Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison

Neuroimaging MRI data in scientific research is increasingly pooled, but the reliability of such studies may be hampered by the use of different hardware elements. This might introduce bias, for example when cross-sectional studies pool data acquired with different head coils, or when longitudinal c...

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Main Authors: Jessica L. Panman, Yang Yang To, Emma L. van der Ende, Jackie M. Poos, Lize C. Jiskoot, Lieke H. H. Meeter, Elise G. P. Dopper, Mark J. R. J. Bouts, Matthias J. P. van Osch, Serge A. R. B. Rombouts, John C. van Swieten, Jeroen van der Grond, Janne M. Papma, Anne Hafkemeijer
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-07-01
Series:Frontiers in Neuroscience
Subjects:
MRI
DTI
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2019.00729/full
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author Jessica L. Panman
Jessica L. Panman
Yang Yang To
Emma L. van der Ende
Emma L. van der Ende
Jackie M. Poos
Jackie M. Poos
Lize C. Jiskoot
Lize C. Jiskoot
Lieke H. H. Meeter
Elise G. P. Dopper
Elise G. P. Dopper
Mark J. R. J. Bouts
Mark J. R. J. Bouts
Mark J. R. J. Bouts
Matthias J. P. van Osch
Matthias J. P. van Osch
Serge A. R. B. Rombouts
Serge A. R. B. Rombouts
Serge A. R. B. Rombouts
John C. van Swieten
Jeroen van der Grond
Janne M. Papma
Anne Hafkemeijer
Anne Hafkemeijer
Anne Hafkemeijer
spellingShingle Jessica L. Panman
Jessica L. Panman
Yang Yang To
Emma L. van der Ende
Emma L. van der Ende
Jackie M. Poos
Jackie M. Poos
Lize C. Jiskoot
Lize C. Jiskoot
Lieke H. H. Meeter
Elise G. P. Dopper
Elise G. P. Dopper
Mark J. R. J. Bouts
Mark J. R. J. Bouts
Mark J. R. J. Bouts
Matthias J. P. van Osch
Matthias J. P. van Osch
Serge A. R. B. Rombouts
Serge A. R. B. Rombouts
Serge A. R. B. Rombouts
John C. van Swieten
Jeroen van der Grond
Janne M. Papma
Anne Hafkemeijer
Anne Hafkemeijer
Anne Hafkemeijer
Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison
Frontiers in Neuroscience
MRI
DTI
neuroimaging
gray matter
white matter
multicenter study
author_facet Jessica L. Panman
Jessica L. Panman
Yang Yang To
Emma L. van der Ende
Emma L. van der Ende
Jackie M. Poos
Jackie M. Poos
Lize C. Jiskoot
Lize C. Jiskoot
Lieke H. H. Meeter
Elise G. P. Dopper
Elise G. P. Dopper
Mark J. R. J. Bouts
Mark J. R. J. Bouts
Mark J. R. J. Bouts
Matthias J. P. van Osch
Matthias J. P. van Osch
Serge A. R. B. Rombouts
Serge A. R. B. Rombouts
Serge A. R. B. Rombouts
John C. van Swieten
Jeroen van der Grond
Janne M. Papma
Anne Hafkemeijer
Anne Hafkemeijer
Anne Hafkemeijer
author_sort Jessica L. Panman
title Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison
title_short Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison
title_full Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison
title_fullStr Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison
title_full_unstemmed Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil Comparison
title_sort bias introduced by multiple head coils in mri research: an 8 channel and 32 channel coil comparison
publisher Frontiers Media S.A.
series Frontiers in Neuroscience
issn 1662-453X
publishDate 2019-07-01
description Neuroimaging MRI data in scientific research is increasingly pooled, but the reliability of such studies may be hampered by the use of different hardware elements. This might introduce bias, for example when cross-sectional studies pool data acquired with different head coils, or when longitudinal clinical studies change head coils halfway. In the present study, we aimed to estimate this possible bias introduced by using different head coils to create awareness and to avoid misinterpretation of results. We acquired, with both an 8 channel and 32 channel head coil, T1-weighted, diffusion tensor imaging and resting state fMRI images at 3T MRI (Philips Achieva) with stable acquisition parameters in a large group of cognitively healthy participants (n = 77). Standard analysis methods, i.e., voxel-based morphometry, tract-based spatial statistics and resting state functional network analyses, were used in a within-subject design to compare 8 and 32 channel head coil data. Signal-to-noise ratios (SNR) for both head coils showed similar ranges, although the 32 channel SNR profile was more homogeneous. Our data demonstrates specific patterns of gray and white matter volume differences between head coils (relative volume change of 6 to 9%), related to altered image contrast and therefore, altered tissue segmentation. White matter connectivity (fractional anisotropy and diffusivity measures) showed hemispherical dependent differences between head coils (relative connectivity change of 4 to 6%), and functional connectivity in resting state networks was higher using the 32 channel head coil in posterior cortical areas (relative change up to 27.5%). This study shows that, even when acquisition protocols are harmonized, the results of standardized analysis models can be severely affected by the use of different head coils. Researchers should be aware of this when combining multiple neuroimaging MRI datasets, to prevent coil-related bias and avoid misinterpretation of their findings.
