Evaluation of a compartmental model for estimating tumor hypoxia via FMISO dynamic PET imaging.
This paper systematically evaluates a pharmacokinetic compartmental model for identifying tumor hypoxia using dynamic positron emission tomography (PET) imaging with 18F-fluoromisonidazole (FMISO). A generic irreversible one-plasma two-tissue compartmental model was used. A dynamic PET image dataset was simulated with three tumor regions-normoxic, hypoxic and necrotic-embedded in a normal-tissue background, and with an image-based arterial input function. Each voxelized tissue's time activity curve (TAC) was simulated with typical values of kinetic parameters, as deduced from FMISO-PET data from nine head-and-neck cancer patients. The dynamic dataset was first produced without any statistical noise to ensure that correct kinetic parameters were reproducible. Next, to investigate the stability of kinetic parameter estimation in the presence of noise, 1000 noisy samples of the dynamic dataset were generated, from which 1000 noisy estimates of kinetic parameters were calculated and used to estimate the sample mean and covariance matrix. It is found that a more peaked input function gave less variation in various kinetic parameters, and the variation of kinetic parameters could also be reduced by two region-of-interest averaging techniques. To further investigate how bias in the arterial input function affected the kinetic parameter estimation, a shift error was introduced in the peak amplitude and peak location of the input TAC, and the bias of various kinetic parameters calculated. In summary, mathematical phantom studies have been used to determine the statistical accuracy and precision of model-based kinetic analysis, which helps to validate this analysis and provides guidance in planning clinical dynamic FMISO-PET studies.
Wang
Wenli
W
Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA. wangw1@mskcc.org
Georgi
Jens-Christoph
JC
Nehmeh
Sadek A
SA
Narayanan
Manoj
M
Paulus
Timo
T
Bal
Matthieu
M
O'Donoghue
Joseph
J
Zanzonico
Pat B
PB
Schmidtlein
C Ross
CR
Lee
Nancy Y
NY
Humm
John L
JL
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008920:Misonidazole / Q000031:analogs & derivatives* / Q000493:pharmacokinetics; D008954:Models, Biological; D009369:Neoplasms / Q000000981:diagnostic imaging / Q000378:metabolism*; D010100:Oxygen / Q000032:analysis / Q000378:metabolism*; D010101:Oxygen Consumption; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals / Q000493:pharmacokinetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D019275:Radiopharmaceuticals; C031843:fluoromisonidazole; D008920:Misonidazole; D010100:Oxygen