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.
| Original language | English (US) |
|---|---|
| Title of host publication | Medical Imaging 2018 |
| Subtitle of host publication | Image Processing |
| Publisher | SPIE |
| Volume | 10574 |
| ISBN (Electronic) | 9781510616370 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Event | Medical Imaging 2018: Image Processing - Houston, United States Duration: Feb 11 2018 → Feb 13 2018 |
Other
| Other | Medical Imaging 2018: Image Processing |
|---|---|
| Country | United States |
| City | Houston |
| Period | 2/11/18 → 2/13/18 |
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Keywords
- bias correction
- bootstrap
- HARDI
- multi-site
- Q-ball
- SIMEX
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Biomaterials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging
Cite this
Evaluation of inter-site bias and variance in diffusion-weighted MRI. / Hainline, Allison E.; Nath, Vishwesh; Parvathaneni, Prasanna; Blaber, Justin; Rogers, Baxter; Newton, Allen; Luci, Jeffrey; Edmonson, Heidi; Kang, Hakmook; Landman, Bennett A.
Medical Imaging 2018: Image Processing. Vol. 10574 SPIE, 2018. 1057413.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Evaluation of inter-site bias and variance in diffusion-weighted MRI
AU - Hainline, Allison E.
AU - Nath, Vishwesh
AU - Parvathaneni, Prasanna
AU - Blaber, Justin
AU - Rogers, Baxter
AU - Newton, Allen
AU - Luci, Jeffrey
AU - Edmonson, Heidi
AU - Kang, Hakmook
AU - Landman, Bennett A.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - bias correction
KW - bootstrap
KW - HARDI
KW - multi-site
KW - Q-ball
KW - SIMEX
UR - http://www.scopus.com/inward/record.url?scp=85047342424&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047342424&partnerID=8YFLogxK
U2 - 10.1117/12.2293735
DO - 10.1117/12.2293735
M3 - Conference contribution
VL - 10574
BT - Medical Imaging 2018
PB - SPIE
ER -