Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods
Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration...
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| Veröffentlicht in: | Journal of neuroscience methods 2024-04, Vol.404, p.110056, Article 110056 |
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| creator | Blenkmann, Alejandro Omar Leske, Sabine Liliana Llorens, Anaïs Lin, Jack J. Chang, Edward F. Brunner, Peter Schalk, Gerwin Ivanovic, Jugoslav Larsson, Pål Gunnar Knight, Robert Thomas Endestad, Tor Solbakk, Anne-Kristin |
| description | Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.
We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes’ CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.
We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants ( |
| doi_str_mv | 10.1016/j.jneumeth.2024.110056 |
| format | Article |
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We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes’ CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.
We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.
GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.
GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.
[Display omitted]
•CT noise and unrelated artifacts undermine the localization of intracranial electrodes.•GridFit provides a robust and precise model-based localization approach.•Anatomical registration of intracranial grids requires brain-shift compensation.•CEPA combines several projection methods accounting for brain-shift deformations.•GridFit and CEPA are implemented in the iElectrodes open-source toolbox.</description><identifier>ISSN: 0165-0270</identifier><identifier>ISSN: 1872-678X</identifier><identifier>EISSN: 1872-678X</identifier><identifier>DOI: 10.1016/j.jneumeth.2024.110056</identifier><identifier>PMID: 38224783</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Brain - diagnostic imaging ; Cerebral Cortex - diagnostic imaging ; Depth electrodes ; EEG (iEEG) ; Electrocorticography (ECoG) ; Electrodes ; Electrodes, Implanted ; Electroencephalography - methods ; Humans ; Intracranial ; Magnetic Resonance Imaging - methods ; Simulations ; Stereo electroencephalography (SEEG) ; Subcortical grids ; Subdural grids</subject><ispartof>Journal of neuroscience methods, 2024-04, Vol.404, p.110056, Article 110056</ispartof><rights>2024 The Authors</rights><rights>Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c416t-5edc3c7f4ad6846ce320f1c0b90468b064e2448db01d869a8b75202d332a05663</citedby><cites>FETCH-LOGICAL-c416t-5edc3c7f4ad6846ce320f1c0b90468b064e2448db01d869a8b75202d332a05663</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jneumeth.2024.110056$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,778,782,3539,27911,27912,45982</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38224783$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Blenkmann, Alejandro Omar</creatorcontrib><creatorcontrib>Leske, Sabine Liliana</creatorcontrib><creatorcontrib>Llorens, Anaïs</creatorcontrib><creatorcontrib>Lin, Jack J.</creatorcontrib><creatorcontrib>Chang, Edward F.</creatorcontrib><creatorcontrib>Brunner, Peter</creatorcontrib><creatorcontrib>Schalk, Gerwin</creatorcontrib><creatorcontrib>Ivanovic, Jugoslav</creatorcontrib><creatorcontrib>Larsson, Pål Gunnar</creatorcontrib><creatorcontrib>Knight, Robert Thomas</creatorcontrib><creatorcontrib>Endestad, Tor</creatorcontrib><creatorcontrib>Solbakk, Anne-Kristin</creatorcontrib><title>Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods</title><title>Journal of neuroscience methods</title><addtitle>J Neurosci Methods</addtitle><description>Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.
We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes’ CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.
We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.
GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.
GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.
[Display omitted]
•CT noise and unrelated artifacts undermine the localization of intracranial electrodes.•GridFit provides a robust and precise model-based localization approach.•Anatomical registration of intracranial grids requires brain-shift compensation.•CEPA combines several projection methods accounting for brain-shift deformations.•GridFit and CEPA are implemented in the iElectrodes open-source toolbox.</description><subject>Brain - diagnostic imaging</subject><subject>Cerebral Cortex - diagnostic imaging</subject><subject>Depth electrodes</subject><subject>EEG (iEEG)</subject><subject>Electrocorticography (ECoG)</subject><subject>Electrodes</subject><subject>Electrodes, Implanted</subject><subject>Electroencephalography - methods</subject><subject>Humans</subject><subject>Intracranial</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Simulations</subject><subject>Stereo electroencephalography (SEEG)</subject><subject>Subcortical grids</subject><subject>Subdural grids</subject><issn>0165-0270</issn><issn>1872-678X</issn><issn>1872-678X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc-KFDEQxoMo7uzqKyw5euneJJ1OZ24ui6vCgiAK3kL-VDsZOsmYpAV9BJ_aDL3r1VNR1O-ror4PoWtKekqouDn2xwhrgHroGWG8p5SQUTxDOyon1olJfnuOdg0cO8ImcoEuSzkSQvieiJfoYpCM8UkOO_TnNuqagrd6wRm--1Kzrj5FnGbsY2ts1tG3ISxga04OSo8_J7OWikPrls7oAg4vqW3wvzetjg47mFMO2iyAS0ipHrDJ2seuHPxcsU3hBLFs-PmL5Mor9GLWS4HXj_UKfb1_9-XuQ_fw6f3Hu9uHznIqajeCs4OdZq6dkFxYGBiZqSVmT7iQhggOjHPpDKFOir2WZhqbRW4YmG4WieEKvdn2nnL6sUKpKvhiYVl0hLQWxfZ0HCdKxqmhYkNtTqVkmNUp-6DzL0WJOuegjuopB3XOQW05NOH1443VBHD_ZE_GN-DtBkD79KeHrIr1EC04n5vRyiX_vxt_AbZvoCc</recordid><startdate>20240401</startdate><enddate>20240401</enddate><creator>Blenkmann, Alejandro Omar</creator><creator>Leske, Sabine Liliana</creator><creator>Llorens, Anaïs</creator><creator>Lin, Jack J.</creator><creator>Chang, Edward F.</creator><creator>Brunner, Peter</creator><creator>Schalk, Gerwin</creator><creator>Ivanovic, Jugoslav</creator><creator>Larsson, Pål Gunnar</creator><creator>Knight, Robert Thomas</creator><creator>Endestad, Tor</creator><creator>Solbakk, Anne-Kristin</creator><general>Elsevier B.V</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20240401</creationdate><title>Anatomical registration of intracranial electrodes. 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Robust model-based localization and deformable smooth brain-shift compensation methods</atitle><jtitle>Journal of neuroscience methods</jtitle><addtitle>J Neurosci Methods</addtitle><date>2024-04-01</date><risdate>2024</risdate><volume>404</volume><spage>110056</spage><pages>110056-</pages><artnum>110056</artnum><issn>0165-0270</issn><issn>1872-678X</issn><eissn>1872-678X</eissn><abstract>Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.
We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes’ CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.
We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.
GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.
GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.
[Display omitted]
•CT noise and unrelated artifacts undermine the localization of intracranial electrodes.•GridFit provides a robust and precise model-based localization approach.•Anatomical registration of intracranial grids requires brain-shift compensation.•CEPA combines several projection methods accounting for brain-shift deformations.•GridFit and CEPA are implemented in the iElectrodes open-source toolbox.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>38224783</pmid><doi>10.1016/j.jneumeth.2024.110056</doi><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; ScienceDirect Journals (5 years ago - present) |
| subjects | Brain - diagnostic imaging Cerebral Cortex - diagnostic imaging Depth electrodes EEG (iEEG) Electrocorticography (ECoG) Electrodes Electrodes, Implanted Electroencephalography - methods Humans Intracranial Magnetic Resonance Imaging - methods Simulations Stereo electroencephalography (SEEG) Subcortical grids Subdural grids |
| title | Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods |
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