Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM)
Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that uses a coil to induce an electric field (E-field) in the brain and modulate its activity. Many applications of TMS call for the repeated execution of E-field solvers to determine the E-field induced in the bra...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922009715 |
| _version_ | 1811177288297873408 |
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| author | Dezhi Wang Nahian I. Hasan Moritz Dannhauer Abdulkadir C. Yucel Luis J. Gomez |
| author_facet | Dezhi Wang Nahian I. Hasan Moritz Dannhauer Abdulkadir C. Yucel Luis J. Gomez |
| author_sort | Dezhi Wang |
| collection | DOAJ |
| description | Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that uses a coil to induce an electric field (E-field) in the brain and modulate its activity. Many applications of TMS call for the repeated execution of E-field solvers to determine the E-field induced in the brain for different coil placements. However, the usage of solvers for these applications remains impractical because each coil placement requires the solution of a large linear system of equations. We develop a fast E-field solver that enables the rapid evaluation of the E-field distribution for a brain region of interest (ROI) for a large number of coil placements, which is achieved in two stages. First, during the pre-processing stage, the mapping between coil placement and brain ROI E-field distribution is approximated from E-field results for a few coil placements. Specifically, we discretize the mapping into a matrix with each column having the ROI E-field samples for a fixed coil placement. This matrix is approximated from a few of its rows and columns using adaptive cross approximation (ACA). The accuracy, efficiency, and applicability of the new ACA approach are determined by comparing its E-field predictions with analytical and standard solvers in spherical and MRI-derived head models. During the second stage, the E-field distribution in the brain ROI from a specific coil placement is determined by the obtained rows and columns in milliseconds. For many applications, only the E-field distribution for a comparatively small ROI is required. For example, the solver can complete the pre-processing stage in approximately 4 hours and determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error enabling its use for neuro-navigation and other applications. Highlight: We developed a fast solver for TMS computational E-field dosimetry, which can determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error. |
| first_indexed | 2024-04-10T22:59:17Z |
| format | Article |
| id | doaj.art-944a69ec384845f1a56509948726fa2b |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-10T22:59:17Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-944a69ec384845f1a56509948726fa2b2023-01-14T04:26:18ZengElsevierNeuroImage1095-95722023-02-01267119850Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM)Dezhi Wang0Nahian I. Hasan1Moritz Dannhauer2Abdulkadir C. Yucel3Luis J. Gomez4Elmore Family School of Electrical and Computer Engineering, Purdue University, 516 Northwestern Ave, West Lafayette, 47906, IN, USAElmore Family School of Electrical and Computer Engineering, Purdue University, 516 Northwestern Ave, West Lafayette, 47906, IN, USANational Institute of Mental Health (NIMH), National Institute of Health (NIH), 6001 Executive Boulevard, Bethesda, 20892, MD, USADepartment of Electrical and Computer Engineering, Nanyang Technological University (NTU), 50 Nanyang Ave, 639798, SingaporeCorresponding author.; Elmore Family School of Electrical and Computer Engineering, Purdue University, 516 Northwestern Ave, West Lafayette, 47906, IN, USATranscranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation technique that uses a coil to induce an electric field (E-field) in the brain and modulate its activity. Many applications of TMS call for the repeated execution of E-field solvers to determine the E-field induced in the brain for different coil placements. However, the usage of solvers for these applications remains impractical because each coil placement requires the solution of a large linear system of equations. We develop a fast E-field solver that enables the rapid evaluation of the E-field distribution for a brain region of interest (ROI) for a large number of coil placements, which is achieved in two stages. First, during the pre-processing stage, the mapping between coil placement and brain ROI E-field distribution is approximated from E-field results for a few coil placements. Specifically, we discretize the mapping into a matrix with each column having the ROI E-field samples for a fixed coil placement. This matrix is approximated from a few of its rows and columns using adaptive cross approximation (ACA). The accuracy, efficiency, and applicability of the new ACA approach are determined by comparing its E-field predictions with analytical and standard solvers in spherical and MRI-derived head models. During the second stage, the E-field distribution in the brain ROI from a specific coil placement is determined by the obtained rows and columns in milliseconds. For many applications, only the E-field distribution for a comparatively small ROI is required. For example, the solver can complete the pre-processing stage in approximately 4 hours and determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error enabling its use for neuro-navigation and other applications. Highlight: We developed a fast solver for TMS computational E-field dosimetry, which can determine the ROI E-field in approximately 40 ms for a 100 mm diameter ROI with less than 2% error.http://www.sciencedirect.com/science/article/pii/S1053811922009715Transcranial magnetic stimulation (TMS)Fast simulationLow rank approximationAdaptive cross approximation (ACA)Motor mapping |
| spellingShingle | Dezhi Wang Nahian I. Hasan Moritz Dannhauer Abdulkadir C. Yucel Luis J. Gomez Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM) NeuroImage Transcranial magnetic stimulation (TMS) Fast simulation Low rank approximation Adaptive cross approximation (ACA) Motor mapping |
| title | Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM) |
| title_full | Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM) |
| title_fullStr | Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM) |
| title_full_unstemmed | Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM) |
| title_short | Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM) |
| title_sort | fast computational e field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method aca adm |
| topic | Transcranial magnetic stimulation (TMS) Fast simulation Low rank approximation Adaptive cross approximation (ACA) Motor mapping |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922009715 |
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