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10.18383/j.tom.2016.00214
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Article

QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials

by
Dariya I. Malyarenko
1,*,
Lisa J. Wilmes
2,
Lori R. Arlinghaus
3,
Michael A. Jacobs
4,
Wei Huang
5,
Karl G. Helmer
6,
Bachir Taouli
7,
Thomas E. Yankeelov
8,
David Newitt
2 and
Thomas L. Chenevert
1
1
Department of Radiology, University of Michigan, 1500 E. Medical Center Ann Arbor, MI 48109-5030, USA
2
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
3
Vanderbilt University (VU) Institute of Imaging Science, VU Medical Center, Nashville, TN, USA
4
Russel H. Morgan Department of Radiology and Radiological Science, John Hopkins University School of Medicine, Baltimore, MD, USA
5
Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, USA
6
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
7
Translational and Molecular Imaging Institute, Icahn School of Medicine at Mt Sinai, New York, NY, USA
8
Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA
*
Author to whom correspondence should be addressed.
Tomography 2016, 2(4), 396-405; https://doi.org/10.18383/j.tom.2016.00214
Submission received: 2 September 2016 / Revised: 4 October 2016 / Accepted: 5 November 2016 / Published: 1 December 2016
Versions Notes

Abstract

Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multi-site clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, −35% to +10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI.
Keywords: nonuniform diffusion weighting; gradient nonlinearity bias; correction validation nonuniform diffusion weighting; gradient nonlinearity bias; correction validation

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MDPI and ACS Style

Malyarenko, D.I.; Wilmes, L.J.; Arlinghaus, L.R.; Jacobs, M.A.; Huang, W.; Helmer, K.G.; Taouli, B.; Yankeelov, T.E.; Newitt, D.; Chenevert, T.L. QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials. Tomography 2016, 2, 396-405. https://doi.org/10.18383/j.tom.2016.00214

AMA Style

Malyarenko DI, Wilmes LJ, Arlinghaus LR, Jacobs MA, Huang W, Helmer KG, Taouli B, Yankeelov TE, Newitt D, Chenevert TL. QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials. Tomography. 2016; 2(4):396-405. https://doi.org/10.18383/j.tom.2016.00214

Chicago/Turabian Style

Malyarenko, Dariya I., Lisa J. Wilmes, Lori R. Arlinghaus, Michael A. Jacobs, Wei Huang, Karl G. Helmer, Bachir Taouli, Thomas E. Yankeelov, David Newitt, and Thomas L. Chenevert. 2016. "QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials" Tomography 2, no. 4: 396-405. https://doi.org/10.18383/j.tom.2016.00214

APA Style

Malyarenko, D. I., Wilmes, L. J., Arlinghaus, L. R., Jacobs, M. A., Huang, W., Helmer, K. G., Taouli, B., Yankeelov, T. E., Newitt, D., & Chenevert, T. L. (2016). QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials. Tomography, 2(4), 396-405. https://doi.org/10.18383/j.tom.2016.00214

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