title: A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET.
abstract: Compartmental modeling of dynamic PET data enables quantification of tracer kinetics in vivo, through the calculated model parameters. In this study, we aimed to investigate the effect of early frame sampling and reconstruction method on pharmacokinetic parameters obtained from a 2-tissue model, in terms of bias and uncertainty (SD).
  
  
  METHODS
  The GATE Monte Carlo software was used to simulate 2 × 15 dynamic 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) brain PET studies, typical in terms of noise level and kinetic parameters. The data were reconstructed by both 3-dimensional (3D) filtered backprojection with reprojection (3DRP) and 3D ordered-subset expectation maximization (OSEM) into 6 dynamic image sets with different early frame durations of 1, 2, 4, 6, 10, and 15 s. Bias and SD were evaluated for fitted parameter estimates, calculated from regions of interest.
  
  
  RESULTS
  The 2-tissue-model parameter estimates K1, k2, and fraction of arterial blood in tissue depended on early frame sampling, and a sampling of 6-15 s generally minimized bias and SD. The shortest sampling of 1 s yielded a 25% and 42% larger bias than the other schemes, for 3DRP and OSEM, respectively, and a parameter uncertainty that was 10%-70% higher. The schemes from 4 to 15 s were generally not significantly different in regards to bias and SD. Typically, the reconstruction method 3DRP yielded less frame-sampling dependence and less uncertain results, compared with OSEM, but was on average more biased.
  
  
  CONCLUSIONS
  Of the 6 sampling schemes investigated in this study, an early frame duration of 6-15 s generally kept both bias and uncertainty to a minimum, for both 3DRP and OSEM reconstructions. Very-short frames of 1 s should be avoided because they typically resulted in the largest parameter bias and uncertainty. Furthermore, 3DRP may be preferred over OSEM for short frames with poor statistics.
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, Sweden ida.haggstrom@radfys.umu.se.
authors.forename: Ida
authors.initials: I
authors.lastname: Häggström
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, Sweden.
authors.forename: Jan
authors.initials: J
authors.lastname: Axelsson
authors.affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York; and.
authors.forename: Charles Ross
authors.initials: CR
authors.lastname: Schmidtlein
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, Sweden.
authors.forename: Mikael
authors.initials: M
authors.lastname: Karlsson
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, Sweden.
authors.forename: Anders
authors.initials: A
authors.lastname: Garpebring
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, Sweden.
authors.forename: Lennart
authors.initials: L
authors.lastname: Johansson
authors.affiliation: Nuclear Medicine and PET, Department of Radiology, Oncology and Radiation Sciences, Uppsala University, Uppsala, Sweden.
authors.forename: Jens
authors.initials: J
authors.lastname: Sörensen
authors.affiliation: Department of Radiation Sciences, Umeå University, Umeå, Sweden.
authors.forename: Anne
authors.initials: A
authors.lastname: Larsson
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging; D015224:Dideoxynucleosides / Q000493:pharmacokinetics*; D007091:Image Processing, Computer-Assisted; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography; D013997:Time Factors; D035501:Uncertainty
chemical_list: D015224:Dideoxynucleosides; C002854:alovudine
keywords: GATE; Monte Carlo; compartment modeling; dynamic PET; frame sampling