A novel neural network method predicts non-acquired brain diffusion MRI to promote HARDI in children

Motivation: High angular resolution diffusion imaging has great potential but is time-consuming so is limited in pediatric clinical studies. Goal(s): To assess the utility of novel deep learning techniques for predicting non-acquired brain diffusion MRI for equivalent HARDI analyses. Approach: A multilayer perceptron (MLP) and convolutional neural network (CNN) were trained to predict b=2000s/mm2 data from b=750s/mm2 data. The neurite orientation dispersion and density index (NODDI) outcomes were computed with quality evaluated with PSNR and SSIM. Results: Both deep learning methods achieved the goal but the CNN outperformed the MLP. Impact: By applying a competitive neural network method, high angular resolution diffusion imaging can be made possible for the pediatric population in a typical clinical setting based only on half of the data typically required.