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Abstract #1734

Non-invasive separation of low and high grade gliomas with diffusion kurtosis decomposition (DKD)

Sirui Li1, Wenbo Sun1, Yuan Zheng2, Qing Wei3, Samo Lasic4, Shihong Han3, Shuheng Zhang3, Danielle van Westen5, Karin Karin Bryskhe4, Daniel Topgaard4,5, and Haibo Xu1
1Zhongnan Hospital of Wuhan University, Wuhan, China, 2UIH America, Inc., Houston, TX, United States, 3United Imaging Healthcare, Shanghai, China, 4Random Walk Imaging, Lund, Sweden, 5Lund University, Lund, Sweden

Diffusion kurtosis decomposition (DKD) is a novel advanced diffusion MRI modality relying on customized pulse sequences and high-performance hardware to assess cell shapes and density heterogeneity via the anisotropic and isotropic mean kurtosis parameters, MKa and MKi, which are fundamentally different microstructural properties that are inextricably entangled in conventional diffusion kurtosis imaging (DKI). We have investigated DKD imaging of gliomas in a clinical setting, and for the first time established the correlations between MKa, MKi, and tumor grade. In comparison to conventional diffusion methods, DKD more accurately describes the microstructural changes and provides a useful tool for glioma diagnosis.

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