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 T1ρ 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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