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Calibrating 3D Scanner in the Coordinate System of Optical Tracker for Image-To-Patient Registration

Three-dimensional scanners have been widely applied in image-guided surgery (IGS) given its potential to solve the image-to-patient registration problem. How to perform a reliable calibration between a 3D scanner and an external tracker is especially important for these applications. This study prop...

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Autores principales: Li, Wenjie, Fan, Jingfan, Li, Shaowen, Tian, Zhaorui, Zheng, Zhao, Ai, Danni, Song, Hong, Yang, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160243/
https://www.ncbi.nlm.nih.gov/pubmed/34054454
http://dx.doi.org/10.3389/fnbot.2021.636772
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author Li, Wenjie
Fan, Jingfan
Li, Shaowen
Tian, Zhaorui
Zheng, Zhao
Ai, Danni
Song, Hong
Yang, Jian
author_facet Li, Wenjie
Fan, Jingfan
Li, Shaowen
Tian, Zhaorui
Zheng, Zhao
Ai, Danni
Song, Hong
Yang, Jian
author_sort Li, Wenjie
collection PubMed
description Three-dimensional scanners have been widely applied in image-guided surgery (IGS) given its potential to solve the image-to-patient registration problem. How to perform a reliable calibration between a 3D scanner and an external tracker is especially important for these applications. This study proposes a novel method for calibrating the extrinsic parameters of a 3D scanner in the coordinate system of an optical tracker. We bound an optical marker to a 3D scanner and designed a specified 3D benchmark for calibration. We then proposed a two-step calibration method based on the pointset registration technique and nonlinear optimization algorithm to obtain the extrinsic matrix of the 3D scanner. We applied repeat scan registration error (RSRE) as the cost function in the optimization process. Subsequently, we evaluated the performance of the proposed method on a recaptured verification dataset through RSRE and Chamfer distance (CD). In comparison with the calibration method based on 2D checkerboard, the proposed method achieved a lower RSRE (1.73 mm vs. 2.10, 1.94, and 1.83 mm) and CD (2.83 mm vs. 3.98, 3.46, and 3.17 mm). We also constructed a surgical navigation system to further explore the application of the tracked 3D scanner in image-to-patient registration. We conducted a phantom study to verify the accuracy of the proposed method and analyze the relationship between the calibration accuracy and the target registration error (TRE). The proposed scanner-based image-to-patient registration method was also compared with the fiducial-based method, and TRE and operation time (OT) were used to evaluate the registration results. The proposed registration method achieved an improved registration efficiency (50.72 ± 6.04 vs. 212.97 ± 15.91 s in the head phantom study). Although the TRE of the proposed registration method met the clinical requirements, its accuracy was lower than that of the fiducial-based registration method (1.79 ± 0.17 mm vs. 0.92 ± 0.16 mm in the head phantom study). We summarized and analyzed the limitations of the scanner-based image-to-patient registration method and discussed its possible development.
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spelling pubmed-81602432021-05-29 Calibrating 3D Scanner in the Coordinate System of Optical Tracker for Image-To-Patient Registration Li, Wenjie Fan, Jingfan Li, Shaowen Tian, Zhaorui Zheng, Zhao Ai, Danni Song, Hong Yang, Jian Front Neurorobot Neuroscience Three-dimensional scanners have been widely applied in image-guided surgery (IGS) given its potential to solve the image-to-patient registration problem. How to perform a reliable calibration between a 3D scanner and an external tracker is especially important for these applications. This study proposes a novel method for calibrating the extrinsic parameters of a 3D scanner in the coordinate system of an optical tracker. We bound an optical marker to a 3D scanner and designed a specified 3D benchmark for calibration. We then proposed a two-step calibration method based on the pointset registration technique and nonlinear optimization algorithm to obtain the extrinsic matrix of the 3D scanner. We applied repeat scan registration error (RSRE) as the cost function in the optimization process. Subsequently, we evaluated the performance of the proposed method on a recaptured verification dataset through RSRE and Chamfer distance (CD). In comparison with the calibration method based on 2D checkerboard, the proposed method achieved a lower RSRE (1.73 mm vs. 2.10, 1.94, and 1.83 mm) and CD (2.83 mm vs. 3.98, 3.46, and 3.17 mm). We also constructed a surgical navigation system to further explore the application of the tracked 3D scanner in image-to-patient registration. We conducted a phantom study to verify the accuracy of the proposed method and analyze the relationship between the calibration accuracy and the target registration error (TRE). The proposed scanner-based image-to-patient registration method was also compared with the fiducial-based method, and TRE and operation time (OT) were used to evaluate the registration results. The proposed registration method achieved an improved registration efficiency (50.72 ± 6.04 vs. 212.97 ± 15.91 s in the head phantom study). Although the TRE of the proposed registration method met the clinical requirements, its accuracy was lower than that of the fiducial-based registration method (1.79 ± 0.17 mm vs. 0.92 ± 0.16 mm in the head phantom study). We summarized and analyzed the limitations of the scanner-based image-to-patient registration method and discussed its possible development. Frontiers Media S.A. 2021-05-14 /pmc/articles/PMC8160243/ /pubmed/34054454 http://dx.doi.org/10.3389/fnbot.2021.636772 Text en Copyright © 2021 Li, Fan, Li, Tian, Zheng, Ai, Song and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Wenjie
Fan, Jingfan
Li, Shaowen
Tian, Zhaorui
Zheng, Zhao
Ai, Danni
Song, Hong
Yang, Jian
Calibrating 3D Scanner in the Coordinate System of Optical Tracker for Image-To-Patient Registration
title Calibrating 3D Scanner in the Coordinate System of Optical Tracker for Image-To-Patient Registration
title_full Calibrating 3D Scanner in the Coordinate System of Optical Tracker for Image-To-Patient Registration
title_fullStr Calibrating 3D Scanner in the Coordinate System of Optical Tracker for Image-To-Patient Registration
title_full_unstemmed Calibrating 3D Scanner in the Coordinate System of Optical Tracker for Image-To-Patient Registration
title_short Calibrating 3D Scanner in the Coordinate System of Optical Tracker for Image-To-Patient Registration
title_sort calibrating 3d scanner in the coordinate system of optical tracker for image-to-patient registration
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160243/
https://www.ncbi.nlm.nih.gov/pubmed/34054454
http://dx.doi.org/10.3389/fnbot.2021.636772
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