:2307.13220, 2023. 1

    work page arXiv 2023

  33. [33]

    Zbontar, F

    Jure Zbontar, Florian Knoll, Anuroop Sriram, and et al. Mur- rell. fastmri: An open dataset and benchmarks for acceler- ated mri.arXiv preprint arXiv:1811.08839, 2018. 2

    work page arXiv 2018