Abstract #3672

Grading of glioma with histogram analysis of multiparameter using advanced diffusion models

Gao Eryuan¹, Gao Ankang¹, Zhang Huiting², Wang Shaoyu², Yan Xu², Bai Jie¹, and Cheng Jingliang¹

¹Dept. of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, Zhengzhou, China, ²MR Scientific Marketing, Siemens Healthcare, Shanghai, China, Shanghai, China

This study aimed to investigate the efficiency of four advanced diffusion models, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP) in grading of glioma. Through histogram analysis of parameters, we found that axial diffusivity (AD)_maximum, mean diffusivity (MD)_maximum and radial diffusivity (RD)_maximum from DTI, and Q-space inverse variance (QIV)_maximum and QIV_range from MAP had significant differences and good diagnostic efficiency in all comparisons among different grading of glioma.

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