title: Functional segmentation of dynamic PET studies: Open source implementation and validation of a leader-follower-based algorithm.
abstract: 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 (68)Ga-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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Montreal Neurological Institute, McGill University, Montreal, Québec, Canada. Electronic address: jmmateos@hggm.es.
authors.forename: José María
authors.initials: JM
authors.lastname: Mateos-Pérez
authors.affiliation: Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.
authors.forename: María Luisa
authors.initials: ML
authors.lastname: Soto-Montenegro
authors.affiliation: Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.
authors.forename: Santiago
authors.initials: S
authors.lastname: Peña-Zalbidea
authors.affiliation: Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.
authors.forename: Manuel
authors.initials: M
authors.lastname: Desco
authors.affiliation: Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.
authors.forename: Juan José
authors.initials: JJ
authors.lastname: Vaquero
mesh_terms: D000465:Algorithms; D000818:Animals; D045744:Cell Line, Tumor; D064593:Heterografts; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D051379:Mice; D008819:Mice, Nude; D009368:Neoplasm Transplantation; D009374:Neoplasms, Experimental / Q000000981:diagnostic imaging*; D049268:Positron-Emission Tomography / Q000379:methods*; D011859:Radiography
keywords: Clustering; Dynamic PET; Functional segmentation; Kinetic modeling; Open source