Cardiac imaging

Introduction

Ultrasound and microbubble contrast agents offer imaging and quantifying microcirculation / perfusion within the myocardium. Perfusion defects are an early indicator of coronary heart diseases as opposed to the current clinical practice which still relies on detection of cardiac wall motion abnormalities.

Myocardial microvasculature and haemodynamics are indicative of potential microvascular diseases for patients with symptoms of coronary heart disease in the absence of obstructive coronary arteries. However, imaging microvascular structure and flow within the myocardium is challenging with clinically available imaging modalities owing to the small size of the vessels and the constant movement of the patient’s heart.  Our group is focusing on cardiovascular imaging by using high-frame-rate ultrasound and super-resolution ultrasound, with cardiac motion correction algorithms.

Current Projects

• Project – Super resolution imaging of the myocardial microvasculature.
• Project – 3D Coronary Ultrasound Localisation Microscopy
• Project – Image-based motion correction for cardiac imaging.

Previous work

• High Frame-Rate Imaging of Myocardium Perfusion

We developed a High Frame Rate contrast Echocardiography (HFR CE) diverging wave imaging system using a cardiac probe and pulse inversion, and demonstrate its feasibility for heart perfusion applications. The technique has been first evaluated on a simple test phantom in the laboratory, then on sheep model and finally on healthy human volunteers.

• Computer-aided perfusion quantification in myocardial contrast echocardiography.

The assessment of blood perfusion in the microvessels of the heart is important in cardiology, especially for the diagnosis of coronary heart diseases (CAD). Myocardial contrast echocardiography (MCE) is one non-invasive way of assessing myocardial perfusion and uses the microbubbles as contrast agents. It is based on the bubble destruction and replenishment method. As MCE is noisy and highly variable, human visual assessments are subjective and unreliable. The project aimed to develop novel semi-automatic computer-assisted tools and quantification methods to improve the accuracy and reproducibility of myocardial perfusion quantification using MCE.

• Automatic myocardium segmentation in MCE.

Myocardial contrast echocardiography (MCE) shows great potential for early detection and diagnosis of coronary artery diseases. But its quantitative analysis requires accurate myocardium segmentation. This is challenging due to the large sources of variations and noise found in MCE data. We aimed to develop semi or fully  automatic myocardium segmentation algorithms that are specifically designed for MCE data. Our work has focused on using random forest, statistical shape modelling and image registration to solve the segmentation problem.