topic MRI
DTI
neuroimaging
gray matter
white matter
multicenter study
url https://www.frontiersin.org/article/10.3389/fnins.2019.00729/full
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spelling doaj-0d539ca8367b43feb1ba163d233088ea2020-11-24T22:17:19ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2019-07-011310.3389/fnins.2019.00729460195Bias Introduced by Multiple Head Coils in MRI Research: An 8 Channel and 32 Channel Coil ComparisonJessica L. Panman0Jessica L. Panman1Yang Yang To2Emma L. van der Ende3Emma L. van der Ende4Jackie M. Poos5Jackie M. Poos6Lize C. Jiskoot7Lize C. Jiskoot8Lieke H. H. Meeter9Elise G. P. Dopper10Elise G. P. Dopper11Mark J. R. J. Bouts12Mark J. R. J. Bouts13Mark J. R. J. Bouts14Matthias J. P. van Osch15Matthias J. P. van Osch16Serge A. R. B. Rombouts17Serge A. R. B. Rombouts18Serge A. R. B. Rombouts19John C. van Swieten20Jeroen van der Grond21Janne M. Papma22Anne Hafkemeijer23Anne Hafkemeijer24Anne Hafkemeijer25Department of Radiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, NetherlandsDepartment of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, NetherlandsDepartment of Radiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, NetherlandsDepartment of Radiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, NetherlandsDepartment of Radiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, NetherlandsDepartment of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, NetherlandsDepartment of Radiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, NetherlandsDepartment of Radiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, NetherlandsLeiden Institute for Brain and Cognition, Leiden University, Leiden, NetherlandsDepartment of Radiology, Leiden University Medical Center, Leiden, NetherlandsLeiden Institute for Brain and Cognition, Leiden University, Leiden, NetherlandsDepartment of Radiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, NetherlandsLeiden Institute for Brain and Cognition, Leiden University, Leiden, NetherlandsDepartment of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, NetherlandsDepartment of Radiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Neurology, Erasmus University Medical Center Rotterdam, Rotterdam, NetherlandsDepartment of Radiology, Leiden University Medical Center, Leiden, NetherlandsDepartment of Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, NetherlandsLeiden Institute for Brain and Cognition, Leiden University, Leiden, NetherlandsNeuroimaging MRI data in scientific research is increasingly pooled, but the reliability of such studies may be hampered by the use of different hardware elements. This might introduce bias, for example when cross-sectional studies pool data acquired with different head coils, or when longitudinal clinical studies change head coils halfway. In the present study, we aimed to estimate this possible bias introduced by using different head coils to create awareness and to avoid misinterpretation of results. We acquired, with both an 8 channel and 32 channel head coil, T1-weighted, diffusion tensor imaging and resting state fMRI images at 3T MRI (Philips Achieva) with stable acquisition parameters in a large group of cognitively healthy participants (n = 77). Standard analysis methods, i.e., voxel-based morphometry, tract-based spatial statistics and resting state functional network analyses, were used in a within-subject design to compare 8 and 32 channel head coil data. Signal-to-noise ratios (SNR) for both head coils showed similar ranges, although the 32 channel SNR profile was more homogeneous. Our data demonstrates specific patterns of gray and white matter volume differences between head coils (relative volume change of 6 to 9%), related to altered image contrast and therefore, altered tissue segmentation. White matter connectivity (fractional anisotropy and diffusivity measures) showed hemispherical dependent differences between head coils (relative connectivity change of 4 to 6%), and functional connectivity in resting state networks was higher using the 32 channel head coil in posterior cortical areas (relative change up to 27.5%). This study shows that, even when acquisition protocols are harmonized, the results of standardized analysis models can be severely affected by the use of different head coils. Researchers should be aware of this when combining multiple neuroimaging MRI datasets, to prevent coil-related bias and avoid misinterpretation of their findings.https://www.frontiersin.org/article/10.3389/fnins.2019.00729/fullMRIDTIneuroimaginggray matterwhite mattermulticenter study