Establishing lung ultrasound as a key tool in the stratification and monitoring of COVID-19 patients

肺部超声（LUS）是诊断肺部不同病变的有力工具。由于COVID-19患者的主要死因是肺炎，因此用于监测患者肺部的简单、低成本和有效的技术至关重要。该提案旨在开发必要的工具，以确保LUS能够在短期和长期内实现这一目标。该提案的第一个目标是纠正目前没有LUS的计算机模拟可供研究人员进行模拟的事实。在免费使用的软件中实现这一模型将允许研究社区快速研究和优化LUS。该提案的第二个目标是实现最近开发的超声波束形成器，用于LUS中使用的换能器范围。这种新型波束成形技术已被证明可以提高超声图像的对比度噪声比和空间分辨率，从而增强对与COVID-19患者相关的肺部病变的检测。一旦在肺部模拟体模和健康志愿者身上得到验证，该技术将发表在开放获取期刊上，以便在任何其他超声系统上实施。最终目标是建立临床LUS图像的安全存储库，可用于训练深度学习网络，以“去除”LUS的使用。此外，我们将实现一个弱监督深度学习网络来测试这些数据集和从测试幻影中获取的数据集。

Lung ultrasound (LUS) is a powerful tool for the diagnosis of different pathologies of the lung. As the main cause of death in COVID-19 patients is from pneumonia, simple, low cost and effective techniques for monitoring the lungs of patients is critical. This proposal seeks to develop the necessary tools to ensure LUS can achieve this in the short and long term. The first goal of this proposal is to rectify the fact that there are currently no computer simulations of LUS for researchers to run simulations with. Implementing this model in free to use software will allow for the rapid study and optimisation of LUS by the research community. The second goal of this proposal is to implement a recently developed ultrasound beamformer for use with the range of transducers used in LUS. This novel beamforming technique has been demonstrated to improve the contrast to noise ratio and spatial resolution of ultrasound images, which enhance the detection of lung pathologies associated with COVID-19 patients. Once validated on lung mimicking phantoms and a healthy volunteer, this technique will be published in an open access journal for implementation on any other ultrasound system. The final goal is to establish a secure repository of clinical LUS images, which can be used to train deep learning networks in order to 'de-skill' the use of LUS. Furthermore, we will implement a weakly supervised deep learning network to test these datasets and those acquired from test phantoms.