Deep Generative Models of Fetal Brain Development: forward modelling of the mechanisms of neurodevelopmental impairment

Aim of the PhD Project:

Develop novel techniques for surface-to-volume image registration consolidating concepts from discrete and deep optimisation
Incorporate biomechanical and biophysical models of brain growth and folding
Build generative forward models of neurodevelopment from fetal and neonatal MRI
Investigate extensive opportunities for clinical translation following preterm birth, congenital heart disease and epilepsy
Project description:

Over the period of late gestation, the fetal brain undergoes a period of rapid development. Neuronal cells make distant connections, laying down a network of communication that will later support complex cognition. The rapid growth of cells in the cortex, the outer layer of the brain, creates biomechanical tensions which cause the surface to fold.

Disruptions to this process, for example resulting from preterm birth or prenatal conditions such as congenital heart disease, can result in long-term neurodevelopmental impairments such as Autism, ADHE and epilepsy. The objective of this project is to build a forward model of this process, through which pathways of brain injury may be simulated, and the causes of neurodevelopmental impairment be understood.

This project extends from previous work that built average models of brain growth using a combination of image registration and Gaussian process regression [1]. This model showed strong potential for the modelling the deviation of preterm development from healthy, at the centre of the brain. But struggled to model the cortex, where considerable variation of individual's brain shape and patterns of cortical organisation make comparison across scans much more difficult.

The work also incorporates ideas from [2,3] which built a deep generative model for image-to-image translation and used it to derive feature attribution (FA) maps that highlight all evidence of pathology in individual brains. This is achieved by learning a mapping that changes an image backwards from categorised as diseased towards being classified as healthy.

Neither of these projects presented a forward or mechanistic model of disease; however, in [4,5] we propose a novel biomechanical models of brain atrophy following dementia. This supports simulation of the trajectory of brain atrophy under different disease states or conditions: healthy ageing, mild cognitive impairment or full Alzheimer's disease.

Accordingly in this project we seek to integrate these ideas to develop a deep generative biomechanical model of cortical growth from late gestation to birth. This will extend the ideas of [4,5] integrating novel models of cortical folding [5] and techniques from the domain of cortical surface registration [6,7] to learn to model longitudinal mappings between an individual's fetal and neonatal scans. Then will adapt ideas from image-to-image models for phenotype translation [2,3] to change tissue contrast and appearance. In this way simultaneously simulating changes in shape, size and tissue maturation.

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.