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Extraction of Vascular Intensity Directional Derivative on Computed Tomography Angiography

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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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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Acknowledgments

Prof. Scalzo was partially supported by a AHA grant 16BGIA27760152, a Spitzer grant, and received hardware donations from Gigabyte, Nvidia, and Intel.

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Authors and Affiliations

  1. Neurovascular Imaging Research Core, Department of Neurology and Computer Science, University of California, Los Angeles (UCLA), Los Angeles, USA

    Elijah Agbayani, Baixue Jia, Graham Woolf, David Liebeskind & Fabien Scalzo

Authors
  1. Elijah Agbayani
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  2. Baixue Jia
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  3. Graham Woolf
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  4. David Liebeskind
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  5. Fabien Scalzo
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Corresponding author

Correspondence to Fabien Scalzo .

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Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Agbayani, E., Jia, B., Woolf, G., Liebeskind, D., Scalzo, F. (2016). Extraction of Vascular Intensity Directional Derivative on Computed Tomography Angiography. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_45

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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