NOWCLOSED - Towards 10-minute Magnetic Resonance Imaging scans in children with machine learning

3-year PhD studentship funded by Great Ormond Street BRC and the i4health CDT - NOW CLOSED

Primary Supervisor: Dr. Jennifer Steeden (UCL Centre for Cardiovascular Imaging, Institute of Cardiovascular Science)
Secondary Supervisor: Dr. Owen Arthurs (Great Ormond Street Hospital) and Prof. Vivek Muthurangu (UCL Centre for Cardiovascular Imaging, Institute of Cardiovascular Science)

Project Summary
A 3-year PhD studentship is available in the UCL Centre for Cardiovascular Imaging, working closely with Great Ormond Street Hospital (GOSH). The funding covers an annual tax-free stipend and tuition fees. As this is a BRC/EPSRC funded studentship the standard EPSRC eligibility criteria apply, please see the EPSRC website for details. The successful candidate will be enrolled onto the UCL CDT in Intelligent Integrated Imaging in Healthcare (i4health) and benefit from the being part of a cohort of PhD students as well as participation in the activities and events organised by the centre.

Background
Magnetic Resonance Imaging (MRI) has enabled significant advances in the diagnosis and management of many childhood diseases. However, MRI is challenging in the pediatric population as it is time consuming (~1 hour to perform) and requires patient cooperation. Hence it is often necessary to use general anesthesia (GA) in children below 8 years of age, which is both costly and carries some risk. 

One way of overcoming these problems would be to speed up the MRI scans so children do not have to keep still or hold their breath. The simplest way of doing this is to acquire less data (data undersampling), however this results in artefacts that make the images unusable. Current reconstruction methods for removing these artefacts, allow limited acceleration, or use time consuming algorithms which hamper their clinical uptake. A new approach is Machine Learning that aims to 'learn' how to remove undersampling, as well as motion artefacts. 

The purpose of this project is to develop fast MRI acquisitions, with rapid Machine Learning reconstruction technologies for use in childhood diseases of the abdomen.
Building on previous work in motion correction, super resolution, deep artefact suppression, as well as more traditional MRI reconstruction technologies, this project aims to improve reconstructions of MRI data, both in terms of image quality and reconstruction time using ML. In particular, convolutional neural networks (CNN) have been shown to be well suited to MRI reconstruction. These ML strategies often require application-specific training data. However, because some pediatric diseases are rare, we may not have access to large amounts of training data. Part of this proposal is to look into the use of methods that do not require large training data sets, as well as transfer learning techniques to enable development of accurate networks from small prospective or retrospective data sets. 

By the end of this project, the student will have an excellent understanding of machine learning algorithms particularly for reconstruction of MRI data. The student will also be able to use an MRI scanner, understand MRI sequence design, as well as traditional and state-of-the-art MRI reconstruction algorithms. This is a very translational project and will include working closely with clinical partners. All work packages will be integrated into standard clinical workflow to enable clinical validation studies, and simple translation into routine clinical practise.

Research Aims
This study aims to develop novel accelerated magnetic resonance imaging (MRI) technologies which will allow scan times to be reduced from ~1 hour to ~10 minutes in children with diseases within the abdomen. This will be achieved this through development of optimised MR acquisition strategies combined with Machine Learning (ML) reconstruction techniques. 
Specific work packages include development of machine learning techniques to remove aliasing caused by undersampling, correct for artefacts caused by motion, as well as improve image contrast in the abdomen. The resulting networks will be integrated into standard clinical workflow at GOSH to enable clinical validation studies, as well as simple translation into routine clinical practise.
 

How to apply:
Please complete the following steps to apply.

•    Send an expression of interest and current CV to Dr Jennifer Steeden  and cdtadmin@ucl.ac.uk. Please use the subject title: Project Code 22008
•    Make a formal application to via the UCL application portal https://www.ucl.ac.uk/prospective-students/graduate/apply . Please select the programme code MPhil Medical Imaging RRDMEISING01 and enter Towards 10-minute Magnetic Resonance Imaging scans in children with machine learning Project Code 22008 under ‘Name of Award 1’.

This studentship is strictly for those who pay Home fees. We cannot consider overseas students.

Application Deadline - Friday 1 April 2022

If shortlisted, you will be invited for an interview.