ExploreQC: A toolbox for MRI quality control in the EPAD multicentre study: Neuroimaging / New imaging methods. (7th December 2020)

• Record Type: Journal Article
• Authors: Lorenzini, Luigi; Ingala, Silvia; Wottschel, Viktor; Wink, Alle Meije; Kuijer, Joost; Sudre, Carole H; Haller, Sven; Molinuevo, Jose Luis; Gispert, Juan Domingo; Cash, David M; Thomas, David L; Vos, Sjoerd; Petr, Jan; Wolz, Robin; Pernet, Cyril; Waldman, Adam; Barkhof, Frederik; Mutsaerts, Henri JMM
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.041952
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Magnetic Resonance Imaging (MRI) of the brain is prone to artefacts that may worsen image quality and subsequent analyses. Despite the growing number of large‐scale multi‐institutional imaging studies, standardized approaches for defining inclusion and exclusion criteria on the basis of image data quality are still lacking and quality assessment is often based on visual inspection. We introduce ExploreQC, a MATLAB‐based toolbox which implements a semi‐automatic pipeline to assess QC metrics, select the most relevant parameters, and to derive informed inclusion thresholds. Methods: The efficacy of ExploreQC was demonstrated on scans from 436 participants of the multi‐centre European Prevention of Alzheimer's Dementia (EPAD) longitudinal cohort study. Five different MRI sequences from five sites of the EPAD study were used: structural (T1w, FLAIR, diffusion MRI), resting state functional (rs‐fMRI) and ASL perfusion MRI. For each sequence, QC features were selected following a literature review of recently published studies on multi‐parameter QC (Alfaro‐Almagro, F. et al. 2018; Bastiani, M. et al. 2019; Esteban, O. et al. 2017; Fallatah, S. M. 2018; Shehzad, Z. 2015). Similar parameters were grouped in image feature domains (IFDs). Results: Table 1 shows the 38 QC features within 19 IFDs computed in the EPAD cohort covering each MRI scan‐type. ExploreQC generated within‐ and between‐site distribution plots of each QC parameter to identify outliers for subsequent visual inspection in order to establish the relevance of the parameter (Fig 1). Conclusions: ExploreQC facilitates the computation and visualization of informative QC parameters, within and between sites and modalities. Application of ExploreQC to the EPAD data showed that the investigation of distributions of different quantitative QC features can provide an overview of the general quality of the data, identify outliers and derive informed reference thresholds for exclusion criteria.