Introduction: CineCT is increasingly being used to evaluate cardiac dynamics. However, current
evaluation typically depends on visual assessment, 3D segmentation, or evaluation
based on wall thickening. Echocardiography and CMR have demonstrated the utility of
global longitudinal shortening (GLS). Currently measuring GLS with CT requires reformatting
the 4D dataset into long-axis imaging planes and delineating the endocardial boundary
across time. In this work, we demonstrate the ability of a deep learning framework
to automatically and accurately measure GLS for detection of wall motion abnormalities
(WMA).
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114: Poster Session 6 - LV/RV Function, Chamber Dimensions Abstracts 440-446
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© 2022 Published by Elsevier Inc.
