Conditional Score-Based Reconstructions for Multi-contrast MRI

Brett Levac, Ajil Jalal, Kannan Ramchandran, Jon Tamir

Abstract:

Magnetic resonance imaging (MRI) exam protocols consist of multiple contrast-weighted images of
the same anatomy to emphasize different tissue properties. Due to the long acquisition times required to
collect fully sampled k-space measurements, it is common to only collect a fraction of k-space for some,
or all, of the scans and subsequently solve an inverse problem for each contrast to recover the desired
image from sub-sampled measurements. Recently, there has been a push to further accelerate MRI exams
using data-driven priors, and generative models in particular, to regularize the ill-posed inverse problem
of image reconstruction. These methods have shown promising improvements over classical methods.
However, many of the approaches neglect the multi-contrast nature of clinical MRI exams and treat each
scan as an independent reconstruction. In this work we show that by learning a joint Bayesian prior over
multi-contrast data with a score-based generative model we are able to leverage the underlying structure
between multi-contrast images and thus improve image reconstruction fidelity over generative models
that only reconstruct images of a single contrast.