Chengpei Li, Linton T Evans, Jennifer Hong, Scott C Davis, David W Roberts, Keith D Paulsen, Xiaoyao Fan
{"title":"硬脑膜打开后脑移补偿的全自动图像更新。","authors":"Chengpei Li, Linton T Evans, Jennifer Hong, Scott C Davis, David W Roberts, Keith D Paulsen, Xiaoyao Fan","doi":"10.3171/2025.4.JNS242786","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>In open cranial procedures, intraoperative brain shift can degrade the accuracy of surgical navigation on the basis of preoperative MR (pMR) images as soon as the cortical surface is exposed. The aim of this study was to develop a fully automated image updating system to address brain shift at the start of open cranial surgery and to evaluate its accuracy and efficiency.</p><p><strong>Methods: </strong>This study included patients undergoing open cranial procedures at a single center. Intraoperative stereovision (iSV) images of the surgical field were acquired as an easily integrated nondisruptive source of high-resolution image data on surgical surface deformation and were integrated with a computational model to compensate for volumetric brain shift after dural opening by updating the coregistered preoperative images. A Fast Segment Anything Model algorithm segmented the exposed cortical surface on iSV images automatically. Vessel and sulcus features were also segmented automatically from both iSV and pMR images and registered using a two-step registration method. Extracted nonrigid cortical displacements were assimilated by a finite element model to estimate whole-brain deformation. Updated MR (uMR) images were generated by deforming pMR by the resulting displacement field. A tracked stylus sampled the exposed cortical surface to provide independent measurements for error assessments. The uMR images were evaluated in terms of the misfit between model estimates and measured displacements, target registration error (TRE), and point-to-surface distance (PSD) relative to their pMR counterparts.</p><p><strong>Results: </strong>Fifteen patients (age range 45-85 years) who underwent open cranial procedures were included in the study. The overall accuracy of reconstructed iSV surfaces relative to stylus positions was 0.8 ± 0.7 mm. The overall mean misfit, TRE, and PSD of uMR images were 2.1 ± 1.2 mm, 1.9 ± 1.0 mm, and 1.6 ± 1.0 mm, respectively, compared with 6.5 ± 1.3 mm, 6.2 ± 1.2 mm, and 4.5 ± 1.2 mm for pMR images. Image updating was completed automatically without any user intervention in an overall mean of 3.9 ± 0.6 minutes.</p><p><strong>Conclusions: </strong>Automatic image updating compensated for brain shift due to dural opening and achieved clinically acceptable accuracy and efficiency. The system required no user intervention or expertise and caused minimal interruptions to surgical flow, suggesting it has potential for future integration into open cranial procedures.</p>","PeriodicalId":16505,"journal":{"name":"Journal of neurosurgery","volume":" ","pages":"1-11"},"PeriodicalIF":3.6000,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fully automated image updating for brain shift compensation after dural opening.\",\"authors\":\"Chengpei Li, Linton T Evans, Jennifer Hong, Scott C Davis, David W Roberts, Keith D Paulsen, Xiaoyao Fan\",\"doi\":\"10.3171/2025.4.JNS242786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>In open cranial procedures, intraoperative brain shift can degrade the accuracy of surgical navigation on the basis of preoperative MR (pMR) images as soon as the cortical surface is exposed. The aim of this study was to develop a fully automated image updating system to address brain shift at the start of open cranial surgery and to evaluate its accuracy and efficiency.</p><p><strong>Methods: </strong>This study included patients undergoing open cranial procedures at a single center. Intraoperative stereovision (iSV) images of the surgical field were acquired as an easily integrated nondisruptive source of high-resolution image data on surgical surface deformation and were integrated with a computational model to compensate for volumetric brain shift after dural opening by updating the coregistered preoperative images. A Fast Segment Anything Model algorithm segmented the exposed cortical surface on iSV images automatically. Vessel and sulcus features were also segmented automatically from both iSV and pMR images and registered using a two-step registration method. Extracted nonrigid cortical displacements were assimilated by a finite element model to estimate whole-brain deformation. Updated MR (uMR) images were generated by deforming pMR by the resulting displacement field. A tracked stylus sampled the exposed cortical surface to provide independent measurements for error assessments. The uMR images were evaluated in terms of the misfit between model estimates and measured displacements, target registration error (TRE), and point-to-surface distance (PSD) relative to their pMR counterparts.