Abstract #1071

Alternating low-rank tensor reconstruction for more precise and repeatable multiparametric mapping with Cardiovascular MR Multitasking

Tianle Cao^1,2, Xianglun Mao², Alan C. Kwan^2,3, Daniel S. Berman³, Yibin Xie², Debiao Li^2,4, and Anthony G. Christodoulou^1,2

¹Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Biomedical imaging research institute, Cedars Sinai Medical Center, Los Angeles, CA, United States, ³Departments of Imaging and Cardiology, Cedars Sinai Medical Center, Los Angeles, CA, United States, ⁴Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Quantitative Imaging, Sparse & Low-Rank Models

Motivation: While MR Multitasking shows initial promise as a free-breathing, non-ECG approach for multiparametric CMR, its precision and repeatability still require further improvement to match the widely adopted clinical protocols.

Goal(s): To improve precision and repeatability of multiparametric mapping by cardiovascular MR Multitasking.

Approach: A novel low-rank tensor reconstruction strategy was developed to improve the reconstruction performance. Numerical simulations and in-vivo studies on healthy volunteers and cardiomyopathy patients were used to evaluate the proposed technique.

Results: Compared to conventional recontruction, the proposed approach showed lower RMSE in numerical simulations, and improved precision by ~20% and repeatability by ~30% in in-vivo studies.

Impact: The improved cardiovascular MR Multitasking has the potential to be an efficient and subject friendly (free-breathing, non-ECG) alternative for diagnosis of CMR patients whose T1 and T2 changes are greater than 100 ms and 2 ms, e.g., amyloidosis patients.

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