Optimized Diffusion Imaging for Brain Structural Connectome Analysis

TL;DR

This paper introduces a statistical method that uses prior data to optimize q-space sampling in diffusion MRI, enabling accurate brain connectome analysis with fewer diffusion directions, thus reducing scan time.

Contribution

It presents a novel prior-based approach for selecting q-space directions, improving diffusion imaging efficiency and accuracy in brain connectome studies.

Findings

Accurately estimates brain connectomes with only 15-20 q-space samples.

Demonstrates significant advantages over existing HARDI sampling methods.

Validates effectiveness on Human Connectome Project and aging adult datasets.

Abstract

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. To more accurately estimate the structural connectome, dense samples in q-space are often acquired, potentially resulting in long scanning times and logistical challenges. This paper proposes a statistical method to select q-space directions optimally and estimate the local diffusion function from sparse observations. The proposed approach leverages relevant historical dMRI data to calculate a prior distribution to characterize local diffusion variability in each voxel in a template space. For a new subject to be scanned, the priors are mapped into the subject-specific coordinate and used to help select the best q-space samples. Simulation studies demonstrate big advantages over the existing HARDI sampling and analysis framework. We also applied the proposed method to the Human Connectome Project data and a dataset of aging adults with mild cognitive impairment. The results indicate that with very few q-space samples (e.g., 15 or 20), we can recover structural brain networks comparable to the ones estimated from 60 or more diffusion directions with the existing methods. n Connectome Project data demonstrate that our proposed method provides substantial advantages over its competitors.