Towards multi-sequence MR image recovery from undersampled k-space data

Undersampled MR image recovery has been widely studied for accelerated MR acquisition. However, it has been mostly studied under a single sequence scenario, despite the fact that multi-sequence MR scan is common in practice. In this paper, we aim to optimize multi-sequence MR image recovery from undersampled k-space data under an overall time constraint while considering the difference in acquisition time for various sequences. We first formulate it as a {\it constrained optimization} problem and then show that finding the optimal sampling strategy for all sequences and the best recovery model at the same time is {\it combinatorial} and hence computationally prohibitive. To solve this problem, we propose a {\it blind recovery model} that simultaneously recovers multiple sequences, and an efficient approach to find proper combination of sampling strategy and recovery model. Our experiments demonstrate that the proposed method outperforms sequence-wise recovery, and sheds light on how to decide the undersampling strategy for sequences within an overall time budget.