Image segmentation and activity estimation for microPET 11C-raclopride images using an expectation-maximum algorithm with a mixture of Poisson distributions.

The objective of this study was to use a mixture of Poisson (MOP) model expectation maximum (EM) algorithm for segmenting microPET images. Simulated rat phantoms with partial volume effect and different noise levels were generated to evaluate the performance of the method. The partial volume correction was performed using an EM deblurring method before the segmentation. The EM-MOP outperforms the EM-MOP in terms of the estimated spatial accuracy, quantitative accuracy, robustness and computing efficiency. To conclude, the proposed EM-MOP method is a reliable and accurate approach for estimating uptake levels and spatial distributions across target tissues in microPET (11)C-raclopride imaging studies.

Partial volume correction of the microPET blood input function using ensemble learning independent component analysis.

Medical images usually suffer from a partial volume effect (PVE), which may degrade the accuracy of any quantitative information extracted from the images. Our aim was to recreate accurate radioactivity concentration and time-activity curves (TACs) by microPET R4 quantification using ensemble learning independent component analysis (EL-ICA). We designed a digital cardiac phantom for this simulation and in order to evaluate the ability of EL-ICA to correct the PVE, the simulated images were convoluted using a Gaussian function (FWHM = 1-4 mm). The robustness of the proposed method towards noise was investigated by adding statistical noise (SNR = 2-16). During further evaluation, another set of cardiac phantoms were generated from the reconstructed images, and Poisson noise at different levels was added to the sinogram. In real experiments, four rat microPET images and a number of arterial blood samples were obtained; these were used to estimate the metabolic rate of FDG (MR(FDG)). Input functions estimated using the FastICA method were used for comparison. The results showed that EL-ICA could correct PVE in both the simulated and real cases. After correcting for the PVE, the errors for MR(FDG), when estimated by the EL-ICA method, were smaller than those when TACs were directly derived from the PET images and when the FastICA approach was used.

A novel blood-cell-two-compartment model for transferring a whole blood time activity curve to plasma in rodents.

The term input function usually refers to the tracer plasma time activity curve (pTAC), which is necessary for quantitative positron emission tomography (PET) studies. The purpose of this study was to acquire the pTAC by independent component analysis (ICA) estimation from the whole blood time activity curve (wTAC) using a novel method, namely the FDG blood-cell-two-compartment model (BCM). This approach was compared to a number of published models, including linear haematocrit (HCT) correction, non-linear HCT correction and two-exponential correction. The results of this study show that the normalized root mean square error (NRMSE) and the error of the area under curve (EAUC) for the BCM estimate of the pTAC were the smallest. Compartmental and graphic analyses were used to estimate the metabolic rate of the FDG (MR(FDG)). The percentage error for the MR(FDG) (PE(MRFDG)) was estimated from the BCM corrected pTAC and this was also the smallest. It is concluded that the BCM is a better choice when transferring wTAC into pTAC for quantification.

Evaluation of reference tissue model and tissue ratio method for 5-HTT using [(123)I] ADAM tracer.

The serotonin (5-hydroxytryptamine, or 5-HT) transporters (5-HTT) are target-sites for commonly used antidepressants. [(123)I] ADAM is a novel radiotracer that selectively binds the 5-HTT of the central nervous system. The aim for this study was to compare four-parameter model (FPM) with three-parameter model (TPM) from non-invasive reference tissue model (RTM) for 5-HTT quantification using the cerebellum as indirect input function. Furthermore, we compared tracer kinetic model with the tissue ratio (TR) method. The binding potential (BP) values derived from both models were almost the same, but ratio of delivery (R(1)) in TPM had smaller standard deviation than the FPM. There was also significant correlation between BP and specific uptake ratio (SUR). In conclusion, simplified reference tissue model (SRTM) was the better choice because of its stability and convenient implementation for non-invasive quantification of brain SPECT studies. The correlation found between BP and SUR supports the use of TR method for quantification of 5-HTT to avoid arterial sampling in dynamic SPECT scan.