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Abstract #1094

Accelerated and Accurate Myocardial T1 Mapping with PENGUIN: Combining Deep Learning with Extended Phase Graph Modeling

Catarina N. Carvalho1,2, Andreia S. Gaspar1, Rita G. Nunes1, and Teresa Correia2,3
1Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal, 2Center of Marine Sciences (CCMAR), Faro, Portugal, 3School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Synopsis

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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Keywords