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

A scientific image of a lung ultrasound.

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.


In the UK, LUS can play an important role in the detection and monitoring of patients with COVID-19. It is an imaging modality that is currently under-employed in NHS hospital trusts. This research project seeks to increase the use of LUS in both the short (<8 months) and long term (>12 months).

Firstly, this proposal will develop and test a computer-simulated LUS phantom, capable of recreating the pathologies seen in COVID-19 patients (B-lines, consolidations, and white lung). This software will be made freely available for the UK primary and secondary care community to test new techniques for identifying pathologies associated with COVID-19 and other complications.

Secondly, a new ultrasound beamforming algorithm already under development by the research team will be repurposed for use in improving the contrast-to-noise ratio and spatial resolution of LUS. This algorithm will be published in an open access journal and validated by an industry-led consortium of ultrasound researchers. It will then be available for use on any commercial or research imaging system to maximise impact from this research.

Finally, a secure database of anonymised LUS images of COVID-19 and non COVID-19 patients will be sourced from clinical contacts. Access to this database will be provided to authorised research groups in the UK or worldwide for performing research into COVID-19. It will also be used to train a deep learning network to help ‘de-skill’ this technique, allowing for a more widespread adoption by POCUS practitioners in the primary and secondary critical care settings.

Publications and outputs

We have an in press article with BJR called 'Lung Ultrasound Education: simulation and hands-on', which the attached figure is from.