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Abstract #1554

A Neural Network for Rapid Generation of T1, T2, T1Ρ Dictionaries for Cardiac MR Fingerprinting

Thomas James Fletcher1, Carlos Velasco1, Talent Fong1, Gastão Cruz1, René Michael Botnar1, and Claudia Prieto1
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Dictionary generation for multi-parametric cardiac Magnetic Resonance Fingerprinting (MRF) is a significant bottleneck as subject-specific dictionaries must be created accounting for the subject’s heart rate variability and dictionaries grow exponentially with the number of parameters considered. Here we propose a feedforward neural network to generate cardiac MRF dictionaries for T1, T2 and T. The proposed approach was tested on simulations and in-vivo data, generating dictionaries in 3 seconds. The proposed method achieved a good match to dictionaries generated with Extended Phase Graph (EPG) simulations with mean relative errors for myocardium T1, T2 and T ranging from 1.7% to 5.1%.

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