Signal Processing for Ultrasound Diagnosis of Cardiac Output

EPSRC产品组合：EPSRC主题：医疗保健技术，挑战：通过有效诊断、患者特定预测和循证干预优化治疗/优化护理，研究领域：数字信号处理(GROW)心输出量对于急诊科诊断休克和监测患者对治疗的反应非常重要，可以使用先进的非侵入性、使用连续波多普勒技术的独立设备进行测量。然而，尽管越来越多的证据表明，测量心血管血流动力学参数与患者的预后直接相关，但监测很少进行，因为这一过程需要高技能医疗专业人员的参与。手术包括在胸骨上切迹放置一个小装置，直接沿纵轴指向升主动脉并穿过主动脉根部。该装置传输连续波多普勒超声信号，用于计算血液离开主动脉瓣时的射血速度。心跳也会受到监测。然而，除了年轻和健康的人外，这种方法通常对所有人都很困难，因为进入胸骨上切迹的途径有限，有时甚至是痛苦的。因此，如果装置直接放在心脏的前面，通过肺动脉瓣的心输出量通常可以更方便地在左侧胸骨旁边缘进行监测。这是一个具有挑战性的程序，因为它需要(I)准确的换能器和患者定位，(Ii)准确的换能器操作，手的移动和压力控制，(Iii)识别多普勒轮廓的外观并优化其特征的能力，(Iv)了解每个患者可能需要对标准方法进行微小的变化，以及(V)了解在低流量状态下可能会出现不佳的外观。拟议的PhD项目将有助于开发一种新的、低成本的非侵入性机器人解决方案，用于精细定位和调整超声设备，使该程序能够由个人进行最少的培训。这将使及时和准确的诊断和重复监测成为可能，从而改善任何后续干预的选择和降低成本，并增加成功健康结果的可能性。该项目将涉及调查如何在不同的临床情况和不同的身体素质下实施该方法，以收集经验和收集机器人设备所需的数据。将开发先进的新信号处理算法，以识别多普勒轮廓并为超声设备的自动定位提供反馈，以优化其特性。将研究超声设备的定位，以评估通过肺动脉瓣的血液流动，并将开发新的先进信号处理算法，以识别多普勒轮廓并为超声设备的自动定位提供反馈，以优化其特性。这名博士生将接受跨学科领域的培训，包括信号处理、超声波和心脏评估和监测。

EPSRC Portfolio: EPSRC Theme: Healthcare Technologies, Challenge: Optimising Treatment/ Optimising care through effective diagnosis, patient-specific prediction and evidence-based intervention, Research areas: Digital signal processing (grow)Cardiac output is important for diagnosing shock and monitoring the patients' response to therapy at Emergency Departments, and could be measured using advanced non-invasive, stand-alone devices using Continuous Wave Doppler technology. However, despite the growing evidence that measuring cardiovascular haemodynamic parameters is directly linked to the prospects of the patients, monitoring is rarely administered as the procedure requires the involvement of highly-skilled medical professionals. The procedure involves placing a small device in the suprasternal notch aiming directly down the longitudinal axis at the ascending aorta and across the aortic root. The device transmits continuous wave Doppler ultrasound signal, which is used to calculate the ejection velocity of the blood as it exits the aortic valve. Heart beat is also monitored. This approach, however, is often difficult in all but young and fit individuals, because of restricted and sometimes painful access to the suprasternal notch. For this reason, cardiac output via the pulmonary valve can generally be more conveniently monitored at the left parasternal edge if the device is placed directly anterior to the heart. This is a challenging procedure as it requires (i) accurate transducer and patient positioning, (ii) accurate transducer manipulation, hand movement and pressure control, (iii) abilities to recognise the appearance of the Doppler profile and optimise its characteristics, (iv) an understanding that each patient may need slight variation to the standard approach, and (v) an understanding that suboptimal appearances may occur with low flow states.The proposed PhD project will contribute to developing a novel, low-cost, non-invasive robotic solution for fine positioning and adjustments of the ultrasound device that will enable the procedure to be administered by individuals with minimal training. This will enable timely and accurate diagnosis and repeated monitoring, therefore improving the choice and reducing the cost of any subsequent intervention, and increasing the likelihood of successful health outcomes.The project will involve an investigation of how the method is administered in different clinical situations and variable body physique in order to gather experience and to collect required data for the robotic device. Advanced new signal processing algorithms will be developed for recognising the Doppler profile and providing feedback for automatic positioning of the ultrasound device in order to optimise its characteristics. The positioning of the ultrasound device will be investigated in order to assess the blood flow through the pulmonary valve, and new advanced signal processing algorithms will be developed for recognising the Doppler profile and providing feedback for automatic positioning of the ultrasound device in order to optimise its characteristics. The PhD student will receive training in cross-disciplinary areas including signal processing, ultrasound and cardiac assessment and monitoring.