Signal Processing for Ultrasound Diagnosis of Cardiac Output

EPSRC项目组合：EPSRC主题：医疗技术，挑战：通过有效诊断，患者特异性预测和循证干预来优化治疗/优化护理，研究领域：数字信号处理（增长）心输出量对于诊断休克和监测急诊科患者对治疗的反应很重要，可以使用先进的无创，独立的连续波多普勒技术设备进行测量。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前景直接相关，但很少进行监测，因为该过程需要高技能的医疗专业人员的参与。手术过程包括在胸骨上切迹中放置一个小装置，直接沿着升主动脉的纵轴穿过主动脉根部。该装置传输连续波多普勒超声信号，用于计算血液流出主动脉瓣时的射速。心跳也会被监测。然而，除了年轻和健康的个体外，这种方法通常很困难，因为进入胸骨上切迹的途径有限，有时会感到疼痛。因此，如果将该装置直接置于心脏前方，通常可以更方便地在左胸骨旁边缘监测经肺动脉瓣的心输出量。这是一个具有挑战性的过程，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，手部运动和压力控制，（iii）识别多普勒剖面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轻微的改变，以及(v)了解低流量状态可能会出现次优外观。拟议的博士项目将有助于开发一种新颖、低成本、无创的机器人解决方案，用于超声设备的精细定位和调整，使该过程能够由个人进行最少的培训。这将有助于及时和准确的诊断和反复监测，从而改善选择并降低任何后续干预措施的成本，并增加取得成功健康结果的可能性。该项目将包括调查如何在不同的临床情况和不同的身体体质中使用该方法，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，用于识别多普勒剖面，并为超声设备的自动定位提供反馈，以优化其特性。超声设备的定位将被研究，以评估通过肺动脉瓣的血流量，新的先进的信号处理算法将被开发，以识别多普勒谱，并为超声设备的自动定位提供反馈，以优化其特性。该博士生将接受跨学科领域的培训，包括信号处理、超声和心脏评估与监测。

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.