Kris Thielemans

Dr. Kris Thielemans

Imaging Research Solutions, Ltd.

Hammersmith, UK

Tuesday, November 19, 2002
4:00-5:30 pm
1500 EECS

Resolution and noise characterisation of iterative reconstruction algorithms for PET that are regularised with inter-filtering

Abstract -

It is generally accepted in PET that reconstruction algorithms based on statistical principles can provide better performance for certain tasks than those based on analytic inversion formulas of the X-ray transform. However, due to the ill-conditioned nature of the problem, it is necessary to introduce some kind of regularisation. It is then important to be able to predict the properties of the reconstructed images in terms of the measured object and the regularisation parameters, such that these parameters can be optimised for a particular task.

This talk concentrates on a regularisation method where the image is filtered after every iteration of the algorithm. Analytic formulas for the resolution and noise behaviour are derived. These formulas are first used to show that these properties are in general object dependent, and then to derive various approaches to obtain object-independent resolution. Finally, we obtain a connection between this inter-filtering regularisation method and penalised likelihood reconstructions.

Biosketch -

Dr. Thielemans has a Ph.D. in Theoretical Physics, and in particular in String Theory. He now works as a researcher at Imaging Research Solutions Ltd, Hammersmith, UK. His particular interest is understanding and characterising the performance of image reconstruction algorithms based on statistical principles, as applied to Positron Emission Tomography.

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