Transthoracic Ultrasound Coronary Angiography

心血管疾病（CVD）仍然是全球死亡的主要原因（根据世卫组织的数据，每年有1790万人死亡），其中最常见的表现是IHD，仍然是主要原因。据欧洲心脏病学会（ESC）报告，IHD的死亡率相似，分别占男性和女性所有死亡人数的17%和18%。心血管疾病的流行给卫生保健系统带来了巨大的经济负担。英国公共卫生部估计，英国每年心血管疾病的医疗费用为74亿英镑，预计未来还会上升。最近的一项医学和侵入性方法（ISCHEMIA）比较健康效果的国际研究发现，与保守治疗策略相比，在LMCA没有动脉粥样硬化的情况下，侵入性策略并没有降低冠心病主要心脏事件的总体发生率。该试验的结果对改善中度或重度稳定IHD患者的安全生活质量具有重要意义，通过对LMCA进行良好的解剖成像，避免了无数可能不必要的侵入性手术。然而，这给CTCA无法单独满足的诊断程序带来巨大负担。该建议所开发的成像技术为ct冠状动脉造影（CTCA）提供了急需和及时的补充，用于成像左冠状动脉主动脉（LMCA）的详细解剖。经胸超声冠状动脉造影（TUSCA）是一种非电离的方式，可在护理点。它将为稳定性缺血性心脏病（IHD）患者提供重要的预后信息，并为IHD提供一种更广泛适用的具有成本效益的诊断工具。它将消除与专用CTCA套件、设备短缺和扫描延迟相关的问题，这些问题因COVID-19而加剧，同时为床边的临床医生提供即时反馈，这是目前CTCA技术无法做到的。虽然传统的二维超声（US）已经成功地成像了左前降支（LAD），但LMCA相对于LAD的后胸部位置，在当前的系统中成像是具有挑战性的。然而，由于空间分辨率有限，从这些图像中获得可靠和定量的解剖信息更加困难，这些图像受到杂波和噪声的影响。换能器技术、超声造影剂（UCAs）和超声增强成像（CEUS）的进步使超声成像成为LMCA成像的可行方式。在这个项目中，我们将使用最先进的高通道计数系统来解决成像挑战，该系统结合了运动锁定，通过深度学习（DL）实现自动传输适应。我们将利用超声造影和三维经胸超声（3DTUS）来更好地成像解剖。该解剖成像将与其他DL结构相结合，以量化LMCA狭窄程度。通过结合解剖学和血流成像，我们将获得具有重要预后价值的患者特异性指标，如血流储备分数（FFR）。近年来，深度学习技术已应用于美国成像的各个阶段，包括波束形成和后处理，可以为提高图像质量、高效数据处理和自动图像分析提供解决方案。FPGA和GPU技术的进步意味着LMCA在护理点的实时4D成像现在是可以实现的，为当前的CTCA实践提供了一种低成本的替代方案。所开发的技术将使临床相关的图像能够在床边获得，同时减少所需的专业知识水平、观察者之间的可变性和额外的测试。

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.