Simultaneous iterative reconstruction of emission and attenuation images in positron emission tomography from emission data only.
For quantitative image reconstruction in positron emission tomography attenuation correction is mandatory. In case that no data are available for the calculation of the attenuation correction factors one can try to determine them from the emission data alone. However, it is not clear if the information content is sufficient to yield an adequate attenuation correction together with a satisfactory activity distribution. Therefore, we determined the log likelihood distribution for a thorax phantom depending on the choice of attenuation and activity pixel values to measure the crosstalk between both. In addition an iterative image reconstruction (one-dimensional Newton-type algorithm with a maximum likelihood estimator), which simultaneously reconstructs the images of the activity distribution and the attenuation coefficients is used to demonstrate the problems and possibilities of such a reconstruction. As result we show that for a change of the log likelihood in the range of statistical noise, the associated change in the activity value of a structure is between 6% and 263%. In addition, we show that it is not possible to choose the best maximum on the basis of the log likelihood when a regularization is used, because the coupling between different structures mediated by the (smoothing) regularization prevents an adequate solution due to crosstalk. We conclude that taking into account the attenuation information in the emission data improves the performance of image reconstruction with respect to the bias of the activities, however, the reconstruction still is not quantitative.
D000465:Algorithms; D016477:Artifacts; D001842:Bone and Bones / Q000000981:diagnostic imaging; D003198:Computer Simulation; D003238:Connective Tissue / Q000000981:diagnostic imaging; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D016013:Likelihood Functions; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D013909:Thorax / Q000000981:diagnostic imaging*; D014055:Tomography, Emission-Computed / Q000379:methods*; D014132:Trachea / Q000000981:diagnostic imaging