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Abstract #2867

Repeatability and reproducibility of diffusional variance decomposition (DIVIDE) imaging

Shohei Inui1, Tsuyoshi Ueyama2, Yuichi Suzuki2, Tetsuya Wakayama3, and Osamu Abe1
1Radiology, The University of Tokyo, Tokyo, Japan, 2Radiology, The University of Tokyo Hospital, Tokyo, Japan, 3GE HealthCare, Tokyo, Japan

Synopsis

Keywords: DWI/DTI/DKI, Diffusion/other diffusion imaging techniques

Motivation: Novel Multidimensional diffusion encoding (MDE) technique, diffusional variance decomposition (DIVIDE), may provide more detailed insights into tissue microstructure.

Goal(s): To evaluate the feasibility of DIVIDE imaging for human brain.

Approach: Ten healthy-subjects underwent MDE (2D-EPI sequence with 29 linear and 26 spherical b-tensors) twice using 3T-MRI. Regional values of 20 ROIs was extracted for 10 DIVIDE metrics. Coefficient of variation (CV) and interclass correlation coefficient (ICC) were calculated.

Results: Intra-subject CV was less than 5% in almost all regional metrics. Intra-subject CV was lower than that of inter-subject CV in all regional metrics. ICC showed almost perfect agreements for almost all regional metrics.

Impact: Recently developed MDE technique, diffusional variance decomposition (DIVIDE), may be reliably used for measuring diffusion metrics with a potential to provide more detailed insights into tissue microstructure in complex tissues, such as crossing or kissing fiber configurations in the brain.

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Keywords