MBSS-T1: Model-based subject-specific self-supervised motion correction for robust cardiac T1 mapping

Cardiac T1 mapping is a valuable quantitative MRI technique for diagnosing diffuse myocardial diseases. Traditional methods, relying on breath-hold sequences and cardiac triggering based on an ECG signal, face challenges with patient compliance, limiting their effectiveness. Image registration can e...

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Vydáno v:Medical image analysis Ročník 102; s. 103495
Hlavní autoři: Hanania, Eyal, Zehavi-Lenz, Adi, Volovik, Ilya, Link-Sourani, Daphna, Cohen, Israel, Freiman, Moti
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier B.V 01.05.2025
Témata:
Algorithms
Deep Learning
Free-breathing MRI
Heart - diagnostic imaging
Humans
Image Interpretation, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Model-based deep learning
Motion
Motion correction
Myocardial imaging
T1 mapping
Motion correction
Deep learning
Free-breathing MRI
T1 mapping
Myocardial imaging
Model-based deep learning
ISSN:1361-8415, 1361-8423, 1361-8423
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Shrnutí:Cardiac T1 mapping is a valuable quantitative MRI technique for diagnosing diffuse myocardial diseases. Traditional methods, relying on breath-hold sequences and cardiac triggering based on an ECG signal, face challenges with patient compliance, limiting their effectiveness. Image registration can enable motion-robust cardiac T1 mapping, but inherent intensity differences between time points pose a challenge. We present MBSS-T1, a subject-specific self-supervised model for motion correction in cardiac T1 mapping. Physical constraints, implemented through a loss function comparing synthesized and motion-corrected images, enforce signal decay behavior, while anatomical constraints, applied via a Dice loss, ensure realistic deformations. The unique combination of these constraints results in motion-robust cardiac T1 mapping along the longitudinal relaxation axis. In a 5-fold experiment on a public dataset of 210 patients (STONE sequence) and an internal dataset of 19 patients (MOLLI sequence), MBSS-T1 outperformed baseline deep-learning registration methods. It achieved superior model fitting quality (R2: 0.975 vs. 0.941, 0.946 for STONE; 0.987 vs. 0.982, 0.965 for MOLLI free-breathing; 0.994 vs. 0.993, 0.991 for MOLLI breath-hold), anatomical alignment (Dice: 0.89 vs. 0.84, 0.88 for STONE; 0.963 vs. 0.919, 0.851 for MOLLI free-breathing; 0.954 vs. 0.924, 0.871 for MOLLI breath-hold), and visual quality (4.33 vs. 3.38, 3.66 for STONE; 4.1 vs. 3.5, 3.28 for MOLLI free-breathing; 3.79 vs. 3.15, 2.84 for MOLLI breath-hold). MBSS-T1 enables motion-robust T1 mapping for broader patient populations, overcoming challenges such as suboptimal compliance, and facilitates free-breathing cardiac T1 mapping without requiring large annotated datasets. Our code is available at https://github.com/TechnionComputationalMRILab/MBSS-T1. •A self-supervised model for motion correction in cardiac T1 mapping.•Integration of physical and anatomical constraints for motion-robust T1 mapping.•Effective motion correction across different T1 mapping sequences.•Robust motion correction in both free-breathing and breath-hold scans.•Motion-resilient T1 mapping that does not require large training datasets.
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ISSN:1361-8415
1361-8423
1361-8423
DOI:10.1016/j.media.2025.103495