Abstract #4398

DeepBLESS: learning inverse Bloch equations for rapid prediction of myocardial relaxation parameters

Jiaxin Shao¹, Vahid Ghodrati ¹, Kim-Lien Nguyen^2,3, and Peng Hu^1,4

¹Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States, ²Department of Medicine, Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States, ³Division of Cardiology, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, United States, ⁴Biomedical Physics Inter-Departmental Graduate Program, University of California, Los Angeles, Los Angeles, CA, United States

Bloch equation simulation provides accurate estimation of soft tissue relaxation parameters for many applications. To speed up using Bloch equation for relaxation parameter estimation, we propose a general approach - deep learning with Bloch equation simulations (DeepBLESS) - to learn inverse Bloch equation for rapid myocardial relaxation parameter prediction. Using the Modified Look-Locker inversion recovery (MOLLI) sequence and a self-designed simultaneous radial T1 and T2 mapping sequence as examples, we demonstrated that DeepBLESS was adaptive to heart rate variation with good estimation accuracy and precision while reducing the inline computation time compared to the conventional Bloch-equation-based approaches.

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