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Reconstruction and optimization of the 3D geometric anatomy structure model for subject-specific human knee joint based on CT and MRI images
BACKGROUND: Nowadays, the total knee arthroplasty (TKA) technique plays an important role in surgical treatment for patients with severe knee osteoarthritis (OA). However, there are still several key issues such as promotion of osteotomy accuracy and prosthesis matching degree that need to be addres...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
IOS Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150550/ https://www.ncbi.nlm.nih.gov/pubmed/33682761 http://dx.doi.org/10.3233/THC-218022 |
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author | Niu, Junlong Qin, Xiansheng Bai, Jing Li, Haiyan |
author_facet | Niu, Junlong Qin, Xiansheng Bai, Jing Li, Haiyan |
author_sort | Niu, Junlong |
collection | PubMed |
description | BACKGROUND: Nowadays, the total knee arthroplasty (TKA) technique plays an important role in surgical treatment for patients with severe knee osteoarthritis (OA). However, there are still several key issues such as promotion of osteotomy accuracy and prosthesis matching degree that need to be addressed. OBJECTIVE: It is significant to construct an accurate three-dimensional (3D) geometric anatomy structure model of subject-specific human knee joint with major bone and soft tissue structures, which greatly contributes to obtaining personalized osteotomy guide plate and suitable size of prosthesis. METHODS: Considering different soft tissue structures, magnetic resonance imaging (MRI) scanning sequences involving two-dimensional (2D) spin echo (SE) sequence T1 weighted image (T1WI) and 3D SE sequence T2 weighted image (T2WI) fat suppression (FS) are selected. A 3D modeling methodology based on computed tomography (CT) and two sets of MRI images is proposed. RESULTS: According to the proposed methods of image segmentation and 3D model registration, a novel 3D knee joint model with high accuracy is finally constructed. Furthermore, remeshing is used to optimize the established model by adjusting the relevant parameters. CONCLUSIONS: The modeling results demonstrate that reconstruction and optimization model of 3D knee joint can clearly and accurately reflect the key characteristics, including anatomical structure and geometric morphology for each component. |
format | Online Article Text |
id | pubmed-8150550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | IOS Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-81505502021-06-09 Reconstruction and optimization of the 3D geometric anatomy structure model for subject-specific human knee joint based on CT and MRI images Niu, Junlong Qin, Xiansheng Bai, Jing Li, Haiyan Technol Health Care Research Article BACKGROUND: Nowadays, the total knee arthroplasty (TKA) technique plays an important role in surgical treatment for patients with severe knee osteoarthritis (OA). However, there are still several key issues such as promotion of osteotomy accuracy and prosthesis matching degree that need to be addressed. OBJECTIVE: It is significant to construct an accurate three-dimensional (3D) geometric anatomy structure model of subject-specific human knee joint with major bone and soft tissue structures, which greatly contributes to obtaining personalized osteotomy guide plate and suitable size of prosthesis. METHODS: Considering different soft tissue structures, magnetic resonance imaging (MRI) scanning sequences involving two-dimensional (2D) spin echo (SE) sequence T1 weighted image (T1WI) and 3D SE sequence T2 weighted image (T2WI) fat suppression (FS) are selected. A 3D modeling methodology based on computed tomography (CT) and two sets of MRI images is proposed. RESULTS: According to the proposed methods of image segmentation and 3D model registration, a novel 3D knee joint model with high accuracy is finally constructed. Furthermore, remeshing is used to optimize the established model by adjusting the relevant parameters. CONCLUSIONS: The modeling results demonstrate that reconstruction and optimization model of 3D knee joint can clearly and accurately reflect the key characteristics, including anatomical structure and geometric morphology for each component. IOS Press 2021-03-25 /pmc/articles/PMC8150550/ /pubmed/33682761 http://dx.doi.org/10.3233/THC-218022 Text en © 2021 – The authors. Published by IOS Press. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Niu, Junlong Qin, Xiansheng Bai, Jing Li, Haiyan Reconstruction and optimization of the 3D geometric anatomy structure model for subject-specific human knee joint based on CT and MRI images |
title | Reconstruction and optimization of the 3D geometric anatomy structure model for subject-specific human knee joint based on CT and MRI images |
title_full | Reconstruction and optimization of the 3D geometric anatomy structure model for subject-specific human knee joint based on CT and MRI images |
title_fullStr | Reconstruction and optimization of the 3D geometric anatomy structure model for subject-specific human knee joint based on CT and MRI images |
title_full_unstemmed | Reconstruction and optimization of the 3D geometric anatomy structure model for subject-specific human knee joint based on CT and MRI images |
title_short | Reconstruction and optimization of the 3D geometric anatomy structure model for subject-specific human knee joint based on CT and MRI images |
title_sort | reconstruction and optimization of the 3d geometric anatomy structure model for subject-specific human knee joint based on ct and mri images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150550/ https://www.ncbi.nlm.nih.gov/pubmed/33682761 http://dx.doi.org/10.3233/THC-218022 |
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