Tractography informed by anatomical and microstructure priors

White matter tractography is a highly promising method for assessing the trajectories of nerve fibers in the brain, based on diffusion MRI (dMRI) [1,2]. This technique utilizes the directionality of diffusion of water molecules in brain tissue to estimate neuronal fiber orientation [3]. This process allows us to delineate white matter fiber pathways, offering unprecedented insights into the structural connections within the human brain. They hold enormous potential for studying brain anatomy, development, and function [3]. Furthermore, tractography has demonstrated its substantial worth in the field of neurosurgery, playing a pivotal role in surgical planning, particularly in the preservation of critical white matter pathways during brain resections [4]. Despite advancements in dMRI acquisition and tracking methods tractography continues to grapple with certain limitations. Recent studies reported the existence of a significant number of connections that remain undetected by tractography, resulting in false negatives [5]. This issue poses a critical challenge, particularly in applications like surgical planning. Furthermore, the outcomes of other studies indicate that state-of-the-art tractography algorithms produce substantial numbers of false positives as well [6]. This drawback hampers the accurate exploration of network properties within the brain’s connectome [7]. In this thesis, our aim was to propose innovative methods for improving fiber estimation.

To overpass that, new approaches have been proposed that include anatomical priors to guide the tractography algorithm. This is especially useful in complex white matter regions, or in situations where the quality of the data is not sufficient to accurately estimate the orientation distribution function (ODF) [7]. In the Empenn team, we recently developed methods for creating and combining anatomical priors using Riemannian geometry, applicable to any ODF-based tractography algorithms. Complementary to diffusion MRI, T2 relaxometry is a magnetic resonance technique providing tissue-specific signals which can be used to estimate their intra-voxel volume fraction [8]. This provides microstructural information complementary to diffusion MRI, in particular to characterize the myelin compartment volume fraction, to which diffusion MRI is blind. This, combined with diffusion MRI, can improve the estimation of brain connectivity and characterization of white matter alterations along the tracts [9].

The proposed method will be based on the track orientation distribution (TOD) [7] from an atlas of segmented fiber bundles and incorporates it during the tracking process, using a Riemannian framework. From this approach, this PhD will aim to 1/ improve a priori estimation using microstructural features to guide tractography, proposed during the PhD thesis of Thomas Durantel [10], by taking into account the variability of track orientation and the TOD estimation; and 2/ Incorporate of new anatomical a priori - fiber bundle atlas, microstructural information from relaxometry or diffusion imaging along known, manually delineated fibers. The developed approach will be tested on a cohort of patients suffering from depression, with the aim of better estimating the microstructure and thus better understanding the neuronal modifications caused by this disease.