</p><p><strong>Results: </strong>Fifteen patients (age range 45-85 years) who underwent open cranial procedures were included in the study. The overall accuracy of reconstructed iSV surfaces relative to stylus positions was 0.8 ± 0.7 mm. The overall mean misfit, TRE, and PSD of uMR images were 2.1 ± 1.2 mm, 1.9 ± 1.0 mm, and 1.6 ± 1.0 mm, respectively, compared with 6.5 ± 1.3 mm, 6.2 ± 1.2 mm, and 4.5 ± 1.2 mm for pMR images. Image updating was completed automatically without any user intervention in an overall mean of 3.9 ± 0.6 minutes.</p><p><strong>Conclusions: </strong>Automatic image updating compensated for brain shift due to dural opening and achieved clinically acceptable accuracy and efficiency. The system required no user intervention or expertise and caused minimal interruptions to surgical flow, suggesting it has potential for future integration into open cranial procedures.</p>\",\"PeriodicalId\":16505,\"journal\":{\"name\":\"Journal of neurosurgery\",\"volume\":\" \",\"pages\":\"1-11\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of neurosurgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3171/2025.4.JNS242786\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neurosurgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3171/2025.4.JNS242786","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Fully automated image updating for brain shift compensation after dural opening.
Objective: In open cranial procedures, intraoperative brain shift can degrade the accuracy of surgical navigation on the basis of preoperative MR (pMR) images as soon as the cortical surface is exposed. The aim of this study was to develop a fully automated image updating system to address brain shift at the start of open cranial surgery and to evaluate its accuracy and efficiency.
Methods: This study included patients undergoing open cranial procedures at a single center. Intraoperative stereovision (iSV) images of the surgical field were acquired as an easily integrated nondisruptive source of high-resolution image data on surgical surface deformation and were integrated with a computational model to compensate for volumetric brain shift after dural opening by updating the coregistered preoperative images. A Fast Segment Anything Model algorithm segmented the exposed cortical surface on iSV images automatically. Vessel and sulcus features were also segmented automatically from both iSV and pMR images and registered using a two-step registration method. Extracted nonrigid cortical displacements were assimilated by a finite element model to estimate whole-brain deformation. Updated MR (uMR) images were generated by deforming pMR by the resulting displacement field. A tracked stylus sampled the exposed cortical surface to provide independent measurements for error assessments. The uMR images were evaluated in terms of the misfit between model estimates and measured displacements, target registration error (TRE), and point-to-surface distance (PSD) relative to their pMR counterparts.
Results: Fifteen patients (age range 45-85 years) who underwent open cranial procedures were included in the study. The overall accuracy of reconstructed iSV surfaces relative to stylus positions was 0.8 ± 0.7 mm. The overall mean misfit, TRE, and PSD of uMR images were 2.1 ± 1.2 mm, 1.9 ± 1.0 mm, and 1.6 ± 1.0 mm, respectively, compared with 6.5 ± 1.3 mm, 6.2 ± 1.2 mm, and 4.5 ± 1.2 mm for pMR images. Image updating was completed automatically without any user intervention in an overall mean of 3.9 ± 0.6 minutes.
Conclusions: Automatic image updating compensated for brain shift due to dural opening and achieved clinically acceptable accuracy and efficiency. The system required no user intervention or expertise and caused minimal interruptions to surgical flow, suggesting it has potential for future integration into open cranial procedures.
期刊介绍:
The Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of Neurosurgery: Pediatrics, and Neurosurgical Focus are devoted to the publication of original works relating primarily to neurosurgery, including studies in clinical neurophysiology, organic neurology, ophthalmology, radiology, pathology, and molecular biology. The Editors and Editorial Boards encourage submission of clinical and laboratory studies. Other manuscripts accepted for review include technical notes on instruments or equipment that are innovative or useful to clinicians and researchers in the field of neuroscience; papers describing unusual cases; manuscripts on historical persons or events related to neurosurgery; and in Neurosurgical Focus, occasional reviews. Letters to the Editor commenting on articles recently published in the Journal of Neurosurgery, Journal of Neurosurgery: Spine, and Journal of Neurosurgery: Pediatrics are welcome.