DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template

• Published in: NeuroImage (Orlando, Fla.) Vol. 257; p. 119266
• Main Authors: Fonov, Vladimir S., Dadar, Mahsa, ADNI, The PREVENT-AD Research Group, Collins, D. Louis
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15-08-2022; Elsevier Limited; Elsevier
• Subjects: Algorithms; Alzheimer's disease; Automation; Brain; Classification; Datasets; Deep learning; Image processing; Linear registration; Magnetic resonance imaging; Medical imaging; Medical research; Methods; Multiple sclerosis; Neural networks; Neuroimaging; Quality control; Registration; Scanners; Success; Deep learning; Linear registration; Quality control
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119266

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans.
In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans and 64476 registrations from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200).
In terms of agreement with a manual rater, our automated QC method was able to achieve 89% accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%).
The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.