Combining data from multiple sources to study mechanisms of aneurysm disease: Tools and techniques.

Int J Numer Method Biomed Eng

Mechanical Engineering and Materials Science and Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Published: November 2018

Introduction: Connecting local hemodynamics, biomechanics, and tissue properties in cerebral aneurysms is important for understanding the processes of wall degeneration and subsequent aneurysm progression and rupture. This challenging problem requires integration of data from multiple sources.

Methods: This paper describes the tools and techniques developed to integrate data from multiple sources, including clinical information, 3D imaging, intraoperative videos, ex vivo micro-computed tomography (CT), and multiphoton microscopy. Central to this approach is a 3D tissue model constructed from micro-CT images of aneurysm samples resected during neurosurgery. This model is aligned to vascular models constructed from 3D clinical images and is used to map and compare flow, biomechanics, and tissue data.

Results: The approach is illustrated with data of three human intracranial aneurysms. These case studies demonstrated the ability of this approach to study relationships between different factors affecting the aneurysm wall and produced provocative observations that will be further studied with larger series. For instance, "atherosclerotic" and "hyperplastic" looking parts of the aneurysm corresponded to thicker walls and occurred in regions of recirculating flow and low wall shear stress (WSS); thin regions were associated with inflow jets, flow impingement, and high WSS; blebs had walls of varying structures, including calcified, thin, or hyperplastic walls.

Conclusions: The current approach enables the study of interactions of multiple factors thought to be responsible for the progressive degradation and weakening of the aneurysm wall during its evolution.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231954PMC
http://dx.doi.org/10.1002/cnm.3133DOI Listing

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