Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models

Sriram Ravula, Brett Levac, Ajil Jalal, Jonathan I. Tamir, Alexandros G. Dimakis
6/5/2023
cs.LGeess.IV

No official code found. Best implementation: Sriram-Ravula/MRI_Sampling_Diffusion (13 stars, MIT, last commit 2023-06-30).

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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Code Implementations(1)

Sriram-Ravula/MRI_Sampling_DiffusionCommunity100%
MIT

Code for "Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models"

133PythonJun 2, 20233 years agoMIT

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Cite this paper

@article{ravula2023optimizing,
  title  = {Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models},
  author = {Sriram Ravula and Brett Levac and Ajil Jalal and Jonathan I. Tamir and Alexandros G. Dimakis},
  year   = {2023},
  eprint = {2306.03284},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.03284v1}
}

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