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Table of Contents    
Year : 2012  |  Volume : 60  |  Issue : 3  |  Page : 355-357

Glioma progression as revealed by diffusion tensor metrics

Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India

Date of Submission13-Apr-2012
Date of Decision16-Apr-2012
Date of Acceptance09-May-2012
Date of Web Publication14-Jul-2012

Correspondence Address:
Chandrasekharan Kesavadas
Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0028-3886.98543

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How to cite this article:
Muthusami P, Basti RS, Thomas B, Kapilamoorthy TR, Kesavadas C. Glioma progression as revealed by diffusion tensor metrics. Neurol India 2012;60:355-7

How to cite this URL:
Muthusami P, Basti RS, Thomas B, Kapilamoorthy TR, Kesavadas C. Glioma progression as revealed by diffusion tensor metrics. Neurol India [serial online] 2012 [cited 2021 Oct 17];60:355-7. Available from:


Mathematically modeling the diffusion tensor (DT) yields its eigenvectors and their eigenvalues, from which a number of metrics can be derived. [1] Of these, the two most commonly described in literature are the mean diffusivity (MD or Dav) and fractional anisotropy [FA]. [2] An idea of tissue microstructure can be formulated from knowing how water diffusion occurs in a given voxel and how this is distributed among various voxels. This has recently received much interest in the field of tumor imaging, where such information can impact treatment and outcomes. A number of other metrics extracted from the DT have been described, [3] each representing a different facet of the directionality information present in the tensor. These include p, q, and L, which represent the components of pure isotropy, pure anisotropy, and the total magnitude of the diffusion tensor, respectively. FA can be separated into its linear, planar, and spherical components, designated CL, CP, and CS respectively, which give a further understanding of the distribution of the tensor ellipsoids and their orientation within selected voxels.

A 39-year-old male presented with two episodes of right focal seizures. Neurological evaluation revealed no deficits. Brain magnetic resonance imaging (MRI) [[Figure 1], top row] showed left temporo-insular and deep gray FLAIR hyperintensities with inhomogeneous contrast enhancement. MR spectroscopy showed no significant alteration of spectra in and around the lesion. A diagnosis of focal encephalitis was considered in view of the clinical presentation, gray matter involvement, and elevated cerebrospinal fluid protein. However, in view of a focus of hyperperfusion, a low-grade glioma was also considered, and follow-up imaging after 2 months was suggested [[Figure 1], middle row]. Second MRI done when the patient was asymptomatic showed increase in the size of the lesion and the hyperperfused focus. The patient refused a biopsy of the lesion. Six months after his initial presentation, the patient returned with headache and blurring of vision. MRI [[Figure 1], bottom row] showed an aggressive lesion with necrosis, hemorrhages, patchy enhancement, and increased perfusion. The patient underwent gross total decompression of the tumor. Histopathology confirmed a glioblastoma multiforme.
Figure 1: Left fronto-insular glioma, progressing from low grade to high grade, followed by DT metrics. MRI of the patient done at three time periods is illustrated in three separate rows. Each column displays axial sections in the following order: T2-weighted, contrast-enhanced T1-weighted, relative CBV, MR spectroscopy, and FA map. The gradual increase in extent of the lesion can be appreciated along with increasing mass effect. The initial focus of hyperperfusion is seen to increase significantly while there is increase in the Cho/Cr and decrease in NAA/Cr ratios. FA maps reveal an initial displacement of white matter tracts in the forceps minor with progressive decrease in anisotropy in this region, suggesting possible infiltration

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We retrospectively analyzed the sequence of MRIs with DT metrics. DT maps including trace-weighted, ADC, FA, CL, CP, CS, and individual eigenvector maps were generated. Regions of interest (ROIs) were drawn in three regions: The hyperperfused focus within the tumor, the peritumoral white matter (PTWM), and the mirror-image contralateral white matter (WM). The DT metrics were calculated from ROIs in their respective maps, and p, q, and L were then calculated. FA and MD values were normalized using values in the contralateral WM, yielding FA-r and MD-r, respectively. The graphical representations of the change in DT metrics within the tumor and in PTWM [Figure 2] depict a temporal decrease in MD-r and larger increase of FA-r within the tumor. There was a large reduction in the spherical component of anisotropy, resulting from reduced directions of diffusion in a disorganized microstructure. Findings in the PTWM were more striking than within the tumor. While there was a large increase in the MD-r, there was a large decrease in the FA-r. Interpreting the above information with knowledge of p, q, and L data proved more promising. A large increase in L with a smaller decrease in q in the PTWM suggests that the former is the cause of the large FA decrease. Along with a marked increase in p, these suggest infiltration in the PTWM. This is also corroborated by the large increase in CS and lesser decrease in CL. Price et al. [4] in their study on 35 gliomas had also noted a marked increase in p and lesser decrease in q in infiltrated PTWM. This case depicts the potential that the gamut of DT metrics has in glioma imaging, including the follow-up of low-grade gliomas, in prognostication and treatment follow-up.
Figure 2: Left fronto-insular glioma, progressing from low grade to high grade, followed by DT metrics. Graphical representation of the change in the metrics within the tumor [top row] and in the PTWM [bottom row] with increasing aggressiveness of the glioma. The columns represent normalized FA and ADC values (a); p, q, and L (b); and CL, CP and CS (c)

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  References Top

1.Jolapara M, Kesavadas C, Radhakrishnan VV, Thomas B, Gupta AK, Bodhey N, et al. Role of diffusion tensor imaging in differentiating subtypes of meningiomas. J Neuroradiol 2010;37:277-83.   Back to cited text no. 1
2.Wang W, Steward CE, Desmond PM. Diffusion tensor imaging in glioblastoma multiforme and brain metastases: The role of p, q, L, and fractional anisotropy. AJNR Am J Neuroradiol 2009;30:203-8.   Back to cited text no. 2
3.Lu S, Ahn D, Johnson G, Cha S. Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol 2003;24:937-41.  Back to cited text no. 3
4.Price SJ, Peña A, Burnet NG, Jena R, Green HA, Carpenter TA, et al. Tissue signature characterisation of diffusion tensor abnormalities in cerebral gliomas. Eur Radiol 2004;14:1909-17.  Back to cited text no. 4


  [Figure 1], [Figure 2]

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