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

Nico U.F. Dosenbach, Jonathan M. Koller, Eric A. Earl, Oscar Miranda Dominguez, Rachel L. Klein, Andrew N. Van, Abraham Z. Snyder, Bonnie Nagel, Joel Nigg, Annie L. Nguyen, Victoria Wesevich, Deanna J. Greene, Damien Fair

Research output: Contribution to journalArticle

24 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)80-93
Number of pages14
JournalNeuroImage
Volume161
DOIs
StatePublished - Nov 1 2017

Fingerprint

Costs and Cost Analysis
Brain
Head
Artifacts
Magnetic Resonance Imaging
Buffers
Software
Data Accuracy
Pediatrics
Research

Keywords

  • Functional MRI
  • Head motion distortion
  • MRI acquisition
  • MRI methods
  • Real-time quality control
  • Resting state functional connectivity MRI
  • Structural MRI

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

Real-time motion analytics during brain MRI improve data quality and reduce costs. / Dosenbach, Nico U.F.; Koller, Jonathan M.; Earl, Eric A.; Miranda Dominguez, Oscar; Klein, Rachel L.; Van, Andrew N.; Snyder, Abraham Z.; Nagel, Bonnie; Nigg, Joel; Nguyen, Annie L.; Wesevich, Victoria; Greene, Deanna J.; Fair, Damien.

In: NeuroImage, Vol. 161, 01.11.2017, p. 80-93.

Research output: Contribution to journalArticle

Dosenbach, NUF, Koller, JM, Earl, EA, Miranda Dominguez, O, Klein, RL, Van, AN, Snyder, AZ, Nagel, B, Nigg, J, Nguyen, AL, Wesevich, V, Greene, DJ & Fair, D 2017, 'Real-time motion analytics during brain MRI improve data quality and reduce costs', NeuroImage, vol. 161, pp. 80-93. https://doi.org/10.1016/j.neuroimage.2017.08.025
Dosenbach, Nico U.F. ; Koller, Jonathan M. ; Earl, Eric A. ; Miranda Dominguez, Oscar ; Klein, Rachel L. ; Van, Andrew N. ; Snyder, Abraham Z. ; Nagel, Bonnie ; Nigg, Joel ; Nguyen, Annie L. ; Wesevich, Victoria ; Greene, Deanna J. ; Fair, Damien. / Real-time motion analytics during brain MRI improve data quality and reduce costs. In: NeuroImage. 2017 ; Vol. 161. pp. 80-93.
@article{60518155574041a18961556bf98f9fa6,
title = "Real-time motion analytics during brain MRI improve data quality and reduce costs",
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.",
keywords = "Functional MRI, Head motion distortion, MRI acquisition, MRI methods, Real-time quality control, Resting state functional connectivity MRI, Structural MRI",
author = "Dosenbach, {Nico U.F.} and Koller, {Jonathan M.} and Earl, {Eric A.} and {Miranda Dominguez}, Oscar and Klein, {Rachel L.} and Van, {Andrew N.} and Snyder, {Abraham Z.} and Bonnie Nagel and Joel Nigg and Nguyen, {Annie L.} and Victoria Wesevich and Greene, {Deanna J.} and Damien Fair",
year = "2017",
month = "11",
day = "1",
doi = "10.1016/j.neuroimage.2017.08.025",
language = "English (US)",
volume = "161",
pages = "80--93",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Real-time motion analytics during brain MRI improve data quality and reduce costs

AU - Dosenbach, Nico U.F.

AU - Koller, Jonathan M.

AU - Earl, Eric A.

AU - Miranda Dominguez, Oscar

AU - Klein, Rachel L.

AU - Van, Andrew N.

AU - Snyder, Abraham Z.

AU - Nagel, Bonnie

AU - Nigg, Joel

AU - Nguyen, Annie L.

AU - Wesevich, Victoria

AU - Greene, Deanna J.

AU - Fair, Damien

PY - 2017/11/1

Y1 - 2017/11/1

N2 - 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.

AB - 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.

KW - Functional MRI

KW - Head motion distortion

KW - MRI acquisition

KW - MRI methods

KW - Real-time quality control

KW - Resting state functional connectivity MRI

KW - Structural MRI

UR - http://www.scopus.com/inward/record.url?scp=85027516860&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027516860&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2017.08.025

DO - 10.1016/j.neuroimage.2017.08.025

M3 - Article

VL - 161

SP - 80

EP - 93

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -