Multicomponent T2 Analysis of Glioblastoma in a Mouse Model


  • Shefali Pandey University of Calgary
  • Tonima Ali University of Calgary
  • Susobhan Sarkar University of Calgary
  • V. W. Yong University of Calgary
  • Jeffrey F. Dunn University of Calgary


Glioblastoma Multiforme (GBM) is the most common and aggressive malignant primary brain tumour and average survival rates, even with aggressive treatment, is less than a year. A primary concern when imaging patients with tumors using MRI (Magnetic Resonance Imaging) is not being able to detect treatment response or tumor tissue characteristics adequately.

MRI methods use the information obtained from the distribution of hydrogen protons from water-filled biological tissues. T2, the spin-spin relaxation time, is affected by the water environment and will increase with edema (excess water within tissues) and specific changes in cell type. Hence, unique T2 times reveal distinctive tissue characteristics. To date, T2 analysis of tumors has largely used monoexponential fitting. However, this method is not sensitive to the multicomponent nature of tissues within a defined volume (voxel).

Using a mouse model implanted with tumor cells and multiexponential T2 analysis, superior differentiation between tissues can be detected within the mouse gliobastoma. We will use a novel visualization software to determine how the multicomponent T2 analysis can improve our sensitivity to specific tumor microenvironments. A study showing proof of principle has previously been published using mice implanted with patient-derived brain tumor initiating cells (BTICs). This project will use human derived glioblastoma cells in mice models to examine whether T2 can be used to detect treatment response. In addition MRI spectroscopy will be performed to detect the grade/type of tumor using the levels of metabolites present in and around the tumor volume.




How to Cite

S. Pandey, T. Ali, S. Sarkar, V. W. Yong, and J. F. Dunn, “Multicomponent T2 Analysis of Glioblastoma in a Mouse Model”, CMBES Proc., vol. 39, no. 1, May 2016.