About the Project
3.5 years EPSRC Funded PhD scholarship for start in October 2020.
Currently Interviewing - apply as soon as possible - final deadline: 20-23 July 2020
Location: King’s College London, Department of Bioengineering, School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, London SE1 7EH (https://www.kcl.ac.uk/bmeis)
Short Project Description.
Measuring metabolite concentration in the fetal brain with MR Spectroscopy (MRS) has potential to assess the effects of placental dysfunction or alterations in the fetal circulation from congenital heart disease on brain development. However, due to continuous/unpredictable fetal motion, with standard MRS methodology, current success rate is low (50-70%).
We aim to develop a motion-robust MRS method resilient to fetal motion. This will be based on 3D volume navigator images collected concurrently with each MRS acquisition (every ~2 seconds), and fast real-time feedback (based on machine-learning algorithms) allowing adjustment of the VOI position to maintain it in the same anatomical position irrespective of motion. Monitoring extent of motion and data similarity between repeats will allow quality assessment of the cumulative data collected and extension of the acquisition until data of sufficient signal-to-noise ratio for meaningful analysis has been collected. The aim is for fetal MRS to become a usable, reliable and robust clinical investigation tool.
To apply, follow the links at: https://www.kcl.ac.uk/health/study/studentships/div-studentships/beis/de-vita-pushparajah
You will learn about, and use, machine learning algorithms, MR Imaging and MR spectroscopy pulse sequences, advanced data acquisition and analysis methods. You will be trained in MR-scanner software and hardware in collaboration with scanner manufacturers. You will be able to attend relevant post-graduate courses in artificial intelligence, machine learning, medical imaging and relevant healthcare technologies as well as other transferrable skills through the School’s extensive educational programme.
You will also be aligned with a large cohort of PhD students enrolled in the Smart Medical Imaging CDT (https://www.imagingcdt.com/phd-programme/)
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