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

• Published in: NeuroImage (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
• Language: English
• 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
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.08.025

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]