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

• URL: http://arxiv.org/abs/2509.21531v1
• Date: Thu, 25 Sep 2025 20:18:56 GMT
• Title: Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction
• Authors: Rohan Sanda, Asad Aali, Andrew Johnston, Eduardo Reis, Jonathan Singh, Gordon Wetzstein, Sara Fridovich-Keil,
• Abstract summary: Patch-based diffusion models have shown promise in learning effective data-driven priors over small real-valued datasets.<n>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.
• Score: 30.203473273516895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Magnetic resonance imaging (MRI) requires long acquisition times, raising costs, reducing accessibility, and making scans more susceptible to motion artifacts. Diffusion probabilistic models that learn data-driven priors can potentially assist in reducing acquisition time. 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 7x 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 uncertainty, cross-contrast 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.

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