title: Simultaneous iterative reconstruction for emission and attenuation images in positron emission tomography.
abstract: The quality of the attenuation correction strongly influences the outcome of the reconstructed emission scan in positron emission tomography. Usually the attenuation correction factors are calculated from the transmission and blank scan and thereafter applied during the reconstruction on the emission data. However, this is not an optimal treatment of the available data, because the emission data themselves contain additional information about attenuation: The optimal treatment must use this information for the determination of the attenuation correction factors. Therefore, our purpose is to investigate a simultaneous emission and attenuation image reconstruction using a maximum likelihood estimator, which takes the attenuation information in the emission data into account. The total maximum likelihood function for emission and transmission is used to derive a one-dimensional Newton-like algorithm for the calculation of the emission and attenuation image. Log-likelihood convergence, mean differences, and the mean of squared differences for the emission image and the attenuation correction factors of a mathematical thorax phantom were determined and compared. As a result we obtain images improved with respect to log likelihood in all cases and with respect to our figures of merit in most cases. We conclude that the simultaneous reconstruction can improve the performance of image reconstruction.
authors.affiliation: Abteilung Nuklearmedizin, Universität Ulm, Germany. gerhard.glatting@medizin.uni-ulm.de
authors.forename: G
authors.initials: G
authors.lastname: Glatting
authors.forename: M
authors.initials: M
authors.lastname: Wuchenauer
authors.forename: S N
authors.initials: SN
authors.lastname: Reske
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D019047:Phantoms, Imaging; D014055:Tomography, Emission-Computed / Q000379:methods*