Exploring early fetal brain development: a deep learning approach

Aim of the PhD Project:

In this project we will develop deep learning image analysis tools to enable understanding of early brain development as depicted by multi-modal MRI. We will develop methods to delineate transient and emerging brain structures and characterise their microstructure in average healthy brain development as well as on individual level. We will extract relevant biomarkers of normal and abnormal early brain development, including anatomy known to be involved in epilepsy and autism spectrum disorder.

Project Description / Background:

During the second half of pregnancy the brain undergoes rapid development, including formation of white matter tracts, the onset of myelination and cortical folding (Figure 1). Understanding precise timing and variation of these developmental processes would shed light on origins of various conditions, such as Autism Spectrum Disorder, Epilepsy, and effects of congenital abnormalities or infection in in womb. Neuroimaging using multi-modal MRI can offer insights into fetal brain development [1,2]. However, imaging moving fetus inside the mother is difficult, resulting in variable image quality, and fast brain development interferes with interpretation of the fetal brain MRI images, requiring dedicated fetal image analysis techniques [3].

In this project we propose to build deep learning tools dedicated to analysis of rapid early fetal brain development as observed by multi-modal MRI. We will build on Developing Human Connectome project that produced a large database of state-of-the-art motion-corrected multi-modal fetal MRI [4]. We will use artificial intelligence to

Detect and delineate rapidly evolving transient fetal structures, by fusing morphological, microstructural and connectivity information extracted from structural and diffusion MRI.
Derive quantitative indices, including volumes, shape and microstructure to accurately stage the fetal brain development
Develop advance deep learning techniques for fetal MRI image enhancement to reduce artefacts and enable reliable quantitative assessment of individual babies
Derive quantitative indices of structural development for brain regions like hippocampus (rotation/folding indices) and Sylvian fissure (opercularisation indices), known to be involved in Epilepsy
Develop spatio-temporal deep learning models to predict risk of ASD and preterm birth and interpret these models to find multi-modal MRI biomarkers that characterise these conditions.

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.