This study assesses the feasibility of training a convolutional neural network (CNN) for IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting to diffusion-weighted (DW) data and evaluates its performance on a brain tumor (poorly differentiated adenocarcinoma) patient data directly acquired from clinical MR scanner. Comparisons were made with the results calculated from the non-linear least squares (NLLS) algorithm. More accurate and robust results were obtained by our CNN method, with processing speed several orders of magnitude faster than the reference method (from 5 min to 1 s).
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