@inproceedings{
ravula2023optimizing,
title={Optimizing Sampling Patterns for Compressed Sensing {MRI} with Diffusion Generative Models},
author={Sriram Ravula and Brett Levac and Ajil Jalal and Jon Tamir and Alex Dimakis},
booktitle={NeurIPS 2023 Workshop on Deep Learning and Inverse Problems},
year={2023},
url={https://openreview.net/forum?id=P2r4AxyeE2}
}
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 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.
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Sriram Ravula Brett Levac Ajil Jalal Jon Tamir Alex Dimakis