Intraoperative Real-Time Querying of White Matter Tracts During Frameless Stereotactic Neuronavigation

BACKGROUND: Brain surgery faces important challenges when trying to achieve maximum tumor resection while avoiding postoperative neurological deficits. OBJECTIVE: For surgeons to have optimal intraoperative information concerning white matter (WM) anatomy, we developed a platform that allows the intraoperative real-time querying of tractography data sets during frameless stereotactic neuronavigation. METHODS: Structural magnetic resonance imaging, functional magnetic resonance imaging, and diffusion tensor imaging were performed on 5 patients before they underwent lesion resection using neuronavigation. During the procedure, the tracked surgical tool tip position was transferred from the navigation system to the 3-dimensional Slicer software package, which used this position to seed the WM tracts around the tool tip location, rendering a geometric visualization of these tracts on the preoperative images previously loaded onto the navigation system. The clinical feasibility of this approach was evaluated in 5 cases of lesion resection. In addition, system performance was evaluated by measuring the latency between surgical tool tracking and visualization of the seeded WM tracts. RESULTS: Lesion resection was performed successfully in all 5 patients. The seeded WM tracts close to the lesion and other critical structures, as defined by the functional and structural images, were interactively visualized during the intervention to determine their spatial relationships relative to the lesion and critical cortical areas. Latency between tracking and visualization of tracts was less than a second for a fiducial radius size of 4 to 5 mm. CONCLUSION: Interactive tractography can provide an intuitive way to inspect critical WM tracts in the vicinity of the surgical region, allowing the surgeon to have increased intraoperative WM information to execute the planned surgical resection

Diffusion directions imaging (high resolution reconstruction of white matter fascicles from low angular resolution diffusion MRI)

L'objectif de cette thèse est de fournir une chaine de traitement complète pour la reconstruction des faisceaux de la matière blanche à partir d'images pondérées en diffusion caractérisées par une faible résolution angulaire. Cela implique (i) d'inférer en chaque voxel un modèle de diffusion à partir des images de diffusion et (ii) d'accomplir une ''tractographie", i.e., la reconstruction des faisceaux à partir de ces modèles locaux. Notre contribution en modélisation de la diffusion est une nouvelle distribution statistique dont les propriétés sont étudiées en détail. Nous modélisons le phénomène de diffusion par un mélange de telles distributions incluant un outil de sélection de modèle destiné à estimer le nombre de composantes du mélange. Nous montrons que le modèle peut être correctement estimé à partir d'images de diffusion ''single-shell" à faible résolution angulaire et qu'il fournit des biomarqueurs spécifiques pour l'étude des tumeurs. Notre contribution en tractographie est un algorithme qui approxime la distribution des faisceaux émanant d'un voxel donné. Pour cela, nous élaborons un filtre particulaire mieux adapté aux distributions multi-modales que les filtres traditionnels. Pour démontrer l'applicabilité de nos outils en usage clinique, nous avons participé aux trois éditions du MICCAI DTI Tractography challenge visant à reconstruire le faisceau cortico-spinal à partir d'images de diffusion ''single-shell" à faibles résolutions angulaire et spatiale. Les résultats montrent que nos outils permettent de reconstruire toute l'étendue de ce faisceau.The objective of this thesis is to provide a complete pipeline that achieves an accurate reconstruction of the white matter fascicles using clinical diffusion images characterized by a low angular resolution. This involves (i) a diffusion model inferred in each voxel from the diffusion images and (ii) a tractography algorithm fed with these local models to perform the actual reconstruction of fascicles. Our contribution in diffusion modeling is a new statistical distribution, the properties of which are extensively studied. We model the diffusion as a mixture of such distributions, for which we design a model selection tool that estimates the number of mixture components. We show that the model can be accurately estimated from single shell low angular resolution diffusion images and that it provides specific biomarkers for studying tumors. Our contribution in tractography is an algorithm that approximates the distribution of fascicles emanating from a seed voxel. We achieve that by means of a particle filter better adapted to multi-modal distributions than the traditional filters. To demonstrate the clinical applicability of our tools, we participated to all three editions of the MICCAI DTI Tractography challenge aiming at reconstructing the cortico-spinal tract from single-shell low angular and low spatial resolution diffusion images. Results show that our pipeline provides a reconstruction of the full extent of the CST.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Combinatorial fiber-tracking of the human brain

This paper presents a novel fiber-tracking algorithm, termed combinatorial tracking, which uses stochastic process modeling and global optimization algorithm for tractography. Combinatorial tracking is a probabilistic tracking algorithm that transforms the brain's white matter into a grid in which each voxel has 26 weighted connections with adjacent voxels. We model the random walk on this graph using a Markov Chain model and suggest two approaches for fiber reconstruction. In the first approach, we find the most probable paths between two voxels with prior connectivity knowledge using a shortest path algorithm. In the second approach, the all-pairs mean first passage time (MFPT) matrix M (or hitting time as referred to in the Spectral Graph theory literature) is calculated analytically. We suggest that M can be interpreted as a global connectivity matrix and use it for fiber reconstruction. We also introduce a simulation framework that can be used to calculate specific elements of the matrix M, and show how it can be employed to select the target of a fiber in a high resolution diffusion tensor imaging (DTI) dataset. Because any source and any target voxel can be connected, combinatorial tracking permits true connectivity analysis, overcoming the limitations of conventional tracking, especially stopping criteria (e.g. low FA). We applied combinatorial tracking to a standard DTI dataset and demonstrated the reconstruction of the cortico-thalamic pathway, the pyramidal decussation, and the medial cerebellar peduncle fibers. While the DTI ellipsoid served as input for the algorithms, any diffusion imaging based orientation density function (ODF) can be used. This framework can potentially turn diffusion imaging tractography into a true connectivity measure