Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion
  Generative Models
Sriram Ravula Brett Levac Ajil Jalal Jonathan I. Tamir Alexandros G. Dimakis
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.