Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model

Abstract

Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the valid connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.

An afferent white matter pathway from the pulvinar to the amygdala facilitates fear recognition

“…This method takes a Bayesian approach to reconstructing a full-brain fibre configuration using a generative signal model to best explain the underlying data. It is less sensitive to noise that may accumulate for longer distance tracts in other "local" tractography methods throughout their stepwise approach 51,52 . Hence, for comparison, we computed probabilistic tractography between our regions of interest 53 .…”

“…As opposed to local streamline tracking, global tractography accounts for the spatial continuity of fibres and thus is better able to discriminate crossing and fanning fibre geometries 51 . Furthermore, because the simultaneously-reconstructed fibre configurations are optimised with respect to the data at hand, the density of the final tractogram quantitatively represents the apparent fibre density (AFD; i.e.…”

Convexity-constrained and nonnegativity-constrained spherical factorization in diffusion-weighted imaging

“…Current literature therefore relies on tissue segmentation of T 1 -weighted images (T1) to define GM and CSF kernels, which requires the T1 to be aligned to the DWI data (Jeurissen et al, 2014). As this is rarely the case in practice, direct DWI tissue segmentation methods have been introduced independently and simultaneously, based on sparsity-constrained NMF (Jeurissen et al, 2015) or convexity-constrained NMF (Christiaens et al, 2015b, Appendix A) of the isotropic mean DWI signal per shell. These methods circumvent T1 requirement and are thus applicable in any reference frame without external input, but still rely on the diffusion tensor model for reorienting the DWI data in each single-fibre voxel.…”

A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals

“…Only diffusion-weighted MRI (DW-MRI) can provide a unique, non-invasive technique to study the microscopic structure of brain white matter (WM) in vivo [2–4]. DW-MRI provides valuable information about the fiber architecture of tissue by measuring the diffusion of water in three-dimensional space [5, 6].…”

“…While the computational cost and intractable computation will arise when the models are more sophisticated. To make full use of spatially constraints of brain fibers, many global tractography methods considered PVEs [4, 31]have been proposed in the last two years. But there are always many disadvantages, including computing space occupied, convergence property, sub-optimal solution and so on.…”