Anisotropic compressed sensing for non-Cartesian MRI acquisitions

TL;DR

This paper develops theoretical results in anisotropic compressed sensing tailored for non-Cartesian MRI acquisitions, aiming to optimize sampling strategies by considering structured sparsity and variable density sampling.

Contribution

It introduces new theoretical insights into anisotropic compressed sensing that incorporate structured sparsity and variable density sampling for non-Cartesian MRI.

Findings

Theoretical framework for anisotropic compressed sensing in MRI

Guidelines for optimal non-Cartesian sampling strategies

Potential improvements in MRI image reconstruction

Abstract

In the present note we develop some theoretical results in the theory of anisotropic compressed sensing that allow to take structured sparsity and variable density structured sampling into account. We expect that the obtained results will be useful to derive explicit expressions for optimal sampling strategies in the non-Cartesian (radial, spiral, etc.) setting in MRI.