Keywords: Machine Learning/Artificial Intelligence, Relaxometry, Reconstruction; Cardiovascular; T1 mapping; Myocardium; AI/Machine Learning
Motivation: Myocardial $$$T_1$$$ mapping sequences typically require multiple breath-hold scans, leading to limited spatial resolution, patient discomfort and motion artifacts. Moreover, mapping is generally accomplished through three-parameter exponential fitting, which may compromise the accuracy of the estimation due to the model’s simplicity.
Goal(s): Improve $$$T_1$$$ mapping estimation accuracy, while also reducing acquisition and reconstruction times.
Approach: We propose a physics-informed deep learning network to obtain myocardial $$$T_1$$$ maps directly from undersampled k-space following the Extended Phase Graph formulation.
Results: Our method is able to estimate $$$T_1$$$ maps for acceleration factors 4 and 8 with minimal error.
Impact: We propose a novel physics-based deep learning method that performs accelerated myocardial $$$T_1$$$ mapping directly from undersampled k-space acquisitions considering the Extended Phase Graph formulation, greatly improving the accuracy of the estimated $$$T_1$$$ values while shortening acquisition/reconstruction times.
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