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Abstract #1763

Deep subspace transfer learning for SMS image reconstruction from single-slice training: Application to CMR Multitasking

Zihao Chen1,2, Hsu-Lei Lee1, Xianglun Mao1, Tianle Cao1,2, Yibin Xie1, Debiao Li1,2, and Anthony Christodoulou1,2
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


CMR multitasking is promising for quantitative cardiac imaging, and can achieve fast three-slice quantification when combined with simultaneous multi-slice (SMS) acquisition. However, slow non-Cartesian iterative reconstruction is a barrier to clinical adoption. Deep learning can accelerate reconstruction, but the SMS training data are currently limited. Here we propose a data-consistent deep subspace transfer learning strategy that trains on single-slice T1 CMR multitasking data but is applied to SMS-encoded T1 CMR multitasking image reconstruction. The proposed strategy is >40x faster than the conventional SMS reconstruction, resulting in an equally better image quality and comparably precise T1 as in single-slice reconstruction.

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