| Title: | Optimizing OPM-MEG sensor layouts using the sequential selection algorithm with simulated sources and individual anatomy |
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| Authors: | ID Marhl, Urban (Author)
ID Hren, Rok (Author)
ID Sander, Tilmann (Author)
ID Jazbinšek, Vojko (Author) |
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| Files: |  PDF - Presentation file, download (3,51 MB)
MD5: 0D5B92F5756FB800C98FC1878EC8C111
 URL - Source URL, visit https://www.mdpi.com/1424-8220/26/4/1292
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IMFM - Institute of Mathematics, Physics, and Mechanics
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| Abstract: | 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. |
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| Keywords: | medical imaging, magnetoencephalography, sensor optimization |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 01.02.2026 |
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| Year of publishing: | 2026 |
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| Number of pages: | 25 str. |
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| Numbering: | Vol. 26, iss. 4, art. no. 1292 |
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| PID: | 20.500.12556/DiRROS-27637  |
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| UDC: | 616-073 |
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| ISSN on article: | 1424-8220 |
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| DOI: | 10.3390/s26041292 |
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| COBISS.SI-ID: | 268680195 |
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| Note: |
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| Publication date in DiRROS: | 17.02.2026 |
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| Views: | 242 |
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| Downloads: | 90 |
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