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 J. Nagel, Joel T. Nigg, Annie L. Nguyen, Victoria Wesevich, Deanna J. Greene, Damien A. Fair

Research output: Contribution to journalArticle

42 Scopus citations

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

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

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  • Cite this

    Dosenbach, N. U. F., Koller, J. M., Earl, E. A., Miranda-Dominguez, O., Klein, R. L., Van, A. N., Snyder, A. Z., Nagel, B. J., Nigg, J. T., Nguyen, A. L., Wesevich, V., Greene, D. J., & Fair, D. A. (2017). Real-time motion analytics during brain MRI improve data quality and reduce costs. NeuroImage, 161, 80-93. https://doi.org/10.1016/j.neuroimage.2017.08.025