This project aims to achieve state-of-the-art high precision, fast and accurate motion correction for dynamic contrast-enhanced (DCE) MR imaging by developing solutions based on convolutional neural networks. Clinical applications of DCE MR include imaging the uterus, liver or prostate gland. For the uterus, a major source of motion is the bladder, which can double in volume during scanning – lifting and rotating the uterus. The uterus is, therefore, a good model case to develop a broader motion correction method. There have been no dynamic uterine studies to date that explicitly corrected for bladder motion.

For details of this project delivered by the University of Edinburgh [Supervisors: Dr Lucy Kershaw and Dr Scott Semple] and Canon Medical Research Europe Ltd [Company supervisor: Dr Keith Goatman], go to https://www.findaphd.com/phds/project/convolutional-neural-networks-to-correct-motion-in-dynamic-mr-imaging/?p107848

The deadline for application is 22 August 2019