Abstract
In this paper, we investigate anomalous diffusion models in a mouse model of glioblastoma, a grade IV brain tumour, and study how the anomalous diffusion model parameters reflect the change in tumour tissue microstructure. Diffusion-weighted MRI data with multiple b-values at 9.4 T was acquired from mice bearing U87 brain tumour cells at four time points. Voxel-level fitting of the MRI data was performed on the classical mono-exponential model, and four anomalous diffusion models, namely, the stretched exponential model, the sub-diffusion model, the continuous time random walk model and the fractional Bloch-Torrey equation. The performance of the anomalous diffusion parameters for differentiating the three-concentric layers of tumour tissue (i.e., core; intermediate zone; peripheral and hyper-vascularised tumour layer) was evaluated with multinomial logistic regression and multi-class classification analysis. We found that parameter \(\alpha \) from the stretched exponential model, parameter \(\upbeta \) from the sub-diffusion model and parameter \(\upbeta \) from the continuous time random walk model provide a clear delineation of the three layers of tumour tissue. The analysis revealed that the combination of diffusion coefficient D and anomalous diffusion parameter (\(\alpha \) and/or \(\upbeta \)) greatly improved the classification power in terms of F1-scores compared with the current approach in clinics, in which D is used alone. Hence, our mouse brain tumour study demonstrated that anomalous diffusion model parameters are useful for differentiating different tumour layers and normal brain tissue.
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Acknowledgments
Qianqian Yang acknowledges the Australian Research Council for the Discovery Early Career Researcher Award (DE150101842). Simon Puttick acknowledges the Cure Brain Cancer Foundation Innovation Grant (R14/2173).
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Yang, Q., Puttick, S., Bruce, Z.C., Day, B.W., Vegh, V. (2020). Investigation of Changes in Anomalous Diffusion Parameters in a Mouse Model of Brain Tumour. In: Bonet-Carne, E., Hutter, J., Palombo, M., Pizzolato, M., Sepehrband, F., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-52893-5_14
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