Evaluation of inter-site bias and variance in diffusion-weighted MRI

Allison E. Hainline; Vishwesh Nath; Prasanna Parvathaneni; Justin Blaber; Baxter Rogers; Allen Newton; Jeffrey Luci; Heidi Edmonson; Hakmook Kang; Bennett A. Landman

doi:10.1117/12.2293735

2 March 2018 Evaluation of inter-site bias and variance in diffusion-weighted MRI

Allison E. Hainline, Vishwesh Nath, Prasanna Parvathaneni, Justin Blaber, Baxter Rogers, Allen Newton, Jeffrey Luci, Heidi Edmonson, Hakmook Kang, Bennett A. Landman

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 1057413 (2018) https://doi.org/10.1117/12.2293735
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

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Citation Download Citation

Allison E. Hainline, Vishwesh Nath, Prasanna Parvathaneni, Justin Blaber, Baxter Rogers, Allen Newton, Jeffrey Luci, Heidi Edmonson, Hakmook Kang, and Bennett A. Landman "Evaluation of inter-site bias and variance in diffusion-weighted MRI", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057413 (2 March 2018); https://doi.org/10.1117/12.2293735

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