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An image or volume of interest in positron emission tomography (PET) isreconstructed from pairs of gamma rays emitted from a radioactive substance.Many image reconstruction methods are based on estimation of pixels or voxelson some predefined grid. Such an approach is usually associated with limitedresolution of the reconstruction, high computational complexity due to slowconvergence and noisy results. This paper explores reconstruction of PET imagesusing the underlying assumption that the originals of interest can be modeledusing Gaussian mixture models. A robust segmentation method based onstatistical properties of the model is presented, with an iterative algorithmresembling the expectation-maximization algorithm. Use of parametric models forimage description instead of pixels circumvent some of the mentionedlimitations.

2D PET Image Reconstruction Using Robust L1 Estimation of the Gaussian Mixture Model