Beyond the resolution limit: Diffusion parameter estimation in partial volume

Abstract

Diffusion MRI is a frequently-used imaging modality that can infer microstructural properties of tissue,down to the scale of microns. For single-compartment models,such as the diffusion tensor (DT),the model interpretation depends on voxels having homogeneous composition. This limitation makes it difficult to measure diffusion parameters for small structures such as the fornix in the brain,because of partial volume. In this work,we use a segmentation from a structural scan to calculate the tissue composition for each diffusion voxel. We model the measured diffusion signal as a linear combination of signals from each of the tissues present in the voxel,and fit parameters on a per-region basis by optimising over all diffusion data simultaneously. We test the proposed method by using diffusion data from the Human Connectome Project (HCP). We down sample the HCP data,and show that our method returns parameter estimates that are closer to the higher solution ground truths than for classical methods. We show that our method allows accurate estimation of diffusion parameters for regions with partial volume. Finally,we apply the method to compare diffusion in the fornix for adults born extremely preterm and matched controls.