用于急性缺血性卒中治疗的Silico试验的虚拟患者队列的生成

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY
Sara Bridio, Giulia Luraghi, Anna Ramella, J. F. Rodriguez Matas, G. Dubini, Claudio A. Luisi, Michael Neidlin, P. Konduri, N. Arrarte Terreros, H. Marquering, C. Majoie, Francesco Migliavacca
{"title":"用于急性缺血性卒中治疗的Silico试验的虚拟患者队列的生成","authors":"Sara Bridio, Giulia Luraghi, Anna Ramella, J. F. Rodriguez Matas, G. Dubini, Claudio A. Luisi, Michael Neidlin, P. Konduri, N. Arrarte Terreros, H. Marquering, C. Majoie, Francesco Migliavacca","doi":"10.3390/app131810074","DOIUrl":null,"url":null,"abstract":"The development of in silico trials based on high-fidelity simulations of clinical procedures requires the availability of large cohorts of three-dimensional (3D) patient-specific anatomy models, which are often hard to collect due to limited availability and/or accessibility and imaging quality. Statistical shape modeling (SSM) allows one to identify the main modes of shape variation and to generate new samples based on the variability observed in a training dataset. In this work, a method for the automatic 3D reconstruction of vascular anatomies based on SSM is used for the generation of a virtual cohort of cerebrovascular models suitable for computational simulations, useful for in silico stroke trials. Starting from 88 cerebrovascular anatomies segmented from stroke patients’ images, an SSM algorithm was developed to generate a virtual population of 100 vascular anatomies, defined by centerlines and diameters. An acceptance criterion was defined based on geometric parameters, resulting in the acceptance of 83 generated anatomies. The 3D reconstruction method was validated by reconstructing a cerebrovascular phantom lumen and comparing the result with an STL geometry obtained from a computed tomography scan. In conclusion, the final 3D models of the generated anatomies show that the proposed methodology can produce a reliable cohort of cerebral arteries.","PeriodicalId":48760,"journal":{"name":"Applied Sciences-Basel","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments\",\"authors\":\"Sara Bridio, Giulia Luraghi, Anna Ramella, J. F. Rodriguez Matas, G. Dubini, Claudio A. Luisi, Michael Neidlin, P. Konduri, N. Arrarte Terreros, H. Marquering, C. Majoie, Francesco Migliavacca\",\"doi\":\"10.3390/app131810074\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of in silico trials based on high-fidelity simulations of clinical procedures requires the availability of large cohorts of three-dimensional (3D) patient-specific anatomy models, which are often hard to collect due to limited availability and/or accessibility and imaging quality. Statistical shape modeling (SSM) allows one to identify the main modes of shape variation and to generate new samples based on the variability observed in a training dataset. In this work, a method for the automatic 3D reconstruction of vascular anatomies based on SSM is used for the generation of a virtual cohort of cerebrovascular models suitable for computational simulations, useful for in silico stroke trials. Starting from 88 cerebrovascular anatomies segmented from stroke patients’ images, an SSM algorithm was developed to generate a virtual population of 100 vascular anatomies, defined by centerlines and diameters. An acceptance criterion was defined based on geometric parameters, resulting in the acceptance of 83 generated anatomies. The 3D reconstruction method was validated by reconstructing a cerebrovascular phantom lumen and comparing the result with an STL geometry obtained from a computed tomography scan. In conclusion, the final 3D models of the generated anatomies show that the proposed methodology can produce a reliable cohort of cerebral arteries.\",\"PeriodicalId\":48760,\"journal\":{\"name\":\"Applied Sciences-Basel\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Sciences-Basel\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.3390/app131810074\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Sciences-Basel","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.3390/app131810074","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

基于临床程序高保真模拟的计算机试验的发展需要大量的三维(3D)患者特定解剖模型的可用性,由于可用性和/或可访问性和成像质量有限,这些模型通常很难收集。统计形状建模(SSM)允许识别形状变化的主要模式,并基于在训练数据集中观察到的变化生成新的样本。在这项工作中,一种基于SSM的血管解剖结构的自动3D重建方法被用于生成适用于计算模拟的脑血管模型的虚拟队列,这对计算机中风试验很有用。从中风患者图像中分割的88个脑血管解剖结构开始,开发了一种SSM算法来生成由中心线和直径定义的100个血管解剖结构的虚拟群体。根据几何参数定义了验收标准,从而验收了83个生成的解剖结构。通过重建脑血管体模管腔并将结果与计算机断层扫描获得的STL几何结构进行比较,验证了3D重建方法。总之,生成的解剖结构的最终3D模型表明,所提出的方法可以产生可靠的脑动脉队列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments
The development of in silico trials based on high-fidelity simulations of clinical procedures requires the availability of large cohorts of three-dimensional (3D) patient-specific anatomy models, which are often hard to collect due to limited availability and/or accessibility and imaging quality. Statistical shape modeling (SSM) allows one to identify the main modes of shape variation and to generate new samples based on the variability observed in a training dataset. In this work, a method for the automatic 3D reconstruction of vascular anatomies based on SSM is used for the generation of a virtual cohort of cerebrovascular models suitable for computational simulations, useful for in silico stroke trials. Starting from 88 cerebrovascular anatomies segmented from stroke patients’ images, an SSM algorithm was developed to generate a virtual population of 100 vascular anatomies, defined by centerlines and diameters. An acceptance criterion was defined based on geometric parameters, resulting in the acceptance of 83 generated anatomies. The 3D reconstruction method was validated by reconstructing a cerebrovascular phantom lumen and comparing the result with an STL geometry obtained from a computed tomography scan. In conclusion, the final 3D models of the generated anatomies show that the proposed methodology can produce a reliable cohort of cerebral arteries.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信