Koehler Sven, Kuhm Julian, Huffaker Tyler, Young Daniel, Tandon Animesh, André Florian, Frey Norbert, Greil Gerald, Hussain Tarique, Engelhardt Sandy
Department of Internal Medicine III, Heidelberg University Hospital, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany.
German Centre for Cardiovascular Research (DZHK), Partner Sites Heidelberg and Mannheim, Germany.
Radiol Artif Intell. 2025 May;7(3):e240303. doi: 10.1148/ryai.240303.
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 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 (mean age [± SD], 15.2 years ± 3.1), and reproducibility was assessed in 82 patients by comparing the study method with existing feature-tracking and DL-based methods. Statistical analysis compared strain values using tests, mixed models, and more than 2000 machine learning models; accuracy, F1 score, sensitivity, and specificity are reported. Results DL-based aligned strain identified five times more differences (29 vs five; < .01) between fibrotic and nonfibrotic segments compared with traditional strain values and identified abnormal diastolic deformation patterns often missed with traditional methods. In addition, aligned strain values enhanced performance of predictive models for myocardial fibrosis detection, improving specificity by 40%, overall accuracy by 17%, and accuracy in patients with preserved ejection fraction by 61%. Conclusion The proposed aligned strain technique enables motion-based detection of myocardial dysfunction at noncontrast cardiac MRI, facilitating detailed interpatient strain analysis and allowing precise tracking of disease progression in DMD. Pediatrics, Image Postprocessing, Heart, Cardiac, Convolutional Neural Network (CNN) Duchenne Muscular Dystrophy © RSNA, 2025.
目的 开发一种深度学习（DL）模型，该模型可从电影（非对比）心脏磁共振成像（MRI）中得出对齐的应变值，并评估这些值预测杜氏肌营养不良症（DMD）患者心肌纤维化的性能。材料与方法 这项回顾性研究纳入了2018年2月至2023年4月期间在单一中心接受心脏MRI检查的139例男性DMD患者。开发了一种DL流程，以检测整个心动周期中的五个关键帧以及相应的密集变形场，从而能够对患者之间以及从一个关键帧到下一个关键帧进行特定相位的应变分析。在57例患者（平均年龄[±标准差]，15.2岁±3.1岁）中评估了这些应变值在识别与纤维化节段相关的异常变形方面的有效性，并通过将研究方法与现有的特征跟踪和基于DL的方法进行比较，在82例患者中评估了其可重复性。统计分析使用检验、混合模型和2000多个机器学习模型比较应变值；报告了准确性、F1分数、敏感性和特异性。结果 与传统应变值相比，基于DL的对齐应变在纤维化和非纤维化节段之间识别出的差异多五倍（29对5；P <.01），并识别出传统方法经常遗漏的异常舒张期变形模式。此外，对齐应变值提高了心肌纤维化检测预测模型的性能，特异性提高了40%，总体准确性提高了17%，射血分数保留患者的准确性提高了61%。结论 所提出的对齐应变技术能够在非对比心脏MRI上基于运动检测心肌功能障碍，便于进行详细的患者间应变分析，并允许精确跟踪DMD疾病进展。儿科学、图像后处理、心脏、心脏、卷积神经网络（CNN）、杜氏肌营养不良症 © RSNA，2025年
