Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact

• Published in: NeuroImage (Orlando, Fla.) Vol. 296; p. 120661
• Main Authors: Wang, Ruonan, Fu, Kaiwen, Zhao, Ruochen, Wang, Dawei, Yang, Zhimin, Bin, Wei, Gao, Yang, Ning, Xiaolin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2024; Elsevier Limited; Elsevier
• Subjects: Algorithms; Artifact suppression; Clinical applications; Comparative analysis; Magnetoencephalography; Metallic artifacts; Neuroimaging; Optically pumped magnetometers; Orthodontics; Therapeutic applications; Timing; Vagus nerve; Optically pumped magnetometers; Metallic artifacts; Magnetoencephalography; Clinical applications; Artifact suppression
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2024.120661

Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time–frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5–8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG. [Display omitted] •The atomic magnetometers are getting more and more attention.•The metallic artifacts hinder the clinical application of wearable OPM-MEG.•The proposed method in this article can effectively suppress the dental metal artifact.