2020 SINAPSE ASM Jun 19, 2020 09:00 AM - 05:00 PM — Virtual Meeting (online)
3rd International Conference on Medical Imaging with Deep Learning Jul 06, 2020 - Jul 08, 2020 — Virtual Meeting (online)
Medical Image Understanding and Analysis Conference 2020 Jul 15, 2020 - Jul 17, 2020 — Virtual Meeting (online)
CAFACHEM 2020 Summer School on Organic & Halogen Radiochemistry Aug 25, 2020 - Aug 28, 2020 — KCL Waterloo Campus, London
Scottish Dementia Research Consortium Annual Conference 2020 [rescheduled] Sep 07, 2020 10:00 AM - 04:00 PM — Radisson Blu, 301 Argyle St, Glasgow

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

Innovation Partnership Fund awards to projects across SINAPSE partner institutions

April 2020 - Innovation Partnership Fund awards to projects across SINAPSE partner institutions

Congratulations to the following research project leaders who have been awarded a total of £55,000 in internal funding to enhance intersectoral engagement through facilitating innovative imaging developments in SINAPSE partner institutions:

 

University of Aberdeen

  • Towards a cardiac magnetic resonance protocol for Fast Field-Cycling MRI - Dr James Ross is leading this project to initiate cardiac imaging on Aberdeen's fast field-cycling MRI (FFC-MRI) scanner, in collaboration with NHS Grampian cardiology research. FFC-MRI is a novel technique which involves rapidly switching the external magnetic field during the imaging procedure; it has been pioneered by University of Aberdeen imaging researchers, and the only human-scale FFC-MRI scanner in the world is located in Aberdeen. While cardiac imaging is well-established at clinical MRI field-strengths of 1.5T and 3T, to date no studies have been performed on a FFC-MRI scanner. Funding from SINAPSE combined with support from the British Heart Foundation will enable the promise of FFC-MRI to be expanded to human cardiac imaging for the first time. 

 

University of Dundee

  • Breast Research Imaging Group Integrated Dataset (BRIGID) - Prof Andrew Evans is leading this project to develop a Scottish database of breast imaging patients, their demographics, multimodality imaging findings, treatment, pathology details and outcomes, in collaboration with NHS Tayside breast cancer research and the scientific computing department at The Royal Surrey County Hospital. A first line of new research will explore relationships between the pre-treatment imaging features of breast cancer and breast cancer specific survival in women receiving neoadjuvant chemotherapy. Funding from SINAPSE will enable the existing BRIGID infrastructure to be improved and expanded in order to facilitate translational and clinical breast imaging research, increasing the value of a database which could eventually be exploited Scotland-wide.
  • Prediction of brain tumour progression - Prof Douglas Steele and Mr Kismet Hossain-Ibrahim, Consultant Neurosurgeon, are leading this project to predict tumour progression in glioblastoma multiforme – the most aggressive type of brain cancer – using a combination of quantitative processing of routinely acquired brain image data and computational modelling of tumour growth, in collaboration with neurosurgery researchers in NHS Tayside and at the University of Cambridge. Funding from SINAPSE will support a feasibility study on expanding the scope of current work on developing predictive methods using routinely acquired NHS imaging data.

 

University of Edinburgh

  • Vascular calcification on routine thoracic CT to predict and improve cardiovascular outcomes - Dr Michelle Williams is leading this project to automatically identify, classify, and quantify coronary calcification on chest CT, in collaboration with machine learning researchers at Cedars-Sinai Medical Center. Although incidental calcification on chest CT can identify previously undiagnosed coronary artery disease, currently it is frequently ignored when reporting routine CT. Funding from SINAPSE will enable analysis to be performed on a large number of cardiac and non-cardiac CT scans, in order to develop a machine learning model that could ultimately be used to support radiologist reporting by improving the speed and accuracy of coronary heart disease diagnoses.

 

University of Glasgow

  • Development of 7T MRI technology for improved neurovascular imaging in neurological conditions - Dr Shajan Gunamony is leading this project to construct a novel radiofrequency coil optimised for ultra-high field MRI scanning of neurovascular pathologies in the head and neck, in collaboration with neurology and neuroradiology researchers in NHS Greater Glasgow & Clyde. Funding from SINAPSE will be combined with support from the Dr Christine Rodger Research Endowment to enable development of a bespoke 7T MRI coil that will extend the anatomical coverage of standard head coils to allow high-quality carotid imaging and to enable arterial spin labelling for quantitative measurement of cerebral perfusion in neurological conditions including small vessel diseases, vascular dementia, neuro-oncology, and epilepsy.

 

University of St Andrews

  • Squeezing new information out of old data – developing multi-level modelling as a novel approach to the (re)analysis of EEG experiments - Prof David Donaldson is leading this project to develop local expertise in the application of multi-level modeling (MLM) to EEG neuroimaging data, in collaboration with SINAPSE colleagues at the University of Aberdeen and the University of Stirling. Traditional approaches to the analysis of EEG reveal differences between conditions within an experiment, but ignore changes that occur across trials and between participants. MLM is a statistical method that has had limited use within EEG research to date but can be used to re-examine existing EEG data sets, building more sophisticated models that account for previously ignored sources of variability. Funding from SINAPSE will enable MLM as a novel approach to the analysis of EEG to be developed and shared across the SINAPSE network.

 

University of Stirling

  • Mobile Cognition Innovation - Dr Magdalena Ietswaart is leading this project to improve the integration of mobile EEG brain recordings with body movement recordings, in collaboration with neurology researchers in Italy and an American industry partner. University of Stirling researchers in the Centre for Mobile Cognition are pioneering methods for neuroimaging of real world behaviour, including applications in sporting and clinical contexts. Funding from SINAPSE will support research on the impact of football heading and new studies identifying neural signatures from mobile EEG synchronised with measurements of body movement in motor disorders.

 

University of Strathclyde

  • The brainstem in autism: A pilot 7T MRI neuroimaging study - Prof Jonathan Delafield-Butt is leading this project to perform ultra-high field MRI scanning of brainstem structures hypothesised to underpin autistic symptomology, in collaboration with autism neuroimaging researchers in Italy and SINAPSE colleagues at the University of Glasgow and the University of Edinburgh. A consistent finding in brain imaging studies of children and adults with autism is a significant difference in brainstem volume and morphology. Although this could present a new target for early diagnosis in young children, to date no study has examined in vivo how the internal organisation of the brainstem is disrupted. Funding from SINAPSE will enable collection of new 7T MRI pilot data to resolve the principal substructures of brainstem that make up its size difference in autism.