Scatter Correction Based on GPU-Accelerated Full Monte Carlo Simulation for Brain PET/MRI.

Accurate scatter correction is essential for qualitative and quantitative PET imaging. Until now, scatter correction based on Monte Carlo simulation (MCS) has been recognized as the most accurate method of scatter correction for PET. However, the major disadvantage of MCS is its long computational time, which makes it unfeasible for clinical usage. Meanwhile, single scatter simulation (SSS) is the most widely used method for scatter correction. Nevertheless, SSS has the disadvantage of limited robustness for dynamic measurements and for the measurement of large objects. In this work, a newly developed implementation of MCS using graphics processing unit (GPU) acceleration is employed, allowing full MCS-based scatter correction in clinical 3D brain PET imaging. Starting from the generation of annihilation photons to their detection in the simulated PET scanner, all relevant physical interactions and transport phenomena of the photons were simulated on GPUs. This resulted in an expected distribution of scattered events, which was subsequently used to correct the measured emission data. The accuracy of the approach was validated with simulations using GATE (Geant4 Application for Tomography Emission), and its performance was compared to SSS. The comparison of the computation time between a GPU and a single-threaded CPU showed an acceleration factor of 776 for a voxelized brain phantom study. The speedup of the MCS implemented on the GPU represents a major step toward the application of the more accurate MCS-based scatter correction for PET imaging in clinical routine.

EduGATE - basic examples for educative purpose using the GATE simulation platform.

EduGATE is a collection of basic examples to introduce students to the fundamental physical aspects of medical imaging devices. It is based on the GATE platform, which has received a wide acceptance in the field of simulating medical imaging devices including SPECT, PET, CT and also applications in radiation therapy. GATE can be configured by commands, which are, for the sake of simplicity, listed in a collection of one or more macro files to set up phantoms, multiple types of sources, detection device, and acquisition parameters. The aim of the EduGATE is to use all these helpful features of GATE to provide insights into the physics of medical imaging by means of a collection of very basic and simple GATE macros in connection with analysis programs based on ROOT, a framework for data processing. A graphical user interface to define a configuration is also included.

Dynamic contrast-enhanced MR imaging of carotid atherosclerotic plaque: model selection, reproducibility, and validation.

PURPOSE: To compare four known pharmacokinetic models for their ability to describe dynamic contrast material-enhanced magnetic resonance (MR) imaging of carotid atherosclerotic plaques, to determine reproducibility, and to validate the results with histologic findings. MATERIALS AND METHODS: The study was approved by the institutional medical ethics committee. Written informed consent was obtained from all patients. Forty-five patients with 30%-99% carotid stenosis underwent dynamic contrast-enhanced MR imaging. Plaque enhancement was measured at 16 time points at approximately 25-second image intervals by using a gadolinium-based contrast material. Pharmacokinetic parameters (volume transfer constant, K(trans); extracellular extravascular volume fraction, v(e); and blood plasma fraction, v(p)) were determined by fitting a two-compartment model to plaque and blood gadolinium concentration curves. The relative fit errors and parameter uncertainties were determined to find the most suitable model. Sixteen patients underwent imaging twice to determine reproducibility. Carotid endarterectomy specimens from 16 patients who were scheduled for surgery were collected for histologic validation. Parameter uncertainties were compared with the Wilcoxon signed rank test. Reproducibility was assessed by using the coefficient of variation. Correlation with histologic findings was evaluated with the Pearson correlation coefficient. RESULTS: The mean relative fit uncertainty (±standard error) for K(trans) was 10% ± 1 with the Patlak model, which was significantly lower than that with the Tofts (20% ± 1), extended Tofts (33% ± 3), and extended graphical (29% ± 3) models (P < .001). The relative uncertainty for v(p) was 20% ± 2 with the Patlak model and was significantly higher with the extended Tofts (46% ± 9) and extended graphical (35% ± 5) models (P < .001). The reproducibility (coefficient of variation) for the Patlak model was 16% for K(trans) and 26% for v(p). Significant positive correlations were found between K(trans) and the endothelial microvessel content determined on histologic slices (Pearson ρ = 0.72, P = .005). CONCLUSION: The Patlak model is most suited for describing carotid plaque enhancement. Correlation with histologic findings validated K(trans) as an indicator of plaque microvasculature, and the reproducibility of K(trans) was good.