Data from: Diffusion tensor imaging in patients with glioblastoma multiforme using the supertoroidal model

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.