Real-time computation of brain E-field for enhanced transcranial magnetic stimulation neuronavigation and optimization

• Published in: Imaging neuroscience (Cambridge, Mass.) Vol. 3
• Main Authors: Hasan, Nahian I., Dannhauer, Moritz, Wang, Dezhi, Deng, Zhi-De, Gomez, Luis J.
• Format: Journal Article
• Language: English
• Published: 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA MIT Press 02.01.2025
• Subjects: FEM; Huygens’ principle; neuronavigation; PMD; real-time; transcranial magnetic stimulation; neuronavigation; transcranial magnetic stimulation; real-time; PMD; Huygens’ principle; FEM
• ISSN: 2837-6056; 2837-6056
• DOI: 10.1162/imag_a_00412

Transcranial Magnetic Stimulation (TMS) coil placement and pulse waveform current are often chosen to achieve a specified E-field dose on targeted brain regions. TMS neuronavigation could be improved by including real-time accurate distributions of the E-field dose on the cortex. We introduce a method and develop software for computing brain E-field distributions in real-time enabling easy integration into neuronavigation and with the same accuracy as -order finite element method (FEM) solvers. Initially, a spanning basis set (<400) of E-fields generated by white noise magnetic currents on a surface separating the head and permissible coil placements are orthogonalized to generate the modes. Subsequently, Reciprocity and Huygens’ principles are utilized to compute fields induced by the modes on a surface separating the head and coil by FEM, which are used in conjunction with online (real-time) computed primary fields on the separating surface to evaluate the mode expansion. We conducted a comparative analysis of E-fields computed by FEM and in real-time for eight subjects, utilizing two head model types (SimNIBS’s ‘headreco’ and ‘mri2mesh’ pipeline), three coil types (circular, double-cone, and Figure-8), and 1000 coil placements (48,000 simulations). The real-time computation for any coil placement is within 4 milliseconds (ms), for 400 modes, and requires less than 4 GB of memory on a GPU. Our solver is capable of computing E-fields within 4 ms, making it a practical approach for integrating E-field information into the neuronavigation systems without imposing a significant overhead on frame generation. The software is available at .