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Abstract #1090

Diving into Extended Phase Graph-based Deep Learning for accurate T2 mapping with PENGUIN

Catarina N. Carvalho1, Teresa M. Correia2,3, and Rita G. Nunes1,4
1Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico – Universidade de Lisboa, Lisbon, Portugal, Lisbon, Portugal, 2School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom, London, United Kingdom, 3Center of Marine Sciences - CCMAR, Faro, Portugal, Faro, Portugal, 4Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College, London, UK, London, United Kingdom

Synopsis

Keywords: Quantitative Imaging, Relaxometry

Model-based deep learning approaches have shown promising results to accelerate T2 relaxometry, but most adopt a pure exponential curve to model the signal, which does not account for indirect and stimulated echoes. A PhasE graph sigNal and Gradients QUantitative Inference MachiNe (PENGUIN) is proposed, which implements a dictionary of pre-calculated echo-modulation curves following the Extended Phase Graph (EPG) formulation and respective gradients as the inputs of a Recurrent Inference Machine to perform accurate T2 mapping from the reconstructed images. PENGUIN is 25-fold faster than a pattern recognition approach with a T2 dictionary step of 2 ms.


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Keywords