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