Learning-Based Compressive MRI

TL;DR

This paper introduces a learning-based framework to optimize MRI subsampling patterns tailored to specific reconstruction algorithms and anatomies, improving sampling efficiency in both noiseless and noisy conditions.

Contribution

It proposes a novel, parameter-free greedy mask selection method and provides theoretical justification using statistical learning theory.

Findings

Effective for various reconstruction rules

Works well in noisy and noiseless settings

Theoretically justified through statistical learning

Abstract

In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. In this paper, we propose a learning-based framework for optimizing MRI subsampling patterns for a specific reconstruction rule and anatomy, considering both the noiseless and noisy settings. Our learning algorithm has access to a representative set of training signals, and searches for a sampling pattern that performs well on average for the signals in this set. We present a novel parameter-free greedy mask selection method, and show it to be effective for a variety of reconstruction rules and performance metrics. Moreover we also support our numerical findings by providing a rigorous justification of our framework via statistical learning theory.