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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Mekkaoui, Choukri [1] ; Metellus, Philippe [2] ; Kostis, William J. [1, 3] ; Martuzzi, Roberto [4] ; Pereira, Fabricio R. [5, 6] ; Beregi, Jean-Paul [5, 6] ; Reese, Timothy G. [1] ; Constable, Todd R. [7] ; Jackowski, Marcel P. [8]
Total Authors: 9
Affiliation:
[1] Harvard Univ, Sch Med, Dept Radiol, Massachusetts Gen Hosp, Athinoula Martinos Ctr Bio, Boston, MA 02115 - USA
[2] Hop Timone Adultes Marseille, Dept Neurosurg, Marseille, Bouches Du Rhon - France
[3] Rutgers Robert Wood Johnson Med Sch, New Brunswick, NJ - USA
[4] Ecole Polytech Fed Lausanne, Sch Life Sci, Brain Mind Inst, Lab Cognit Neurosci, Lausanne - Switzerland
[5] Univ Hosp Ctr Nimes, Dept Radiol, Nimes, Gard - France
[6] Res Team EA 2415, Nimes, Gard - France
[7] Yale Univ, Sch Med, Dept Diagnost Radiol, Magnet Resonance Res Ctr, New Haven, CT 06510 - USA
[8] Univ Sao Paulo, Dept Comp Sci, Inst Math & Stat, Sao Paulo - Brazil
Total Affiliations: 8
Document type: Journal article
Source: PLoS One; v. 11, n. 1 JAN 13 2016.
Web of Science Citations: 3
Abstract

Purpose 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). Materials and Methods 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). Results 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. Conclusions 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. (AU)

FAPESP's process: 15/01587-0 - Storage, modeling and analysis of dynamical systems for e-Science applications
Grantee:João Eduardo Ferreira
Support Opportunities: Research Grants - eScience and Data Science Program - Thematic Grants