Bridging the gap between constrained spherical deconvolution and diffusional variance decomposition via tensor-valued diffusion MRI

Abstract

Diffusion tensor imaging (DTI) is widely used to extract valuable tissue measurements and white matter (WM) fiber orientations, even though its lack of specificity is now well-known, especially for WM fiber crossings. Models such as constrained spherical deconvolution (CSD) take advantage of high angular resolution diffusion imaging (HARDI) data to compute fiber orientation distribution functions (fODF) and tackle the orientational part of the DTI limitations. Furthermore, the recent introduction of tensor-valued diffusion MRI allows for diffusional variance decomposition (DIVIDE), enabling the computation of measures more specific to microstructure than DTI measures, such as microscopic fractional anisotropy (μFA). Recent work on making CSD compatible with tensor-valued diffusion MRI data opens the door for methods combining CSD and DIVIDE to get both fODFs and microstructure measures. However, the impacts of such atypical data on fODF reconstruction with CSD are yet to be fully known and understood. In this work, we use simulated data to explore the effects of various combinations of diffusion encodings on the angular resolution of extracted fOFDs and on the versatility of CSD in multiple realistic situations. We also compare the combinations with regards to their performance at producing accurate and precise μFA with DIVIDE, and present an optimized 10 min protocol combining linear and spherical b-tensor encodings for both methods. We show that our proposed protocol enables the reconstruction of both fODFs and μFA on in vivo data.

Keywords: Constrained spherical deconvolution; Diffusional variance decomposition; Tensor-valued dMRI.