An improved iterative thresholding method to delineate PET volumes using the delineation-averaged signal instead of the enclosed maximum signal

Abstract

An improved thresholding method for segmentation of PET volumes is proposed that uses the delineation-averaged activity concentration (AC) within an iterative procedure instead of the enclosed maximum AC. Methods: The average-based iterative thresholding method (avg-ITM) needs the background-corrected relative boundary-reproducing threshold (BRT) curve applied to segment the tumor. On the basis of a previous study, which developed an iterative thresholding method using the maximum AC (max-ITM), the average-based BRT curve was derived from the AC profiles of a sphere model. Numerous phantom scans (using glass spheres, wax spheres, and bihemispheres) under different conditions were obtained to verify and assess the avg-ITM. Clinically, the avg-ITM was tested in 2 patients bearing tumors, and the interobserver and intraobserver variability in the volume segmentation was assessed using 5 tumors analyzed by 5 observers. In the entire study, the max-ITM was also applied to compare the respective results. Results: The phantom measurements verified the averagebased BRT curve and demonstrated that spheric tumors down to a diameter equaling the spatial resolution could be delineated. In contrast to the max-ITM, the avg-ITM yielded reliable volumes (within the 95% confidence intervals) for standardized uptake values and signal-to-background ratios larger than 3. The volumes derived using the max-ITM were significantly underestimated for object sizes with diameters considerably larger than the spatial resolution. Phantom measurements using bihemispheres with decreasing AC ratios demonstrated that the avg-ITM was robust down to an AC ratio of 0.5 and, therefore, is less prone to nonuniformity than the max-ITM, which was corroborated by real tumor imaging. The observer-related variability was small (mean absolute relative SD < 4%). Conclusion: Compared with the max-ITM, the avg-ITM improves the segmentation results for large objects and is less sensitive against image noise and nonuniformity. A clinical comparison study is warranted to assess the benefits of the avg-ITM method compared with other segmentation methods.