Estimation of Parameters from Sparsely Sampled in-vivo Magnetization Transfer Data Using Artificial Neural Networks

Henrik Marschner¹, Dirk K. Mller¹, Andr Pampel¹, Jane Neumann¹, Harald E. Mller¹

We examine whether Artificial Neural Networks (ANN) can be trained to estimate MT parameter sets from sparsely sampled in-vivo MT data. ANNs were trained using densely sampled MT data from healthy volunteers and the MT parameters obtained using conventional fitting as input. ANNs were used to extract MT parameters from sparsely sampled data. The obtained parameters were compared with those that come out using the conventional method. It is shown that parameters obtained with both methods are highly correlated (R>0.97). Once ANNs are trained subsequent measurements of other individuals and parameter estimation can be notably accelerated.