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

Rohan Sanda, Asad Aali, Andrew Johnston, Eduardo Pontes Reis, Gordon Wetzstein, Sara Fridovich-Keil

Keywords: MRI, patch-based priors, diffusion models, data-efficiency, robustness

Track: Proceedings

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.

General Area: Applications and Practice

Specific Subject Areas: Medical Imaging

Data And Code Availability: No

Ethics Board Approval: No

Entered Conflicts: I confirm the above

Anonymity: I confirm the above

Submission Number: 221