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Automatic image registration on intraoperative CBCT compared to Surface Matching registration on preoperative CT for spinal navigation: accuracy and workflow

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Title: Automatic image registration on intraoperative CBCT compared to Surface Matching registration on preoperative CT for spinal navigation: accuracy and workflow
Authors: Henrik Frisk, Gustav Burström, Oscar Persson, Victor Gabriel El-Hajj, Luisa Coronado, Susanne Hager, Erik Edström, Adrian Elmi-Terander
Source: Int J Comput Assist Radiol Surg
Publisher Information: Springer Science and Business Media LLC, 2024.
Publication Year: 2024
Subject Terms: Adult, CBCT, Spiral Cone-Beam Computed Tomography, Patient tracking, Spine/diagnostic imaging [MeSH], Spiral Cone-Beam Computed Tomography [MeSH], Workflow [MeSH], Adult [MeSH], Humans [MeSH], Surgery, Computer-Assisted/methods [MeSH], Neurosurgical Procedures [MeSH], Original Article, Surface Matching, Reference frame, Surgical navigation, Spine/surgery [MeSH], Spine surgery, Spine, Neurosurgical Procedures, Workflow, 03 medical and health sciences, Medical Imaging, 0302 clinical medicine, Medicinsk bildvetenskap, Surgery, Computer-Assisted, Humans, Radiologi och bildbehandling, Radiology, Nuclear Medicine and Medical Imaging
Description: Introduction Spinal navigation solutions have been slower to develop compared to cranial ones. To facilitate greater adoption and use of spinal navigation, the relatively cumbersome registration processes need to be improved upon. This study aims to validate a new solution for automatic image registration and compare it to a traditional Surface Matching method. Method Adult patients undergoing spinal surgery requiring navigation were enrolled after providing consent. A registration matrix—Universal AIR (= Automatic Image Registration)—was placed in the surgical field and used for automatic registration based on intraoperative 3D imaging. A standard Surface Matching method was used for comparison. Accuracy measurements were obtained by comparing planned and acquired coordinates on the vertebrae. Results Thirty-nine patients with 42 datasets were included. The mean accuracy of Universal AIR registration was 1.20 ± 0.42 mm, while the mean accuracy of Surface Matching registration was 1.94 ± 0.64 mm. Universal AIR registration was non-inferior to Surface Matching registration. Post hoc analysis showed a significantly greater accuracy for Universal AIR registration. In Surface Matching, but not automatic registration, user-related errors such as incorrect identification of the vertebral level were seen. Conclusion Automatic image registration for spinal navigation using Universal AIR and intraoperative 3D imaging provided improved accuracy compared to Surface Matching registration. In addition, it minimizes user errors and offers a standardized workflow, making it a reliable registration method for navigated spinal procedures.
Document Type: Article
Other literature type
File Description: application/pdf
Language: English
ISSN: 1861-6429
DOI: 10.1007/s11548-024-03076-4
Access URL: https://pubmed.ncbi.nlm.nih.gov/38378987
https://repository.publisso.de/resource/frl:6524468
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-541930
Rights: CC BY
Accession Number: edsair.doi.dedup.....f0b460c8cdd1238eb58e9ac66ad16d2e
Database: OpenAIRE
Description
Abstract:Introduction Spinal navigation solutions have been slower to develop compared to cranial ones. To facilitate greater adoption and use of spinal navigation, the relatively cumbersome registration processes need to be improved upon. This study aims to validate a new solution for automatic image registration and compare it to a traditional Surface Matching method. Method Adult patients undergoing spinal surgery requiring navigation were enrolled after providing consent. A registration matrix—Universal AIR (= Automatic Image Registration)—was placed in the surgical field and used for automatic registration based on intraoperative 3D imaging. A standard Surface Matching method was used for comparison. Accuracy measurements were obtained by comparing planned and acquired coordinates on the vertebrae. Results Thirty-nine patients with 42 datasets were included. The mean accuracy of Universal AIR registration was 1.20 ± 0.42 mm, while the mean accuracy of Surface Matching registration was 1.94 ± 0.64 mm. Universal AIR registration was non-inferior to Surface Matching registration. Post hoc analysis showed a significantly greater accuracy for Universal AIR registration. In Surface Matching, but not automatic registration, user-related errors such as incorrect identification of the vertebral level were seen. Conclusion Automatic image registration for spinal navigation using Universal AIR and intraoperative 3D imaging provided improved accuracy compared to Surface Matching registration. In addition, it minimizes user errors and offers a standardized workflow, making it a reliable registration method for navigated spinal procedures.
ISSN:18616429
DOI:10.1007/s11548-024-03076-4