# Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI

## TL;DR

This paper introduces a scalable, learning-based method for optimizing sampling masks in dynamic MRI, significantly reducing computational costs while maintaining high-quality image reconstruction from undersampled data.

## Contribution

It presents a novel stochastic greedy algorithm for designing optimal sampling masks, addressing scalability issues in dynamic MRI compressed sensing.

## Key findings

- Reduces computational burden by nearly 200 times.
- Maintains reconstruction performance comparable to existing methods.
- Provides a deterministic optimal sampling mask solution.

## Abstract

Compressed sensing applied to magnetic resonance imaging (MRI) allows to reduce the scanning time by enabling images to be reconstructed from highly undersampled data. In this paper, we tackle the problem of designing a sampling mask for an arbitrary reconstruction method and a limited acquisition budget. Namely, we look for an optimal probability distribution from which a mask with a fixed cardinality is drawn. We demonstrate that this problem admits a compactly supported solution, which leads to a deterministic optimal sampling mask. We then propose a stochastic greedy algorithm that (i) provides an approximate solution to this problem, and (ii) resolves the scaling issues of [1,2]. We validate its performance on in vivo dynamic MRI with retrospective undersampling, showing that our method preserves the performance of [1,2] while reducing the computational burden by a factor close to 200.