Improving Lung MRI using Computational Motion Models and Machine Learning (26001)

Primary Supervisors:

Dr Mina Kim (mina.kim@ucl.ac.uk)

Subsidiary Supervisors: Dr Jamie McClelland and Prof. Joseph Jacob

Introduction:

A 3-year funded PhD studentship is available for Home Fee applicants in the UCL Centre for Doctoral Training (CDT) in Intelligent, Integrated Imaging in Healthcare (i4Health). Funding will be at least the UCL Minimum Stipend rate and Home Tuition Fees. Stipend details can be found here. 

The successful candidate will join our MPhil/PhD Medical Imaging programme in October 2026, and benefit from the activities and events organised by the department. The application portal can be found on the Medical Imaging MRes + MPhil/PhD prospectus page.

Project Background: 

Lung MRI during free breathing has many potential applications including guiding interventions (e.g. radiotherapy), motion compensating functional imaging (e.g. Oxygen-enhanced MRI), and studying lung anatomy and physiology in health and diseases such as lung cancer, asthma, and COPD. However, breathing motion makes obtaining high-quality free-breathing lung MRI very challenging, limiting its use in these and other applications. Typical solutions assume reproducible motion from one breath to another, or require very long acquisition times, in order to reconstruct a 4D dataset i.e. a series of 3D volumes over the breathing-cycle/time. Alternatively, the motion during the acquisition can be modelled and compensated for during the image reconstruction, producing a single high quality 3D image from all the available image data. A key challenge to this approach is accurately modelling the motion. 

The project will leverage motion modelling expertise within the supervisory team, combined with expertise in MRI physics and quantitative imaging. It aims to develop new methods to model and compensate for respiratory motion during advanced MRI acquisitions. Achieving this will require methodological innovations in motion modelling and image reconstruction, including approaches that do not rely on external breathing signals and the use of emerging machine-learning techniques.

Research aims: 

• Develop new methods to model and compensate for respiratory motion in MRI.
• Improve the quality and robustness of lung MRI acquired during free breathing.
• Explore the use of advanced computational approaches, including machine learning, for motion modelling and image reconstruction.
• Evaluate the developed methods in respiratory imaging applications, such as functional lung MRI and radiotherapy planning.

Person specification & requirements:

• This studentship is open to Home Fee-paying candidates only. Read more information about fee status criteria.
• We are looking a highly motivated and capable student with a strong background in computing and an interest in its application to medical imaging.
• Previous experience with MRI, and motion modelling and compensation, is desirable but not essential.
• Candidates should hold a UK (or international equivalent) first or upper-second Bachelor’s degree.

Funding:

This is a full studentship available to Home Fee applicants only. Please see the page on UCL fee status.

The successful student will receive a stipend starting from at least the UCL minimum (£23,805 in 2026/27) as well as the cost of tuition fees for Home fee students (£6,400 in 2026/27). 

The stipends awarded to PhD students at UCL are tax free and incur no income tax or national insurance contributions. The amount received increases each year over the duration of the studentship. 

How to apply:

Application Deadline: Friday 17th April 2026

Please complete the following steps to apply.

•    Send an expression of interest and current CV to Dr Mina Kim (mina.kim@ucl.ac.uk) and cdtadmin@ucl.ac.uk, quoting Project Code 26001 in the email subject line.
•    Make a formal application via the UCL application portal: Apply | Prospective Students Graduate - UCL – University College London. Please select the programme code ‘Medical Imaging RRDMEISING01’ and enter Project Code 26001 under ‘Name of Award 1’ 
•    If shortlisted, candidates will be invited for an interview.

Application Timeline:

• After the deadline, all applicants that expressed their interest and specified Project 26001 in their Portico application will be considered for interview.
• Candidates will normally be invited for interview within three weeks of the deadline. If you have not been contacted within this time-period, you have unfortunately not been successful in being shortlisted.
• The interview panel will normally consist of the supervision team on the project.
• Note that applications without specifying the project they are applying for and/or making a formal Portico application will be automatically rejected.
• If you are offered and accept a studentship position, a formal UCL Offer of Admission will be sent to you as well as an offer of studentship funding.