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Abstract #1760

Dictionary Generation and Matching with Conditional Invertible Neural Networks for Cardiac MR Fingerprinting

Thomas James Fletcher1, Carlos Velasco1, Gastão Cruz1, Alina Schneider1, René Michael Botnar1, and Claudia Prieto1
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom


Dictionary generation and pattern matching are two important bottlenecks in cardiac MRF. Dictionaries must be recalculated for each new scan as they depend on the subject’s heart rate variability and both dictionaries and the length of pattern matching grow exponentially with the number of parameters being considered. We propose a conditional invertible neural network capable of both dictionary generation and parameter estimation for T1, T2 and T cardiac MRF. The network achieves excellent results on EPG-generated data (inner product >0.999, parameters’ relative error <1.5%) and good results for in-vivo data (mean relative errors for myocardium ranging from 2.2% to 15.1%).

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