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We introduce a method for denoising dynamic PET data, spatio-temporal expectation-maximization (STEM) filtering, that combines four-dimensional Gaussian filtering with EM deconvolution. The initial Gaussian filter suppresses noise at a broad range of spatial and temporal frequencies and EM deconvolution quickly restores the frequencies most important to the signal. We aim to demonstrate that STEM filtering can improve variance in both individual time frames and in parametric images without introducing significant bias. We evaluate STEM filtering with a dynamic phantom study, and with simulated and human dynamic PET studies of a tracer with reversible binding behaviour, [C-11]raclopride, and a tracer with irreversible binding behaviour, [F-18]FDOPA. STEM filtering is compared to a number of established three and four-dimensional denoising methods. STEM filtering provides substantial improvements in variance in both individual time frames and in parametric images generated with a number of kinetic analysis techniques while introducing little bias. STEM filtering does bias early frames, but this does not affect quantitative parameter estimates. STEM filtering is shown to be superior to the other simple denoising methods studied. STEM filtering is a simple and effective denoising method that could be valuable for a wide range of dynamic PET applications.

We have previously developed a model that provides relative dosimetry estimates for targeted radionuclide therapy (TRT) agents. The whole-body and tumor pharmacokinetic (PK) parameters of this model can be noninvasively measured with molecular imaging, providing a means of comparing potential TRT agents. Parameter sensitivities and noise will affect the accuracy and precision of the estimated PK values and hence dosimetry estimates. The aim of this work is to apply a PK model for TRT to two agents with different magnitudes of clearance rates, NM404 and FLT, explore parameter sensitivity with respect to time and investigate the effect of noise on parameter precision and accuracy. Twenty-three tumor bearing mice were injected with a 'slow-clearing' agent, (124)I-NM404 (n = 10), or a 'fast-clearing' agent, (18)F-FLT (3'-deoxy-3'-fluorothymidine) (n = 13) and imaged via micro-PET/CT pseudo-dynamically or dynamically, respectively. Regions of interest were drawn within the heart and tumor to create time-concentration curves for blood pool and tumor. PK analysis was performed to estimate the mean and standard error of the central compartment efflux-to-influx ratio (k(12)/k(21)), central elimination rate constant (k(el)), and tumor influx-to-efflux ratio (k(34)/k(43)), as well as the mean and standard deviation of the dosimetry estimates. NM404 and FLT parameter estimation results were used to analyze model accuracy and parameter sensitivity. The accuracy of the experimental sampling schedule was compared to that of an optimal sampling schedule found using Cramer-Rao lower bounds theory. Accuracy was assessed using correlation coefficient, bias and standard error of the estimate normalized to the mean (SEE/mean). The PK parameter estimation of NM404 yielded a central clearance, k(el) (0.009 ± 0.003 h(-1)), normal body retention, k(12)/k(21) (0.69 ± 0.16), tumor retention, k(34)/k(43) (1.44 ± 0.46) and predicted dosimetry, D(tumor) (3.47 ± 1.24 Gy). The PK parameter estimation of FLT yielded a central elimination rate constant, k(el) (0.050 ± 0.025 min(-1)), normal body retention, k(12)/k(21) (2.21 ± 0.62) and tumor retention, k(34)/k(43) (0.65 ± 0.17), and predicted dosimetry, D(tumor) (0.61 ± 0.20 Gy). Compared to experimental sampling, optimal sampling decreases the dosimetry bias and SEE/mean for NM404; however, it increases bias and decreases SEE/mean for FLT. For both NM404 and FLT, central compartment efflux rate constant, k(12), and central compartment influx rate constant, k(21), possess mirroring sensitivities at relatively early time points. The instantaneous concentration in the blood, C(0), was most sensitive at early time points; central elimination, k(el), and tumor efflux, k(43), are most sensitive at later time points. A PK model for TRT was applied to both a slow-clearing, NM404, and a fast-clearing, FLT, agents in a xenograft murine model. NM404 possesses more favorable PK values according to the PK TRT model. The precise and accurate measurement of k(12), k(21), k(el), k(34) and k(43) will translate into improved and precise dosimetry estimations. This work will guide the future use of this PK model for assessing the relative effectiveness of potential TRT agents.