Overview
This project will explore changes in respiratory kinetics measured using dynamic digital radiography (DDR) on treatment in people with asthma and COPD, to establish the utility of DDR as a biomarker for future therapeutics studies. It seeks to develop automation tools and novel DDR radiomic lung health biomarkers using an AI image-processing workflow.
About this opportunity
Liverpool has amongst the highest morbidity of asthma and chronic obstructive pulmonary disease (COPD) in England, driven by high smoking rates and marked socioeconomic deprivation. Understanding response to treatment, identification of non-responders and predicting trends in long-term lung function in these conditions are of vital importance.
Air trapping – abnormal retention of air in the lungs on expiration – is associated with increased symptoms, exacerbations and mortality in asthma and COPD. Changes in air-trapping with treatment are challenging to quantify in day-to-day clinical practice. Well-established in asthma, monoclonal antibodies have recently become clinically available in the UK for treating people with COPD. Being able to easily measure improvements in air trapping may help establish novel biomarkers and facilitate response prediction and targeted treatment.
Dynamic digital radiography (DDR) is a low-radiation, high temporal and spatial resolution chest imaging system that measures respiratory mechanics and ventilation/perfusion. It measures moving respiratory structures to provide diagnostic imaging alongside lung function markers.
This project will explore changes in respiratory kinetics, ventilation and perfusion measured using quantitative imaging techniques (such as quantitative computed tomography [qCT] of the thorax, and DDR) and pulmonary physiology (lung function testing) after treatment in people with asthma and COPD, to establish the utility of DDR as a biomarker for future therapeutics studies. It seeks to develop automation tools and novel qCT / DDR radiomic lung health biomarkers using an AI-assisted image-processing workflow.
The PhD candidate will be expected to lead the study with support from supervisors, and work closely with UoL’s medical AI expert to develop further DDR radiomics biomarkers using state-of-the-art AI technologies. The candidate will be expected to work across the University of Liverpool campus and clinical sites of the University of Liverpool Hospitals Group (honorary contracts can be provided).
The supervisory team consists of Professor Paul McNamara (professor of respiratory medicine at the University of Liverpool), Dr Hassan Burhan (consultant respiratory physician and severe asthma service lead at the Royal Liverpool Hospital, and Ronald Finn Senior Research Fellow at the University of Liverpool), Dr Thomas FitzMaurice (NIHR Academic Clinical Lecturer in Respiratory Medicine at the University of Liverpool and senior registrar in respiratory medicine) and Professor Yalin Zheng (professor of artificial intelligence in healthcare at the University of Liverpool). The supervisory team have a track record of successful supervision and publication.
The University of Liverpool offers a dynamic, collaborative and innovative research community. You will have access to expertise in respiratory medicine (McNamara, Burhan), respiratory imaging and physiology (FitzMaurice) and artificial intelligence in healthcare (Zheng). We pride ourselves on a positive and inclusive working environment that will support the successful candidate in the conduct of a clinical study, training in research methodology, scientific writing and communication. You will receive regular supervision and feedback from the supervisory team.
Timescale:
Year 1: protocol design and submission, training, ethics approvals, start of study
Year 2: conduct of study, data collection, study closeout, presentation of interim results at conference
Year 3: analysis and write up of results, publication of results