Optic radiation fiber tractography in glioma patients based on high angular resolution diffusion imaging with compressed sensing compared with diffusion tensor imaging - initial experience.

Objective: Up to now, fiber tractography in the clinical routine is mostly based on diffusion tensor imaging (DTI). However, there are known drawbacks in the resolution of crossing or kissing fibers and in the vicinity of a tumor or edema. These restrictions can be overcome by tractography based on High Angular Resolution Diffusion Imaging (HARDI) which in turn requires larger numbers of gradients resulting in longer acquisition times.

Reconstruction of white matter tracts via repeated deterministic streamline tracking--initial experience.

Diffusion Tensor Imaging (DTI) and fiber tractography are established methods to reconstruct major white matter tracts in the human brain in-vivo. Particularly in the context of neurosurgical procedures, reliable information about the course of fiber bundles is important to minimize postoperative deficits while maximizing the tumor resection volume. Since routinely used deterministic streamline tractography approaches often underestimate the spatial extent of white matter tracts, a novel approach to improve fiber segmentation is presented here, considering clinical time constraints.

Fiber tractography based on diffusion tensor imaging compared with high-angular-resolution diffusion imaging with compressed sensing: initial experience.

Background: The most frequently used method for fiber tractography based on diffusion tensor imaging (DTI) is associated with restrictions in the resolution of crossing or kissing fibers and in the vicinity of tumor or edema. Tractography based on high-angular-resolution diffusion imaging (HARDI) is capable of overcoming this restriction. With compressed sensing (CS) techniques, HARDI acquisitions with a smaller number of directional measurements can be used, thus enabling the use of HARDI-based fiber tractography in clinical practice.

Atlas-based fiber reconstruction from diffusion tensor MRI data.

Purpose: Develop a neural fiber reconstruction method based on diffusion tensor imaging, which is not as sensitive to user-defined regions of interest as streamline tractography.

Methods: A simulated annealing approach is employed to find a non-rigid transformation to map a fiber bundle from a fiber atlas to another fiber bundle, which minimizes a specific energy functional. The energy functional describes how well the transformed fiber bundle fits the patient's diffusion tensor data.

Brain shift compensation and neurosurgical image fusion using intraoperative MRI: current status and future challenges.

Navigation systems are commonly used in neurosurgical operating theaters. Generally, they either rely on the use of preoperative or intraoperative image data. Using preoperative image data, the phenomenon of brain shift contributes most to errors, in addition to various other sources of decreased reliability, such as image-related errors or registration inaccuracy.

DTI segmentation via the combined analysis of connectivity maps and tensor distances.

We describe a novel approach to extract the neural tracts of interest from a diffusion tensor image (DTI). Compared to standard streamline tractography, existing probabilistic methods are able to capture fiber paths that deviate from the main tensor diffusion directions. At the same time, tensor clustering methods are able to more precisely delimit the border of the bundle.

Intraoperative visualization of fiber tracking based reconstruction of language pathways in glioma surgery.

Background: For neuroepithelial tumors, the surgical goal is maximum resection with preservation of neurological function. This is contributed to by intraoperative magnetic resonance imaging (iMRI) combined with multimodal navigation.

Objective: We evaluated the contribution of diffusion tensor imaging (DTI)-based fiber tracking of language pathways with 2 different algorithms (tensor deflection, connectivity analysis [CA]) integrated in the navigation on the surgical outcome.

Segmentation of fiber tracts based on an accuracy analysis on diffusion tensor software phantoms.

Due to its unique sensitivity to tissue microstructure, one of the primary applications of diffusion-weighted magnetic resonance imaging is the reconstruction of neural fiber pathways by means of fiber-tracking algorithms. In this work, we make use of realistic diffusion-tensor software phantoms in order to carry out an analysis of the precision of streamline tractography by systematically varying certain properties of the simulated image data (noise, tensor anisotropy, and image resolution) as well as certain fiber-tracking parameters (number of seed points and step length). Building upon the gained knowledge about the precision of the analyzed fiber-tracking algorithm, we proceed by suggesting a fuzzy segmentation algorithm for diffusion tensor images which better estimates the precise spatial extent of a tracked fiber bundle.

Boundary estimation of fiber bundles derived from diffusion tensor images.

Purpose: Diffusion tensor imaging (DTI) is a non-invasive imaging technique that allows estimating the location of white matter tracts based on the measurement of water diffusion properties. Using DTI data, the fiber bundle boundary can be determined to gain information about eloquent structures, which is of major interest for neurosurgical interventions. In this paper, a novel approach for boundary estimation is presented.