TY - JOUR
T1 - Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments
AU - Bridio, Sara
AU - Luraghi, Giulia
AU - Ramella, Anna
AU - Rodriguez Matas, Jose Felix
AU - Dubini, Gabriele
AU - Luisi, Claudio A.
AU - Neidlin, Michael
AU - Konduri, Praneeta
AU - Arrarte Terreros, Nerea
AU - Marquering, Henk A.
AU - Majoie, Charles B. L. M.
AU - Migliavacca, Francesco
N1 - Funding Information: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: H.A.M. reports being a co-founder and shareholder of Nico.lab, a company that focuses on the use of artificial intelligence for medical image analysis. C.B.L.M.M. received funds from the European Commission (related to this project, paid to institution) and from CVON/Dutch Heart Foundation, Stryker, TWIN Foundation, Health Evaluation Program Netherlands (unrelated; all paid to institution). C.B.L.M.M. is a shareholder of Nico.lab, a company that focuses on the use of artificial intelligence for medical imaging analysis. H.A.M., N.A.T. and P.K. are co-founders and shareholders of inSteps B.V. Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 777072 and from the MIUR FISR-FISR2019_03221 CECOMES. F.M. is partially supported by the MUSA—Multilayered Urban Sustainability Action—project, funded by the European Union—NextGenerationEU, under the National Recovery and Resilience Plan (NRRP) Mission 4 Component 2 Investment Line 1.5: Strengthening of research structures and creation of R&D “innovation ecosystems”, set up of “territorial leaders in R&D”. Publisher Copyright: © 2023 by the authors.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - 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.
AB - 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.
KW - cerebral arteries
KW - in silico trials
KW - statistical shape modeling
KW - stroke
KW - virtual populations
UR - http://www.scopus.com/inward/record.url?scp=85173020559&partnerID=8YFLogxK
U2 - https://doi.org/10.3390/app131810074
DO - https://doi.org/10.3390/app131810074
M3 - Article
SN - 2076-3417
VL - 13
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 18
M1 - 10074
ER -