Student: Helena Sousa
1st Supervisor: Maria Deprez, King’s College London
Second Supervisor: Alexander Hammers, King’s College London
Third Supervisor: J-Donald Tournier, King’s College London
Clinical Supervisor: Alexander Hammers, King’s College London
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.
The project would suit a candidate from a quantitative discipline, interested in image analysis, machine learning, deep learning as well as early brain development.
Maturing fetal brain as shown by structural MRI (top) and diffusion MRI (bottom).
References:
- Jakab, A: Developmental Pathoconnectomics and Advanced Fetal MRI, Topics in MRI, 2019
- Biegon, A.,Hoffmann, C. Quantitative magnetic resonance imaging of the fetal brain in utero: Methods and applications. World journal of radiology, 6(8), 2014.
- Studholme, C. Mapping the developing human brain in utero using quantitative MR imaging techniques. Seminars in perinatology 39(2), 2015.
- Deprez, M., et al. Higher Order Spherical Harmonics Reconstruction of Fetal Diffusion MRI With Intensity Correction. IEEE Transactions on Medical Imaging 39(4), 2020.