Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Master of Engineering Science

Program

Biomedical Engineering

Supervisor

Peters, Terry M.

Abstract

An arteriovenous malformation (AVM) is an abnormal collection of blood vessels which causes blood to travel from arteries to veins through an abnormal twisted network of vessels. This network has an elevated risk of rupture, which can lead to permanent disability and death if the rupture occurs in the brain. The gold standard treatment for AVM is surgical resection, and it is crucial to know which vessels are bringing blood towards and away from the AVM. Unfortunately, it is almost impossible to know this by looking at the surgical scene. The primary limitations of current methods to address this are requirements for extra hardware and a lack of intraoperative blood flow information.

Here, preliminary results are presented for two video-based methods to provide this important clinical information. Videos from routinely-used surgical microscopes are enhanced and analyzed to determine the identity of vessels of interest and guide AVM resection surgery.

Summary for Lay Audience

There are a variety of diseases involving blood vessels in the brain that require corrective surgery. One of these diseases is known as an arteriovenous malformation, or AVM. A key step in this surgery is correctly identifying which blood vessels are bringing blood towards the diseased site, and which ones are bringing blood away from it. It is impossible to distinguish between these vessels by visually looking at the surgical site, so other methods must be used to obtain this information.

Currently available methods that surgeons use to get this information rely on medical imaging, injectable contrast agents, and augmented reality displays. Although these methods provide useful information, they also have inherent limitations that make them sub-optimal.

The work presented in this thesis aims to achieve the primary goal of these other guidance systems, characterizing which vessels are feeding and draining the diseased area, while addressing their limitations. This is done by enhancing and analyzing subtle colour changes in videos taken by routinely-used surgical microscopes. These colour changes occur when blood flows through these vessels, and both the strength and timing of these changes are used to discern the identity of vessels.

Future work will include further investigating these preliminary results and making this workflow more automated and efficient.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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