Data Sciences and Brain Health across the Life Course session in 2020 SICSA Conference Oct 01, 2020 01:15 PM - 03:15 PM — Virtual Meeting (online)
Ophthalmic Medical Image Analysis MICCAI 2020 Workshop Oct 08, 2020 12:00 AM — Virtual Meeting (online)
Predictive Intelligence in Medicine MICCAI 2020 Workshop Oct 08, 2020 12:00 AM — Virtual Meeting (online)
PET is Wonderful Annual Meeting 2020 Oct 27, 2020 02:00 PM - 05:40 PM — Virtual Meeting (online)

eLearning

SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.


Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

PhD studentship at Edinburgh: Convolutional neural networks to correct motion in dynamic MR imaging

Convolutional neural networks to correct motion in dynamic MR imaging

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