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Updated: Jul 24, 2025
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
Published on: February 12, 2011
StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE
Yu Wang1, Changyu Sun1, Sona Ghadimi1
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.
Purpose:
To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI.
Materials And Methods:
In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models.
Results:
The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc.
Conclusion:
StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023.
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