From simulation to clinic: Assessing the required channel count for effective clinical use of OPM-MEG systems
The channel count in an Optically Pumped Magnetometer Magnetoencephalography (OPM-MEG) system plays a pivotal role in determining its overall performance. While existing research consistently highlights that a greater number of channels enhances system capabilities, practical constraints such as sensor placement on the head, inter-channel interference, and cost-efficiency impose limitations on channel scalability. Additionally, the optimal channel count required for clinical applications of OPM-MEG remains unclear. In this study, we systematically investigate the impact of channel count on OPM-MEG performance by integrating simulations, phantom experiments, and human MEG experiments. Four configurations with varying channel counts (16, 32, 64, and 128) are evaluated. Specifically, systems with fewer channels (e.g., 16 channels) encounter significant challenges in meeting the demands of clinical MEG applications. In contrast, a 64-channel OPM-MEG system demonstrates performance metrics-such as signal-to-noise ratio (SNR) and localization accuracy-that are comparable to those of a 306-channel Superconducting Quantum Interference Device MEG (SQUID-MEG) system. Notably, a 128-channel OPM-MEG system surpasses the 306-channel SQUID-MEG system, achieving superior results. This work provides a detailed exploration of the relationship between channel count and OPM-MEG system performance, analyzing how many channels of the OPM-MEG system are suitable for clinical applications. By combining simulation-based evaluations with empirical measurements, we found that it is crucial to carefully select the appropriate number of channels based on the specific usage requirements in clinical applications.
Authors
- Yixiang Zhang (ORCID: https://orcid.org/0009-0001-0848-3129)
- Ming Ding (ORCID: https://orcid.org/0000-0003-2373-6494)
- Haonan Zhang (ORCID: https://orcid.org/0000-0002-4239-6141)
- Chang Sun (ORCID: https://orcid.org/0000-0002-8713-0160)
- Bing Yan (ORCID: https://orcid.org/0000-0003-3746-6560)
- Yuming Peng (ORCID: https://orcid.org/0009-0001-2702-821X)
- Yun Zhang (ORCID: https://orcid.org/0000-0001-9457-7801)
Institutions
- Beihang University (CN)
Publication Details
- Journal
- NeuroImage
- Published
- 2025-05-09
- DOI
- https://doi.org/10.1016/j.neuroimage.2025.121262
- Citations
- 5
- Primary Topic
- Atomic and Subatomic Physics Research
- Type
- article
- Field-Weighted Citation Impact
- 6.23