The applicants proposed to develop and optimize three dimensional (3-D) iterative reconstruction algorithms. Recently, the removal of interplane septa in multiring PET cameras has allowed oblique coincidence events to be collected and has proved to have significantly increased the sensitivity of signal detection in PET volume imaging. From the results of studies in two dimensional space, most iterative reconstructions which allow the incorporation of a model in the reconstruction algorithm can improve the image quality over the filtered backprojection reconstruction. The results of O-15 water subtraction studies, which have been shown to have increased signal-to-noise ratio (SNR) using the volume imaging technique, can be further improved by the use of 3-D iterative reconstructions.
The specific aims are: 1) To investigate the signal and noise tradeoff as a function of resolution recovery, in particular in the axial direction compared to 3-D filtered backprojection; 2) To further study the impact of a more accurate model which includes scatter, attenuation, randoms and detector homogeneity for the two iterative reconstructions on the signal and noise tradeoff. The results will be compared to 3-D filtered backprojection; and 3) To optimize the iterative 3-D reconstructions for O-15 water subtraction studies. The two iterative reconstruction algorithms interative filtered back projection (IFBP) and maximum likelihood - EM (ML-EM), will be applied to the 3-D O-15 water data sets, consisting of two groups of normal volunteers that are given a motor and speech stimulation task respectively. The improvement of SNR will be measured by change distribution analysis (CDA) over the data set and compared with the results from filtered backprojection. Efforts will also be made to optimize the reconstruction code over four CSPI supercard II array processors to reduce the reconstruction time for clinical studies.
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