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Inter-scanner brain MRI volumetric biases persist even in a harmonized multi-subject study of multiple sclerosis

View ORCID ProfileKelly A. Clark, Carly M. O’Donnell, Mark A. Elliott, Shahamat Tauhid, View ORCID ProfileBlake E. Dewey, Renxin Chu, Samar Khalil, View ORCID ProfileGovind Nair, Pascal Sati, Anna DuVal, Nicole Pellegrini, View ORCID ProfileAmit Bar-Or, Clyde Markowitz, Matthew K. Schindler, Jonathan Zurawski, Peter A. Calabresi, Daniel S. Reich, View ORCID ProfileRohit Bakshi, Russell T. Shinohara, the NAIMS Cooperative
doi: https://doi.org/10.1101/2022.05.05.490645
Kelly A. Clark
1Penn Statistics in Imaging and Visualization Endeavor, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
2Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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  • ORCID record for Kelly A. Clark
  • For correspondence: kelly.clark2@pennmedicine.upenn.edu
Carly M. O’Donnell
1Penn Statistics in Imaging and Visualization Endeavor, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
2Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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Mark A. Elliott
3Department of Radiology, University of Pennsylvania, Philadelphia, PA
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Shahamat Tauhid
4Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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Blake E. Dewey
5Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
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Renxin Chu
4Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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Samar Khalil
4Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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Govind Nair
6Quantitative MRI core facility, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD
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Pascal Sati
7Neuroimaging Program, Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA
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Anna DuVal
5Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
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Nicole Pellegrini
5Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
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Amit Bar-Or
8Center for Neuroinflammation and Neurotherapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
9Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Clyde Markowitz
8Center for Neuroinflammation and Neurotherapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
9Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Matthew K. Schindler
8Center for Neuroinflammation and Neurotherapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
9Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Jonathan Zurawski
4Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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Peter A. Calabresi
5Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD
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Daniel S. Reich
10Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institute of Health, Bethesda, MD
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Rohit Bakshi
4Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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Russell T. Shinohara
1Penn Statistics in Imaging and Visualization Endeavor, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
2Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
8Center for Neuroinflammation and Neurotherapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Abstract

Background/Purpose Multicenter study designs involving a variety of MRI scanners have become increasingly common. However, these present the issue of biases in image-based measures due to scanner or site differences. To assess these biases, we imaged 11 volunteers with multiple sclerosis (MS) with scan and rescan data at 4 sites.

Materials and Methods Images were acquired on Siemens or Philips scanners at 3-tesla. Automated white matter lesion detection and whole brain, gray and white matter, and thalamic volumetry were performed, as well as expert manual delineations of T1 and T2 (FLAIR) lesions. Random effect and permutation-based nonparametric modeling was performed to assess differences in estimated volumes within and across sites.

Results Random effect modeling demonstrated model assumption violations for most comparisons of interest. Non-parametric modeling indicated that site explained > 50% of the variation for most estimated volumes. This expanded to > 75% when data from both Siemens and Philips scanners were included. Permutation tests revealed significant differences between average inter- and intra-scanner differences in most estimated brain volumes (P < .05). The automatic activation of spine coil elements during some acquisitions resulted in a shading artifact in these images. Permutation tests revealed significant differences between thalamic volume measurements from acquisitions with and without this artifact.

Conclusion Differences in brain volumetry persisted across MR scanners despite protocol harmonization. These differences were not well explained by variance component modeling; however, statistical innovations for mitigating inter-scanner differences show promise in reducing biases in multi-center studies of MS.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 05, 2022.
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Inter-scanner brain MRI volumetric biases persist even in a harmonized multi-subject study of multiple sclerosis
Kelly A. Clark, Carly M. O’Donnell, Mark A. Elliott, Shahamat Tauhid, Blake E. Dewey, Renxin Chu, Samar Khalil, Govind Nair, Pascal Sati, Anna DuVal, Nicole Pellegrini, Amit Bar-Or, Clyde Markowitz, Matthew K. Schindler, Jonathan Zurawski, Peter A. Calabresi, Daniel S. Reich, Rohit Bakshi, Russell T. Shinohara, the NAIMS Cooperative
bioRxiv 2022.05.05.490645; doi: https://doi.org/10.1101/2022.05.05.490645
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Inter-scanner brain MRI volumetric biases persist even in a harmonized multi-subject study of multiple sclerosis
Kelly A. Clark, Carly M. O’Donnell, Mark A. Elliott, Shahamat Tauhid, Blake E. Dewey, Renxin Chu, Samar Khalil, Govind Nair, Pascal Sati, Anna DuVal, Nicole Pellegrini, Amit Bar-Or, Clyde Markowitz, Matthew K. Schindler, Jonathan Zurawski, Peter A. Calabresi, Daniel S. Reich, Rohit Bakshi, Russell T. Shinohara, the NAIMS Cooperative
bioRxiv 2022.05.05.490645; doi: https://doi.org/10.1101/2022.05.05.490645

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