Removing inter-subject technical variability in magnetic resonance imaging studies

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variabili...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Jg. 132; S. 198 - 212
Hauptverfasser: Fortin, Jean-Philippe, Sweeney, Elizabeth M., Muschelli, John, Crainiceanu, Ciprian M., Shinohara, Russell T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 15.05.2016
Elsevier Limited
Schlagworte:
ADNI
Algorithms
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - pathology
Alzheimer's disease
Biomarkers
Brain - anatomy & histology
Brain - pathology
Datasets
Female
Gene expression
Humans
Image Processing, Computer-Assisted
Linear Models
Magnetic Resonance Imaging - methods
Male
Medical imaging
Methods
MRI
Neuroimaging - methods
NMR
Normalization
Nuclear magnetic resonance
Reproducibility of Results
ROC Curve
Scan effect
Scanners
Signal Processing, Computer-Assisted
Studies
ADNI
Normalization
MRI
Alzheimer's disease
Scan effect
ISSN:1053-8119, 1095-9572
Online-Zugang:Volltext
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Abstract Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities. •Between-scan unwanted variation is still present after intensity normalization.•We propose a scan-effect removal tool for removing post-normalization artifacts.•We model the unwanted variability between MRI T1-w scans using CSF region.•We use a large subset of the ADNI database to showcase our method.•We show that our method improves replicability of the voxels associated with AD.•MRI intensities corrected by our method improves prediction of AD and MCI.
AbstractList Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities.
Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect, and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area, and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities. •Between-scan unwanted variation is still present after intensity normalization.•We propose a scan-effect removal tool for removing post-normalization artifacts.•We model the unwanted variability between MRI T1-w scans using CSF region.•We use a large subset of the ADNI database to showcase our method.•We show that our method improves replicability of the voxels associated with AD.•MRI intensities corrected by our method improves prediction of AD and MCI.
Author Crainiceanu, Ciprian M.
Sweeney, Elizabeth M.
Shinohara, Russell T.
Fortin, Jean-Philippe
Muschelli, John
AuthorAffiliation 2 Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania
1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, University of Pennsylvania
AuthorAffiliation_xml – name: 1 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, University of Pennsylvania
– name: 2 Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania
Author_xml – sequence: 1
  givenname: Jean-Philippe
  surname: Fortin
  fullname: Fortin, Jean-Philippe
  organization: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
– sequence: 2
  givenname: Elizabeth M.
  surname: Sweeney
  fullname: Sweeney, Elizabeth M.
  organization: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
– sequence: 3
  givenname: John
  surname: Muschelli
  fullname: Muschelli, John
  organization: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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  givenname: Ciprian M.
  surname: Crainiceanu
  fullname: Crainiceanu, Ciprian M.
  organization: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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  givenname: Russell T.
  surname: Shinohara
  fullname: Shinohara, Russell T.
  email: rshi@upenn.edu
  organization: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/26923370$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright 2016
Published by Elsevier Inc.
Copyright Elsevier Limited May 15, 2016
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CorporateAuthor The Alzheimer's Disease Neuroimaging Initiative
Alzheimer's Disease Neuroimaging Initiative
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Keywords ADNI
Normalization
MRI
Alzheimer's disease
Scan effect
Language English
License Published by Elsevier Inc.
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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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Snippet Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity...
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SubjectTerms ADNI
Algorithms
Alzheimer Disease - diagnostic imaging
Alzheimer Disease - pathology
Alzheimer's disease
Biomarkers
Brain - anatomy & histology
Brain - pathology
Datasets
Female
Gene expression
Humans
Image Processing, Computer-Assisted
Linear Models
Magnetic Resonance Imaging - methods
Male
Medical imaging
Methods
MRI
Neuroimaging - methods
NMR
Normalization
Nuclear magnetic resonance
Reproducibility of Results
ROC Curve
Scan effect
Scanners
Signal Processing, Computer-Assisted
Studies
Title Removing inter-subject technical variability in magnetic resonance imaging studies
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https://dx.doi.org/10.1016/j.neuroimage.2016.02.036
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