PaDIS-MRI: Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction

Rohan Sanda, Asad Aali, Andrew Johnston, Eduardo Reis, Gordon Wetzstein, Sara Fridovich-Keil
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:1316-1335, 2026.

Abstract

Magnetic resonance imaging ({MRI}) requires long acquisition times, which raise costs, reduce accessibility, and increase susceptibility to motion artifacts. Diffusion probabilistic models that learn data-driven priors may reduce acquisition time by enabling reconstruction from undersampled k-space measurements. However, they typically require large training datasets that can be prohibitively expensive to collect. Patch-based diffusion models have shown promise in learning effective data-driven priors over small real-valued datasets, but have not yet demonstrated clinical value in {MRI}. We extend the Patch-based Diffusion Inverse Solver ({PaDIS}) to complex-valued, multi-coil {MRI} reconstruction, and compare it against a state-of-the-art whole-image diffusion baseline ({FastMRI-EDM}) for $7\times$ undersampled {MRI} reconstruction on the {FastMRI} brain dataset. We show that {PaDIS-MRI} models trained on small datasets of as few as 25 k-space images outperform {FastMRI-EDM} on image quality metrics ({PSNR}, {SSIM}, {NRMSE}), pixel-level mask-induced variability, cross-contrast/-modality generalization, and robustness to severe k-space undersampling. In a blinded study with three radiologists, {PaDIS-MRI} reconstructions were chosen as diagnostically superior in 91.7% of cases, compared to baselines (i) {FastMRI-EDM} and (ii) classical convex reconstruction with wavelet sparsity. These findings highlight the potential of patch-based diffusion priors for high-fidelity {MRI} reconstruction in data-scarce clinical settings where diagnostic confidence matters.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-sanda26a, title = {{PaDIS-MRI}: Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred {MRI} Reconstruction}, author = {Sanda, Rohan and Aali, Asad and Johnston, Andrew and Reis, Eduardo and Wetzstein, Gordon and Fridovich-Keil, Sara}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {1316--1335}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/sanda26a/sanda26a.pdf}, url = {https://proceedings.mlr.press/v297/sanda26a.html}, abstract = {Magnetic resonance imaging ({MRI}) requires long acquisition times, which raise costs, reduce accessibility, and increase susceptibility to motion artifacts. Diffusion probabilistic models that learn data-driven priors may reduce acquisition time by enabling reconstruction from undersampled k-space measurements. However, they typically require large training datasets that can be prohibitively expensive to collect. Patch-based diffusion models have shown promise in learning effective data-driven priors over small real-valued datasets, but have not yet demonstrated clinical value in {MRI}. We extend the Patch-based Diffusion Inverse Solver ({PaDIS}) to complex-valued, multi-coil {MRI} reconstruction, and compare it against a state-of-the-art whole-image diffusion baseline ({FastMRI-EDM}) for $7\times$ undersampled {MRI} reconstruction on the {FastMRI} brain dataset. We show that {PaDIS-MRI} models trained on small datasets of as few as 25 k-space images outperform {FastMRI-EDM} on image quality metrics ({PSNR}, {SSIM}, {NRMSE}), pixel-level mask-induced variability, cross-contrast/-modality generalization, and robustness to severe k-space undersampling. In a blinded study with three radiologists, {PaDIS-MRI} reconstructions were chosen as diagnostically superior in 91.7% of cases, compared to baselines (i) {FastMRI-EDM} and (ii) classical convex reconstruction with wavelet sparsity. These findings highlight the potential of patch-based diffusion priors for high-fidelity {MRI} reconstruction in data-scarce clinical settings where diagnostic confidence matters.} }
Endnote
%0 Conference Paper %T PaDIS-MRI: Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction %A Rohan Sanda %A Asad Aali %A Andrew Johnston %A Eduardo Reis %A Gordon Wetzstein %A Sara Fridovich-Keil %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-sanda26a %I PMLR %P 1316--1335 %U https://proceedings.mlr.press/v297/sanda26a.html %V 297 %X Magnetic resonance imaging ({MRI}) requires long acquisition times, which raise costs, reduce accessibility, and increase susceptibility to motion artifacts. Diffusion probabilistic models that learn data-driven priors may reduce acquisition time by enabling reconstruction from undersampled k-space measurements. However, they typically require large training datasets that can be prohibitively expensive to collect. Patch-based diffusion models have shown promise in learning effective data-driven priors over small real-valued datasets, but have not yet demonstrated clinical value in {MRI}. We extend the Patch-based Diffusion Inverse Solver ({PaDIS}) to complex-valued, multi-coil {MRI} reconstruction, and compare it against a state-of-the-art whole-image diffusion baseline ({FastMRI-EDM}) for $7\times$ undersampled {MRI} reconstruction on the {FastMRI} brain dataset. We show that {PaDIS-MRI} models trained on small datasets of as few as 25 k-space images outperform {FastMRI-EDM} on image quality metrics ({PSNR}, {SSIM}, {NRMSE}), pixel-level mask-induced variability, cross-contrast/-modality generalization, and robustness to severe k-space undersampling. In a blinded study with three radiologists, {PaDIS-MRI} reconstructions were chosen as diagnostically superior in 91.7% of cases, compared to baselines (i) {FastMRI-EDM} and (ii) classical convex reconstruction with wavelet sparsity. These findings highlight the potential of patch-based diffusion priors for high-fidelity {MRI} reconstruction in data-scarce clinical settings where diagnostic confidence matters.
APA
Sanda, R., Aali, A., Johnston, A., Reis, E., Wetzstein, G. & Fridovich-Keil, S.. (2026). PaDIS-MRI: Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:1316-1335 Available from https://proceedings.mlr.press/v297/sanda26a.html.

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