  High resolution diffusion MRI (dMRI) data is often constrained by limited
scanning time in clinical settings, thus restricting the use of downstream
analysis techniques that would otherwise be available. In this work we develop
a 3D recurrent convolutional neural network (RCNN) capable of super-resolving
dMRI volumes in the angular (q-space) domain. Our approach formulates the task
of angular super-resolution as a patch-wise regression using a 3D autoencoder
conditioned on target b-vectors. Within the network we use a convolutional long
short term memory (ConvLSTM) cell to model the relationship between q-space
samples. We compare model performance against a baseline spherical harmonic
interpolation and a 1D variant of the model architecture. We show that the 3D
model has the lowest error rates across different subsampling schemes and
b-values. The relative performance of the 3D RCNN is greatest in the very low
angular resolution domain. Code for this project is available at
https://github.com/m-lyon/dMRI-RCNN.

Image and Video Processing;Computer Vision and Pattern Recognition