Participant factors that contribute to magnetic resonance imaging motion artifacts in children with mild traumatic brain injury or orthopedic injury

Abstract

Background: Motion can compromise image quality and confound results, especially in pediatric research. This study evaluated qualitative and quantitative approaches to motion artifacts detection and correction, and whether motion artifacts relate to injury history, age, or sex in children with mild traumatic brain injury or orthopedic injury relative to typically developing children. The concordance between qualitative and quantitative motion ratings was also examined.Method: Children aged 8-16 years with mild traumatic brain injury (n=141) or orthopedic injury (n=73) were recruited from the emergency department and completed an MRI scan roughly 2 weeks post-injury.Typically developing children (n=41) completed a single MRI scan. T1-and diffusion-weighted images were visually inspected and rated for motion artifacts by trained examiners. Quantitative estimates of motion artifacts were derived from FreeSurfer and FSL.Results: Age (younger > older) and sex (boys > girls) were signi cantly associated with motion artifacts on both T1-and diffusion-weighted images. Children with mild traumatic brain or orthopedic injury had signi cantly more motion-corrupted diffusion-weighted volumes than typically developing children, but mild traumatic brain injury and orthopedic injury groups did not differ from each other. The exclusion of motion-corrupted volumes did not signi cantly change diffusion tensor imaging metrics.Discussion: Results indicate that automated quantitative estimates of motion artifacts, which are less labour-intensive than manual methods, are appropriate. Results have implications for the reliability of structural magnetic resonance imaging research and highlight the importance of considering motion artifacts in studies of pediatric mild traumatic brain injury.

Longitudinal white matter microstructural changes in pediatric mild traumatic brain injury: AnA‐CAPstudy

“…Diffusion‐weighted volumes with severe motion artifact were removed. Datasets with >7 volumes with severe motion artifact were excluded from subsequent analysis (Ware et al, 2021 ).…”

Longitudinal Gray Matter Trajectories in Pediatric Mild Traumatic Brain Injury

“…34 Discrepancies in findings likely reflect the use of different comparison groups, given the growing evidence that an OI comparison group provides a more conservative basis for comparisons in pediatric mTBI research than uninjured, typically developing children. 17,36,37 Inconsistencies could also reflect methodological differences across known studies (e.g., age ranges, postinjury time, sex ratios, acquisition parameters, etc). 2…”

“…As detailed previously, 17 initial visual quality assurance of raw images was conducted to identify and exclude scans that demonstrated structural abnormalities at the time of the scan, scanner artifact, incomplete acquisition, reduced FOV (i.e., cropped), or were not collected using the default scan parameters. Data that passed the initial quality checks were subsequently rated for motion by at least 2 trained analysts using a 0-2 ordinal scale, with "0" for gross artifacts, "1" for minor artifacts (i.e., acceptable), and "2" for images free from visible artifact (i.e., excellent).…”

Cerebral Hemodynamics and Microvasculature Changes in Relation to White Matter Microstructure After Pediatric Mild Traumatic Brain Injury: An A-CAP Pilot Study

“…Children with OI were included if they sustained an upper or lower extremity fracture, sprain, or strain because of blunt force/physical trauma associated with an Abbreviated Injury Scale score ≤4. 19 Children were excluded from the OI group if they had any TBI or signs or symptoms of concussion at the time of recruitment, or any injury requiring surgical intervention or procedural sedation.…”

“…MRI acquisition and processing are described elsewhere. 19 Briefly, after image pre-processing and quality assurance procedures, 19 automated deterministic tractography was performed using the open-source Automated Fiber Quantification software package (v1.2) 21 using the default parameters. 22 Non-linear transformation was used to apply a waypoint region-of-interest template to individual images in native (diffusion-weighted) space to identify the forceps major (splenium) and forceps minor (genu) of the corpus callosum, along with five major white matter tracts in each hemisphere: corticospinal tract, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, thalamic radiations, and uncinate fasciculus.…”