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Abstract #0866

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

Tianle Cao1,2, Yibin Xie1, Debiao Li1, and Anthony G. Christodoulou1
1Cedars Sinai Medical Center, Los Angeles, CA, United States, 2University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Sparse & Low-Rank Models, Sparse & Low-Rank ModelsLow-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.

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Keywords