Evaluation of amplitude-based sorting algorithm to reduce lung tumor blurring in PET images using 4D NCAT phantom

Abstract

Purpose: develop and validate a PET sorting algorithm based on the respiratory amplitude to correct for abnormal respiratory cycles. Method and materials: using the 4D NCAT phantom model, 3D PET images were simulated in lung and other structures at different times within a respiratory cycle and noise was added. To validate the amplitude binning algorithm, NCAT phantom was used to simulate one case of five different respiratory periods and another case of five respiratory periods alone with five respiratory amplitudes. Comparison was performed for gated and un-gated images and for the new amplitude binning algorithm with the time binning algorithm by calculating the mean number of counts in the ROI (region of interest). Results: an average of 8.87 ± 5.10% improvement was reported for total 16 tumors with different tumor sizes and different T/B (tumor to background) ratios using the new sorting algorithm. As both the T/B ratio and tumor size decreases, image degradation due to respiration increases. The greater benefit for smaller diameter tumor and lower T/B ratio indicates a potential improvement in detecting more problematic tumors.