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

Overview of attention for article published in PLOS ONE, January 2016
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Article details
Title
Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model
Published in
PLOS ONE, January 2016
DOI 10.1371/journal.pone.0146693
Pubmed ID
Authors
Abstract

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). 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). 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. 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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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 11 20%
Researcher 11 20%
Student > Bachelor 4 7%
Other 3 6%
Student > Doctoral Student 3 6%
Other 11 20%
Unknown 11 20%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 15 28%
Neuroscience 10 19%
Engineering 5 9%
Agricultural and Biological Sciences 3 6%
Nursing and Health Professions 2 4%
Other 6 11%
Unknown 13 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 July 2016.
All research outputs
#13,453,089
of 22,840,638 outputs
Outputs from PLOS ONE
#107,533
of 194,876 outputs
Outputs of similar age
#190,336
of 395,522 outputs
Outputs of similar age from PLOS ONE
#2,428
of 4,991 outputs
Altmetric has tracked 22,840,638 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 194,876 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 395,522 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 4,991 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.