Technical Report (v1.0)--Pseudo-random Cartesian Sampling for Dynamic MRI

TL;DR

This technical report introduces five pseudo-random Cartesian sampling methods for dynamic MRI that enhance compressed sensing by providing incoherent undersampling patterns, with four methods enabling fast, parameterized sampling mask generation.

Contribution

The report presents five novel pseudo-random Cartesian sampling techniques for dynamic MRI, including fast, parameterized methods with publicly available MATLAB code.

Findings

Four methods enable fast on-the-fly sampling mask generation

Sampling distribution can be controlled via parameters

Methods applicable to various MRI imaging types

Abstract

For an effective application of compressed sensing (CS), which exploits the underlying compressibility of an image, one of the requirements is that the undersampling artifact be incoherent (noise-like) in the sparsifying transform domain. For cardiovascular MRI (CMR), several pseudo-random sampling methods have been proposed that yield a high level of incoherence. In this technical report, we present a collection of five pseudo-random Cartesian sampling methods that can be applied to 2D cine and flow, 3D volumetric cine, and 4D flow imaging. Four out of the five presented methods yield fast computation for on-the-fly generation of the sampling mask, without the need to create and store pre-computed look-up tables. In addition, the sampling distribution is parameterized, providing control over the sampling density. For each sampling method in the report, (i) we briefly describe the methodology, (ii) list default values of the pertinent parameters, and (iii) provide a publicly available MATLAB implementation.