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