Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping.

PURPOSE: Hybrid dynamic imaging allows not only the estimation of whole-body (WB) macroparametric maps but also the estimation of microparameters in the initial bed position targeting the blood pool region containing the pathology owing to the limited axial field of view of PET scanners. In this work, we assessed the capability of multipass WB F-FDG PET parametric imaging in terms of lesion detectability through qualitative and quantitative evaluation of simulation and clinical studies. METHODS: Simulation studies were conducted by generating data incorporating 3 liver and 3 lung lesions produced by 3 noise levels and 20 noise realizations for each noise level to estimate bias and lesion detection features. The total scan time for the clinical studies of 8 patients addressed for lung and liver lesions staging, including dynamic and static WB imaging, lasted 80 minutes. An in-house-developed MATLAB code was utilized to derive the microparametric and macroparametric maps. We compared lesion detectability and different image-derived PET metrics including the SUVs, Patlak-derived influx rate constant (Ki) and distribution volume (V) and K1, k2, k3, blood volume (bv) microparameters, and Ki estimated using the generalized linear least square approach. RESULTS: In total, 104 lesions were detected, among which 47 were located in the targeted blood pool bed position where all quantitative parameters were calculated, thus enabling comparative analysis across all parameters. The evaluation encompassed visual interpretation performed by an expert nuclear medicine specialist and quantitative analysis. High correlation coefficients were observed between SUVmax and Kimax derived from the generalized linear least square approach, as well as Ki generated by Patlak graphical analysis. Moreover, 3 contrast-enhanced CT-proven malignant lesions located in the liver and a biopsy-proven malignant liver lesion not visible on static SUV images and Patlak maps were clearly pinpointed on K1 and k2 maps. CONCLUSIONS: Our results demonstrate that full compartmental modeling for the region containing the pathology has the potential of providing complementary information and, in some cases, more accurate diagnosis than conventional static SUV imaging, favorably comparing to Patlak graphical analysis.

Comparison of TG-43 dosimetric parameters of brachytherapy sources obtained by three different versions of MCNP codes.

Monte Carlo simulations are widely used for calculation of the dosimetric parameters of brachytherapy sources. MCNP4C2, MCNP5, MCNPX, EGS4, EGSnrc, PTRAN, and GEANT4 are among the most commonly used codes in this field. Each of these codes utilizes a cross-sectional library for the purpose of simulating different elements and materials with complex chemical compositions. The accuracies of the final outcomes of these simulations are very sensitive to the accuracies of the cross-sectional libraries. Several investigators have shown that inaccuracies of some of the cross section files have led to errors in 125I and 103Pd parameters. The purpose of this study is to compare the dosimetric parameters of sample brachytherapy sources, calculated with three different versions of the MCNP code - MCNP4C, MCNP5, and MCNPX. In these simulations for each source type, the source and phantom geometries, as well as the number of the photons, were kept identical, thus eliminating the possible uncertainties. The results of these investigations indicate that for low-energy sources such as 125I and 103Pd there are discrepancies in gL(r) values. Discrepancies up to 21.7% and 28% are observed between MCNP4C and other codes at a distance of 6 cm for 103Pd and 10 cm for 125I from the source, respectively. However, for higher energy sources, the discrepancies in gL(r) values are less than 1.1% for 192Ir and less than 1.2% for 137Cs between the three codes.