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Activity Number: 195 - Section on Statistics in Imaging Student Paper Award Winners
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #322171
Title: Optimized Diffusion Imaging for Brain Structural Connectome Analysis
Author(s): William Consagra* and Zhengwu Zhang and Arun Venkataraman
Companies: University of Rochester and University of North Carolina at Chapel Hill and University of Rochester
Keywords: diffusion MRI; structural connectome; optimal design ; functional data; Gaussian process; greedy algorithm

High angular resolution diffusion imaging (HARDI) is a type of diffusion magnetic resonance imaging (dMRI) that measures diffusion signals on a sphere in q-space. It has been widely used in data acquisition for human brain structural connectome analysis. For accurate structural connectome estimation, dense samples in q-space are often acquired, resulting in long scanning times and logistical challenges. To overcome these issues, we develop a statistical framework that incorporates relevant dMRI data from prior large-scale imaging studies in order to improve the efficiency of human brain structural connectome estimation under sparse sampling. Our approach leverages the historical dMRI data to calculate a prior distribution characterizing local diffusion variability in each voxel in a template space. The priors are used to parameterize a sparse sample estimator and corresponding approximate optimal design algorithm to select the most informative q-space samples. Through both simulation studies and real data analysis using Human Connectome Project data, we demonstrate significant advantages of our method over existing HARDI sampling and estimation frameworks.

Authors who are presenting talks have a * after their name.

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