Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models

• URL: http://arxiv.org/abs/2306.03284v1
• Date: Mon, 5 Jun 2023 22:09:06 GMT
• Title: Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
• Authors: Sriram Ravula, Brett Levac, Ajil Jalal, Jonathan I. Tamir, Alexandros G. Dimakis
• Abstract summary: We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI. We use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process. Our method requires as few as five training images to learn effective sampling patterns.
• Score: 75.52575380824051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-based generative models have been used as powerful priors for magnetic resonance imaging (MRI) reconstruction. We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI that leverages pre-trained diffusion generative models. Crucially, during training we use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process. Experiments across varying anatomies, acceleration factors, and pattern types show that sampling operators learned with our method lead to competitive, and in the case of 2D patterns, improved reconstructions compared to baseline patterns. Our method requires as few as five training images to learn effective sampling patterns.

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