Original project description:
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. There have been some previous works like the dataparallel approach proposed in . The project will study the consequences on the final results of the local learning used in it. As a by product, the project will produce a data processing framework prototype reusable for other types of image processing.