Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI
Deep brain stimulation (DBS) requires accurate localization of the anatomical target structure, and the precise placement of the DBS electrode within it. Ultra-high field 7 Tesla (T) MR images can be utilized to create patient-specific anatomical 3D models of the subthalamic nuclei (STN) to enhance...
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| creator | Duchin, Yuval Shamir, Reuben R Patriat, Remi Kim, Jinyoung Vitek, Jerrold L Sapiro, Guillermo Harel, Noam |
| description | Deep brain stimulation (DBS) requires accurate localization of the anatomical target structure, and the precise placement of the DBS electrode within it. Ultra-high field 7 Tesla (T) MR images can be utilized to create patient-specific anatomical 3D models of the subthalamic nuclei (STN) to enhance pre-surgical DBS targeting as well as post-surgical visualization of the DBS lead position and orientation. We validated the accuracy of the 7T imaging-based patient-specific model of the STN and measured the variability of the location and dimensions across movement disorder patients.
72 patients who underwent DBS surgery were scanned preoperatively on 7T MRI. Segmentations and 3D volume rendering of the STN were generated for all patients. For 21 STN-DBS cases, microelectrode recording (MER) was used to validate the segmentation. For 12 cases, we computed the correlation between the overlap of the STN and volume of tissue activated (VTA) and the monopolar review for a further validation of the model's accuracy and its clinical relevancy.
We successfully reconstructed and visualized the STN in all patients. Significant variability was found across individuals regarding the location of the STN center of mass as well as its volume, length, depth and width. Significant correlations were found between MER and the 7T imaging-based model of the STN (r = 0.86) and VTA-STN overlap and the monopolar review outcome (r = 0.61).
The results suggest that an accurate visualization and localization of a patient-specific 3D model of the STN can be generated based on 7T MRI. The imaging-based 7T MRI STN model was validated using MER and patient's clinical outcomes. The significant variability observed in the STN location and shape based on a large number of patients emphasizes the importance of an accurate direct visualization of the STN for DBS targeting. An accurate STN localization can facilitate postoperative stimulation parameters for optimized patient outcome. |
| doi_str_mv | 10.1371/journal.pone.0201469 |
| format | Article |
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72 patients who underwent DBS surgery were scanned preoperatively on 7T MRI. Segmentations and 3D volume rendering of the STN were generated for all patients. For 21 STN-DBS cases, microelectrode recording (MER) was used to validate the segmentation. For 12 cases, we computed the correlation between the overlap of the STN and volume of tissue activated (VTA) and the monopolar review for a further validation of the model's accuracy and its clinical relevancy.
We successfully reconstructed and visualized the STN in all patients. Significant variability was found across individuals regarding the location of the STN center of mass as well as its volume, length, depth and width. Significant correlations were found between MER and the 7T imaging-based model of the STN (r = 0.86) and VTA-STN overlap and the monopolar review outcome (r = 0.61).
