High Angular Resolution Diffusion Imaging (HARDI) is a promising method for the analysis of microstructural changes. However, HARDI acquisition is time-consuming and therefore impractical in clinical settings. We developed 2 neural networks for predicting non-acquired diffusion datasets based on diffusion MRI: Multi-layer Perceptron (MLP) and Convolutional Neural Network (CNN). Through systemic training and evaluation with healthy public data and local MS patient MRI, we found that both the MLP and CNN models could predict high b-value from low b-value data that allowed the assessment of Neurite Orientation Dispersion and Density Imaging (NODDI) outcomes. Neural networks can make NODDI clinically viable.