Abstract #2233

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

Ricardo A Gonzales^1,2, John Onofrey¹, Jérôme Lamy¹, Felicia Seemann^3,4, Einar Heiberg^3,4,5, 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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