Computed Tomography Coronary Angiography (CTCA) is a non-invasive imaging technique utilized for assessing coronary artery disease, along with the evaluation and reconstruction of cardiac and coronary vessel structures. Coronary artery disease is a significant cardiovascular condition characterized by the narrowing or blockage of these vital blood vessels. Nowadays artificial intelligence (AI) is heavily applied to enhance the process of segmenting coronary arteries and detecting coronary artery disease (CAD) using images obtained from CTCA. Nonetheless, there remain unresolved research questions aimed at overcoming challenges in particular areas, including refining tasks such as centerline extraction, quantifying stenosis, and segmenting specific artery segments.

This project's primary objective is to conduct an initial exploration and implementation of AI techniques for accurately and efficiently identifying the intricate structure of coronary arteries within CTCA images. Leveraging AI capabilities, the research aims to enhance the precision of coronary artery segmentation, essential for precise diagnosis and treatment planning. Additionally, as an extension of this endeavor, the research envisions automating the process of detecting signs of coronary artery disease, enabling early identification and timely intervention. These applications hold substantial promise for assisting cardiologists.


Electrical Engineering and Telecommunications

Research Area

Machine learning | Medical imaging analysis

The project will be conducted within the Signals, Information and Machine Intelligence (SIMI) Lab, situated within the School of Electrical Engineering and Telecommunications. This dynamic group comprises two full-time academic staff members, a Postdoctoral Research Fellow, and multiple research students. Collectively, they are immersed in diverse research endeavors that involve the application of AI/ML techniques to an array of disciplines, encompassing audio processing, image processing, and time-series data analytics.

Upon the completion of this project, students will acquire a comprehensive knowledge of AI/Machine learning (ML) techniques within medical imaging analysis. Furthermore, they will substantially enhance their proficiency in technical research methods and programming (Python/Julia), particularly in utilizing AI/ML frameworks (Tensorflow/ Pythorch/Flux). By the project's conclusion, students are expected to have successfully developed and validated AI/ML algorithms that assist with the identification and modelling of coronary arteries from CTCA images. The outcomes will be manifested in user-friendly code or toolbox implementations which can make significant contributions to the field of cardiology.