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Abstract #0389

Multi-task Deep Learning for Late-activation Detection of Left Ventricular Myocardium

Jiarui Xing1, Sona Ghadimi2, Mohammad Abdishektaei2, Kenneth C Bilchick3, Frederick H Epstein2, and Miaomiao Zhang1
1Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States, 2Department of Biomedical Engineering, University of VIrginia, Charlottesville, VA, United States, 3School of Medicine, University of Virginia, Charlottesville, VA, United States

Implantation of the left ventricular pacing lead at the area with delayed activation is critical to Cardiac Resynchronization Therapy (CRT) response. Current approaches of detecting late-activated regions of left ventricles (LV) are slow with unsatisfied accuracy, particularly in cases where scar tissues exist in the patient’s heart. This work presents a multi-task deep learning algorithm to automatically identify late-activated regions of LV, as well as estimating the Time to the Onset of circumferential Shortening (TOS) using spatio-temporal cardiac DENSE MR images. Experimental results show that our algorithm provides ultra-fast identification of late-activated regions and estimated TOS with increased accuracy.

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