StrainNet:通过DENSE的深度学习改进了Cine MRI的心肌应变分析

Yu Wang¹, Changyu Sun¹, Sona Ghadimi¹+16

• ¹From the Department of Biomedical Engineering, University of Virginia, Biomedical Engineering and Medical Science Building, Room 2013, MR5, Charlottesville, VA 22903 (Y.W., C.S., S.G., D.C.A., F.H.E.); Department of Biomedical, Biological and Chemical Engineering and Department of Radiology, University of Missouri, Columbia, Mo (C.S.); Department of Radiology, University Hospital of Saint Etienne, Saint Etienne, France (P.C.); CREATIS (UMR CNRS 5220, U1206 INSERM), INSA Lyon, Lyon, France (P.C., M.V.); BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland (K.M., C.B.); Department of Translational Data Science and Informatics, Geisinger Health System, Danville, Pa (C.M.H., L.J., B.K.F.); Cardiovascular Research Center, University of Kentucky, Lexington, Ky (C.M.H., L.J., B.K.F.); The Heart Center, St Francis Hospital, Roslyn, NY (J.J.C., J.C.); Cardiovascular Magnetic Resonance Unit, The Royal Brompton Hospital and National Heart and Lung Institute, Imperial College London, London, England (A.D.S., P.F.F.); Department of Radiology & Imaging Sciences and Biomedical Engineering, Emory University, Atlanta, Ga (J.N.O.); Department of Radiology, Stanford University, Stanford, Calif (D.B.E.); Department of Medicine (K.C.B.) and Department of Radiology and Medical Imaging (F.H.E.), University of Virginia Health System, Charlottesville, Va.