  Accurate local fiber orientation distribution (FOD) modeling based on
diffusion magnetic resonance imaging (dMRI) capable of resolving complex fiber
configurations benefits from specific acquisition protocols that sample a high
number of gradient directions (b-vecs), a high maximum b-value(b-vals), and
multiple b-values (multi-shell). However, acquisition time is limited in a
clinical setting and commercial scanners may not provide such dMRI sequences.
Therefore, dMRI is often acquired as single-shell (single b-value). In this
work, we learn improved FODs for commercially acquired MRI. We evaluate
patch-based 3D convolutional neural networks (CNNs)on their ability to regress
multi-shell FOD representations from single-shell representations, where the
representation is a spherical harmonics obtained from constrained spherical
deconvolution (CSD) to model FODs. We evaluate U-Net and HighResNet 3D CNN
architectures on data from the Human Connectome Project and an in-house
dataset. We evaluate how well each CNN model can resolve local fiber
orientation 1) when training and testing on datasets with the same dMRI
acquisition protocol; 2) when testing on a dataset with a different dMRI
acquisition protocol than used to train the CNN models; and 3) when testing on
a dataset with a fewer number of gradient directions than used to train the CNN
models. Our approach may enable robust CSD model estimation on single-shell
dMRI acquisition protocols with few gradient directions, reducing acquisition
times, facilitating translation of improved FOD estimation to time-limited
clinical environments.
