Deep learning-based aligned strain from cine cardiac MRI for detection of fibrotic myocardial tissue in patients with Duchenne muscular dystrophy - Köhler, Sven; Kuhm, Julian; Huffaker, Tyler; Young, Daniel; Tandon, Animesh; André, Florian; Frey, Norbert; Greil, Gerald; Hussain, Tarique; Engelhardt, Sandy (Radiology: artificial intelligence, 2025)
“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. - - Purpose - - To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these values to predict myocardial fibrosis in patients with Duchenne muscular dystrophy (DMD). - - Materials and Methods - - This retrospective study included 139 male patients with DMD who underwent cardiac MRI examinations at a single center between February 2018 and April 2023. A DL pipeline was developed to detect five key frames throughout the cardiac cycle, and respective dense deformation fields, allowing for phase-specific strain analysis across patients and from one key frame to the next. Effectiveness of these strain values in identifying abnormal deformations associated with fibrotic segments was evaluated in 57 patients (15.2 ± 3.1 years), and reproducibility was assessed in 82 patients (12.8 ± 2.7 years), comparing our method with existing feature-tracking and DL-based methods. Statistical analysis compared strain values using t tests, mixed models, and 2000+ ML models, reporting accuracy, F1 score, sensitivity, and specificity. - - Results - - DL-based aligned strain identified five times more differences (29 versus 5, P < .01) between fibrotic and nonfibrotic segments compared with traditional strain values and identified abnormal diastolic deformation patterns often missed by traditional methods. Additionally, aligned strain values enhanced performance of predictive models for myocardial fibrosis detection, improving specificity by 40%, overall accuracy by 17%, and accuracy patients with preserved ejection fraction by 61%. - - Conclusion - - The proposed aligned strain technique enables motion-based detection of myocardial dysfunction on contrast free cardiac MRI, facilitating detailed interpatient strain analysis, and allowing precise tracking of disease progression in DMD. - - ©RSNA, 2025
Gesehen am 01.04.2025