Abstract #4928
Improved strain analysis of cine images by deep learning from DENSE: Comparison of a 3D Unet and an optical-flow net
Yu Wang1, Changyu Sun1, Sona Ghadimi1, Auger C. Daniel1, Pierre Croisille2,3, Magalie Viallon2,3, Jie Jane Cao4, Yang Joshua Cheng4, Andrew D. Scott5,6, Pedro F. Ferreira5,6, John N. Oshinski7, Daniel B. Ennis8, Kenneth C. Bilchick9, and Frederick H. Epstein1,10
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2University of Lyon, UJM-Saint-Etienne, INSA, CNRS UMR 5520, INSERM U1206, CREATIS, Saint-Etienne, France, 3Department of Radiology, University Hospital Saint-Etienne, Saint-Etienne, France, 4St. Francis Hospital, DeMatteis Center for Research and Education, Cardiac Imaging, Greenvale, NY, United States, 5Cardiovascular Magnetic Resonance Unit, The Royal Brompton Hospital, London, United Kingdom, 6National Heart and Lung Institute, Imperial College, London, United Kingdom, 7Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States, 8Department of Radiology, Stanford University, Stanford, CA, United States, 9Cardiovascular Division, Department of Medicine, University of Virginia Health System, Charlottesville, VA, United States, 10Radiology, University of Virginia, Charlottesville, VA, United States
Synopsis
Cine DENSE provides both myocardial contours and intramyocardial displacements. We propose to use DENSE to train deep networks to predict intramyocardial motion from contour motion. Two workflows were implemented: a two-step FlowNet2-based framework with a through-time correction network and a 3D (2D+t) Unet framework. Both networks depicted cardiac contraction and abnormal motion patterns. The 3D Unet showed excellent reliability for global circumferential strain (Ecc) and good reliability for segmental Ecc, and it outperformed commercial FT for both global and segmental Ecc.
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