Построение трехмерной модели кровеносной системы головного мозга для задач планирования и тренировки проведения нейрохирургических вмешательств

Abstract

Работа посвящена построению трехмерной модели кровеносной системы головного мозга человека, применительно к задачам планирования и тренировки проведения нейрохирургических вмешательств. Предложены основные этапы обеспечивающие построение трехмерной модели на основании параметрических данных, а также её математического описания. Показана возможность использования разработанного подхода для обеспечения обучения проведения нейрохирургического виртуального вмешательства, а в последующем, с использованием быстрого 3D прототипирования, и реального.Currently, functional surgery on the human brain are closely linked to the planning stage. One of the most dangerous during surgery, are excision of a brain blood vessels, this can be fatal. Therefore, the main objective of this work was to develop a method for constructing a three-dimensional model of the brain circulatory system for planning tasks and training tasks for neurosurgery. The study examined the anatomical structure of the vascular system of the brain, from the point of view of the possibility of its three-dimensional parametric reconstruction; formed principles describe a model using the graph, as well as a three-dimensional mathematical formalism; defined stages of building such a model. The developed method for constructing a three-dimensional model of the brain circulatory system is an important component in providing training neurosurgeons. Construction of three-dimensional model of the vascular system of the brain is carried by the following steps: initialization parameters, build a skeleton, detalization of the skeleton; construction of the volume of model and rasterization. This approach, in addition the possibility of simulating different structural variations of the circulatory system does not require real tomographic data, and thus meets the requirements of medical ethics. The next step is to obtain the data for the full three-dimensional model of the bloodstream. For a more accurate reproduction of the models, we should use the data of digital subtraction angiography, together with methods of machine learning. In the future, this approach, together with the pre-planning, as well as three-dimensional printing technology should provide substantive training of the surgery.