title: PETSTEP: Generation of synthetic PET lesions for fast evaluation of segmentation methods.
abstract: OBJECTIVE
  This work describes PETSTEP (PET Simulator of Tracers via Emission Projection): a faster and more accessible alternative to Monte Carlo (MC) simulation generating realistic PET images, for studies assessing image features and segmentation techniques.
  
  
  METHODS
  PETSTEP was implemented within Matlab as open source software. It allows generating three-dimensional PET images from PET/CT data or synthetic CT and PET maps, with user-drawn lesions and user-set acquisition and reconstruction parameters. PETSTEP was used to reproduce images of the NEMA body phantom acquired on a GE Discovery 690 PET/CT scanner, and simulated with MC for the GE Discovery LS scanner, and to generate realistic Head and Neck scans. Finally the sensitivity (S) and Positive Predictive Value (PPV) of three automatic segmentation methods were compared when applied to the scanner-acquired and PETSTEP-simulated NEMA images.
  
  
  RESULTS
  PETSTEP produced 3D phantom and clinical images within 4 and 6 min respectively on a single core 2.7 GHz computer. PETSTEP images of the NEMA phantom had mean intensities within 2% of the scanner-acquired image for both background and largest insert, and 16% larger background Full Width at Half Maximum. Similar results were obtained when comparing PETSTEP images to MC simulated data. The S and PPV obtained with simulated phantom images were statistically significantly lower than for the original images, but led to the same conclusions with respect to the evaluated segmentation methods.
  
  
  CONCLUSIONS
  PETSTEP allows fast simulation of synthetic images reproducing scanner-acquired PET data and shows great promise for the evaluation of PET segmentation methods.
authors: Wales Research & Diagnostic PET Imaging Centre, Cardiff University, Cardiff, Wales, UK. Electronic address: BerthonB@cardiff.ac.uk.
authors: Beatrice
authors: B
authors: Berthon
authors: Department of Radiation Sciences, Umeå University, Umeå, Sweden.
authors: Ida
authors: I
authors: Häggström
authors: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
authors: Aditya
authors: A
authors: Apte
authors: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
authors: Bradley J
authors: BJ
authors: Beattie
authors: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
authors: Assen S
authors: AS
authors: Kirov
authors: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
authors: John L
authors: JL
authors: Humm
authors: Wales Research & Diagnostic PET Imaging Centre, Cardiff University, Cardiff, Wales, UK.
authors: Christopher
authors: C
authors: Marshall
authors: School of Engineering, Cardiff University, Cardiff, Wales, UK.
authors: Emiliano
authors: E
authors: Spezi
authors: Department of Radiation Sciences, Umeå University, Umeå, Sweden.
authors: Anne
authors: A
authors: Larsson
authors: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
authors: C Ross
authors: CR
authors: Schmidtlein
mesh_terms: D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography; D012984:Software; D013997:Time Factors
keywords: Digital phantoms; Image segmentation; Positron emission tomography; Simulation; Synthetic lesions