Keywords: Myocardium, Machine Learning/Artificial Intelligence, Motion Correction
Motivation: Cardiac T1 mapping is often limited by the need for breath-holding to prevent motion artifacts, which restricts its use in patients who cannot hold their breath.
Goal(s): To create a self-supervised deep learning method for motion-corrected, free-breathing cardiac T1 mapping without requiring large datasets or worrying about data variability.
Approach: We present a new self-supervised model that combines a signal relaxation model with anatomical constraints and employs the voxel-morph framework for motion correction. Our model's performance was assessed using a publicly available myocardial T1 mapping dataset.
Results: Our approach outperformed other state-of-the-art registration methods in terms of R2, DICE, and Hausdorff distance.
Impact: Our model offers the possibility of extending cardiac T1 mapping to patients who cannot perform breath-hold MRI procedures by ensuring robust motion correction for accurate T1 mapping, all without the necessity for large training datasets or worries about data anomalies.
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