The results suggest that an accurate visualization and localization of a patient-specific 3D model of the STN can be generated based on 7T MRI. The imaging-based 7T MRI STN model was validated using MER and patient's clinical outcomes. The significant variability observed in the STN location and shape based on a large number of patients emphasizes the importance of an accurate direct visualization of the STN for DBS targeting. An accurate STN localization can facilitate postoperative stimulation parameters for optimized patient outcome.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0201469</identifier><identifier>PMID: 30133472</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Artificial intelligence ; Biology and Life Sciences ; Biomedical engineering ; Brain ; Brain research ; Brain stimulation ; Computer engineering ; Computer science ; Correlation ; Deep brain stimulation ; Drug therapy ; Image processing ; Image segmentation ; Information science ; Localization ; Magnetic resonance imaging ; Medical imaging ; Medicine and Health Sciences ; Microelectrodes ; Model accuracy ; Neuroimaging ; Parkinson's disease ; Patients ; Position (location) ; Public access ; Recording ; Research and Analysis Methods ; Signal to noise ratio ; Stimulation ; Surgeons ; Surgery ; Thalamus ; Three dimensional models ; Variability ; Visualization</subject><ispartof>PloS one, 2018-08, Vol.13 (8), p.e0201469-e0201469</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 Duchin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 Duchin et al 2018 Duchin et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-c46031f851af3d0a50d0ffefbf444fcf29157741180a9eb7f9fd8b0cd31bd8fc3</citedby><cites>FETCH-LOGICAL-c692t-c46031f851af3d0a50d0ffefbf444fcf29157741180a9eb7f9fd8b0cd31bd8fc3</cites><orcidid>0000-0003-2928-3772 ; 0000-0002-4927-7276</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6104927/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6104927/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30133472$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Duchin, Yuval</creatorcontrib><creatorcontrib>Shamir, Reuben R</creatorcontrib><creatorcontrib>Patriat, Remi</creatorcontrib><creatorcontrib>Kim, Jinyoung</creatorcontrib><creatorcontrib>Vitek, Jerrold L</creatorcontrib><creatorcontrib>Sapiro, Guillermo</creatorcontrib><creatorcontrib>Harel, Noam</creatorcontrib><title>Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Deep brain stimulation (DBS) requires accurate localization of the anatomical target structure, and the precise placement of the DBS electrode within it. Ultra-high field 7 Tesla (T) MR images can be utilized to create patient-specific anatomical 3D models of the subthalamic nuclei (STN) to enhance pre-surgical DBS targeting as well as post-surgical visualization of the DBS lead position and orientation. We validated the accuracy of the 7T imaging-based patient-specific model of the STN and measured the variability of the location and dimensions across movement disorder patients.
72 patients who underwent DBS surgery were scanned preoperatively on 7T MRI. Segmentations and 3D volume rendering of the STN were generated for all patients. For 21 STN-DBS cases, microelectrode recording (MER) was used to validate the segmentation. For 12 cases, we computed the correlation between the overlap of the STN and volume of tissue activated (VTA) and the monopolar review for a further validation of the model's accuracy and its clinical relevancy.
We successfully reconstructed and visualized the STN in all patients. Significant variability was found across individuals regarding the location of the STN center of mass as well as its volume, length, depth and width. Significant correlations were found between MER and the 7T imaging-based model of the STN (r = 0.86) and VTA-STN overlap and the monopolar review outcome (r = 0.61).
The results suggest that an accurate visualization and localization of a patient-specific 3D model of the STN can be generated based on 7T MRI. The imaging-based 7T MRI STN model was validated using MER and patient's clinical outcomes. The significant variability observed in the STN location and shape based on a large number of patients emphasizes the importance of an accurate direct visualization of the STN for DBS targeting. An accurate STN localization can facilitate postoperative stimulation parameters for optimized patient outcome.