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1.	Valastyán, Iván, et al. (author) Full 3-D cluster-based iterative image reconstruction tool for a small animal PET camera 2007 In: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier BV. - 0168-9002 .- 1872-9576. ; 571:1-2, s. 219-222 Journal article (peer-reviewed)abstract Iterative reconstruction methods are commonly used to obtain images with high resolution and good signal-to-noise ratio in nuclear imaging. The aim of this work was to develop a scalable, fast, cluster based, fully 3-D iterative image reconstruction package for our small animal PET camera, the miniPET. The reconstruction package is developed to determine the 3-D radioactivity distribution from list mode type of data sets and it can also simulate noise-free projections of digital phantoms. We separated the system matrix generation and the fully 3-D iterative reconstruction process. As the detector geometry is fixed for a given camera, the system matrix describing this geometry is calculated only once and used for every image reconstruction, making the process much faster. The Poisson and the random noise sensitivity of the ML-EM iterative algorithm were studied for our small animal PET system with the help of the simulation and reconstruction tool. The reconstruction tool has also been tested with data collected by the miniPET from a line and a cylinder shaped phantom and also a rat.

Valastyán, Iván, et al. (author)
Full 3-D cluster-based iterative image reconstruction tool for a small animal PET camera
2007
In: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier BV. - 0168-9002 .- 1872-9576. ; 571:1-2, s. 219-222
Journal article (peer-reviewed)abstract
- Iterative reconstruction methods are commonly used to obtain images with high resolution and good signal-to-noise ratio in nuclear imaging. The aim of this work was to develop a scalable, fast, cluster based, fully 3-D iterative image reconstruction package for our small animal PET camera, the miniPET. The reconstruction package is developed to determine the 3-D radioactivity distribution from list mode type of data sets and it can also simulate noise-free projections of digital phantoms. We separated the system matrix generation and the fully 3-D iterative reconstruction process. As the detector geometry is fixed for a given camera, the system matrix describing this geometry is calculated only once and used for every image reconstruction, making the process much faster. The Poisson and the random noise sensitivity of the ML-EM iterative algorithm were studied for our small animal PET system with the help of the simulation and reconstruction tool. The reconstruction tool has also been tested with data collected by the miniPET from a line and a cylinder shaped phantom and also a rat.

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