Transthoracic Ultrasound Coronary Angiography

经胸超声冠状动脉造影

• 批准号: EP/V04799X/1
• 负责人: Steven Freear
• 金额: $ 146.96万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV04799X%2F1
• 关键词: Transthoracic; Ultrasound; Coronary; Angiography

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.

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)，其中最常见的表现为IHD，仍然是主要原因。根据欧洲心脏病学会(ESC)的报告，IHD的死亡率与男性和女性的死亡率相似，分别占所有男性和女性死亡的17%和18%。心血管疾病的流行给医疗保健系统带来了巨大的经济负担。英国公共卫生估计，英格兰每年因心血管疾病而产生的医疗费用为74亿GB，预计未来还会上升。最近的国际医疗和侵入性方法健康有效性比较研究(缺血)试验发现，与保守治疗策略相比，在LMCA中没有动脉粥样硬化的情况下，侵入性策略并不能降低冠心病中主要心脏事件的总体发生率。该试验的结果对安全改善中重度、稳定的IHD患者的生活质量具有重要意义，通过良好的LMCA解剖成像避免了无数潜在的不必要的侵入性手术。然而，这给诊断程序带来了巨大的负担，仅靠CTCA是不能满足的。这项提议将开发的成像技术为CT冠状动脉造影术(CTCA)提供了急需和及时的补充，用于成像左冠状动脉主干(LMCA)的详细解剖。经胸超声冠状动脉造影术(TUSCA)是一种非电离方式，可在护理地点使用。它将为稳定型缺血性心脏病(IHD)患者提供重要的预后信息，并为IHD提供一种更适用于IHD的经济有效的诊断工具。它将消除与特制的CTCA套件、设备短缺和扫描延迟相关的问题，这些问题因新冠肺炎而加剧，同时为床边的临床医生提供即时反馈，而这是目前CTCA技术无法实现的。虽然左前降支(LAD)已通过常规二维超声(US)成功成像，但LMCA相对于LAD的后胸部位置，在现有系统下的成像是具有挑战性的。由于空间分辨率有限，从这些图像中获取可靠和定量的解剖信息变得更加困难，因为这些图像受到杂波和噪声的影响。超声探头技术、超声造影剂(UCAS)和对比增强超声(CEUS)成像技术的进步是建议US作为LMCA成像的一种可行方法的原因。在这个项目中，我们将使用最先进的高通道计数系统解决成像挑战，该系统结合了运动锁定、通过深度学习(DL)实现的自动传输自适应。我们将使用CEUS和3D经胸超声(3DTUS)来更好地成像解剖结构。这一解剖成像将与其他DL结构相结合，以量化LMCA狭窄程度。通过结合解剖和血流成像，我们将获得具有重要预后价值的患者特有的指标，如血流储备分数(FFR)。最近，DL已被应用于超声成像的各个阶段，包括波束形成和后处理，并可以为提高图像质量、有效的数据处理和自动图像分析提供解决方案。现场可编程门阵列和图形处理器技术的进步意味着，现在可以在护理点实现LMCA的4D实时成像，为目前的CTCA实践提供了一种低成本的替代方案。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水平、观察者之间的可变性和额外的测试。

项目成果

Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part IV

医学图像计算和计算机辅助干预 - MICCAI 2022 - 第 25 届国际会议，新加坡，2022 年 9 月 18-22 日，会议记录，第四部分

• DOI: 10.1007/978-3-031-16440-8_27
• 发表时间: 2022
• 作者: Avisdris N

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

使用贝叶斯形状框架通过超声定位喉返神经

• DOI: 10.48550/arxiv.2206.15254
• 发表时间: 2022
• 作者: Dou H

Predicting myocardial infarction through retinal scans and minimal personal information

• DOI: 10.1038/s42256-021-00427-7
• 发表时间: 2022-01-25
• 期刊: NATURE MACHINE INTELLIGENCE
• 影响因子: 23.8
• 作者: Diaz-Pinto, Andres;Ravikumar, Nishant;Frangi, Alejandro F.
• 通讯作者: Frangi, Alejandro F.

Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology

• DOI: 10.48550/arxiv.2301.02916
• 发表时间: 2023-01
• 期刊: ArXiv
• 作者: Rodrigo Bonazzola;Enzo Ferrante;N. Ravikumar;Yan Xia;B. Keavney;S. Plein;T. Syeda-Mahmood;Alejandro F Frangi