Attention-based two-stage network for non-cartesian multi-coil ASL MRI reconstruction

Motivation: High-resolution arterial spin labeling (ASL) imaging is time-consuming, limiting its clinical applications in studying small brain structures. Goal(s): To reconstruct high-resolution ASL images from 8-time accelerated ASL image acquisition, an under-sampled non-Cartesian k-space sampling. Approach: We proposed an attention-based deep learning (DL) model. Results: The proposed DL model can successfully reconstruct 8-fold under-sampled, non-cartesian, multi-coil data from k-space. Impact: Our proposed attention-based deep learning model can reconstruct under-sampled non-cartesian multi-coil data in k-space and thereby significantly decrease long MRI acquisition time required for high-resolution ASL MRI imaging, which may enable clinical applications in studying small brain structures.