Classification of cerebrovascular pathologies in real-time using nonlinear ODE-based surrogate model | Sciact - Система учета научной деятельности ИМ СО РАН

Classification of cerebrovascular pathologies in real-time using nonlinear ODE-based surrogate model Научная публикация

Журнал

Journal of Inverse and Ill-Posed Problems
ISSN: 0928-0219 , E-ISSN: 1569-3945

Вых. Данные

Год: 2025, DOI: 10.1515/jiip-2025-0028

Ключевые слова

Surrogate model; inverse problem; hemodynamics; neurosurgery; arteriovenous malformation arterial aneurysm; nonlinear oscillator; optimization; initial guess

Авторы

Bugai Yuriy V. ¹ , Cherevko Alexander A. ^1,2 , Shishlenin Maxim A. ³

Организации

1	Lavrentiev Institute of Hydrodynamics SB RAS
2	Novosibirsk State University
3	Sobolev Institute of Mathematics SB RAS

Информация о финансировании (2)

1	Институт математики им. С.Л. Соболева СО РАН	FWNF-2024-0001
2	Институт гидродинамики им. М.А.Лаврентьева СО РАН	FWGG-2021-0009-2.3.1.2.10

In this paper we consider the coefficient inverse problem for a second-order nonlinear ODE surrogate model describing hemodynamic parameters during intraoperative neurosurgical measurements. This mathematical model of cerebral hemodynamics is based on the generalized Van der Pol–Duffing equation and described the local interaction of the velocity and pressure of blood flow in cerebral vessels. For each patient, the coefficients of this equation are individual and are determined from clinical data in real-time by solving the coefficient inverse problem. We apply the gradient method for optimization of the cost functional with the analytical finding of initial guess to get the coefficients by clinical data obtained during neurosurgical operation in the vicinity of arterial pathologies. A good initial guess is based on the analytical Fourier method. Statistical analysis of clinical data has shown that the surrogate model equation is sensitive to different types of pathology, which allows intraoperative monitoring of the patient’s condition and assessment of the type of pathology in real time. Numerical results are presented and it is shown that the proposed mathematical model and numerical method predict clinical data well.

Bugai Y.V. , Cherevko A.A. , Shishlenin M.A.
Classification of cerebrovascular pathologies in real-time using nonlinear ODE-based surrogate model
Journal of Inverse and Ill-Posed Problems. 2025. DOI: 10.1515/jiip-2025-0028 OpenAlex

Поступила в редакцию:	17 апр. 2025 г.
Принята к публикации:	8 сент. 2025 г.
Опубликована online:	1 окт. 2025 г.

OpenAlex:

W4414633943

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