Three-dimensional hybrid guidance system for cardiac interventional procedures

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Minimally invasive cardiac surgeries are the common treatment for cardiovascular disease, involving the insertion of flexible devices (e.g. catheters or stents) into heart chambers. X-ray fluoroscopy is currently used to guide surgeons as the devices are highly visible under X-rays and modern X-ray systems provide real-time (i.e. with no lag) imaging, a large field-of-view and excellent image resolution. However, X-ray images offer very little anatomical information as surgeons cannot see where the heart chamber is and its surrounding blood vessels, unless contrast agents are injected. Furthermore, X-ray images are 2D images and so objects inside the image could overlap each other making it difficult to determine the accurate position of devices relative to the complex heart anatomy. This results in extended procedure times and thus additional harmful radiation doses. To add this anatomical information, hybrid guidance systems have been developed which combine the X-ray information with other information (e.g. from computerised tomography) to add the shadows or contours on the top of the X-ray images. The drawbacks of these systems are that they still heavily rely on X-ray fluoroscopic images to provide guidance, and all information is still 2D.The aim of this project is to develop a new 3D hybrid guidance system superior to these existing approaches. It will provide 3D information to surgeons, increasing their efficiency and thus reducing X-ray exposure. It will also use additional 3D guidance equipment such as the electroanatomical mapping (EAM) system to reduce the frequency of X-ray images, and so further reduce X-ray exposure. The EAM system uses a weak magnetic field rather than harmful X-ray radiation and so it can be switched on throughout the procedure. The primary use of the EAM system is to map electrophysiological activities within the heart. But it also can track catheters within a heart chamber and create low-resolution 3D models of heart chambers. It is not possible to visualise the 3D blood vessel structures clearly when using the EAM system and also some of devices such as stents and balloons might not be tracked. Hence the need for the proposed hybrid system with X-ray information.To develop this system we will use advanced computer vision techniques to detect devices and extract 3D blood vessel models from X-ray images, and then fuse these with existing 3D models inside the EAM system to provide the completed information to guide the procedure. Due to the high-level of noise present in low-dose X-ray images and the interference from overlapping objects, it is a challenging task to achieve accurate and robust detection in real-time. To meet the challenges, a novel approach is proposed to simultaneously detect the electrode catheters by the electrode pattern and the device on the wire by an image classifier. Since all devices are objects attached to the wires, our learning-base image classifiers will only need to search the areas along the wire-like objects. Furthermore, our approach will also be able to solve the challenge of the accurate alignment between 3D models in two systems measured in different coordinate systems. The alignment is based on tracking the 3D position of the same device in both an EAM system and an X-ray system. As it is possible to use the EAM system as the main guidance tool and use less frequent X-ray images, our proposed system will significantly reduce X-ray radiation exposure. This will benefit patients as X-ray radiation might cause the cancer in their later life. We will partner with Abbott Medical UK Ltd, and aim to develop and adapt our approach using Abbott's EAM system so that a research prototype can be made in the near future. But our theoretical contributions will not limited to the EAM system, and could be used to hybridise X-ray images with other image-guidance systems, such as the 3D echo imaging, as well as future robotic surgery systems.