Towards 10 Minute Magnetic Resonance Imaging scans in children with machine learning

1. 1. Background

Magnetic Resonance Imaging (MRI) has enabled significant advances in the diagnosis and management of many 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 collect less data for each image, 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 (ML) that aims to 'learn' how to remove undersampling and 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 work in Super Resolution and Deep Artefact Suppression, this project aims to reframe the reconstruction of MRI data, to remove aliases caused by data undersampling and motion corruption, as an image de-noising problem that can be initially performed by a CNN. This strategy requires specific sampling patterns that produce noise-like aliasing and large amounts of high quality, application-specific training data. Thus, this work will leverage the extremely large amounts image data that is available at Great Ormond Street Hospital (GOSH).

2. 2. Research Aims and Objectives

This study aims to develop novel accelerated 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 ML reconstruction techniques.

Specific work packages include development of techniques to correct for artefacts caused by respiratory motion, development of fast 3D imaging for assessment of small bowel motility in Chron's disease, as well as development of real-time imaging to assess the filling and emptying function of the stomach. The resulting networks will be integrated into standard clinical workflow to enable clinical validation studies, as well as simple translation into routine clinical practise.

3. 3. Novelty of Research Methodology

A few recent papers have shown the benefit of using ML methods for reconstruction of MR images. These have mostly been in knee or cardiac applications, and very few have been in abdominal MR imaging or in paediatrics.

4. 4. Alignment to EPSRC's strategies and research areas

This research aligns with the EPSRC Healthcare Technologies theme. The work falls within the "Optimising Treatment" grand challenge and is thoroughly aligned with the "Novel imaging technologies" cross-cutting research capabilities area. The proposed technologies satisfy the key fields; techniques for image reconstruction, lower cost image acquisition technologies, high throughput, and automated image interpretation. The work is also aligned with the EPSRC Research area, "Medical imaging". In particular, it addresses the high priority areas of this delivery plan; enabling earlier and more effective diagnosis, and novel imaging technologies that offer a significant benefit over current technologies.

5. 5. Any companies or collaborators involved

The National Institute for Health Research Biomedical Research Centre (BRC) is partly funding this PhD studentship. Siemens Healthcare have pledged their support, as Project Partner, for Jennifer Steeden's UKRI grant which is closely related to this work.Great Ormond Street Hospital (GOSH) will be close collaborators in this project, providing access to MRI data and MRI scanning facilities.

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.