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从常规 X 射线血管造影投影图像对冠状动脉进行三维重建和基于 NURBS 的结构网格划分。

Three-dimensional reconstruction and NURBS-based structured meshing of coronary arteries from the conventional X-ray angiography projection images.

机构信息

Faculty of Engineering Sciences, University of Kragujevac, Kragujevac, Serbia.

Research and Development Center for Bioengineering, Kragujevac, Kragujevac, Serbia.

出版信息

Sci Rep. 2018 Jan 26;8(1):1711. doi: 10.1038/s41598-018-19440-9.

DOI:10.1038/s41598-018-19440-9
PMID:29374175
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5786031/
Abstract

Despite its two-dimensional nature, X-ray angiography (XRA) has served as the gold standard imaging technique in the interventional cardiology for over five decades. Accordingly, demands for tools that could increase efficiency of the XRA procedure for the quantitative analysis of coronary arteries (CA) are constantly increasing. The aim of this study was to propose a novel procedure for three-dimensional modeling of CA from uncalibrated XRA projections. A comprehensive mathematical model of the image formation was developed and used with a robust genetic algorithm optimizer to determine the calibration parameters across XRA views. The frames correspondences between XRA acquisitions were found using a partial-matching approach. Using the same matching method, an efficient procedure for vessel centerline reconstruction was developed. Finally, the problem of meshing complex CA trees was simplified to independent reconstruction and meshing of connected branches using the proposed nonuniform rational B-spline (NURBS)-based method. Because it enables structured quadrilateral and hexahedral meshing, our method is suitable for the subsequent computational modelling of CA physiology (i.e. coronary blood flow, fractional flow reverse, virtual stenting and plaque progression). Extensive validations using digital, physical, and clinical datasets showed competitive performances and potential for further application on a wider scale.

摘要

尽管二维性质,X 射线血管造影(XRA)已经作为介入心脏病学中的黄金标准成像技术超过五十年。因此,对于能够提高 XRA 程序效率以进行冠状动脉(CA)定量分析的工具的需求不断增加。本研究旨在提出一种从未经校准的 XRA 投影中对 CA 进行三维建模的新方法。开发了一个全面的图像形成数学模型,并与强大的遗传算法优化器一起使用,以确定 XRA 视图之间的校准参数。使用部分匹配方法找到 XRA 采集之间的帧对应关系。使用相同的匹配方法,开发了一种用于血管中心线重建的有效方法。最后,通过提出的基于非均匀有理 B 样条(NURBS)的方法,将复杂 CA 树的网格问题简化为独立的重建和连接分支的网格。由于它能够进行结构化的四边形和六面体网格划分,因此我们的方法适用于 CA 生理学的后续计算建模(即冠状动脉血流、反向分数流量、虚拟支架和斑块进展)。使用数字、物理和临床数据集进行的广泛验证表明,具有竞争力的性能和进一步更广泛应用的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/f9c0f0500c32/41598_2018_19440_Fig14_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/5983ac2a725f/41598_2018_19440_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/83d114c546d0/41598_2018_19440_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/ec3f6dbe2007/41598_2018_19440_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/47a44229e3bb/41598_2018_19440_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/e3af5eac601e/41598_2018_19440_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/2f7bb182a4f4/41598_2018_19440_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/f9c0f0500c32/41598_2018_19440_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/a5161804131c/41598_2018_19440_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/875b4b389f86/41598_2018_19440_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/3e40b4e9f9ce/41598_2018_19440_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/bebe2b2d8793/41598_2018_19440_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/dd101dd1da85/41598_2018_19440_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/0ebc36caaf1f/41598_2018_19440_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/1a0fd0b53646/41598_2018_19440_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/5983ac2a725f/41598_2018_19440_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/83d114c546d0/41598_2018_19440_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/ec3f6dbe2007/41598_2018_19440_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/47a44229e3bb/41598_2018_19440_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/e3af5eac601e/41598_2018_19440_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/2f7bb182a4f4/41598_2018_19440_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0f93/5786031/f9c0f0500c32/41598_2018_19440_Fig14_HTML.jpg

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