Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks.

Purpose: This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (K) from standardized uptake value (SUV) and/or dynamic PET image series.

Methods: Whole-body F-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 K-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme.

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.

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.