Empirical Estimation of Intravoxel Structure with Persistent Angular Structure and Q-ball Models of Diffusion Weighted MRI

Overview

Journal J Med Imaging (Bellingham)

Specialty Radiology

Date 2018 Mar 14

PMID 29531965

Citations 4

Authors

Vishwesh Nath

Kurt G Schilling

Prasanna Parvathaneni

Justin Blaber

Allison E Hainline

Zhaohua Ding

Adam Anderson

Bennett A Landman

Affiliations

Soon will be listed here.

Abstract

The diffusion tensor model is nonspecific in regions where micrometer structural patterns are inconsistent at the millimeter scale (i.e., brain regions with pathways that cross, bend, branch, fan, etc.). Numerous models have been proposed to represent crossing fibers and complex intravoxel structure from diffusion weighted magnetic resonance imaging (e.g., high angular resolution diffusion imaging-HARDI). Here, we present an empirical comparison of two HARDI approaches-persistent angular structure MRI (PAS-MRI) and Q-ball-using a newly acquired reproducibility dataset. Briefly, a single subject was scanned 11 times with 96 diffusion weighted directions and 10 reference volumes for each of two [Formula: see text] values (1000 and [Formula: see text] for a total of 2144 volumes). Empirical reproducibility of intravoxel fiber fractions (number/strength of peaks), angular orientation, and fractional anisotropy was compared with metrics from a traditional tensor analysis approach, focusing on [Formula: see text] values of 1000 and [Formula: see text]. PAS-MRI is shown to be more reproducible than Q-ball and offers advantages at low [Formula: see text] values. However, there are substantial and biologically meaningful differences between the intravoxel structures estimated both in terms of analysis method as well as by [Formula: see text] value. The two methods suggest a fundamentally different microarchitecture of the human brain; therefore, it is premature to perform meta-analysis or combine results across HARDI studies using a different analysis model or acquisition sequences.

Citing Articles

Harmonizing 1.5T/3T Diffusion Weighted MRI through Development of Deep Learning Stabilized Microarchitecture Estimators.

Nath V, Remedios S, Parvathaneni P, Hansen C, Bayrak R, Bermudez C Proc SPIE Int Soc Opt Eng. 2020; 10949.

PMID: 32089583 PMC: 7034942. DOI: 10.1117/12.2512902.

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI.

Nath V, Schilling K, Parvathaneni P, Hansen C, Hainline A, Huo Y Magn Reson Imaging. 2019; 62:220-227.

PMID: 31323317 PMC: 6748654. DOI: 10.1016/j.mri.2019.07.012.

Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge.

Nath V, Schilling K, Parvathaneni P, Huo Y, Blaber J, Hainline A J Magn Reson Imaging. 2019; 51(1):234-249.

PMID: 31179595 PMC: 6900461. DOI: 10.1002/jmri.26794.

A deep learning approach to estimation of subject-level bias and variance in high angular resolution diffusion imaging.

Hainline A, Nath V, Parvathaneni P, Schilling K, Blaber J, Anderson A Magn Reson Imaging. 2019; 59:130-136.

PMID: 30926560 PMC: 6818965. DOI: 10.1016/j.mri.2019.03.021.

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