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Enhancing the depth perception of DSA images with 2D–3D registration

OBJECTIVE: Today, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important...

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Autores principales: Zhang, Xiaofeng, Deng, Yongzhi, Tian, Congyu, Chen, Shu, Wang, Yuanqing, Zhang, Meng, Wang, Qiong, Liao, Xiangyun, Si, Weixin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944716/
https://www.ncbi.nlm.nih.gov/pubmed/36846131
http://dx.doi.org/10.3389/fneur.2023.1122021
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author Zhang, Xiaofeng
Deng, Yongzhi
Tian, Congyu
Chen, Shu
Wang, Yuanqing
Zhang, Meng
Wang, Qiong
Liao, Xiangyun
Si, Weixin
author_facet Zhang, Xiaofeng
Deng, Yongzhi
Tian, Congyu
Chen, Shu
Wang, Yuanqing
Zhang, Meng
Wang, Qiong
Liao, Xiangyun
Si, Weixin
author_sort Zhang, Xiaofeng
collection PubMed
description OBJECTIVE: Today, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D–3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images. METHODS: To make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D–3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm. RESULT: In this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI). CONCLUSION: The experimental results in this study show that in the 2D–3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation.
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spelling pubmed-99447162023-02-23 Enhancing the depth perception of DSA images with 2D–3D registration Zhang, Xiaofeng Deng, Yongzhi Tian, Congyu Chen, Shu Wang, Yuanqing Zhang, Meng Wang, Qiong Liao, Xiangyun Si, Weixin Front Neurol Neurology OBJECTIVE: Today, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D–3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images. METHODS: To make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D–3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm. RESULT: In this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI). CONCLUSION: The experimental results in this study show that in the 2D–3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation. Frontiers Media S.A. 2023-02-08 /pmc/articles/PMC9944716/ /pubmed/36846131 http://dx.doi.org/10.3389/fneur.2023.1122021 Text en Copyright © 2023 Zhang, Deng, Tian, Chen, Wang, Zhang, Wang, Liao and Si. 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 Neurology
Zhang, Xiaofeng
Deng, Yongzhi
Tian, Congyu
Chen, Shu
Wang, Yuanqing
Zhang, Meng
Wang, Qiong
Liao, Xiangyun
Si, Weixin
Enhancing the depth perception of DSA images with 2D–3D registration
title Enhancing the depth perception of DSA images with 2D–3D registration
title_full Enhancing the depth perception of DSA images with 2D–3D registration
title_fullStr Enhancing the depth perception of DSA images with 2D–3D registration
title_full_unstemmed Enhancing the depth perception of DSA images with 2D–3D registration
title_short Enhancing the depth perception of DSA images with 2D–3D registration
title_sort enhancing the depth perception of dsa images with 2d–3d registration
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944716/
https://www.ncbi.nlm.nih.gov/pubmed/36846131
http://dx.doi.org/10.3389/fneur.2023.1122021
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