Venue: UCL Gordon House 209 https://www.ucl.ac.uk/maps/gordon-house

Presentation by Dr Simon Stute

Abstract: In PET image reconstruction, it would be useful to obtain the entire posterior probability distribution of the image, because it allows for both estimating image intensity and assessing the uncertainty of the estimation, thus leading to more reliable interpretation. We propose a new entirely probabilistic model: the prior is a distribution over possible smooth regions (distance-driven Chinese restaurant process), and the posterior distribution is estimated using a Gibbs MCMC sampler. Data from other modalities (here one or several MR images) are introduced into the model as additional observed data, providing side information about likely smooth regions in the image. The method was compared to MLEM and to a penalized ML method using Bowsher weights from MRI data. Results from simulations and real brain scans from the GE Signa PET/MR are presented.

CV: Simon Stute is a researcher at the French Atomic Energy Commission at Orsay, France. He is working inside the Service Hospitalier Frederic Joliot. His research interests are mainly focused on tomographic image reconstruction, from theoretical to applied developments. He received his PhD degree from Paris-Sud University in 2010 and has a background of applied computer sciences in the field of modelling and simulations in physics.

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