  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.
