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Neuroimage. 2017 Nov 1;161:80-93. doi: 10.1016/j.neuroimage.2017.08.025. Epub 2017 Aug 10.

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

Author information

1
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA; Program in Occupational Therapy, Washington University, St. Louis, MO, USA. Electronic address: ndosenbach@wustl.edu.
2
Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
3
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA.
4
Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA.
5
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
6
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
7
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA.
8
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA.
9
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
10
Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
11
Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA; Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA; Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA. Electronic address: faird@ohsu.edu.

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

PMID:
28803940
PMCID:
PMC5731481
DOI:
10.1016/j.neuroimage.2017.08.025
[Indexed for MEDLINE]
Free PMC Article

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