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Abstract #0772

Deep Learning-based Strain Quantification from CINE Cardiac MRI

Teodora Chitiboi1, Bogdan Georgescu1, Jens Wetzl2, Indraneel Borgohain1, Christian Geppert2, Stefan K Piechnik3, Stefan Neubauer3, Steffen Petersen4, and Puneet Sharma1
1Siemens Healthineers, Princeton, NJ, United States, 2Magnetic Resonance, Siemens Healthcare, Erlangen, Germany, 3Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom, 4NIHR Biomedical Research Centre at Barts, Queen Mary University of London, London, United Kingdom

Deep learning enables fully automatic strain analysis from CINE MRI on large subject cohorts. Deep learning neural nets were trained to segment the heart chambers from CINE MRI using manually annotated ground truth. After validation on more than 1700 different patient datasets, the models were used to generate segmentations as the first step of a fully automatic strain analysis pipeline for 460 subjects. We found significant differences associated with gender (strain magnitude smaller for males), height (lower strain magnitude for patients taller than 170 cm) and age (lower circumferential and longitudinal strain for subjects older than 60 years).

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