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

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.