Paper
17 February 2012 Visualizing the path of blood flow in static vessel images for image guided surgery of cerebral arteriovenous malformations
Sean Jy-Shyang Chen, Marta Kersten-Oertel, Simon Drouin, D. Louis Collins
Author Affiliations +
Abstract
Cerebral arteriovenous malformations (AVMs) are a type of vascular anomaly consisting of large intertwined vascular growth (the nidus) that are prone to serious hemorrhaging and can result in patient death if left untreated. Intervention through surgical clipping of feeding and draining vessels to the nidus is a common treatment. However, identification of which vessels to clip is challenging even to experienced surgeons aided by conventional image guidance systems. In this work, we describe our methods for processing static preoperative angiographic images in order to effectively visualize the feeding and draining vessels of an AVM nidus. Maps from level-set front propagation processing of the vessel images are used to label the vessels by colour. Furthermore, images are decluttered using the topological distances between vessels. In order to aid the surgeon in the vessel clipping decision-making process during surgery, the results are displayed to the surgeon using augmented virtuality.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sean Jy-Shyang Chen, Marta Kersten-Oertel, Simon Drouin, and D. Louis Collins "Visualizing the path of blood flow in static vessel images for image guided surgery of cerebral arteriovenous malformations", Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 831630 (17 February 2012); https://doi.org/10.1117/12.911684
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications and 1 patent.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Visualization

Surgery

Arteries

Blood circulation

Image processing

Veins

Cameras

Back to Top