Share Email Print
cover

Proceedings Paper

Evaluation of inter-site bias and variance in diffusion-weighted MRI
Author(s): Allison E. Hainline; Vishwesh Nath; Prasanna Parvathaneni; Justin Blaber; Baxter Rogers; Allen Newton; Jeffrey Luci; Heidi Edmonson; Hakmook Kang; Bennett A. Landman
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

An understanding of the bias and variance of diffusion weighted magnetic resonance imaging (DW-MRI) acquisitions across scanners, study sites, or over time is essential for the incorporation of multiple data sources into a single clinical study. Studies that combine samples from various sites may be introducing confounding factors due to site-specific artifacts and patterns. Differences in bias and variance across sites may render the scans incomparable, and, without correction, inferences obtained from these data may be misleading. We present an analysis of the bias and variance of scans of the same subjects across different sites and evaluate their impact on statistical analyses. In previous work, we presented a simulation extrapolation (SIMEX) technique for bias estimation as well as a wild bootstrap technique for variance estimation in metrics obtained from a Q-ball imaging (QBI) reconstruction of empirical high angular resolution diffusion imaging (HARDI) data. We now apply those techniques to data acquired from 5 healthy volunteers on 3 independent scanners under closely matched acquisition protocols. The bias and variance of GFA measurements were estimated on a voxel-wise basis for each scan and compared across study sites to identify site-specific differences. Further, we provide model recommendations that can be used to determine the extent of the impact of bias and variance as well as aspects of the analysis to account for these differences. We include a decision tree to help researchers determine if model adjustments are necessary based on the bias and variance results.

Paper Details

Date Published: 2 March 2018
PDF: 11 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057413 (2 March 2018); doi: 10.1117/12.2293735
Show Author Affiliations
Allison E. Hainline, Vanderbilt Univ. (United States)
Vishwesh Nath, Vanderbilt Univ. (United States)
Prasanna Parvathaneni, Vanderbilt Univ. (United States)
Justin Blaber, Vanderbilt Univ. (United States)
Baxter Rogers, Vanderbilt Univ. (United States)
Allen Newton, Vanderbilt Univ. (United States)
Jeffrey Luci, The Univ. of Texas at Austin (United States)
Heidi Edmonson, Mayo Clinic (United States)
Hakmook Kang, Vanderbilt Univ. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)
Vanderbilt Univ. Institute of Imaging Science (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top