Deep Learning-based Aligned Strain from Cine Cardiac MRI for Detection of Fibrotic Myocardial Tissue in Patients with Duchenne Muscular Dystrophy.

Journal: Radiology. Artificial intelligence
PMID:

Abstract

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.

Authors

  • Sven Koehler
    Department of Internal Medicine III, Heidelberg University Hospital, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany.
  • Julian Kuhm
    Department of Internal Medicine III, Heidelberg University Hospital, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany.
  • Tyler Huffaker
    Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern/Children's Health, Dallas, Tex.
  • Daniel Young
    Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern/Children's Health, Dallas, Tex.
  • Animesh Tandon
    Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX, 75235, USA. tandon.animesh@gmail.com.
  • Florian André
    University of Heidelberg, Department of Cardiology, Angiology and Pneumology, Im Neuenheimer Feld 410, Heidelberg, 69120, Germany. Electronic address: florian.andre@med.uni-heidelberg.de.
  • Norbert Frey
    DZHK (German Centre for Cardiovascular Research, All Partner Sites), Munich, Germany.
  • Gerald Greil
    Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern/Children's Health, Dallas, Tex.
  • Tarique Hussain
    Department of Pediatrics, UT Southwestern Medical Center, 1935 Medical District Dr, Dallas, TX, 75235, USA.
  • Sandy Engelhardt