Evaluation of Kernel Low-Rank Compressed Sensing in preclinical Diffusion Magnetic Resonance Imaging

Abstract

Compressed Sensing (CS) is widely used to accelerate clinical diffusion MRI acquisitions, but it remains under-utilized in preclinical settings. In this study, we optimized and compared several CS reconstruction methods for diffusion imaging. Different undersampling patterns and two reconstruction approaches were evaluated: conventional CS, based on Berkeley Advanced Reconstruction Toolbox (BART-CS) toolbox, and a new Kernel Low-Rank (KLR)-CS, based on Kernel Principal Component Analysis and low-resolution-phase maps. 3D CS acquisitions were performed at 9.4T using a 4-element cryocoil on mice (wild type and a MAP6 knockout). Comparison metrics were error and Structural Similarity Index Measure (SSIM) on fractional anisotropy (FA) and mean diffusivity (MD), as well as reconstructions of the anterior commissure and fornix. Acceleration factors (AF) up to 6 were considered. In the case of retrospective undersampling, the proposed KLR-CS outperformed BART-CS up to AF=6 for FA and MD maps and tractography. For instance, for AF=4, the maximum errors were respectively 8.0% for BART-CS and 4.9% for KLR-CS, considering both FA and MD in the corpus callosum. Regarding undersampled acquisitions, these maximum errors became respectively 10.5% for BART-CS and 7.0% for KLR-CS. This difference between simulations and acquisitions arose mainly from repetition noise, but also from differences in resonance frequency drift, signal-to-noise ratio, and in reconstruction noise. Despite this increased error, fully sampled and AF=2 yielded comparable results for FA, MD and tractography, and AF=4 showed minor faults. Altogether, KLR-CS based on low-resolution-phase maps seems a robust approach to accelerate preclinical diffusion MRI.