Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, model-based
Motivation: T2 mapping provides quantitative myocardial tissue characterization. However, current approaches acquire several 2D contrast images which are then fitted to a model to estimate the T2 values, leading to limited coverage, and long acquisition and reconstruction times.
Goal(s): Here we propose to speed up 3D whole-heart T2 mapping using a model-based deep learning unrolling network (MEDAL) that leverages the power of machine learning and physical knowledge.
Approach: MEDAL reconstructs the T2 maps directly without reconstructing any intermediate contrast weighted images or fitting.
Results: The proposed approach was evaluated in iNAV-based free-breathing 3D T2 mapping 4x accelerated showing promising results.
Impact: A novel method for reconstructing parametric maps using a model-based deep learning unrolling network is presented. The method was demonstrated in a highly accelerated free breathing 3D whole-heart T2 mapping sequence allowing for fast and accurate T2 measurements.
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