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Hemodynamic Characterization of Peripheral Arterio-venous Malformations

Overview of attention for article published in Annals of Biomedical Engineering, March 2017
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Title
Hemodynamic Characterization of Peripheral Arterio-venous Malformations
Published in
Annals of Biomedical Engineering, March 2017
DOI 10.1007/s10439-017-1821-9
Pubmed ID
Authors

Sabrina Frey, A. Haine, R. Kammer, H. von Tengg-Kobligk, D. Obrist, I. Baumgartner

Abstract

Peripheral arterio-venous malformations (pAVMs) are congenital vascular anomalies that require treatment, due to their severe clinical consequences. The complexity of lesions often leads to misdiagnosis and ill-planned treatments. To improve disease management, we developed a computational model to quantify the hemodynamic effects of key angioarchitectural features of pAVMs. Hemodynamic results were used to predict the transport of contrast agent (CA), which allowed us to compare our findings to digital subtraction angiography (DSA) recordings of patients. The model is based on typical pAVM morphologies and a generic vessel network that represents realistic vascular feeding and draining components related to lesions. A lumped-parameter description of the vessel network was employed to compute blood pressure and flow rates. CA-transport was determined by coupling the model to a 1D advection-diffusion equation. Results show that the extent of hemodynamic effects of pAVMs, such as arterial steal and venous hypertension, strongly depends on the lesion type and its vascular architecture. Dimensions of shunting vessels strongly influence hemodynamic parameters. Our results underline the importance of the dynamics of CA-transport in diagnostic DSA images. In this context, we identified a set of temporal CA-transport parameters, which are indicative of the presence and specific morphology of pAVMs.

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Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 24%
Student > Ph. D. Student 5 20%
Other 4 16%
Professor 2 8%
Researcher 2 8%
Other 2 8%
Unknown 4 16%
Readers by discipline Count As %
Engineering 12 48%
Medicine and Dentistry 5 20%
Biochemistry, Genetics and Molecular Biology 3 12%
Unknown 5 20%