Patient-Specific Coronary Artery 3D Printing Based on Intravascular Optical Coherence Tomography and Coronary Angiography

Huang, Chenxi, Lan, Yisha, Chen, Sirui, Liu, Qing, Luo, Xin, Xu, Gaowei, Zhou, Wen, Lin, Fan, Peng, Yonghong, Ng, Eddie Y. K., Cheng, Yongqiang, Zeng, Nianyin, Zhang, Guokai and Che, Wenliang (2019) Patient-Specific Coronary Artery 3D Printing Based on Intravascular Optical Coherence Tomography and Coronary Angiography. Complexity, 2019 (571259). pp. 1-10. ISSN 1076-2787

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Abstract

Despite the new ideas were inspired in medical treatment by the rapid advancement of three-dimensional (3D) printing technology, there is still rare research work reported on 3D printing of coronary arteries being documented in the literature. In this work, the application value of 3D printing technology in the treatment of cardiovascular diseases has been explored via comparison study between the 3D printed vascular solid model and the computer aided design (CAD) model. In this paper, a new framework is proposed to achieve a 3D printing vascular model with high simulation. The patient-specific 3D reconstruction of the coronary arteries is performed by the detailed morphological information abstracted from the contour of the vessel lumen. In the process of reconstruction which has 5 steps, the morphological details of the contour view of the vessel lumen are merged along with the curvature and length information provided by the coronary angiography. After comparing with the diameter of the narrow section and the diameter of the normal section in CAD models and 3D printing model, it can be concluded that there is a high correlation between the diameter of vascular stenosis measured in 3D printing models and computer aided design models. The 3D printing model has high-modeling ability and high precision, which can represent the original coronary artery appearance accurately. It can be adapted for prevascularization planning to support doctors in determining the surgical procedures.

Item Type: Article
Divisions: Faculty of Technology > School of Computer Science
Depositing User: Leah Maughan
Date Deposited: 10 Feb 2020 11:47
Last Modified: 10 Feb 2020 11:47
URI: http://sure.sunderland.ac.uk/id/eprint/11518
ORCID for Yonghong Peng: ORCID iD orcid.org/0000-0002-5508-1819

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