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 3 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 9 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.

This study evaluated the positron emission tomography (PET) imaging performance of the Ingenuity TF 128 PET/computed tomography (CT) scanner which has a PET component that was designed to support a wider radioactivity range than is possible with those of Gemini TF PET/CT and Ingenuity TF PET/MR. Spatial resolution, sensitivity, count rate characteristics and image quality were evaluated according to the NEMA NU 2–2007 standard and ACR phantom accreditation procedures; these were supplemented by additional measurements intended to characterize the system under conditions that would be encountered during quantitative cardiac imaging with 82Rb. Image quality was evaluated using a hot spheres phantom, and various contrast recovery and noise measurements were made from replicated images. Timing and energy resolution, dead time, and the linearity of the image activity concentration, were all measured over a wide range of count rates. Spatial resolution (4.8– 5.1 mm FWHM), sensitivity (7.3 cps kBq−1), peak noise-equivalent count rate (124 kcps), and peak trues rate (365 kcps)were similar to those of the Gemini TF PET/CT. Contrast recovery was higher with a 2 mm, body-detail reconstruction than with a 4 mm, body reconstruction, although the precision was reduced. The noise equivalent count rate peak was broad (within 10% of peak from 241–609 MBq). The activity measured in phantom images was within 10% of the true activity for count rates up to those observed in 82Rb cardiac PET studies.

This study used pharmacokinetic analysis of 18 F-labeled fluoromisonidazole ( 18 F-FMISO) dynamic PET to assist the identification of regional tumor hypoxia and to investigate the relationship among a potential tumor hypoxia index (K i ), tumorto-blood ratio (T/B) in the late-time image, plasma-to-tissue transport rate (k 1 ), and local vascular volume fraction (b) for head and neck cancer patients. Methods: Newly diagnosed patients underwent a dynamic 18 F-FMISO PET scan before chemotherapy or radiotherapy. The data were acquired in 3 consecutive PET/CT dynamic scan segments, registered with each other and analyzed using pharmacokinetics software. The (K i , k 1 , b) kinetic parameter images were derived for each patient. Results: Nine patients' data were analyzed. Representative images of 18 F-FDG PET (of the tumor), CT (of the anatomy), and late-time 18 F-FMISO PET (of the T/B) and parametric images of K i (potentially representing tumor hypoxia) are shown. The patient image data could be classified into 3 types: with good concordance between the parametric hypoxia map K i and high T/B, with concordant findings between the parametric hypoxia map and low T/B, and with ambiguity between parametric hypoxia map and T/B. Correlation coefficients are computed between each pair of T/ B, K i , k 1 , and b. Data are also presented for other potential hypoxia surrogate measures, for example, k 3 and k 1 /k 2 . Conclusion: There is a positive correlation of 0.86 between the average T/B and average hypoxia index K i of the region of interest. However, because of the statistical photon counting noise in PET and the amplification of noise in kinetic analysis, the direct correlation between the T/B and hypoxia of the individual pixel is not obvious. For a tumor region of interest, there is a slight negative correlation between k 1 and K i , moderate positive correlation between b and K i , but no correlation between b and k 1 .