Abstract #1061

Single-Shot Adaptation using Score-Based Models for MRI Reconstruction

Marius Arvinte¹, Ajil Jalal¹, Giannis Daras², Eric Price², Alex Dimakis¹, and Jonathan I Tamir^1,3,4

¹Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States, ²Computer Science, The University of Texas at Austin, Austin, TX, United States, ³Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States, ⁴Diagnostic Medicine, Dell Medical School, The University of Texas at Austin, Austin, TX, United States

Synopsis

This work deals with the problem of few-shot adaptation in data-driven MRI reconstruction, where models must efficiently adapt to new distributions. We introduce score-based models for MRI reconstruction and an algorithm for adjusting inference parameters (step size, noise level and stopping point), investigate the impact of these parameters on reconstruction performance, and demonstrate average gains of at least 2 dB in PSNR across a range of acceleration values, all while using a pretrained model that was trained for brain MRI and fine-tuned using only a single fully-sampled 2D knee scan from the fastMRI database.

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