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2016 | OriginalPaper | Buchkapitel

Extraction of Vascular Intensity Directional Derivative on Computed Tomography Angiography

verfasst von : Elijah Agbayani, Baixue Jia, Graham Woolf, David Liebeskind, Fabien Scalzo

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

Collateral flow has been shown to have positive effects in ischemic intracranial vessel disease and can compensate for moderate stenosis and even complete occlusion of a major artery. Despite this, the common method of evaluating collaterals - computed tomography angiography (CTA) - is not effective in fully visualizing collaterals, making evaluation difficult. The spatial derivative of signal intensity, in the direction of flow, computed from standard, single-phase CTA may provide hemodynamic information that can be used to grade collaterals without directly visualizing them. We present in this paper software to compute the directional derivative, as well as to map it and the signal intensity onto a color-coded surface mesh for a 3D visualization. Our approach uses precomputed centerlines to simplify the computation and interpretation. To see if the derivative provided information that was not redundant with intensity, the software was run on a set of 43 CTA cases with stenosis, where the VOI of each was segmented by a neurology expert. Whereas KS tests comparing the intensity distributions of the healthy and affected hemispheres indicated that the two were different for 93% of cases, the distributions of directional derivative values were only different for 52.5% of cases. Therefore this derivative may be used as a tool to discriminate the severity of such cases, although its effectiveness as a collateral evaluation tool remains to be seen. While surface segmentation is time-consuming, the software can otherwise process and render color-coded 3D visualizations quickly.

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Metadaten
Titel
Extraction of Vascular Intensity Directional Derivative on Computed Tomography Angiography
verfasst von
Elijah Agbayani
Baixue Jia
Graham Woolf
David Liebeskind
Fabien Scalzo
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-50835-1_45