Quantitative mapping of developing fetal organs using dynamic MRI and artificial neural networksare Technologies

Quantitative T1 and T2 mapping of the fetus has potential to allow characterisation of subtle changes in tissue properties. Normal developmental processes such as myelination, changing microstructure and water content are known to have profound effect on T1 and T2 relaxation times1, which reflect fundamental biological properties of the tissues. Identifying subtle changes to these values resulting from pathology, inflammation or injury would require fully quantitative measurements.

However, the fetal motion presents a serious challenge to established quantitative methods. Many state-of-the-art neuroimaging qMRI methods require multiple lengthy 3D acquisitions and steady-state sequences2 that are easily disrupted by motion, and are thus incompatible with fetal imaging. Recent advancements have produced high quality 3D non-quantitative anatomical images of moving fetuses by using a "slice to volume registration" (SVR) approach, which assembles a 3D image from multiple 2D images using image registration3 (see Fig. 1 and 2).

The aim of the project is to develop innovative motion resistant slice-selective sequences that can be reconstructed into high resolution consistent quantitative maps. Artificial intelligence methods (deep neural networks) will be employed to provide a tractable solution to a complex inverse problem of estimation of relaxation times from the non-steady state sequences by modelling interaction with fetal motion.

The newly developed motion resistant quantitative techniques will be applied to fetal brain and other organs to assess their diagnostic value and facilitate comparison of fetal brain development with pre-term neonatal population.

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.