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":"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}
引用次数: 0
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.
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.