Compressed Sensing MRI With Variable Density Averaging (CS-VDA) Outperforms Full Sampling At Low SNR

TL;DR

This paper introduces CS-VDA, a novel MRI technique combining k-space undersampling and variable density averaging, which improves image quality in low-SNR conditions surpassing full sampling at the same scan time.

Contribution

The study presents a new compressed sensing method with variable density averaging that enhances low-SNR MRI image quality, outperforming traditional full sampling techniques.

Findings

CS-VDA yields better image quality than full sampling at the same scan time.

Higher averages in the k-space center improve anatomical detail and soft-tissue contrast.

The method is effective in both phantom and in vivo MRI scans.

Abstract

We investigated whether a combination of k-space undersampling and variable density averaging enhances image quality for low-SNR MRI acquisitions. We implemented 3D Cartesian k-space prospective undersampling with a variable number of averages for each k-line. The performance of this compressed sensing with variable-density averaging (CS-VDA) method was evaluated in retrospective analysis of fully sampled phantom MRI measurements, as well as for prospectively accelerated in vivo 3D brain and knee MRI scans. Both phantom and in vivo results showed that acquisitions using the CS-VDA approach resulted in better image quality as compared to full sampling of k-space in the same scan time. Specifically, CS-VDA with a higher number of averages in the center of k-space resulted in the best image quality, apparent from increased anatomical detail with preserved soft-tissue contrast. This novel approach will facilitate improved image quality of inherently low SNR data, such as those with high-resolution or specific contrast-weightings with low SNR efficiency.