Automated quality control of T1-weighted brain MRI scans for clinical research: methods comparison and design of a quality prediction classifier

Introduction: T1-weighted MRI is widely used in clinical neuroimaging for studying brain structure and its changes, including those related to neurodegenerative diseases, and as anatomical reference for analysing other modalities. Ensuring high-quality T1-weighted scans is vital as image quality affects reliability of outcome measures. However, visual inspection can be subjective and time-consuming, especially with large datasets. The effectiveness of automated quality control (QC) tools for clinical cohorts remains uncertain. In this study, we used T1w scans from elderly participants within ageing and clinical populations to test the accuracy of existing QC tools with respect to visual QC and to establish a new quality prediction framework for clinical research use. Methods: Four datasets acquired from multiple scanners and sites were used (N = 2438, 11 sites, 39 scanner manufacturer models, 3 field strengths [1.5T, 3T, 2.9T], patients and controls, average age 71 +/- 8 years). All structural T1w scans were processed with two standard automated QC pipelines (MRIQC and CAT12). The agreement of the accept-reject ratings was compared between the automated pipelines and with visual QC. We then designed a quality prediction framework that combines the QC measures from the existing automated tools and is trained on clinical datasets. We tested the classifier performance using cross-validation on data from all sites together, also examining the performance across diagnostic groups. We then tested the generalisability of our approach when leaving one site out and explored how well our approach generalises to data from a different scanner manufacturer and/or field strength from those used for training. Results: Our results show significant agreement between automated QC tools and visual QC (Kappa=0.30 with MRIQC predictions; Kappa=0.28 with CAT12 rating) when considering the entire dataset, but the agreement was highly variable across datasets. Our proposed robust undersampling boost (RUS) classifier achieved 87.7% balanced accuracy on the test data combined from different sites (with 86.6% and 88.3% balanced accuracy on scans from patients and controls respectively). This classifier was also found to be generalisable on different combinations of training and test datasets (leave-one-site-out = 78.2% average balanced accuracy; exploratory models = 77.7% average balanced accuracy). Conclusion: While existing QC tools may not be robustly applicable to datasets comprised of older adults who have a higher rate of atrophy, they produce quality metrics that can be leveraged to train a more robust quality control classifiers for ageing and clinical cohorts. Keywords: Brain MRI, Classifier, DPUK, Multisite, Prediction, T1w, Quality control ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the UK Medical Research Council Dementias Platform UK (MR/T033371/1), an Alzheimer's Association Grant (AARF-21-846366), the Wellcome Centre for Integrative Neuroimaging (203139/Z/16/Z), the NIHR Oxford Cognitive Health Clinical Research Facility and by the NIHR Oxford Health Biomedical Research Centre (NIHR203316). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Work on the Whitehall II MRI substudy was funded by the Lifelong Health and Wellbeing Programme Grant: Predicting MRI abnormalities with longitudinal data of the Whitehall II Substudy (UK Medical Research Council: G1001354), the Horizon 2020 Grant: Lifebrain (Agreement number: 732592), and the HDH Wills 1965 Charitable Trust (No: 1117747). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Access to ADNI data is available to researchers upon request and approval of a data usage agreement (https://adni.loni.usc.edu/). The datasets were approved through the submission of an application via the DPUK data portal (https://portal.dementiasplatform.uk/Apply) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Access to ADNI data is available to researchers upon request and approval of a data usage agreement (https://adni.loni.usc.edu/). Details on how to request access to the data can be found at http://adni.loni.usc.edu/data-samples/access-data/. Other datasets used in this study can be accessed through the submission of an application via the DPUK data portal (https://portal.dementiasplatform.uk/Apply)