Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model.

<h4>Purpose</h4>Diffusion Tensor Imaging (DTI) is a powerful imaging technique that has led to improvements in the diagnosis and prognosis of cerebral lesions and neurosurgical guidance for tumor resection. Traditional tensor modeling, however, has difficulties in differentiating tumor-i...

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Main Authors: Choukri Mekkaoui, Philippe Metellus, William J Kostis, Roberto Martuzzi, Fabricio R Pereira, Jean-Paul Beregi, Timothy G Reese, Todd R Constable, Marcel P Jackowski
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2016-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0146693
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author Choukri Mekkaoui
Philippe Metellus
William J Kostis
Roberto Martuzzi
Fabricio R Pereira
Jean-Paul Beregi
Timothy G Reese
Todd R Constable
Marcel P Jackowski
author_facet Choukri Mekkaoui
Philippe Metellus
William J Kostis
Roberto Martuzzi
Fabricio R Pereira
Jean-Paul Beregi
Timothy G Reese
Todd R Constable
Marcel P Jackowski
author_sort Choukri Mekkaoui
collection DOAJ
description <h4>Purpose</h4>Diffusion Tensor Imaging (DTI) is a powerful imaging technique that has led to improvements in the diagnosis and prognosis of cerebral lesions and neurosurgical guidance for tumor resection. Traditional tensor modeling, however, has difficulties in differentiating tumor-infiltrated regions and peritumoral edema. Here, we describe the supertoroidal model, which incorporates an increase in surface genus and a continuum of toroidal shapes to improve upon the characterization of Glioblastoma multiforme (GBM).<h4>Materials and methods</h4>DTI brain datasets of 18 individuals with GBM and 18 normal subjects were acquired using a 3T scanner. A supertoroidal model of the diffusion tensor and two new diffusion tensor invariants, one to evaluate diffusivity, the toroidal volume (TV), and one to evaluate anisotropy, the toroidal curvature (TC), were applied and evaluated in the characterization of GBM brain tumors. TV and TC were compared with the mean diffusivity (MD) and fractional anisotropy (FA) indices inside the tumor, surrounding edema, as well as contralateral to the lesions, in the white matter (WM) and gray matter (GM).<h4>Results</h4>The supertoroidal model enhanced the borders between tumors and surrounding structures, refined the boundaries between WM and GM, and revealed the heterogeneity inherent to tumor-infiltrated tissue. Both MD and TV demonstrated high intensities in the tumor, with lower values in the surrounding edema, which in turn were higher than those of unaffected brain parenchyma. Both TC and FA were effective in revealing the structural degradation of WM tracts.<h4>Conclusions</h4>Our findings indicate that the supertoroidal model enables effective tensor visualization as well as quantitative scalar maps that improve the understanding of the underlying tissue structure properties. Hence, this approach has the potential to enhance diagnosis, preoperative planning, and intraoperative image guidance during surgical management of brain lesions.
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spelling doaj.art-e1a36ef90f2d45959cccdbb681dbad672022-12-21T18:24:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01111e014669310.1371/journal.pone.0146693Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model.Choukri MekkaouiPhilippe MetellusWilliam J KostisRoberto MartuzziFabricio R PereiraJean-Paul BeregiTimothy G ReeseTodd R ConstableMarcel P Jackowski<h4>Purpose</h4>Diffusion Tensor Imaging (DTI) is a powerful imaging technique that has led to improvements in the diagnosis and prognosis of cerebral lesions and neurosurgical guidance for tumor resection. Traditional tensor modeling, however, has difficulties in differentiating tumor-infiltrated regions and peritumoral edema. Here, we describe the supertoroidal model, which incorporates an increase in surface genus and a continuum of toroidal shapes to improve upon the characterization of Glioblastoma multiforme (GBM).<h4>Materials and methods</h4>DTI brain datasets of 18 individuals with GBM and 18 normal subjects were acquired using a 3T scanner. A supertoroidal model of the diffusion tensor and two new diffusion tensor invariants, one to evaluate diffusivity, the toroidal volume (TV), and one to evaluate anisotropy, the toroidal curvature (TC), were applied and evaluated in the characterization of GBM brain tumors. TV and TC were compared with the mean diffusivity (MD) and fractional anisotropy (FA) indices inside the tumor, surrounding edema, as well as contralateral to the lesions, in the white matter (WM) and gray matter (GM).<h4>Results</h4>The supertoroidal model enhanced the borders between tumors and surrounding structures, refined the boundaries between WM and GM, and revealed the heterogeneity inherent to tumor-infiltrated tissue. Both MD and TV demonstrated high intensities in the tumor, with lower values in the surrounding edema, which in turn were higher than those of unaffected brain parenchyma. Both TC and FA were effective in revealing the structural degradation of WM tracts.<h4>Conclusions</h4>Our findings indicate that the supertoroidal model enables effective tensor visualization as well as quantitative scalar maps that improve the understanding of the underlying tissue structure properties. Hence, this approach has the potential to enhance diagnosis, preoperative planning, and intraoperative image guidance during surgical management of brain lesions.https://doi.org/10.1371/journal.pone.0146693
spellingShingle Choukri Mekkaoui
Philippe Metellus
William J Kostis
Roberto Martuzzi
Fabricio R Pereira
Jean-Paul Beregi
Timothy G Reese
Todd R Constable
Marcel P Jackowski
Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model.
PLoS ONE
title Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model.
title_full Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model.
title_fullStr Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model.
title_full_unstemmed Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model.
title_short Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model.
title_sort diffusion tensor imaging in patients with glioblastoma multiforme using the supertoroidal model
url https://doi.org/10.1371/journal.pone.0146693
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