Abstract
Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterise disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data‐driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of quantitative MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.
Highlights
Movement notoriously degrades magnetic resonance imaging (MRI) data, leading to prolonged examinations and increased costs in clinical applications [1,2]
We propose an alternative method that assigns a weight to each image within a cohort, computed from its motion degradation index (MDI) value using the restricted maximum likelihood (REML) algorithm 25
With QUIQI, the values of the MDI – specific for each image sample – are inserted into the REML algorithm in the form of basis functions that capture the relationship between image noise and the MDI
Summary
Movement notoriously degrades magnetic resonance imaging (MRI) data, leading to prolonged examinations and increased costs in clinical applications [1,2]. Quality control procedures exist that help mitigate the effect of image degradation on analysis results These procedures require an assessment of data quality, provided by a motion degradation index (MDI) 10–16. The weights are specific to each image and down-weight low quality images in subsequent analyses We illustrate this method through the analysis of a large cohort (1,432 participants) of quantitative MRI (qMRI) data. The proposed method, called QUIQI for ‘analysis of QUantitative Imaging data using a Quality Index’, restores homoscedasticity, ensuring the validity of statistical tests. This global approach provides near optimal results in whole-brain analysis of neuroimaging data, despite local effects of motion. The framework is flexible and amenable to other MDIs and to the analysis of other types of MRI data
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
