9th SINAPSE Neuro-oncology Imaging Meeting [rescheduled] Mar 11, 2021 09:30 AM - 03:30 PM — West Park Conferencing & Events, 319 Perth Road, Dundee DD2 1NN
Total Body PET 2020 conference [rescheduled] Jun 05, 2021 - Jun 07, 2021 — McEwan Hall, University of Edinburgh
Medical Imaging Convention [rescheduled] Sep 15, 2021 - Sep 16, 2021 — National Exhibition Centre, Birmingham, England

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

CSO Award for Clinical Imaging Innovation and Partnership to support machine learning project for radiolucency detection in clinical x-rays

December 2020 - CSO Award for Clinical Imaging Innovation and Partnership to support machine learning project for radiolucency detection in clinical x-rays

To facilitate innovative, clinically focussed imaging developments in Scotland, £10,000 from the Chief Scientist Office (CSO) of the Scottish Government Health and Social Care Directorates has been awarded to the research project Clinical importance of radiolucent lines in knee surgery. Dr Matthew Banger from University of Strathclyde is leading this project, in collaboration with orthopaedic clinicians at the Glasgow Royal Infirmary (Mr Mark Blyth and Dr James Doonan), to develop a method of detecting radiolucent lines with machine learning in a new database of clinical x-ray images from knee replacement patients. Bone density (radiolucency) underneath the knee replacement implant is an indicator of physiological changes in the joint following surgery; however, its detection on x-ray is currently dependent upon a highly subjective scoring system. This award will enable an automated process for the detection of radiolucency to be created and validated in a large imaging database, and evaluated against relevant clinical outcome scores, in order to provide an objective measure of changing bone densities to be used in clinical interpretations. A refined machine learning approach to define the presence or absence of radiolucency in x-ray data will support well informed clinical decisions in care for arthroplasty patients, particularly around the requirement for revision surgery.