Multi-constraint point set registration with redundant point removal for the registration of coronary arteries

Bu Xu; Lu Wang; Jinzhong Yang; Benqiang Yang; Lisheng Xu; Yang Chen; Dingchang Zheng

doi:10.1016/j.compbiomed.2023.107438

Multi-constraint point set registration with redundant point removal for the registration of coronary arteries

Bu Xu, Lu Wang, Jinzhong Yang, Benqiang Yang, Lisheng Xu, Yang Chen, Dingchang Zheng

Centre for Intelligent Healthcare

Research output: Contribution to journal › Article › peer-review

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Abstract

Background: Coronary artery disease (CAD) is the leading cause of death worldwide. The registration of the coronary artery at different phases can help radiologists explore the motion patterns of the coronary artery and assist in the diagnosis of CAD. However, there is no automatic and easy-to-execute method to solve the missing data problem that occurs at the endpoints of the coronary artery tree. This paper proposed a non-rigid multi-constraint point set registration with redundant point removal (MPSR-RPR) algorithm to tackle this challenge.

Methods: Firstly, the MPSR-RPR algorithm roughly registered two coronary artery point sets with the pre-set smoothness regularization parameter and Gaussian filter width value. The moving coherent, local feature, and the corresponding relationship between bifurcation point pairs were exploited as the constraints. Next, the spatial geometry information of the coronary artery was utilized to automatically recognize the vessel endpoints and to delete the redundant points of the coronary artery. Finally, the algorithm continued carrying out the multi-constraint registration with another group of the pre-set parameters to improve the alignment performance.

Results: The experimental results demonstrated that the MPSR-RPR algorithm achieved a significantly lower mean value of the modified Hausdorff distance (MHD) compared to the other state-of-the-art methods for addressing the serious missing data in the left and right coronary arteries.

Conclusion: This study demonstrated the effectiveness of the proposed algorithm in aligning coronary arteries, providing significant value in assisting in the diagnosis of coronary artery and myocardial lesions.

Original language	English
Article number	107438
Number of pages	11
Journal	Computers in Biology and Medicine
Volume	165
Early online date	1 Sept 2023
DOIs	https://doi.org/10.1016/j.compbiomed.2023.107438
Publication status	Published - Oct 2023

Bibliographical note

© 2023, Elsevier. Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Funder

This work was supported by the National Natural Science Foundation of China (No. 62273082, and No. 61773110), the Natural Science Foundation of Liaoning Province (No. 20170540312, and No. 2021-YGJC-14), the Liaoning Provincial “Selecting the Best Candidates by Opening Competition Mechanism” Science and Technology Program (No. 2022JH1/10400004), the Basic Scientific Research Project (Key Project) of Liaoning Provincial Department of Education (LJKZ00042021), and Fundamental Research Funds for the Central Universities (No. N2119008). This work was also supported by the Shenyang Science and Technology Plan Fund (No. 21-104-1-24, No. 20-201-4-10, and No. 201375).

Funding

This work was supported by the National Natural Science Foundation of China (No. 62273082, and No. 61773110), the Natural Science Foundation of Liaoning Province (No. 20170540312, and No. 2021-YGJC-14), the Liaoning Provincial “Selecting the Best Candidates by Opening Competition Mechanism” Science and Technology Program (No. 2022JH1/10400004), the Basic Scientific Research Project (Key Project) of Liaoning Provincial Department of Education (LJKZ00042021), and Fundamental Research Funds for the Central Universities (No. N2119008). This work was also supported by the Shenyang Science and Technology Plan Fund (No. 21-104-1-24, No. 20-201-4-10, and No. 201375).

Funders	Funder number
National Natural Science Foundation of China	62273082, 61773110
Natural Science Foundation of Liaoning Province	20170540312, 2021-YGJC-14
Liaoning Provincial Science and Technology Program	2022JH1/10400004
Liaoning Provincial Department of Education Series Project	LJKZ00042021
Fundamental Research Funds for the Central Universities	N2119008
Shenyang Science and Technology Plan Fund	21-104-1-24, 20-201-4-10, 201375

Keywords

Point set registration
Coronary arteries
Non-rigid
Multi-constraint
Missing data

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10.1016/j.compbiomed.2023.107438

