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

Lead Research Organisation: King's College London
Department Name: Imaging & Biomedical Engineering

Abstract

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.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022104/1 01/10/2019 31/03/2028
2271380 Studentship EP/S022104/1 01/10/2019 30/09/2023 Suryava Bhattacharya