engleski

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

An image or volume of interest in positron emission tomography (PET) is reconstructed from gamma rays emitted from a radioactive tracer, which are then captured and used to estimate the tracer’s location. The image or volume of interest is reconstructed by estimating the pixel or voxel values on a grid determined by the scanner. Such an approach is usually associated with limited resolution of the reconstruction, high computational complexity due to slow convergence and noisy results. This paper presents a novel method of PET image reconstruction using the underlying assumption that the originals of interest can be modelled using Gaussian mixture models. Parameters are estimated from one-dimensional projections using an iterative algorithm resembling the expectation- maximization algorithm. This presents a complex computational problem which is resolved by a novel approach that utilizes L1 minimization.

Gaussian mixture models ; positron emission tomography ; Expectation - Maximization (EM) algorithm ; image segmentation

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