Cao Fuzhi, An Nan, Xu Weinan, Wang Wenli, Li Wen, Wang Chunhui, Yang Yanfei, Xiang Min, Gao Yang, Ning Xiaolin
Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Hangzhou, China.
Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China.
Front Neurosci. 2022 Sep 15;16:984036. doi: 10.3389/fnins.2022.984036. eCollection 2022.
Magnetoencephalography (MEG) based on optically pumped magnetometers (OPM-MEG) has shown better flexibility in sensor configuration compared with the conventional superconducting quantum interference devices-based MEG system while being better suited for all-age groups. However, this flexibility presents challenges for the co-registration of MEG and magnetic resonance imaging (MRI), hindering adoption. This study presents a toolbox called OMMR, developed in Matlab, that facilitates the co-registration step for researchers and clinicians. OMMR integrates the co-registration methods of using the electromagnetic digitization system and two types of optical scanners (the structural-light and laser scanner). As the first open-source co-registration toolbox specifically for OPM-MEG, the toolbox aims to standardize the co-registration process and set the ground for future applications of OPM-MEG.
基于光泵磁力仪(OPM-MEG)的脑磁图(MEG)与传统的基于超导量子干涉装置的MEG系统相比,在传感器配置方面表现出更好的灵活性,同时更适合所有年龄段的人群。然而,这种灵活性给MEG与磁共振成像(MRI)的联合配准带来了挑战,阻碍了其应用。本研究展示了一个在Matlab中开发的名为OMMR的工具箱,它为研究人员和临床医生提供了便利的联合配准步骤。OMMR整合了使用电磁数字化系统和两种类型光学扫描仪(结构光扫描仪和激光扫描仪)的联合配准方法。作为首个专门用于OPM-MEG的开源联合配准工具箱,该工具箱旨在规范联合配准过程,并为OPM-MEG的未来应用奠定基础。