Machine learning approaches to enabling ultra-fast diagnostic MRI protocols for neurology

1. Brief description of the context of the research including potential impact

Magnetic resonance imaging (MRI) is proven as the diagnostic imaging method of choice for a wide range of neurological conditions. However, it is used less often than competing modalities such as CT due to its expense and the longer time taken to acquire images. Additionally, the number of installed MRI machines is lower than the number of CT scanners, with many healthcare organisations having older machines not capable of the latest high-quality imaging. This mixture of challenges around the use of MRI means that it is used less often than CT, even though in many cases it is the more appropriate choice, providing greater diagnostic sensitivity and specificity.

An important area where this has impact is in the diagnosis of Alzheimer's disease (AD) where NICE guidelines specify using structural imaging to rule out reversible causes of cognitive decline and to assist with subtype diagnosis. Ideally, this means scheduling an MRI scan, due to its lack of ionising radiation, excellent soft-tissue contrast, and superiority over other imaging techniques in identifying vascular dementia or when the subtype is uncertain. This information allows differential diagnosis, which may alter management and enhance prognostication, unlike CT.

If the scan time, availability, and cost for an MRI scan were comparable to a CT scan, its benefits mean that MRI would be used in almost all cases. The key to solving each of these problems is a substantial reduction in the duration of a diagnostic scan for Alzheimer's disease. Shorter scans would be easier to schedule in the overall diagnostic patient pathway, thereby improving availability of appropriate imaging to patients and providers. Shorter scans are also less expensive, as cost is driven to a large degree by the scan time. The patient experience would also be improved by less time in the scanner as anxiety is reduced, and more time is available for staff.

However, achieving shorter times has been problematic to date because the required scan time reduction leads to unacceptable image quality degradation. Additionally, if older MRI machines acquisitions could be improved in quality, then the overall availability of suitable scanning would also be increased. A key scientific challenge of this PhD project is the development of a combination of new ultra-fast MRI and machine learning methods for reconstruction and analysis that can provide equivalent diagnostic information to conventional diagnostic MRI.

2. Aims and Objectives

The specific objectives are to:
Evaluate existing machine learning methods to accelerate MRI acquisition
Develop and evaluate the use of Image Quality Transfer (IQT) methods as applied to MR image reconstruction and quality improvement
Apply and assess IQT methods on ultra-fast acquired MRI scans for the detection of Alzheimer's disease
Apply and assess IQT methods on ultra-low field acquired MRI scans for the detection of Alzheimer's disease
Develop and maintain tools and workflows for efficient application of IQT and related methods in a translational research environment.

3. Novelty of Research Methodology

Until now there has been no previous attempt of using Image Quality Transfer methods to enable accelerated scans for dementia. The challenge of creating standard of care images (T1, T2, SWI) from significantly degraded rapid scans will require new machine learning methods to be developed, implemented on clinical images, and subsequently evaluated.

4. Alignment to EPSRC's strategies and research areas

Aligned with the EPSRC themes on Artificial Intelligence and Healthcare Technologies

5. Any companies or collaborators involved

There is a possibility that there may be collaboration with MRI scanner manufactures, but this is yet to be confirmed.

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.