Non-invasive imaging techniques and assessment of carotid vasa vasorum neovascularization: promises and pitfalls
Carotid adventitia vasa vasorum neovascularization (VVn) is associated with the initial stages of arteriosclerosis and with the formation of unstable plaque. However, techniques to accurately quantify that neovascularization in a standard, fast, non-invasive, and efficient way are still lacking. The...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Estado: | Versión aceptada para publicación |
| Data de publicação: | 2018 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositório: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10459.1/65168 |
| Acesso em linha: | https://doi.org/10.1016/j.tcm.2018.06.007 http://hdl.handle.net/10459.1/65168 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Carotid imaging techniques Vasa vasorum neovascularization Atherosclerotic process Automatic methods |
| Resumo: | Carotid adventitia vasa vasorum neovascularization (VVn) is associated with the initial stages of arteriosclerosis and with the formation of unstable plaque. However, techniques to accurately quantify that neovascularization in a standard, fast, non-invasive, and efficient way are still lacking. The development of such techniques holds the promise of enabling wide, inexpensive, and safe screening programs that could stratify patients and help in personalized preventive cardiovascular medicine. In this paper, we review the recent scientific literature pertaining to imaging techniques that could set the stage for the development of standard methods for quantitative assessment of atherosclerotic plaque and carotid VVn. We present and discuss the alternative imaging techniques being used in clinical practice and we review the computational developments that are contributing to speed up image analysis and interpretation. We conclude that one of the greatest upcoming challenges will be the use of machine learning techniques to develop automated methods that assist in the interpretation of images to stratify patients according to their risk. |
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