· Dr. Freek Beekman
· Associate professor responsible for R&D on Tomography
·
Image Sciences Institute,
University Medical Centre
· +31 30 2538843
· +31 30 2539032
· Freek Beekman (physicist, Ph.D.’95, associate professor at the Image Science Institute, Utrecht University, authored more than 40 journal peer reviewed journal papers, several book chapters and patent applications. His research interests include image reconstruction (in particular emission CT and X-ray CT), Monte Carlo and analytic modelling, and tomographic instrumentation.
· Johan Nuyts
· Associate professor, responsible for R&D in nuclear medicine imaging.
·
Affiliation: Dept. Nuclear
Medicine,
Address: Nuclear Medicine, UZ Gasthuisberg,
Herestraat 49, B3000
· Tel: +32 16 343715
· Fax: +32 16 343759
· E-mail: Johan.Nuyts@uz.kuleuven.ac.be
· Degree of Electronical Engineering in ’82, of Medical Physics in ’83 and PhD in Applied Sciences in ’91. R&D in image processing hardware until ’87 at ESAT, MI2, K.U.Leuven, and since ’87 R&D in nuclear medicine imaging, UZ Gasthuisberg and K.U.Leuven. His research interests include iterative reconstruction and image processing for nuclear medicine applications.
· Statistical Image Reconstruction Methods
· Description of Course
The introduction of novel PET, SPECT and CT imaging devices, availability of fast computers and algorithms, as well as the increasing demand for improved image reconstruction has brought new relevance to the topic of discrete reconstruction methods. These include methods that are suitable for modelling noise in the projection data, for incorporating prior knowledge about the object to be reconstructed, and for model-based correction of image degrading effects (i.e. detector blurring, photon attenuation and scatter).
The objective of this four hours
course is to provide up-to-date practical knowledge on the emerging area of
discrete image reconstruction, applied to SPECT, PET, and transmission CT. The
course will cover topics like scatter correction for emission tomography and
CT, beam-hardening correction for
Prequisite knowledge should include basics
of the physics of imaging systems, statistics, and elementary linear algebra.
I. Introduction and primer (Freek Beekman)
A. Imaging modalities and their discrete models
B. Iterative methods
C. Primer on block-iterative and dual matrix methods
D. Primer on Regularization
II. Theory (Johan Nuyts)
A. Maximum likelihood reconstructions
B. Bayesian reconstruction
III. Modelling of photon transport (Freek Beekman)
A. SPECT: attenuation, scatter and blurring correction.
B. PET: attenuation, scatter and blurring correction
C. Beam hardening and scatter modelling for
D. Resolution recovery in transmission CT
IV. Capita Selecta
A. Using the Fisher information matrix in iterative reconstruction (Johan Nuyts)
B.
V Various practical implementation tricks
(Spike suppression through pre-processing of images, blurring strategies for resolution recovery, filter versus Bayesian, etc, etc)