(A*STAR) Multimodal data-based modelling for precision obstetrics medicine

The placenta is responsible for sustaining fetal life and enabling fetal growth during pregnancy. It is characterised by a unique complex arrangement of densely packed fetal blood vessels drawing oxygen and nutrients from maternal blood. In up to 10% of pregnancies, placental function is compromised, resulting in babies that fail to grow to their full potential. This condition (fetal growth restriction) can have lifelong consequences, including higher risks of heart disease, diabetes or stroke later in life. Despite rapid developments in the field of medical diagnostics, there are still no significant technological advances in obstetrics medicine over the last 50 years since the introduction of ultrasonography. Thus, there is urgent need to better understand and quantify the placental function and structure relationship to aid in the early detection and management of high-risk pregnancies.

Image-based modelling has the potential to reveal how disruption of this intricate structure in disease impacts placental function. We have developed a system using X-ray micro-CT technology to image vascular casts of the feto- placental vascular tree in normal and abnormal placentas and characterise their structure. The structural data is complemented by a unique large clinical dataset of Doppler ultrasound measurements on the blood flow that enters and leaves the placenta at different stages of pregnancy. Our recent work has only started to explore the potential of the imaging technology; mathematical, computational and machine-learning approaches are now needed to characterise the vessel network structure and to integrate it with functional observations at the whole-organ level. Using existing and emerging data, we will develop multiscale models of the placental circulation, which can be validated against clinical data, and will use these models to assess nutrient transport in normal and abnormal placentas.

The project will suit a student with a degree in applied mathematics, physics, bioengineering or a related area who has enthusiasm to join an interdisciplinary group to work on a challenging biomedical application with strong potential to transform clinical practice.

Entry Requirements:
Applications should be submitted online and candidates should make direct contact with the Manchester supervisor to discuss their application directly. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.