Motivation: Myocardial T₁ 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 T₁ mapping estimation accuracy, while also reducing acquisition and reconstruction times. Approach: We propose a physics-informed deep learning network to obtain myocardial T₁ maps directly from undersampled k-space following the Extended Phase Graph formulation. Results: Our method is able to estimate T₁ maps for acceleration factors 4 and 8 with minimal error. Impact: We propose a novel physics-based deep learning method that performs accelerated myocardial T₁ mapping directly from undersampled k-space acquisitions considering the Extended Phase Graph formulation, greatly improving the accuracy of the estimated T₁ values while shortening acquisition/reconstruction times.
Carvalho et al. (Tue,) studied this question.