Zheng2023AAMAccepted author manuscript, 4.56 MBLicence: CC BY-NC-ND

Cite this

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title = "Multi-constraint point set registration with redundant point removal for the registration of coronary arteries",

abstract = "Background: Coronary artery disease (CAD) is the leading cause of death worldwide. The registration of the coronary artery at different phases can help radiologists explore the motion patterns of the coronary artery and assist in the diagnosis of CAD. However, there is no automatic and easy-to-execute method to solve the missing data problem that occurs at the endpoints of the coronary artery tree. This paper proposed a non-rigid multi-constraint point set registration with redundant point removal (MPSR-RPR) algorithm to tackle this challenge. Methods: Firstly, the MPSR-RPR algorithm roughly registered two coronary artery point sets with the pre-set smoothness regularization parameter and Gaussian filter width value. The moving coherent, local feature, and the corresponding relationship between bifurcation point pairs were exploited as the constraints. Next, the spatial geometry information of the coronary artery was utilized to automatically recognize the vessel endpoints and to delete the redundant points of the coronary artery. Finally, the algorithm continued carrying out the multi-constraint registration with another group of the pre-set parameters to improve the alignment performance. Results: The experimental results demonstrated that the MPSR-RPR algorithm achieved a significantly lower mean value of the modified Hausdorff distance (MHD) compared to the other state-of-the-art methods for addressing the serious missing data in the left and right coronary arteries. Conclusion: This study demonstrated the effectiveness of the proposed algorithm in aligning coronary arteries, providing significant value in assisting in the diagnosis of coronary artery and myocardial lesions.",

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author = "Bu Xu and Lu Wang and Jinzhong Yang and Benqiang Yang and Lisheng Xu and Yang Chen and Dingchang Zheng",

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T1 - Multi-constraint point set registration with redundant point removal for the registration of coronary arteries

AU - Xu, Bu

AU - Wang, Lu

AU - Yang, Jinzhong

AU - Yang, Benqiang

AU - Xu, Lisheng

AU - Chen, Yang

AU - Zheng, Dingchang

N1 - © 2023, Elsevier. Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

PY - 2023/10

Y1 - 2023/10

N2 - Background: Coronary artery disease (CAD) is the leading cause of death worldwide. The registration of the coronary artery at different phases can help radiologists explore the motion patterns of the coronary artery and assist in the diagnosis of CAD. However, there is no automatic and easy-to-execute method to solve the missing data problem that occurs at the endpoints of the coronary artery tree. This paper proposed a non-rigid multi-constraint point set registration with redundant point removal (MPSR-RPR) algorithm to tackle this challenge. Methods: Firstly, the MPSR-RPR algorithm roughly registered two coronary artery point sets with the pre-set smoothness regularization parameter and Gaussian filter width value. The moving coherent, local feature, and the corresponding relationship between bifurcation point pairs were exploited as the constraints. Next, the spatial geometry information of the coronary artery was utilized to automatically recognize the vessel endpoints and to delete the redundant points of the coronary artery. Finally, the algorithm continued carrying out the multi-constraint registration with another group of the pre-set parameters to improve the alignment performance. Results: The experimental results demonstrated that the MPSR-RPR algorithm achieved a significantly lower mean value of the modified Hausdorff distance (MHD) compared to the other state-of-the-art methods for addressing the serious missing data in the left and right coronary arteries. Conclusion: This study demonstrated the effectiveness of the proposed algorithm in aligning coronary arteries, providing significant value in assisting in the diagnosis of coronary artery and myocardial lesions.

AB - Background: Coronary artery disease (CAD) is the leading cause of death worldwide. The registration of the coronary artery at different phases can help radiologists explore the motion patterns of the coronary artery and assist in the diagnosis of CAD. However, there is no automatic and easy-to-execute method to solve the missing data problem that occurs at the endpoints of the coronary artery tree. This paper proposed a non-rigid multi-constraint point set registration with redundant point removal (MPSR-RPR) algorithm to tackle this challenge. Methods: Firstly, the MPSR-RPR algorithm roughly registered two coronary artery point sets with the pre-set smoothness regularization parameter and Gaussian filter width value. The moving coherent, local feature, and the corresponding relationship between bifurcation point pairs were exploited as the constraints. Next, the spatial geometry information of the coronary artery was utilized to automatically recognize the vessel endpoints and to delete the redundant points of the coronary artery. Finally, the algorithm continued carrying out the multi-constraint registration with another group of the pre-set parameters to improve the alignment performance. Results: The experimental results demonstrated that the MPSR-RPR algorithm achieved a significantly lower mean value of the modified Hausdorff distance (MHD) compared to the other state-of-the-art methods for addressing the serious missing data in the left and right coronary arteries. Conclusion: This study demonstrated the effectiveness of the proposed algorithm in aligning coronary arteries, providing significant value in assisting in the diagnosis of coronary artery and myocardial lesions.

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Multi-constraint point set registration with redundant point removal for the registration of coronary arteries

Abstract

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