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Optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization

Arteriovenous malformation (AVM) of the brain is a congenital vascular abnormality, in which the arterial and venous blood pools are intertwined and directly connected. This dangerous disease causes a high risk of intracranial hemorrhage and disrupts brain functioning. The preferred method of AVM tr...

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Autores principales: Sharifullina, Tatiana, Cherevko, Alexander, Ostapenko, Vladimir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789901/
https://www.ncbi.nlm.nih.gov/pubmed/35079058
http://dx.doi.org/10.1038/s41598-022-05231-w
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author Sharifullina, Tatiana
Cherevko, Alexander
Ostapenko, Vladimir
author_facet Sharifullina, Tatiana
Cherevko, Alexander
Ostapenko, Vladimir
author_sort Sharifullina, Tatiana
collection PubMed
description Arteriovenous malformation (AVM) of the brain is a congenital vascular abnormality, in which the arterial and venous blood pools are intertwined and directly connected. This dangerous disease causes a high risk of intracranial hemorrhage and disrupts brain functioning. The preferred method of AVM treating is embolization, which is the endovascular filling of abnormal AVM vessels with a special embolic agent. Despite the fact that this method is widely used in neurosurgery, in some cases its use is accompanied by perioperative AVM vessels rupture. In this regard, the aim of this work is to study the optimal scenarios for multi-stage AVM embolization from the effectiveness and safety of the procedure point of view. Mathematically, the joint movement of blood and embolic agent in the AVM body is described on the basis of a one-dimensional two-phase filtration model, which takes into account the redistribution of blood to surrounding healthy vessels. For the numerical solution of the resulting integro-differential system of equations, a monotonic modification of the CABARET scheme is used. To find optimal embolization scenarios, the optimal control problem with phase constraints arising from medicine is formulated. A modified particle swarm optimization method is used to solve this problem numerically. This technique is used to obtain optimal embolization scenarios on the basis of real patients clinical data collected during neurosurgical operations.
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spelling pubmed-87899012022-01-27 Optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization Sharifullina, Tatiana Cherevko, Alexander Ostapenko, Vladimir Sci Rep Article Arteriovenous malformation (AVM) of the brain is a congenital vascular abnormality, in which the arterial and venous blood pools are intertwined and directly connected. This dangerous disease causes a high risk of intracranial hemorrhage and disrupts brain functioning. The preferred method of AVM treating is embolization, which is the endovascular filling of abnormal AVM vessels with a special embolic agent. Despite the fact that this method is widely used in neurosurgery, in some cases its use is accompanied by perioperative AVM vessels rupture. In this regard, the aim of this work is to study the optimal scenarios for multi-stage AVM embolization from the effectiveness and safety of the procedure point of view. Mathematically, the joint movement of blood and embolic agent in the AVM body is described on the basis of a one-dimensional two-phase filtration model, which takes into account the redistribution of blood to surrounding healthy vessels. For the numerical solution of the resulting integro-differential system of equations, a monotonic modification of the CABARET scheme is used. To find optimal embolization scenarios, the optimal control problem with phase constraints arising from medicine is formulated. A modified particle swarm optimization method is used to solve this problem numerically. This technique is used to obtain optimal embolization scenarios on the basis of real patients clinical data collected during neurosurgical operations. Nature Publishing Group UK 2022-01-25 /pmc/articles/PMC8789901/ /pubmed/35079058 http://dx.doi.org/10.1038/s41598-022-05231-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sharifullina, Tatiana
Cherevko, Alexander
Ostapenko, Vladimir
Optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization
title Optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization
title_full Optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization
title_fullStr Optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization
title_full_unstemmed Optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization
title_short Optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization
title_sort optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8789901/
https://www.ncbi.nlm.nih.gov/pubmed/35079058
http://dx.doi.org/10.1038/s41598-022-05231-w
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