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UBC Theses and Dissertations

Noise propagation in quantitative positron emission tomography Palmer, Matthew Rex


Image noise in Positron Emission Tomography (PET) is the result of statistical fluctuation in projection data. The variance properties of images obtained with the UBC/TRIUMF PETT VI tomograph are studied by analytical methods, computer simulations, and phantom experiments. The PETT VI image reconstruction algorithm is described and analyzed for noise propagation properties. Procedures for estimating both point-wise (pixel) and region of interest (ROI) variances are developed: these include the effects of corrections for non-uniform sampling, detector efficiency variation, object self-attenuation and random coincidences. The analytical expression for image-plane variance is used in computer simulations to isolate the effects of the various data corrections: It is shown that the image precision is degraded due to non-uniform sampling of the projections. The RMS noise is found to be increased by 9% due to the wobble motion employed in PETT VI. Analytical predictions for both pixel and ROI variances are verified with phantom experiments. The average error between measured and predicted ROI variances due to noise in emission data for a set of seven regions placed on a 20 cm cylindrical phantom is 9.5%. Images showing variance distributions due to noise in emission data and due to noise in transmission data are produced from human subject brain scan data collected by the UBC/TRIUMF PET group. The maximum ratio of image variance due to noise in transmission data to that due to noise in emission data is calculated as 2.6 for a typical FDG study, and 0.082 for a typical fluorodopa study. Total RMS noise varies between 0.4% and 11.6% for a typical set of ROI's placed on mid-brain slices reconstructed from these data sets. Procedures are suggested for improving the statistical accuracy of quantitative PET measurements.

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