PET is Wonderful Annual Meeting 2020 Oct 27, 2020 02:00 PM - 05:40 PM — Virtual Meeting (online)
Through the Looking Glass: Breaking Barriers in STEM Oct 28, 2020 12:00 PM - 03:30 PM — Virtual Meeting (online)
NRS Mental Health Network Annual Scientific Meeting 2020 Nov 04, 2020 09:00 AM - 05:30 PM — Virtual Meeting (online)
Scottish Radiological Society Annual General Meeting 2020 Nov 06, 2020 09:30 AM - 03:30 PM — Virtual Meeting (online)
IPEM educational meeting: Artificial Intelligence in MRI Nov 18, 2020 12:00 AM — 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

Mr Fan Zhu

Position: PhD Student
Institute: School of Informatics
Department: SFC Brain Imaging Research Centre


Description of Phd:

 

Deconvolution is used in perfusion imaging to obtain the impulse residue function (IRF) that is then used to create parametric maps of relevant haemodynamic quantities such as CBF, CBV and MTT. A popular method to achieve this is Singular Value Decomposition (SVD), but it has been shown that for MRI Gaussian Process Deconvolution (GPD) is comparable to SVD when determining the maximum of the IRF, and superior estimating the full IRF. Furthermore, it clearly outperforms SVD when the signal-to-noise ratio improves.   Gaussian Process regression arises from a Bayesian approach to the regression problem, and as in the case of other kernel-based methods the scalability with data size is very poor. This constitutes the main drawback of this technique to compute deconvolution when compared with SVD.  The currently running Wyeth-TMRC multicenter project on acute stroke brings the opportunity to test this technique with data from several SINAPSE centres and different modalities. This PhD project will benefit from the expertise in these centres and would seek to collaborate with them through the centres’ contacts: M.J. McLeod (Aberdeen), J. Wardlaw (Edinburgh) and K. Muir (Glasgow).  The project will research the possibilities that distributed (and parallel) computing brings to make this method usable in practice. As a by product, the project will produce a data processing framework prototype reusable for other types of image processing.