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Abstract #0705

Towards Glioma Grading Using Non-Gaussian Diffusion Imaging with a Continuous-time Random Walk Model and A Quantile Histogram Analysis

Muge Karaman1, Jiaxuan Zhang1,2, Zheng Zhong1,3, Kejia Cai1,4, Wenzhen Zhu2, and Xiaohong Joe Zhou5

1Center for MR Research, University of Illinois at Chicago, Chicago, IL, United States, 2Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, People's Republic of China, 3Department of Bioengineering, Chicago, IL, United States, 4Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States, 5Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois at Chicago, IL, United States

Gliomas are the most common primary tumors of the central nervous system. As surgical biopsy may not be always feasible, an accurate noninvasive glioma grading is highly desirable for planning treatments. Recently, a number of non-Gaussian diffusion models were developed to characterize the anomalous diffusion behavior of the complex biological tissue. Among these, the continuous-time random walk (CTRW) model showed a great potential to probe the tissue heterogeneity and complexity that is elevated with tumor progression. In this study, we show that the CTRW parameters are capable of differentiating glioma grades, beyond simply separating low-grade and high-grade as in many diffusion MR studies on gliomas.

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