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14 April 2000 Evaluating image reconstruction methods for tumor detection performance in whole-body PET oncology imaging
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Abstract
This work presents initial results from observer detection performance studies using the same volume visualization software tools that are used in clinical PET oncology imaging. Research into the FORE+OSEM and FORE+AWOSEM statistical image reconstruction methods tailored to whole- body 3D PET oncology imaging have indicated potential improvements in image SNR compared to currently used analytic reconstruction methods (FBP). To assess the resulting impact of these reconstruction methods on the performance of human observers in detecting and localizing tumors, we use a non- Monte Carlo technique to generate multiple statistically accurate realizations of 3D whole-body PET data, based on an extended MCAT phantom and with clinically realistic levels of statistical noise. For each realization, we add a fixed number of randomly located 1 cm diam. lesions whose contrast is varied among pre-calibrated values so that the range of true positive fractions is well sampled. The observer is told the number of tumors and, similar to the AFROC method, asked to localize all of them. The true positive fraction for the three algorithms (FBP, FORE+OSEM, FORE+AWOSEM) as a function of lesion contrast is calculated, although other protocols could be compared. A confidence level for each tumor is also recorded for incorporation into later AFROC analysis.
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Carole Lartizien, Paul E. Kinahan, Claude Comtat, Michael Lin, Richard G. Swensson, Regine Trebossen, and Bernard Bendriem "Evaluating image reconstruction methods for tumor detection performance in whole-body PET oncology imaging", Proc. SPIE 3981, Medical Imaging 2000: Image Perception and Performance, (14 April 2000); https://doi.org/10.1117/12.383125
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