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

Keywords:
Image Reconstruction, Quantitative Susceptibility mapping, EPI, distortion correction
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 applyingartificial 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 resolutionof 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.