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Abstract #0383

Evaluation of multi-shell diffusion MRI acquisition strategy on quantitative analysis of multi-compartment models

Xiaodong Zhang1, Sudeep Patel1, and Chun-Xia Li1

1Yerkes Imaging Center, Emory University, ATLANTA, GA, United States

Multi-shell diffusion MRI (dMRI) allows for analyzing the water diffusion signal using multi-compartment diffusion models, providing more specific characterization of tissue microstructure in grey matter and white matter than standard diffusion tensor imaging (DTI). However, the traditional multi-shell dMRI data acquisition usually demands high gradient strength and long scanning duration and its application is hindered for subjects like fetuses and infants in which fast imaging and reduced gradient strength is required. In the present study, the quantification analysis of NODDI and DBSI was evaluated using different hybrid diffusion imaging (HYDI) acquisition strategy for fast imaging on a clinical 3T setting. The results demonstrated that the data acquisition time for multi-shell dMRI can be reduced dramatically using HYDI gradient encoding strategy, while the quality of derived NODDI, DBSI, and DTI indices is generally maintained, suggesting quantitative analysis of multi-compartment models are applicable for developmental study of whole brain fetuses and infants by using multi-shell dMRI with HYDI encoding scheme.

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