Simulation study of a coincidence detection system for non-invasive determination of arterial blood time-activity curve measurements

Background : Arterial sampling in PET studies for the purposes of kinetic modeling remains an invasive, time intensive and expensive procedure. Alternatives to derive the blood time-activity curve (BTAC) non-invasively are either reliant on large vessels in the field of view or are laborious to implement and analyse as well as being prone to many processing errors. An alternative method is proposed in this work by the simulation of a non-invasive coincidence detection unit. Results: We utilized GATE simulations of a human forearm phantom with a blood flow model, as well as a model for dynamic radioactive bolus activity concentration based on clinical measurements. A fixed configuration of 14, and also separately, 8 detectors were employed around the phantom, and simulations performed to investigate signal detection parameters. BGO crystals proved to show the highest detection efficiency and sensitivity to a simulated BTAC with a maximum coincidence rate of 575 cps. Repeatable location of the blood vessels in the forearm allowed a half-ring design with only 8 detectors. Using this configuration, maximum coincident rates of 250 cps and 42 cps were achieved with simulation of activity concentration determined from 15 O and 18 F arterial blood sampling. NECR simulated in a water phantom at 3 different vertical positions inside the 8-detector system (Y=-1 cm, Y=-2 cm and Y=-3 cm) was 8360 cps, 13041 cps and 20476 cps at an activity of 3.5 MBq. Addition of extra axial detection planes to the half-ring configuration provided increases in system sensitivity by a factor of approximately 10. Conclusions: Initial simulations demonstrated that the configuration of a single half-ring 8 detector of monolithic BGO crystals could describe the a simulated BTAC in a clinically relevant forearm phantom with good signal properties, and an increased number of axial detection planes can provide increased sensitivity of the system. The system would find use in the derivation of the BTAC for use in the application of kinetic models without physical arterial sampling or reliance on image-based techniques.

Algorithm for uptake assessment in small lesions based on dynamic scintigraphy scans

The aim of our research was to develop an algorithm for estimation and visualisation of radiopharmaceutical uptake based on time-activity-curve (TAC) analysis in small regions of interest (ROI) in scintigraphic studies. The algorithm is implemented in Lab view environment (National Instruments, Texas, Austin) and comprises the following steps: 1) delineation of grid of small ROIs over the examined tissue and corresponding TAC processing; 2) background vs tissue separation; 3) the extraction of all ?suspected? ROIs where TACs are not exponentially descendent; 4) correlation analysis between a TAC corresponding to the central suspected ROI and TACs of neghboring ROIs; 5) the extraction of representative TAC for ?suspected? area by Principal Component Analysis technique; and 6) visual interpretation of radiopharmaceutical distribution in the ?suspected? area. The application of algorithm is presented in data recorded in case of histopathologically proven parathyroid tumors.

Combination of MRI and dynamic FET PET for initial glioma grading

Summary Aim: MRI and PET with 18F-fluoro-ethyl-tyro- sine (FET) have been increasingly used to evaluate patients with gliomas. Our purpose was to assess the additive value of MR spectroscopy (MRS), diffusion imaging and dynamic FET-PET for glioma grading. Patients, methods: 38 patients (42 ± 15 aged, F/M: 0.46) with untreated histologically proven brain gliomas were included. All underwent conventional MRI, MRS, diffusion sequences, and FET-PET within 3±4 weeks. Performances of tumour FET time-activity-curve, early-to-middle SUVmax ratio, choline / creatine ratio and ADC histogram distribution pattern for gliomas grading were assessed, as compared to histology. Combination of these parameters and respective odds were also evaluated. Results: Tumour time-activity- curve reached the best accuracy (67%) when taken alone to distinguish between low and high-grade gliomas, followed by ADC histogram analysis (65%). Combination of time-activity-curve and ADC histogram analysis improved the sensitivity from 67% to 86% and the specificity from 63-67% to 100% (p < 0.008). On multivariate logistic regression analysis, negative slope of the tumour FET time-activity-curve however remains the best predictor of high-grade glioma (odds 7.6, SE 6.8, p = 0.022). Conclusion: Combination of dynamic FET-PET and diffusion MRI reached good performance for gliomas grading. The use of FET-PET/MR may be highly relevant in the initial assessment of primary brain tumours.