Enhancing Fiber Orientation Distributions using convolutional Neural Networks (2008.05409v2)

Abstract: 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.