Joint optimization of multi-echo reconstruction and quantitative map estimation in Looping Star

Motivation: Looping-star sequences, despite their advantages, exhibit low SNR and undersampling artifacts compared to standard GRE sequences. Goal(s): This work proposes to jointly reconstruct multi-echo data and estimate quantitative maps in looping-star to boost the SNR, reduce the undersampling artifacts, and improve image quality. Approach: Our approach frames echo image reconstruction and quantitative map estimation as a unified optimization problem. This is then split into two sub-problems, addressed alternately using CG-SENSE. Results: Compared to individual echo reconstruction, our joint optimization improves tSNR of both echo images and T2* maps and effectively mitigates image artifacts. Impact: Our method jointly reconstructs multi-echo data in looping-star, enhancing SNR and reducing artifacts, with a notable tSNR improvement. It can be adapted to the Looping-Star fMRI protocol to potentially improve functional activity estimation.