Artificial Sparsity Enhanced Deep Learning BUDA Imaging Enables Rapid and Distortion-corrected High-Resolution 3D-EPI and QSM

Motivation: MR susceptibility mapping serves as a highly valuable tool in various neuroscientific and clinical applications. Goal(s): This innovative approach is designed to facilitate fast and robust high-resolution whole-brain imaging and quantitative susceptibility mapping (QSM). Approach: In this study, our primary objective was to create a distortion-free 3D-EPI with blip-up/down acquisition (BUDA), incorporating controlled aliasing in parallel imaging (CAIPI) sampling, and applying artificial sparsity enhanced deep learning image reconstruction. Results: Our developed technique holds the potential to produce distortion-free high-resolution whole-brain quantitative susceptibility mapping in just 12s at 3T and 9s at 7T, achieving an impressive resolution of 1 mm isotropic. Impact: The proposed 3D-BUDA, incorporating a 2D CAIPIRINHA acquisition sequence with artificial sparsity-enhanced self-supervised deep learning reconstruction, demonstrated its ability to deliver rapid, distortion-free, high-resolution, whole-brain T2*-weighted imaging and QSM.