Iterative image reconstruction methods have attracted considerable attention in the past decades for applications in Computer Tomography (CT) due to the feasibility of incorporating the physical and statistical properties of the imaging process completely. So far, all statistical reconstruction algorithms are based on the maximum likelihood (ML) or the least squares cost function. The maximum likelihood-expectation maximization (ML-EM) algorithm, which is a general statistical method for seeking the estimate of the image, allows computing projections that are close to the measured projection data. Iterative based ML reconstruction algorithms require a considerable computational cost per iteration. The advantages of the iterative approach include better insensitivity to noise and capability of reconstructing an optimal image in the case of incomplete data. The method has been applied in emission tomography modalities like SPECT and PET, where there is significant attenuation along ray paths and noise statistics are relatively poor. Generally speaking, the tomography reconstruction with a limited number of data appears as a highly underdetermined ill-posed problem. The projection data generated by the CT system are initially noisy and the ML algorithm tends to increase this noise and in particular the noise artifacts through the successive iterations. This accumulation of noise leads to a premature stopping of the ML-EM reconstruction process. Several methods have been developed to decrease this accumulation of noise and improve the quality of the reconstructed images in tomography. The aim of this research is to propose a new penalized likelihood iterative reconstruction algorithm for Positron Emission Tomography, by

T. Arun Prasath

Papers

Pet image reconstruction using soft computing techniques