Combining machine learning and mathematical modeling in estimation of T1 relaxation time
Kateřina Škardová1, Radek Galabov1,2, Kateřina Fricková1, Tomáš Pevný3, Jaroslav Tintěra2, Tomáš Oberhuber1, and Radomír Chabiniok1,4
1Department of Mathematics, FNSPE CTU in Prague, Prague, Czech Republic, 2Department of Radiology, Institute for clinical and experimental medicine, Prague, Czech Republic, 3Artificial Intelligence Center, CTU in Prague, Prague, Czech Republic, 4Department of Pediatrics, UT Southwestern Medical Center, Dallas, TX, United States
A method for estimating tissue parameters using cardiovascular MRI and biophysical model by combining neural network (NN) and numerical optimization (NO) is illustrated on estimating $$$T_1$$$ relaxation time from MOLLI. Compared to the estimation obtained from MOLLI by the scanner, the proposed method provided $$$T_1$$$ closer to turbo spin-echo pseudo-ground in 7 out of 8 phantoms and higher or comparable myocardial and blood $$$T_1$$$ in 6 out of 7 patiens’ datasets. Including the NN-based initial guess accelerated the subsequent NO. NO initialized by NN, trained using simulated data, showed the potential to increase the efficiency and robustness of parameter estimation.
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