scholarly journals Guided Diffusion Tensor Tractography with GTRACT: A Validation Study

2005 ◽  
Author(s):  
Vincent Magnotta
Keyword(s):  
Open Source ◽  
Validation Study ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Diffusion Tensor Tractography ◽  
Tracking Algorithm ◽  
King's College ◽  
Cross Platform ◽  
Digital Phantom ◽  
Better Than

A novel fiber tracking algorithm (GTRACT) was developed to enhance tracking through ambiguous regions where cross fibers, fiber merging or fanning may be occurring. The software was developed using several cross platform open-source toolkits (ITK, VTK, and FLTK). The algorithm was evaluated using a freely available digital phantom dataset provided by King’s College London. The results show that the GTRACT algorithm performed significantly better than standard streamline approaches and is less affected by noise.

Magnetic Resonance Imaging Diffusion Tensor Tractography: Evaluation of Anatomic Accuracy of Different Fiber Tracking Software Packages

2014 ◽  
Vol 81 (1) ◽  
pp. 144-150 ◽  
Author(s):  
Guenther C. Feigl ◽  
Wolfgang Hiergeist ◽  
Claudia Fellner ◽  
Karl-Michael M. Schebesch ◽  
Christian Doenitz ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Diffusion Tensor Tractography ◽  
Resonance Imaging ◽  
Software Packages

scholarly journals A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

2006 ◽  
Author(s):  
F. Prados ◽  
A. Bardera ◽  
M. Sbert ◽  
I. Boada ◽  
M. Feixas
Keyword(s):  
Monte Carlo ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Tracking Algorithm ◽  
Diffusion Tensor Mri

Brain Nerve Fiber Tracking Algorithm Based on Improved Optical Flow in Combination of Bayesian Prior Constraints

2020 ◽  
Vol 10 (2) ◽  
pp. 452-457
Author(s):  
Shen Jian ◽  
Chen Huan ◽  
Zuo Jianjian ◽  
Pan Xuming
Keyword(s):  
Optical Flow ◽  
Nerve Fiber ◽  
Diffusion Tensor ◽  
Three Dimensional ◽  
Fiber Tracking ◽  
Nerve Fibers ◽  
Tracking Algorithm ◽  
Brain Diseases ◽  
Field Estimation ◽  
The Brain

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) can track the brain nerve fiber and reconstruct non-invasively the three-dimensional image by tracing the local tensor orientation. The commonly used tracking method is usually based on the local diffusion information and insufficient to consider the geometrical structure and fractional anisotropy which is constrained by anatomical structure and physiological function of human. Therefore, a novel brain nerve fiber tracking algorithm based on Bayesian optical-flow constrained framework is proposed. The construction of energy function is the core step of global optical flow field estimation technology. In this paper, data fidelity constraint, prior constraint, penalty function and weight factor are introduced to construct Bayesian constraint function. The fiber trend model is displayed intuitively to obtain the structure and direction of the inner nerve fibers of the brain, which can better assist in the diagnosis and treatment of clinical brain diseases, and lay a foundation for subsequent brain tissue research.

scholarly journals CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Evert van Aart ◽  
Neda Sepasian ◽  
Andrei Jalba ◽  
Anna Vilanova
Keyword(s):  
Diffusion Tensor ◽  
Graphics Processing Unit ◽  
Fibrous Tissue ◽  
Real Data ◽  
Fiber Tracking ◽  
Tracking Algorithm ◽  
Processing Unit ◽  
Graphics Processing ◽  
Speedup Factor ◽  
Diffusion Tensor Imaging Dti

Diffusion Tensor Imaging (DTI) allows to noninvasively measure the diffusion of water in fibrous tissue. By reconstructing the fibers from DTI data using a fiber-tracking algorithm, we can deduce the structure of the tissue. In this paper, we outline an approach to accelerating such a fiber-tracking algorithm using a Graphics Processing Unit (GPU). This algorithm, which is based on the calculation of geodesics, has shown promising results for both synthetic and real data, but is limited in its applicability by its high computational requirements. We present a solution which uses the parallelism offered by modern GPUs, in combination with the CUDA platform by NVIDIA, to significantly reduce the execution time of the fiber-tracking algorithm. Compared to a multithreaded CPU implementation of the same algorithm, our GPU mapping achieves a speedup factor of up to 40 times.

Accuracy of diffusion tensor magnetic resonance imaging tractography assessed using intraoperative subcortical stimulation mapping and magnetic source imaging

2007 ◽  
Vol 107 (3) ◽  
pp. 488-494 ◽  
Author(s):  
Jeffrey I. Berman ◽  
Mitchel S. Berger ◽  
Sungwon Chung ◽  
Srikantan S. Nagarajan ◽  
Roland G. Henry
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Bipolar Electrode ◽  
Source Imaging ◽  
Magnetic Source Imaging ◽  
Motor Pathway ◽  
Magnetic Source ◽  
Subcortical Stimulation

Object Resecting brain tumors involves the risk of damaging the descending motor pathway. Diffusion tensor (DT)–imaged fiber tracking is a noninvasive magnetic resonance (MR) technique that can delineate the subcortical course of the motor pathway. The goal of this study was to use intraoperative subcortical stimulation mapping of the motor tract and magnetic source imaging to validate the utility of DT-imaged fiber tracking as a tool for presurgical planning. Methods Diffusion tensor-imaged fiber tracks of the motor tract were generated preoperatively in nine patients with gliomas. A mask of the resultant fiber tracks was overlaid on high-resolution T1- and T2-weighted anatomical MR images and used for stereotactic surgical navigation. Magnetic source imaging was performed in seven of the patients to identify functional somatosensory cortices. During resection, subcortical stimulation mapping of the motor pathway was performed within the white matter using a bipolar electrode. Results A total of 16 subcortical motor stimulations were stereotactically identified in nine patients. The mean distance between the stimulation sites and the DT-imaged fiber tracks was 8.7 ±3.1 mm (±standard deviation). The measured distance between subcortical stimulation sites and DT-imaged fiber tracks combines tracking technique errors and all errors encountered with stereotactic navigation. Conclusions Fiber tracks delineated using DT imaging can be used to identify the motor tract in deep white matter and define a safety margin around the tract.

scholarly journals Pictorial essay: How co-registered BOLD fMRI and DTI data can improve diffusion tensor tractography

2021 ◽  
pp. 101258
Author(s):  
Bojan D. Petrovic ◽  
Doug Burman ◽  
Shakeel Chowdhry ◽  
Julian E. Bailes ◽  
Joel Meyer
Keyword(s):  
Diffusion Tensor ◽  
Diffusion Tensor Tractography ◽  
Bold Fmri ◽  
Pictorial Essay
Sign in / Sign up

Export Citation Format

Share Document