title: Assessing margin expansions of internal target volumes in 3D and 4D PET: a phantom study.
abstract: OBJECTIVE
  To quantify tumor volume coverage and excess normal tissue coverage using margin expansions of mobile target internal target volumes (ITVs) in the lung.
  
  
  METHODS
  FDG-PET list-mode data were acquired for four spheres ranging from 1 to 4 cm as they underwent 1D motion based on four patient breathing trajectories. Both ungated PET images and PET maximum intensity projections (PET-MIPs) were examined. Amplitude-based gating was performed on sequential list-mode files of varying signal-to-background ratios to generate PET-MIPs. ITVs were first post-processed using either a Gaussian filter or a custom two-step module, and then segmented by applying a gradient-based watershed algorithm. Uniform and non-uniform 1 mm margins were added to segmented ITVs until complete target coverage was achieved.
  
  
  RESULTS
  PET-MIPs required smaller uniform margins (4.7 vs. 11.3 mm) than ungated PET, with correspondingly smaller over-coverage volumes (OCVs). Non-uniform margins consistently resulted in smaller OCVs when compared to uniform margins. PET-MIPs and ungated PET had comparable OCVs with non-uniform margins, but PET-MIPs required smaller longitudinal margins (4.7 vs. 8.5 mm). Non-uniform margins were independent of sphere size.
  
  
  CONCLUSIONS
  Gated PET-MIP images and non-uniform margins result in more accurate ITV delineation while reducing normal tissue coverage.
authors.affiliation: Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA, sjani@mednet.ucla.edu.
authors.forename: Shyam S
authors.initials: SS
authors.lastname: Jani
authors.forename: James M
authors.initials: JM
authors.lastname: Lamb
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: White
authors.forename: Magnus
authors.initials: M
authors.lastname: Dahlbom
authors.forename: Clifford G
authors.initials: CG
authors.lastname: Robinson
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Low
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging / Q000503:physiopathology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000503:physiopathology; D008954:Models, Biological; D016011:Normal Distribution; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000295:instrumentation / Q000379:methods*; D012119:Respiration; D012371:Robotics; D047368:Tumor Burden