Alternating Low-Rank Tensor Reconstruction for Improved Multi-Dimensional MRI with MR Multitasking

Low-rank tensor modelling is promising for multi-dimensional MR imaging. In this work, we developed a new low-rank tensor reconstruction approach using alternating minimization of spatial and temporal bases from the whole k-t space data instead of from split subsets of data. The approach was evaluated for 2D motion-resolved myocardial T1/T2/T2*/fat-fraction mapping and could potentially be used for imporving reconstruction quality and/or further reducing scan time.