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We present a novel segmentation algorithm for dynamic PET studies that groups pixels according to the similarity of their time-activity curves. Methods: Sixteen mice bearing a human tumor cell line xenograft (CH-157MN) were imaged with three different Ga-68-DOTA-peptides (DOTANOC, DOTATATE, DOTATOC) using a small animal PET-CT scanner. Regional activities (input function and tumor) were obtained after manual delineation of regions of interest over the image. The algorithm was implemented under the jClustering framework and used to extract the same regional activities as in the manual approach. The volume of distribution in the tumor was computed using the Logan linear method. A Kruskal-Wallis test was used to investigate significant differences between the manually and automatically obtained volumes of distribution. Results: The algorithm successfully segmented all the studies. No significant differences were found for the same tracer across different segmentation methods. Manual delineation revealed significant differences between DOTANOC and the other two tracers (DOTANOC - DOTATATE, p=0.020; DOTANOC - DOTATOC, p=0.033). Similar differences were found using the leader-follower algorithm. Conclusion: An open implementation of a novel segmentation method for dynamic PET studies is presented and validated in rodent studies. It successfully replicated the manual results obtained in small animal studies, thus making it a reliable substitute for this task and, potentially, for other dynamic segmentation procedures. (C) 2016 Elsevier Ltd. All rights reserved.
dynamic pet; functional segmentation; kinetic modeling; clustering; open source