Abstract #3720

Universal sequence-invariant deep learning image reconstruction for cardiovascular MR Multitasking

Zheyuan Hu^1,2, Zihao Chen^1,2, Hsu-Lei Lee¹, Yibin Xie¹, Debiao Li^1,2, and Anthony Christodoulou^1,2

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Quantitative ImagingCardiovascular MR (CMR) Multitasking can quantify various parameter combinations without breath-holding or ECG monitoring. Clinically practical reconstruction time is viable using supervised deep subspace learning, but it depends on sequence-specific training. Here we explore whether universal, sequence-invariant CMR Multitasking deep learning reconstruction is practical by trading temporal awareness (breadth) for added depth in spatial domain. We evaluated the performance and generalizability of both strategies by training on T1 mapping data only and testing on two datasets: a) a matched-sequence T1 mapping data; and b) a novel-sequence T1-T2 mapping data.

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