Transthoracic Ultrasound Coronary Angiography

Lead Research Organisation: University of Leeds
Department Name: Electronic and Electrical Engineering


Cardiovascular disease (CVD) remains the leading cause of deaths globally (17.9m each year according to WHO) of which the most common manifestation, IHD, remains the prominent cause. IHD accounts for similar mortality rates, 17% and 18%, of all deaths in men and women respectively, as reported by the European Society of Cardiology (ESC). The prevalence of CVD presents a significant economic burden on healthcare systems. Public Health England estimates the yearly healthcare costs of CVD for England is £7.4 billion, forecast to rise in the future. The recent International Study of Comparative Health Effectiveness with Medical and Invasive Approaches (ISCHEMIA) trial found that invasive strategies did not reduce the overall rate of a major cardiac events in CAD, in the absence of atheroma in the LMCA as compared to conservative treatment strategies. The outcomes of the trial have significant implications with the potential to improve quality of life safely in patients with moderate or severe, stable IHD, avoiding countless potentially unnecessary invasive procedures through good anatomical imaging of the LMCA. However, this places a dramatic burden on diagnostic procedures which CTCA cannot alone satisfy.
The imaging technology to be developed by this proposal offers a much needed and timely addition to Computed Tomography Coronary Angiography (CTCA) for imaging the detailed anatomy of the Left Main Coronary Artery (LMCA). Transthoracic Ultrasound Coronary Angiography (TUSCA) is a non-ionising modality available at the point-of-care. It will offer important prognostic information for patients with stable Ischaemic Heart Disease (IHD) and provide a cost-effective diagnostic tool of broader applicability for IHD. It will eliminate the problems associated with purpose built CTCA suites, equipment shortages and scanning delays, exacerbated by COVID-19, whilst offering instant feedback for clinicians at the bedside, something which currently eludes CTCA technology. Whilst the Left Anterior Descending artery (LAD) has been imaged successfully by conventional 2D ultrasound (US), the posterior chest location of the LMCA, in relation to the LAD, is challenging to image with current systems. It is yet more difficult to obtain reliable and quantitative anatomical information from these images, degraded by clutter and noise, due to limited spatial resolution. Advances in transducer technology, ultrasound contrast agents (UCAs) and contrast-enhanced ultrasound (CEUS) imaging are reason to propose US as a viable modality for imaging the LMCA. In this project we will address the imaging challenges using a state-of-the-art, high channel count system incorporating motion locked, automatic transmit adaptation enabled through Deep Learning (DL). We will utilise CEUS and 3D transthoracic ultrasound (3DTUS) to better image the anatomy. This anatomical imaging will be combined with additional DL architectures to quantify LMCA stenosis extent. By combining anatomical and flow imaging, we will obtain patient-specific metrics of important prognostic value such as Fractional Flow Reserve (FFR). DL has recently been applied to US imaging at various stages including beamforming and post-processing, and can offer solutions for improving image quality, for efficient data processing and for automatic image analysis. Advances in FPGA and GPU technology mean that real-time, 4D, imaging of the LMCA, at the point-of-care, is now achievable, offering a lower-cost alternative to current CTCA practise. The techniques developed will enable clinically relevant images to be obtained at the bedside, whilst reducing the level of expertise required, inter-observer variability, and additional testing.


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