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Abstract #0990

Synchronizing dimension reduction and parameter inference in 3D multiparametric MRI: A hybrid dual-pathway neural network approach

Carolin M Pirkl1,2, Izabela Horvath1,2, Sebastian Endt1,2, Guido Buonincontri3,4, Marion I Menzel2,5, Pedro A Gómez1, and Bjoern H Menze1
1Informatics, Technical University of Munich, Munich, Germany, 2GE Healthcare, Munich, Germany, 3Fondazione Imago7, Pisa, Italy, 4IRCCS Fondazione Stella Maris, Pisa, Italy, 5Physics, Technical University of Munich, Munich, Germany

Complementing the fast acquisition of coupled multiparametric MR signals, multiple studies have dealt with improving and accelerating parameter quantification using machine learning techniques. Here we synchronize dimension reduction and parameter inference and propose a hybrid neural network with a signal-encoding layer followed by a dual-pathway structure, for parameter prediction and recovery of the artifact-free signal evolution. We demonstrate our model with a 3D multiparametric MRI framework and show that it is capable of reliably inferring T1, T2 and PD estimates, while its trained latent-space projection facilitates efficient data compression already in k-space and thereby significantly accelerates image reconstruction.

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