Magnetic resonance imaging (MRI) remains under-utilised clinically for lung structural imaging compared with computed tomography (CT). High-quality imaging typically requires long periods of breath-holding that are challenging in populations such as infants and patients with severe respiratory disease. However, non-Cartesian MRI acquisition trajectories show promise for motion-robust lung structural imaging, and through retrospective motion compensation, can offer complementary information on breathing dynamics and lung function.
This project will address the need for respiratory motion compensation techniques that are robust to complex motion and challenging populations, through the development of model-based and data- / AI-driven motion resolved MR image reconstruction techniques.
The student will develop, optimise and evaluate MRI acquisition and reconstruction techniques for imaging during respiratory motion. We will compare the use of conventional respiratory “gating” methods (e.g. respiratory bellows) with data-driven approaches, including iterative low-rank reconstructions and supervised deep learning approaches, incorporating models of the respiratory motion and lung physiology. Through this, we propose to develop a versatile, open-source image reconstruction software package.
Developed methods will be evaluated in healthy human volunteers and patients with lung disease, in close collaboration with clinicians at Sheffield Teaching Hospitals and Sheffield Children’s Hospital.
Specifically, the successful PhD student will undertake research in the following themes:
- Model- and AI-based motion-resolved image reconstruction
- Magnetic resonance physics and MR pulse sequence design
- MR pulse programming on GE Healthcare scanners (C language) and reconstruction (e.g. C++, Python)
- Quantitative image analysis pipelines (Matlab, Python etc.)
- Validation studies of developed techniques in humans
This PhD project will be undertaken at the POLARIS (Pulmonary, Lung and Respiratory Imaging Sheffield) research laboratories. The student will have access to our 4 MRI scanners (at both clinically-used field strengths of 1.5T and 3T), state-of-the-art hyperpolarisation and radiofrequency hardware laboratories, and the University of Sheffield high performance computing clusters.
The student will also work closely with expert MR scientists at GE Healthcare on MRI method development.
Upon completion of this PhD, the student will have a deep understanding of:
- Magnetic resonance physics
- Magnetic resonance pulse sequence programming
- Image reconstruction and analysis algorithms
- Deep learning
- Lung and pulmonary-cardiovascular disorders
Entry Requirements:
Candidates must have a first or upper second class honours degree in physics, (biomedical, electrical) engineering, computer science or related subject, or significant research experience.
How to apply:
Please complete a University Postgraduate Research Application form available here: www.shef.ac.uk/postgraduate/research/apply
Please clearly state the prospective main supervisor in the respective box and select Department of Infection, Immunity & Cardiovascular Disease as the department.
Enquiries:
Interested candidates should in the first instance contact Dr Neil J Stewart ([Email Address Removed])
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