title: Sensitivity estimation in time-of-flight list-mode positron emission tomography.
abstract: OBJECTIVE
  An accurate quantification of the images in positron emission tomography (PET) requires knowing the actual sensitivity at each voxel, which represents the probability that a positron emitted in that voxel is finally detected as a coincidence of two gamma rays in a pair of detectors in the PET scanner. This sensitivity depends on the characteristics of the acquisition, as it is affected by the attenuation of the annihilation gamma rays in the body, and possible variations of the sensitivity of the scanner detectors. In this work, the authors propose a new approach to handle time-of-flight (TOF) list-mode PET data, which allows performing either or both, a self-attenuation correction, and self-normalization correction based on emission data only.
  
  
  METHODS
  The authors derive the theory using a fully Bayesian statistical model of complete data. The authors perform an initial evaluation of algorithms derived from that theory and proposed in this work using numerical 2D list-mode simulations with different TOF resolutions and total number of detected coincidences. Effects of randoms and scatter are not simulated.
  
  
  RESULTS
  The authors found that proposed algorithms successfully correct for unknown attenuation and scanner normalization for simulated 2D list-mode TOF-PET data.
  
  
  CONCLUSIONS
  A new method is presented that can be used for corrections for attenuation and normalization (sensitivity) using TOF list-mode data.
authors.affiliation: Madrid-MIT M+Visión Consortium, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 and Grupo de Física Nuclear, Departamento de Física Atómica, Molecular y Nuclear, Universidad Complutense de Madrid, CEI Moncloa, Madrid 28040, Spain.
authors.forename: J L
authors.initials: JL
authors.lastname: Herraiz
authors.affiliation: Center for Advanced Medical Imaging Sciences, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114.
authors.forename: A
authors.initials: A
authors.lastname: Sitek
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D005720:Gamma Rays; D008390:Markov Chains; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000295:instrumentation / Q000379:methods*