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 18F-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 (K i) and distribution volume (V) and K1, k2, k3, blood volume (bv) microparameters, and K i 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 K imax derived from the generalized linear least square approach, as well as K i 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.

Purpose This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (Ki) from standardized uptake value (SUV) and/or dynamic PET image series. Methods Whole-body 18F-FDG dynamic PET images of 19 subjects consisting of 13 frames or passes were employed for training a residual deep learning model with SUV and/or dynamic series as input and Ki-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme. Owing to the availability of SUV images acquired 60 min post-injection (20 min total acquisition time), the data sets used for the training of the models were split into two groups: “With SUV” and “Without SUV.” For “With SUV” group, the model was first trained using only SUV images and then the passes (starting from pass 13, the last pass, to pass 9) were added to the training of the model (one pass each time). For this group, 6 models were developed with input data consisting of SUV, SUV plus pass 13, SUV plus passes 13 and 12, SUV plus passes 13 to 11, SUV plus passes 13 to 10, and SUV plus passes 13 to 9. For the “Without SUV” group, the same trend was followed, but without using the SUV images (5 models were developed with input data of passes 13 to 9). For model performance evaluation, the mean absolute error (MAE), mean error (ME), mean relative absolute error (MRAE%), relative error (RE%), mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between the predicted Ki-Patlak images by the two groups and the reference Ki-Patlak images generated through Patlak analysis using the whole acquired data sets. For specific evaluation of the method, regions of interest (ROIs) were drawn on representative organs, including the lung, liver, brain, and heart and around the identified malignant lesions. Results The MRAE%, RE%, PSNR, and SSIM indices across all patients were estimated as 7.45 ± 0.94%, 4.54 ± 2.93%, 46.89 ± 2.93, and 1.00 ± 6.7 × 10−7, respectively, for models predicted using SUV plus passes 13 to 9 as input. The predicted parameters using passes 13 to 11 as input exhibited almost similar results compared to the predicted models using SUV plus passes 13 to 9 as input. Yet, the bias was continuously reduced by adding passes until pass 11, after which the magnitude of error reduction was negligible. Hence, the predicted model with SUV plus passes 13 to 9 had the lowest quantification bias. Lesions invisible in one or both of SUV and Ki-Patlak images appeared similarly through visual inspection in the predicted images with tolerable bias. Conclusion This study concluded the feasibility of direct deep learning-based approach to estimate Ki-Patlak parametric maps without requiring the input function and with a fewer number of passes. This would lead to shorter acquisition times for WB dynamic imaging with acceptable bias and comparable lesion detectability performance.

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 normalgLfalse(rfalse) 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 normalgLfalse(rfalse) values are less than 1.1% for  192Ir and less than 1.2% for  137Cs between the three codes.PACS number(s): 87.56.bg