scholarly journals Optic Radiation Fiber Tractography in Glioma Patients Based on High Angular Resolution Diffusion Imaging with Compressed Sensing Compared with Diffusion Tensor Imaging - Initial Experience

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e70973 ◽  
Author(s):  
Daniela Kuhnt ◽  
Miriam H. A. Bauer ◽  
Jens Sommer ◽  
Dorit Merhof ◽  
Christopher Nimsky
Keyword(s):  
Diffusion Tensor Imaging ◽  
Compressed Sensing ◽  
Angular Resolution ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
High Angular Resolution ◽  
Initial Experience ◽  
Optic Radiation ◽  
Fiber Tractography

scholarly journals Dimensionality Reduction of Diffusion MRI Measures for Improved Tractometry of the Human Brain

2019 ◽  
Author(s):  
Maxime Chamberland ◽  
Erika P. Raven ◽  
Sila Genc ◽  
Kate Duffy ◽  
Maxime Descoteaux ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Angular Resolution ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
High Angular Resolution ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Tissue Microstructure ◽  
Age Related ◽  
Diffusion Tensor Imaging Dti

AbstractVarious diffusion MRI measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different diffusion measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. In this work, we first demonstrate redundancies in the amount of information captured by 10 diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) measures. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in commonly-used DTI and HARDI measures profiled along 22 brain pathways extracted from typically developing children aged 8 - 18 years (n = 36). The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. Our results also suggest that HARDI measures are more sensitive at detecting age-related changes in tissue microstructure than DTI measures.

scholarly journals Spatially Regularized Compressed Sensing for High Angular Resolution Diffusion Imaging

2011 ◽  
Vol 30 (5) ◽  
pp. 1100-1115 ◽  
Author(s):  
Oleg Michailovich ◽  
Yogesh Rathi ◽  
Sudipto Dolui
Keyword(s):  
Compressed Sensing ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
High Angular Resolution

scholarly journals Deep Learning Improves Pre-Surgical White Matter Visualization in Glioma Patients

2020 ◽  
Author(s):  
Sandip S Panesar ◽  
Vishwesh Nath ◽  
Sudhir K Pathak ◽  
Walter Schneider ◽  
Bennett A. Landman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Angular Resolution ◽  
Diffusion Tensor ◽  
Learning Algorithm ◽  
Real Life ◽  
High Angular Resolution ◽  
Fiber Tractography ◽  
Deep Learning Algorithm ◽  
Learning Technique

BackgroundDiffusion tensor imaging (DTI) is a commonly utilized pre-surgical tractography technique. Despite widespread use, DTI suffers from several critical limitations. These include an inability to replicate crossing fibers and a low angular-resolution, affecting quality of results. More advanced, non-tensor methods have been devised to address DTI’s shortcomings, but they remain clinically underutilized due to lack of awareness, logistical and cost factors.ObjectiveNath et al. (2020) described a method of transforming DTI data into non-tensor high-resolution data, suitable for tractography, using a deep learning technique. This study aims to apply this technique to real-life tumor cases.MethodsThe deep learning model utilizes a residual convolutional neural network architecture to yield a spherical harmonic representation of the diffusion-weighted MR signal. The model was trained using normal subject data. DTI data from clinical cases were utilized for testing: Subject 1 had a right-sided anaplastic oligodendroglioma. Subject 2 had a right-sided glioblastoma. We conducted deterministic fiber tractography on both the DTI data and the post-processed deep learning algorithm datasets.ResultsGenerally, all tracts generated using the deep learning algorithm dataset were qualitatively and quantitatively (in terms of tract volume) superior than those created with DTI data. This was true for both test cases.ConclusionsWe successfully utilized a deep learning technique to convert standard DTI data into data capable of high-angular resolution tractography. This method dispenses with specialized hardware or dedicated acquisition protocols. It presents an economical and logistically feasible method for increasing access to high definition tractography imaging clinically.

Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing

2020 ◽  
Vol 85 (3) ◽  
pp. 1397-1413
Author(s):  
Maarten Naeyaert ◽  
Jan Aelterman ◽  
Johan Van Audekerke ◽  
Vladimir Golkov ◽  
Daniel Cremers ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Angular Resolution ◽  
Spin Echo ◽  
Diffusion Imaging ◽  
Fast Spin Echo ◽  
High Angular Resolution ◽  
Isotropic Resolution ◽  
Fast Spin

scholarly journals Identification of optic radiation in-vivo using diffusion tensor imaging and fiber tractography

2010 ◽  
Vol 8 (6) ◽  
pp. 958-958 ◽  
Author(s):  
A. Sherbondy ◽  
R. Dougherty ◽  
B. Wandell
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Optic Radiation ◽  
Fiber Tractography

scholarly journals Meyer’s Loop Anatomy Demonstrated Using Diffusion Tensor MR Imaging and Fiber Tractography at 3T

2014 ◽  
Vol 60 (5) ◽  
pp. 215-222 ◽  
Author(s):  
Cristina Goga ◽  
Zeynep Firat ◽  
Klara Brinzaniuc ◽  
Is Florian
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Fiber Tractography ◽  
Brain Images ◽  
3 Dimensional ◽  
Software Release ◽  
Magnetic Resonance Imaging Mri ◽  
Meyer’S Loop

Abstract Objective: The ultimate anatomy of the Meyer’s loop continues to elude us. Diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) may be able to demonstrate, in vivo, the anatomy of the complex network of white matter fibers surrounding the Meyer’s loop and the optic radiations. This study aims at exploring the anatomy of the Meyer’s loop by using DTI and fiber tractography. Methods: Ten healthy subjects underwent magnetic resonance imaging (MRI) with DTI at 3 T. Using a region-of-interest (ROI) based diffusion tensor imaging and fiber tracking software (Release 2.6, Achieva, Philips), sequential ROI were placed to reconstruct visual fibers and neighboring projection fibers involved in the formation of Meyer’s loop. The 3-dimensional (3D) reconstructed fibers were visualized by superimposition on 3-planar MRI brain images to enhance their precise anatomical localization and relationship with other anatomical structures. Results: Several projection fiber including the optic radiation, occipitopontine/parietopontine fibers and posterior thalamic peduncle participated in the formation of Meyer’s loop. Two patterns of angulation of the Meyer’s loop were found. Conclusions: DTI with DTT provides a complimentary, in vivo, method to study the details of the anatomy of the Meyer’s loop.

scholarly journals Diffusion tensor imaging and fiber tractography of the median nerve at 1.5T: optimization of b value

2008 ◽  
Vol 38 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Gustav Andreisek ◽  
Lawrence M. White ◽  
Andrea Kassner ◽  
George Tomlinson ◽  
Marshall S. Sussman
Keyword(s):  
Diffusion Tensor Imaging ◽  
Median Nerve ◽  
Diffusion Tensor ◽  
B Value ◽  
Fiber Tractography
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