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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 QIVrange from MAP had significant differences and good diagnostic efficiency in all comparisons among different grading of glioma.

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