Myocardial perfusion imaging by positron emission tomography (PET-MPI) is the current gold standard for quantification of myocardial blood flow. 18F-flurpiridaz was recently introduced as a valid alternative to currently used PET-MPI probes. Nonetheless, optimum scan duration and time interval for image analysis are currently unknown. Further, it is unclear whether rest/stress PET-MPI with 18F-flurpiridaz is feasible in mice.
Rest/stress PET-MPI was performed with 18F-flurpiridaz (0.6-3.0 MBq) in 27 mice aged 7-8 months. Regadenoson (0.1 µg/g) was used for induction of vasodilator stress. Kinetic modeling was performed using a metabolite-corrected arterial input function. Image-derived myocardial 18F-flurpiridaz uptake was assessed for different time intervals by placing a volume of interest in the left ventricular myocardium.
Tracer kinetics were best described by a two-tissue compartment model. K1 ranged from 6.7 to 20.0 mL·cm-3·min-1, while myocardial volumes of distribution (VT) were between 34.6 and 83.6 mL·cm-3. Of note, myocardial 18F-flurpiridaz uptake (%ID/g) was significantly correlated with K1 at rest and following pharmacological vasodilation for all time intervals assessed. However, while Spearman\'s coefficients (rs) ranged between 0.478 and 0.681, R2 values were generally low. In contrast, an excellent correlation of myocardial 18F-flurpiridaz uptake with VT was obtained, particularly when employing the averaged myocardial uptake from 20 to 40 min post tracer injection (R2 ≥ 0.98). Notably, K1 and VT were similarly sensitive to pharmacological vasodilation induction. Further, mean stress-to-rest ratios of K1, VT, and %ID/g 18F-flurpiridaz were virtually identical, suggesting that %ID/g 18F-flurpiridaz can be used to estimate coronary flow reserve (CFR) in mice.
Our findings suggest that a simplified assessment of relative myocardial perfusion and CFR, based on image-derived tracer uptake, is feasible with 18F-flurpiridaz in mice, enabling high-throughput mechanistic CFR studies in rodents.

Logan graphical analysis (LGA) is a method for in vivo quantification of tracer kinetics in positron emission tomography (PET). The shortcoming of LGA is the presence of a negative bias in the estimated parameters for noisy data. Various approaches have been proposed to address this issue. We recently applied an alternative regression method called least-squares cubic (LSC), which considers the errors in both the predictor and response variables to estimate the LGA slope. LSC reduced the bias in non-displaceable binding potential estimates while causing slight increases in the variance. In this study, we combined LSC with a principal component analysis (PCA) denoising technique to counteract the effects of variance on parametric image quality, which was assessed in terms of the contrast between gray and white matter. Tissue time-activity curves were denoised through PCA, prior to estimating the regression parameters using LSC. We refer to this approach as LSC-PCA. LSC-PCA was assessed against OLS-PCA (PCA with ordinary least-squares (OLS)), LSC, and conventional OLS-based LGA. Comparisons were made for simulated11C-carfentanil and11C Pittsburgh compound B (11C-PiB) data, and clinical11C-PiB PET images. PCA-based methods were compared over a range of principal components, varied by the percentage variance they account for in the data. The results showed reduced variances in distribution volume ratio estimates in the simulations for LSC-PCA compared to LSC, and lower bias compared to OLS-PCA and OLS. Contrasts were not significantly improved in clinical data, but they showed a significant improvement in simulation data |indicating a potential advantage of LSC-PCA over OLS-PCA. The effects of bias reintroduction when many principal components are used were also observed in OLS-PCA clinical images. We therefore encourage the use of LSC-PCA. LSC-PCA can allow the use of many principal components with minimal risk of bias, thereby strengthening the interpretation of PET parametric images.

The Logan graphical analysis (LGA) algorithm is widely used to quantify receptor density for parametric imaging in positron emission tomography (PET). Estimating receptor density, in terms of the non-displaceable binding potential (BPND), from the LGA using the ordinary least-squares (OLS) method has been found to be negatively biased owing to noise in PET data. This is because OLS does not consider errors in the X-variable (predictor variable). Existing bias reduction methods can either only reduce the bias slightly or reduce the bias accompanied by increased variation in the estimates. In this study, we addressed the bias reduction problem by applying a different regression method.
We employed least-squares cubic (LSC) linear regression, which accounts for errors in both variables as well as the correlation of these errors. Noise-free PET data were simulated, for 11C-carfentanil kinetics, with known BPND values. Statistical noise was added to these data and the BPNDs were re-estimated from the noisy data by three methods, conventional LGA, multilinear reference tissue model 2 (MRTM2), and LSC-based LGA; the results were compared. The three methods were also compared in terms of beta amyloid (A β) quantification of 11C-Pittsburgh compound B brain PET data for two patients with Alzheimer\'s disease and differing A β depositions.
Amongst the three methods, for both synthetic and actual data, LSC was the least biased, followed by MRTM2, and then the conventional LGA, which was the most biased. Variations in the LSC estimates were smaller than those in the MRTM2 estimates. LSC also required a shorter computational time than MRTM2.
The results suggest that LSC provides a better trade-off between the bias and variability than the other two methods. In particular, LSC performed better than MRTM2 in all aspects; bias, variability, and computational time. This makes LSC a promising method for BPND parametric imaging in PET studies.

Chemokine receptor 4 (CXCR4) is overexpressed in many cancers and a potential drug target. We have recently developed the tracer N-[11C]methyl-AMD3465 for imaging of CXCR4 expression by positron emission tomography (PET). We investigated the pharmacokinetics of N-[11C]methyl-AMD3465 in rats bearing a C6 tumor and assessed whether the CXCR4 occupancy by the drug Plerixafor® can be measured with this PET tracer.
A subcutaneous C6 tumor was grown in Wistar rats. Dynamic N-[11C]methyl-AMD3465 PET scans with arterial blood sampling was performed in control rats and rats pretreated with Plerixafor® (30 mg/kg, s.c). The distribution volume (V T) of the tracer was estimated by compartment modeling with a two-tissue reversible compartment model (2TRCM) and by Logan graphical analysis. The non-displaceable binding potential (BPND) was estimated with the 2TRCM. Next, CXCR4 receptor occupancy of different doses of the drug Plerixafor® (0.5-60 mg/kg) was investigated.
The tumor could be clearly visualized by PET in control animals. Pretreatment with 30 mg/kg Plerixafor® significantly reduced tumor uptake (SUV 0.65 ± 0.08 vs. 0.20 ± 0.01, p < 0.05). N-[11C]Methyl-AMD3465 was slowly metabolized in vivo, with 70 ± 7% of the tracer in plasma still being intact after 60 min. The tracer showed reversible in vivo binding to its receptor. Both 2TRCM modeling and Logan graphical analysis could be used to estimate V T. Pre-treatment with 30 mg/kg Plerixafor® resulted in a significant reduction in V T (2TCRM 0.87 ± 0.10 vs. 0.23 ± 0.12, p < 0.05) and BPND (1.85 ± 0.14 vs. 0.87 ± 0.12, p < 0.01). Receptor occupancy by Plerixafor® was dose-dependent with an in vivo ED50 of 12.7 ± 4.0 mg/kg. Logan analysis gave comparable results.
N-[11C]Methyl-AMD3465 PET can be used to visualize CXCR4 expression and to calculate receptor occupancy. V T determined by Logan graphical analysis is a suitable parameter to assess CXCR4 receptor occupancy. This approach can easily be translated to humans and used for early drug development and optimization of drug dosing schedules.