Visual Quantification of the Circle of Willis: An Automated Identification and Standardized Representation

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s): not specified
• Date: 2016
• ISSN: 1467-8659
• Journal: Computer Graphics Forum
• Keywords: Circle of Willis, medical visualization, information visualization

Abstract

This paper presents a method for the visual quantification of cerebral arteries, known as the Circle of Willis (CoW). It is an arterial structure with the responsibility of supplying the brain with blood, however, dysfunctions can lead to strokes. The diagnosis of such a time-critical/urgent event depends on the expertise of radiologists and the applied software tools. They use basic display methods of the volumetric data without any support of advanced image processing and visualization techniques. The goal of this paper is to present an automated method for the standardized description of cerebral arteries in stroke patients in order to provide an overview of the CoW's configuration. This novel representation provides visual indications of problematic areas as well as straightforward comparisons between multiple patients. Additionally, we offer a pipeline for extracting the CoW from Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data sets together with an enumeration technique for labelling the arterial segments by detecting the main supplying arteries of the CoW. We evaluated the feasibility of our visual quantification approach in a study of 63 TOF-MRA data sets and compared our findings to those of three radiologists. The obtained results demonstrate that our proposed techniques are effective in detecting the arteries and visually capturing the overall configuration of the CoW.

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BibTeX

@article{miao_2016_cgf,
  title =      "Visual Quantification of the Circle of Willis: An Automated
               Identification and Standardized Representation",
  author =     "Haichao Miao and Gabriel Mistelbauer and Christian Nasel and
               Eduard Gr\"{o}ller",
  year =       "2016",
  abstract =   "This paper presents a method for the visual quantification
               of cerebral arteries, known as the Circle of Willis (CoW).
               It is an arterial structure with the responsibility of
               supplying the brain with blood, however, dysfunctions can
               lead to strokes. The diagnosis of such a
               time-critical/urgent event depends on the expertise of
               radiologists and the applied software tools. They use basic
               display methods of the volumetric data without any support
               of advanced image processing and visualization techniques.
               The goal of this paper is to present an automated method for
               the standardized description of cerebral arteries in stroke
               patients in order to provide an overview of the CoW's
               configuration. This novel representation provides visual
               indications of problematic areas as well as straightforward
               comparisons between multiple patients. Additionally, we
               offer a pipeline for extracting the CoW from Time-of-Flight
               Magnetic Resonance Angiography (TOF-MRA) data sets together
               with an enumeration technique for labelling the arterial
               segments by detecting the main supplying arteries of the
               CoW. We evaluated the feasibility of our visual
               quantification approach in a study of 63 TOF-MRA data sets
               and compared our findings to those of three radiologists.
               The obtained results demonstrate that our proposed
               techniques are effective in detecting the arteries and
               visually capturing the overall configuration of the CoW.",
  issn =       "1467-8659",
  journal =    "Computer Graphics Forum",
  keywords =   "Circle of Willis, medical visualization, information
               visualization",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2016/miao_2016_cgf/",
}