Dealing with Gradient Nonlinearities for High-Performance Gradient Diffusion MRI - Application to the Human Connectome Project (HCP) Dataset

Motivation: Correction of gradient nonlinearity effects on diffusion-sensitization is important. However, not all software packages and/or diffusion models can incorporate spatially-varying bvals/bvecs information. Goal(s): Determine how significant nonlinearities are in the Human Connectome Project(HCP) dataset and investigate the feasibility of a spherical harmonics (SH)-based signal regeneration technique to directly incorporate nonlinearity effects and eliminate the need for additional information sharing. Approach: FA, MD and angular error maps from 200 subjects were compared using different formats of correction. Results: Nonlinearities are significant on HCP-dMRI data. The proposed SH-based signal regeneration approach allows the use of spatially invariant diffusion gradient tables with substantially reduced residual error. Impact: Diffusion models and software not designed to incorporate spatially-varying bvals/bvecs can now take advantage of gradient nonlinearity correction. A reprocessed version of HCP dMRI dataset will be made publicly available in this format, along with the conventional gradient deviation tensors.