</description><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Biology and Life Sciences</subject><subject>Biomedical engineering</subject><subject>Brain</subject><subject>Brain research</subject><subject>Brain stimulation</subject><subject>Computer engineering</subject><subject>Computer science</subject><subject>Correlation</subject><subject>Deep brain stimulation</subject><subject>Drug therapy</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Information science</subject><subject>Localization</subject><subject>Magnetic resonance imaging</subject><subject>Medical imaging</subject><subject>Medicine and Health Sciences</subject><subject>Microelectrodes</subject><subject>Model accuracy</subject><subject>Neuroimaging</subject><subject>Parkinson's disease</subject><subject>Patients</subject><subject>Position 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anatomical model for deep brain stimulation based on 7 Tesla MRI</title><author>Duchin, Yuval ; Shamir, Reuben R ; Patriat, Remi ; Kim, Jinyoung ; Vitek, Jerrold L ; Sapiro, Guillermo ; Harel, Noam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-c46031f851af3d0a50d0ffefbf444fcf29157741180a9eb7f9fd8b0cd31bd8fc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Analysis</topic><topic>Artificial intelligence</topic><topic>Biology and Life Sciences</topic><topic>Biomedical engineering</topic><topic>Brain</topic><topic>Brain research</topic><topic>Brain stimulation</topic><topic>Computer engineering</topic><topic>Computer science</topic><topic>Correlation</topic><topic>Deep brain stimulation</topic><topic>Drug therapy</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Information science</topic><topic>Localization</topic><topic>Magnetic resonance imaging</topic><topic>Medical imaging</topic><topic>Medicine and Health Sciences</topic><topic>Microelectrodes</topic><topic>Model accuracy</topic><topic>Neuroimaging</topic><topic>Parkinson's disease</topic><topic>Patients</topic><topic>Position (location)</topic><topic>Public access</topic><topic>Recording</topic><topic>Research and Analysis Methods</topic><topic>Signal to noise ratio</topic><topic>Stimulation</topic><topic>Surgeons</topic><topic>Surgery</topic><topic>Thalamus</topic><topic>Three dimensional models</topic><topic>Variability</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Duchin, Yuval</creatorcontrib><creatorcontrib>Shamir, Reuben R</creatorcontrib><creatorcontrib>Patriat, Remi</creatorcontrib><creatorcontrib>Kim, Jinyoung</creatorcontrib><creatorcontrib>Vitek, Jerrold L</creatorcontrib><creatorcontrib>Sapiro, Guillermo</creatorcontrib><creatorcontrib>Harel, 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One</addtitle><date>2018-08-22</date><risdate>2018</risdate><volume>13</volume><issue>8</issue><spage>e0201469</spage><epage>e0201469</epage><pages>e0201469-e0201469</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Deep brain stimulation (DBS) requires accurate localization of the anatomical target structure, and the precise placement of the DBS electrode within it. Ultra-high field 7 Tesla (T) MR images can be utilized to create patient-specific anatomical 3D models of the subthalamic nuclei (STN) to enhance pre-surgical DBS targeting as well as post-surgical visualization of the DBS lead position and orientation. We validated the accuracy of the 7T imaging-based patient-specific model of the STN and measured the variability of the location and dimensions across movement disorder patients.
72 patients who underwent DBS surgery were scanned preoperatively on 7T MRI. Segmentations and 3D volume rendering of the STN were generated for all patients. For 21 STN-DBS cases, microelectrode recording (MER) was used to validate the segmentation. For 12 cases, we computed the correlation between the overlap of the STN and volume of tissue activated (VTA) and the monopolar review for a further validation of the model's accuracy and its clinical relevancy.
We successfully reconstructed and visualized the STN in all patients. Significant variability was found across individuals regarding the location of the STN center of mass as well as its volume, length, depth and width. Significant correlations were found between MER and the 7T imaging-based model of the STN (r = 0.86) and VTA-STN overlap and the monopolar review outcome (r = 0.61).
The results suggest that an accurate visualization and localization of a patient-specific 3D model of the STN can be generated based on 7T MRI. The imaging-based 7T MRI STN model was validated using MER and patient's clinical outcomes. The significant variability observed in the STN location and shape based on a large number of patients emphasizes the importance of an accurate direct visualization of the STN for DBS targeting. An accurate STN localization can facilitate postoperative stimulation parameters for optimized patient outcome.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30133472</pmid><doi>10.1371/journal.pone.0201469</doi><tpages>e0201469</tpages><orcidid>https://orcid.org/0000-0003-2928-3772</orcidid><orcidid>https://orcid.org/0000-0002-4927-7276</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Artificial intelligence Biology and Life Sciences Biomedical engineering Brain Brain research Brain stimulation Computer engineering Computer science Correlation Deep brain stimulation Drug therapy Image processing Image segmentation Information science Localization Magnetic resonance imaging Medical imaging Medicine and Health Sciences Microelectrodes Model accuracy Neuroimaging Parkinson's disease Patients Position (location) Public access Recording Research and Analysis Methods Signal to noise ratio Stimulation Surgeons Surgery Thalamus Three dimensional models Variability Visualization |
| title | Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI |
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