Resolving the cortex with ultra-high resolution MRI to detect epileptic lesions

Epilepsy is the most common neurological condition in children. The most common cause of drug resistant focal epilepsy in children are malformations of cortical development (MCD). In these conditions alterations to the processes of cell migration and differentiation early in life lead to cortical areas that have aberrant cell types and layering structure. MRI is the principal imaging modality to detect these abnormalities, and it is effective when they are extensive or involve strong changes in MRI image contrast compared to healthy cortex. We have been developing high quality quantitative structural MRI to allow more optimal non-invasive lesion detection and phenotyping. However detection of abnormalities remains challenging in a significant proportion of patients. This may be attributable to the current resolution of MRI (typically ~1mm) being unable to resolve cortical structure (4-6 layers within 2-4mm).

In this project, image resolution in MRI will be increased using longer scan times enabled by: utilising a new high-field 7T MRI, improved image acquisition and reconstruction that obtains more information in the same time; deep learning, computer algorithms that learn how to make high resolution images from limited data.

This will enable us to provide preliminary evidence both of the feasibility of imaging at this increased resolution and the potential advantages of imaging this patient group at 7T, the new frontier of MRI for clinical application. We will obtain pilot data in patients with focal epilepsy with localising electro-clinical features that are consistent with MCD but that have no visible abnormalities in current clinical 3T MR images.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.