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

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 <i>t</i> 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; <i>P</i> < .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. <b>Keywords:</b> Pediatrics, Image Postprocessing, Heart, Cardiac, Convolutional Neural Network (CNN) Duchenne Muscular Dystrophy <i>Supplemental material is available for this article.</i> © RSNA, 2025.

Authors

Institutions

Publication Details

Journal
Radiology Artificial Intelligence
Published
2025-02-26
DOI
https://doi.org/10.1148/ryai.240303
Citations
1
Primary Topic
Cardiac Structural Anomalies and Repair
Type
article
Field-Weighted Citation Impact
2.01
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Gerald Greil, Norbert Frey, Florian André, Sven Koehler et al.
1 citations
Radiology Artificial Intelligence
Cardiac Structural Anomalies and Repair
2.01
article

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

Gerald Greil, Norbert Frey, Florian André, Sven Koehler, Daniel Young, Tyler Huffaker, Julian Kuhm, Animesh Tandon, Sandy Engelhardt, Tarique Hussain
article en
1 citations

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 <i>t</i> 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; <i>P</i> < .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. <b>Keywords:</b> Pediatrics, Image Postprocessing, Heart, Cardiac, Convolutional Neural Network (CNN) Duchenne Muscular Dystrophy <i>Supplemental material is available for this article.</i> © RSNA, 2025.

Radiology Artificial IntelligenceVol. 7(3)
Cleveland Clinic (US), Children’s Institute (US), Heidelberg University (DE), University Hospital Heidelberg (DE), Cook Children's Health Care System (US), German Centre for Cardiovascular Research (DE), Case Western Reserve University (US), Heidelberg University (US), The University of Texas Southwestern Medical Center (US)
Good health and well-being
Openalex Percentile: Top 18%
Cardiac Structural Anomalies and Repair
2.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.