paper-with-me

홈 › Papers

Optimal mechanical operation in the vicinity of curved vasculature

2018-06-20

It has been shown that geometrical, structural properties vary along the length of the aortic arch. There is a scarcity of studies focus on the variation in the vessel wall thickness of aortic arch. The central premise of this study is that considering the variation in the vessel wall thickness along the circumference of the aortic arch to be governed by the uniform stress distribution across the vessel wall, meeting the principle of optimal mechanical operation of which the distribution of stress across the vessel wall is assumed to be uniform so as to create a favorable mechanical environment for the mechanosensitive resident vascular cells. Aortic arch was created with image-derived three-dimensional (3D) reconstruction technique. A structure-motivated constitutive model was utilized in the numerical modeling and direct boundary value problem was solved. Stress distribution across the vessel wall under physiological loading condition was predicted in circumferential direction to test the role of the wall thickness. The results showed the variation of the vessel wall thickness in the circumferential direction and uniform distribution of the circumferential stress in the aortic wall which implies a favorable mechanical environment for the resident mechano-sensitive vascular smooth muscle cells. Correlation of geometrical and simulation data support the proposed principle of optimal mechanical operation for the aortic arch.

📄 PDF Abstract BibTeX arXiv:1806.07718

Code (0)

등록된 구현이 없습니다.

Tasks

3D Reconstruction

Similar Papers 제목 키워드 기반

Modelling mechanically dominated vasculature development

2022-03-22 · Benjamin J. Walker, Adriana T. Dawes

Vascular networks play a key role in the development, function, and survival of many organisms, facilitating transport of nutrients and other critical factors within and between systems. The development of these vessel n…

Toward AI Autonomous Navigation for Mechanical Thrombectomy using Hierarchical Modular Multi-agent Reinforcement Learning (HM-MARL)

2026-02-20 · Harry Robertshaw, Nikola Fischer, Lennart Karstensen, Benjamin Jackson 외 arxiv

Mechanical thrombectomy (MT) is typically the optimal treatment for acute ischemic stroke involving large vessel occlusions, but access is limited due to geographic and logistical barriers. Reinforcement learning (RL) sh…

Multi-agent Reinforcement Learning

World Model for AI Autonomous Navigation in Mechanical Thrombectomy

2025-09-29 · Harry Robertshaw, Han-Ru Wu, Alejandro Granados, Thomas C Booth arxiv

Autonomous navigation for mechanical thrombectomy (MT) remains a critical challenge due to the complexity of vascular anatomy and the need for precise, real-time decision-making. Reinforcement learning (RL)-based approac…

Reinforcement LearningMulti-Task Learning

Progressive Experience Fusion for Multi-Task World Model Control in Endovascular Navigation

2026-08-19 · Harry Robertshaw, Maxence Boels, Nikola Fischer, Sebastien Ourselin 외 arxiv

Autonomous endovascular navigation could support the delivery of mechanical thrombectomy to underserved areas, but controllers must navigate long, multi-stage paths across varying vascular anatomies. This study investiga…

Elastic registration based on compliance analysis and biomechanical graph matching

2019-12-13 · Jaime Garcia Guevara, Igor Peterlik, Marie-Odile Berger, Stéphane Cotin

An automatic elastic registration method suited for vascularized organs is proposed. The vasculature in both the preoperative and intra-operative images is represented as a graph. A typical application of this method is …

Graph Matching