Real-time motion analytics during brain MRI improve data quality and reduce costs

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring c...

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Bibliographic Details
Published inNeuroImage (Orlando, Fla.) Vol. 161; pp. 80 - 93
Main Authors Dosenbach, Nico U.F., Koller, Jonathan M., Earl, Eric A., Miranda-Dominguez, Oscar, Klein, Rachel L., Van, Andrew N., Snyder, Abraham Z., Nagel, Bonnie J., Nigg, Joel T., Nguyen, Annie L., Wesevich, Victoria, Greene, Deanna J., Fair, Damien A.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.11.2017
Elsevier Limited
Subjects
Accuracy
Adolescent
Adult
Alcoholism - diagnostic imaging
Attention Deficit Disorder with Hyperactivity - diagnostic imaging
Autism Spectrum Disorder - diagnostic imaging
Brain - diagnostic imaging
Child
Cost control
Functional magnetic resonance imaging
Functional MRI
Functional Neuroimaging - methods
Functional Neuroimaging - standards
Head motion distortion
Head Movements - physiology
Humans
Image Processing, Computer-Assisted - methods
Image Processing, Computer-Assisted - standards
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - standards
MRI acquisition
MRI methods
Neural networks
NMR
Nuclear magnetic resonance
Quality
Real-time quality control
Resting state functional connectivity MRI
Scanners
Structural MRI
Structure-function relationships
Young Adult
MRI acquisition
Head motion distortion
Real-time quality control
Resting state functional connectivity MRI
Structural MRI
Functional MRI
MRI methods
Online AccessGet full text
ISSN1053-8119
1095-9572
1095-9572
DOI10.1016/j.neuroimage.2017.08.025

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Abstract Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional ‘buffer data’, an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more. [Display omitted]
AbstractList Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional ‘buffer data’, an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.
Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional ‘buffer data’, an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use F ramewise I ntegrated R eal-time M RI M onitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.
Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.
Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional ‘buffer data’, an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more. [Display omitted]
Author Wesevich, Victoria
Fair, Damien A.
Van, Andrew N.
Nigg, Joel T.
Koller, Jonathan M.
Nguyen, Annie L.
Miranda-Dominguez, Oscar
Snyder, Abraham Z.
Dosenbach, Nico U.F.
Earl, Eric A.
Klein, Rachel L.
Nagel, Bonnie J.
Greene, Deanna J.
AuthorAffiliation a Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
d Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
c Program in Occupational Therapy, Washington University, St. Louis, MO, USA
h Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
b Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
g Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
e Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
f Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/28803940$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright 2017 The Authors
Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Copyright Elsevier Limited Nov 1, 2017
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– notice: Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
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Keywords MRI acquisition
Head motion distortion
Real-time quality control
Resting state functional connectivity MRI
Structural MRI
Functional MRI
MRI methods
Language English
License This is an open access article under the CC BY-NC-ND license.
Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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  ident: 10.1016/j.neuroimage.2017.08.025_bib47
  article-title: Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI
  publication-title: Magn. Reson Med.
  doi: 10.1002/mrm.23228
SSID ssj0009148
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Snippet Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI...
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SubjectTerms Accuracy
Adolescent
Adult
Alcoholism - diagnostic imaging
Attention Deficit Disorder with Hyperactivity - diagnostic imaging
Autism Spectrum Disorder - diagnostic imaging
Brain - diagnostic imaging
Child
Cost control
Functional magnetic resonance imaging
Functional MRI
Functional Neuroimaging - methods
Functional Neuroimaging - standards
Head motion distortion
Head Movements - physiology
Humans
Image Processing, Computer-Assisted - methods
Image Processing, Computer-Assisted - standards
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - standards
MRI acquisition
MRI methods
Neural networks
NMR
Nuclear magnetic resonance
Quality
Real-time quality control
Resting state functional connectivity MRI
Scanners
Structural MRI
Structure-function relationships
Young Adult
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  priority: 102
  providerName: ProQuest
Title Real-time motion analytics during brain MRI improve data quality and reduce costs
URI https://www.clinicalkey.com/#!/content/1-s2.0-S1053811917306729
https://dx.doi.org/10.1016/j.neuroimage.2017.08.025
https://www.ncbi.nlm.nih.gov/pubmed/28803940
https://www.proquest.com/docview/1965445393
https://www.proquest.com/docview/1928781526
https://pubmed.ncbi.nlm.nih.gov/PMC5731481
Volume 161
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