Multi-parametric MRI of glioblastoma invasion quantitative evaluation using histological stacks
Haitham Al-Mubarak1, Antoine Vallatos1, Lindsay Gallagher1, Joanna Birch2, Lesley Glmour2, John Foster3, Anthony Chalmers2, and William Holmes1

1Glasgow Experimental MRI center, University of Glasgow, Glasgow, United Kingdom, 2Institute of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom, 3School of medicine, dentistry and nursing, University of Glasgow, Glasgow, United Kingdom


We perform a quantitative histological evaluation of a range of MRI techniques in their ability to probe glioblastoma invasion in a mouse model. Using 3-D histological stacks co-registered with MRI slices allows to achieve high values in Dice, sensitivity and specificity tests (>90%). This approach enables to go beyond the standard evaluation tests, performing direct voxel-to-voxel comparison between MRI and histology, and facilitating the development of multi-parametric analysis models. We also identified promising methods for detecting low tumour concentration regions at the invasion limits.


Magnetic Resonance Imaging (MRI) is an important tool for glioblastoma (GBM) diagnosis, however, several works have reported limitations of diagnostic accuracy1 for tumour invasion. In order to more robustly evaluate MRI techniques, we have developed a protocol for the co-registration of histological stacks with MRI slices. Care was taken to cut the histological slices (HLA stain for human GBM) in exactly the same plane as the MR images, with five evenly distributed 20 mm histological slices then averaged to account for the thicker MRI slice (1.5mm). We have used this protocol to evaluate the ability of a range of MRI techniques to probe invasion in a mouse glioblastoma model. This approach allows us to go beyond the standard evaluation tests and enables a direct voxel-to-voxel comparison between MRI and histology.


This study used ten nude mice injected intra-cranially with human glioblastoma2, presenting highly invasive tumour margins. The mice brains were scanned in vivo and sacrificed at week 12 post-injection. Imaging was performed on a Bruker 7T Biospec with 72 cm volume resonance and 4 channel phase-array surface coil. Several imaging modalities were acquired as following: (Figure 1a): T2 weighted (T2W), T2 value (T2map), Diffusion weighted (DW), Apparent Diffusion Coefficient (ADC), Fractional anisotropy (FA), Kurtosis weighted (KW) and perfusion weighted (ASL). Slice thickness was 1.5 mm and resolution varied according to the MRI technique used. Five evenly distributed 20 mm histological slices (HLA stain for human GBM) were cut in the MRI plane (using high resolution T2 images for reference) and stacked to account for MRI slice thickness. Post processing of the data (with in-house developed MATLAB code) leads to a single matrix of same dimension which includes: noise reduction, surface-coil sensitivity correction, re-gridding and MRI/Histology registration (Figure 1b). Following these steps, the abnormal region on each of image modality was manually selected in order to performed MRI-histology comparison tests3 (Dice, Sensitivity,….) and voxel-to-voxel analysis.


Figure -1a- shows examples of different MR contrast in the mouse model, with the corresponding histological stack shown in figure-1b-. An example of co-registration of a histological stack with an MR image (ASL) is given in figure 1c. ASL, DKI, DWI and FA all showed very high test coefficients, with ASL giving Dice, sensitivity, and specificity coefficients above 90%. Indeed, the high quality of the registration allows for more direct evaluation via a voxel-to-voxel comparison. For example, figure -1c- shows a voxel-by-voxel comparison of histology with ASL, showing a negative correlation in the tumour region.


We introduce a protocol for the direct histological evaluation of MRI to identify glioblastoma invasion in a mouse model. This protocol allowed MRI methods with the greatest sensitivity to tumour invasion to be identified. Furthermore, this approach of using 3-D histological stacks, co-registered with MRI slices, will facilitate the development and calibration of multi-spectral analysis models, allowing more information to be aggregated.


Haitham Al-Mubarak would like to thank the Ministry of Higher Education and Scientific Research in Iraq for funding and support.Also, A. Vallatos would like to thank the Brain Tumor Charity in U.K.


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2- Manninoa M., Gomez-Romanb N. , Helfrid H., Chalmers A.. (2014), Differential sensitivity of Glioma stem cells to Aurora kinase A inhibitors: Implications for stem cell mitosis and centrosome dynamics ,Stem Cell Research (2014) 13, 135–143.

3- García-Lorenzo D., Simon F., Narayanan S., Arnold D. L, Collins D.L.. (2013), Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging ,Medical Image Analysis,1-18.


Figure-1- (a) MRI modalities (b) ASL-Histology and registration (c) ASL-Histology signal scattered.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)