Acoustic Localisation of Coronary Artery Stenosis

2006年，在英国，冠心病（CHD）造成了所有死亡人数的16%以上（571，034人中有94，381人）。其中，95%（89，817）发生在55岁以上的人群中。保健费用估计为32亿美元（人均50美元），因工作日损失而增加的经济费用估计为39亿美元，非正式护理病人的费用估计为18亿美元，冠心病是一个昂贵的杀手。它给纳税人带来了巨大的负担，每年花费近90亿美元，随着公认的人口老龄化的必然趋势，这一数字将继续逐年增长。冠心病是指动脉粥样硬化疾病的众多疾病之一，其中动脉粥样硬化斑块脂肪（脂肪和钙沉积物）在动脉壁中积累以形成部分阻塞，从而引起心肌缺血（流向心肌的血液不足）。随着时间的推移，所谓的脆弱斑块会突然破裂并激活身体的凝血机制。这会阻塞动脉并导致（最常见的）心肌梗死：“心脏病发作”。目前还没有冠心病的“标准”筛查工具。咨询医生的病人已经在一些不适和随后的诊断需要的干预，并检查，高度专业化的医疗practitioner.We提出了一个概念验证的调查，连接计算应用数学的生物技术。一个成功的结果将提供一个相对便宜的筛查和诊断工具，冠心病，可以针对'在风险'的人口groups.A动脉狭窄有一个声学签名（杂音），这是由所产生的湍流血流冲击动脉壁触发。这导致低振幅位移波（剪切波）穿过胸部，然后表现为胸部表面上的扰动。这些扰动可以通过在皮肤上放置传感器来非侵入性地测量。在动脉壁处波的产生、它们通过胸部的传输以及它们在胸部表面的出现都可以通过描述人体组织（心脏、肺、肌肉等）的粘弹性性质的详细数学模型来描述。整个模型可以模拟软件作为一个虚拟的胸部，从而避免了需要，在早期的概念验证的发展阶段，临床试验的真实的people.We建议开发和实施这个虚拟的胸部在理论和软件，并通过实验来验证它，以评估这种方法在一个有效的“早期筛查过程”的风险人群。该任务包括两个部分（正解和逆解），这两个部分都将在胸部的真实力学模型上进行校准和实验测试。具体来说，虚拟胸部将在数学上进行公式化，计算实现和实验测试。动脉扰动的初始猜测将通过直接问题预测胸壁处的表面扰动。这些值和测量值之间的差异将形成迭代逆解算程序，该程序将修改动脉扰动，直到测量值和计算值之间的差异最小化。一旦完成了这种正逆解算迭代，表面测量值就被数学和软件解码为动脉影响。这些可以潜在地用于指示狭窄的存在、大小、位置和形态。正是这种诊断潜力，该项目旨在通过基础研究进行调查和评估，包括数学建模和分析，计算机模拟，并通过充满组织模拟凝胶的胸部模型实验进行验证。

In the UK in 2006 coronary heart disease (CHD) caused over 16% of all deaths (94,381 out of 571,034). Of these, 95% (89,817) occurred in people over the age of fifty-five. The cost of health care was estimated at 3.2 billion (50 per capita), the additional economic cost due to lost working days has been estimated at 3.9 billion, and the cost of informal care of patients, at 1.8 billion.Coronary heart disease is an expensive killer. It places a huge burden on the taxpayer, costing nearly 9 billion per year which, with the acknowledged and inexorable trend toward an ageing population, will continue to grow year by year.The term CHD is one of a number that refer to the disease of atherosclerosis, wherein atheromatous plaques (fat and calcium deposits) accumulate in an artery wall to form a partial blockage and thereby cause myocardial ischaemia (inadequate blood flow to the heart muscle). In time, so-called vulnerable plaques undergo a sudden rupture and activate the body's blood-clotting mechanism. This occludes the artery and leads to (the most common form of) myocardial infarction: a 'heart attack'.There is currently no 'standard' screening tool for CHD. Patients who consult their doctor are already in some discomfort and the subsequent diagnosis requires the intervention of, and examination by, highly specialized medical practitioners.We propose a proof-of-concept investigation which connects computational applied mathematics to biotechnology. A successful outcome would provide a relatively cheap screening and diagnosis tool for CHD which could be targeted towards 'at-risk' population groups.An arterial stenosis has an acoustic signature (bruit) which is triggered by the resulting turbulent blood flow impacting on the artery walls. This causes low amplitude displacement waves (shear waves) to travel through the chest, which then manifest themselves as disturbances on the chest surface. These disturbances can be measured non-invasively by placing sensors on the skin.The generation of waves at the artery wall, their transmission through the chest, and their appearance at the chest surface, can all be described by a detailed mathematical model which describes the viscoelastic nature of human tissue (heart, lungs, muscle etc). The entire model can be simulated in software as a virtual chest thus obviating the need, in the early proof-of-concept development stage, for clinical tests on real people.We propose to develop and implement this virtual chest in theory and in software and to validate it by experiment in order to evaluate this approach in terms of an effective 'early days screening process' for an at-risk population. This task consists of two parts (the direct and inverse solver) both of which will be calibrated and tested by experiments on a realistic mechanical model of the chest.Specifically, the virtual chest will be formulated mathematically, implemented computationally and tested experimentally. An initial guess at the arterial disturbance will, via the direct problem, predict the surface disturbances at the chest wall. The difference between these and measured values will form an iterative inverse solver procedure which will modify the arterial disturbance until the difference between the measured and computed values is minimised.Once this direct-inverse solution iteration has finished, the surface measurements have been decoded by the mathematics and software into the arterial impacts. These can, potentially, be used to indicate the presence, size, location and morphology of the stenosis. It is this potential for diagnosis that this project seeks to investigate and evaluate through a fundamental study involving mathematical modelling and analysis, computational simulation, and validation through experimentation on chest phantoms filled with tissue-mimicking gel.