| Zugriffsnummer | 55842 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | Optimizing OPM-MEG sensor layouts using the sequential selection algorithm with simulated sources and individual anatomy |
| Autor(in); Institution |
Marhl, U.; Institute of Mathematics, Physics and Mechanics, Ljubljana, SLOVENIA; Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, SLOVENIA
Hren, R.; Institute of Mathematics, Physics and Mechanics, Ljubljana, SLOVENIA; Syreon Research Institute, Budapest, HUNGARY
Jazbinsek, V.; Institute of Mathematics, Physics and Mechanics, Ljubljana, SLOVENIA
|
| Quelle/Jahr | Sensors: 26 (2026), 1 - 25 |
| Artikelnummer | 1292 |
| Availability | [online only] |
| ISSN | 1424-8220 (online) |
| DOI | |
| Verlag | Basel: MDPI |
| Freie Schlagworte | magnetoencephalography ; optically pumped magnetometers ; sensor optimization ; sequential selection algorithm ; auditory-evoked fields ; magnetic field maps |
| Zusammenfassung | Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) offers the flexibility to position sensors closer to the scalp, which improves the signal-to-noise ratio compared to conventional superconducting quantum interference device (SQUID) systems. However, the spatial resolution of OPM-MEG critically depends on sensor placement, especially when the number of sensors is limited. In this study, we present a methodology for optimizing OPM-MEG sensor layouts using each subject’s anatomical information derived from individual magnetic resonance imaging (MRI). We generated realistic forward models from reconstructed head surfaces and simulated magnetic fields produced by equivalent current dipoles (ECDs). We compared multiple simulation strategies, including ECDs randomly distributed across the cortical surface and ECDs constrained to regions of interest. For each simulated magnetic field map (MFM) database, we applied the sequential selection algorithm (SSA) to identify sensor positions that maximized information capture. Unlike previous approaches relying on large measurement databases, this simulation-driven strategy eliminates the need for extensive pre-existing recordings. We benchmarked the performance of the personalized layouts using OPM-MEG datasets of auditory evoked fields (AEFs) derived from real whole-head SQUID-MEG measurements. Our results show that simulation-based SSA optimization improves the coverage of cortical regions of interest, reduces the number of sensors required for accurate source reconstruction, and yields sensor configurations that perform comparably to layouts optimized using measured data. To evaluate the quality of estimated MFMs, we applied metrics such as the correlation coefficient (CC), root-mean-square error, and relative error. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95) capture most of the information contained in full-head MFMs. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles and found that localization errors were < 5 mm. The results further indicate that SSA performance is insensitive to individualized head geometry, supporting the feasibility of using representative anatomical models and highlighting the potential of this approach for clinical OPM-MEG applications. This article belongs to the Special Issue Feature Papers in Biomedical Sensors 2025 |
| Kostenfreier Zugang | Open Access Gold |
| Rechteinformation | CC BY 4.0 ; Creative Commons Attribution 4.0 License |
| Themenbereich der Metrologie | Metrologie in der Medizin |