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Abstract #2233

Time-resolved tracking of the atrioventricular plane displacement in long-axis cine images with residual neural networks

Ricardo A Gonzales1,2, John Onofrey1, Jérôme Lamy1, Felicia Seemann3,4, Einar Heiberg3,4,5, and Dana C Peters1
1Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States, 2Department of Electrical Engineering, Universidad de Ingenieria y Tecnologia, Lima, Peru, 3Department of Clinical Physiology, Lund University, Lund, Sweden, 4Department of Biomedical Engineering, Lund University, Lund, Sweden, 5Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden

Diastolic dysfunction is assessed by measurement of mitral annular (MA) early diastolic velocity (e’), commonly performed in echocardiography. Similar measurements can be obtained with valvular plane tracking in MRI long-axis cines. These measurements have been validated and have good reproducibility, yet manual MA points annotations are required. In this work we present a machine learning convolutional neural network with a residual architecture for automatic annotation of MA points in MRI long-axis cine images of the 2 and 4-chamber views. The landmark tracking allowed a fast and accurate evaluation of diastolic parameters improving the clinical applicability of MRI for diastolic assessment.

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