Monte Carlo simulations of clinical PET and SPECT scans: impact of the input data on the simulated images

Abstract

Monte Carlo simulations of emission tomography have proven useful to assist detector design and optimize acquisition and processing protocols. The more realistic the simulations, the more straightforward the extrapolation of conclusions to clinical situations. In emission tomography, accurate numerical models of tomographs have been described and well validated under specific operating conditions (collimator, radionuclide, acquisition parameters, count rates, etc). When using these models under these operating conditions, the realism of simulations mostly depends on the activity distribution used as an input for the simulations. It has been proposed to derive the input activity distribution directly from reconstructed clinical images, so as to properly model the heterogeneity of the activity distribution between and within organs. However, reconstructed patient images include noise and have limited spatial resolution. In this study, we analyse the properties of the simulated images as a function of the properties of the reconstructed images used to define the input activity distributions in (18)F-FDG PET and (131)I SPECT simulations. The propagation through the simulation/reconstruction process of the noise and spatial resolution in the input activity distribution was studied using simulations. We found that the noise properties of the images reconstructed from the simulated data were almost independent of the noise in the input activity distribution. The spatial resolution in the images reconstructed from the simulations was slightly poorer than that in the input activity distribution. However, using high-noise but high-resolution patient images as an input activity distribution yielded reconstructed images that could not be distinguished from clinical images. These findings were confirmed by simulated highly realistic (131)I SPECT and (18)F-FDG PET images from patient data. In conclusion, we demonstrated that (131)I SPECT and (18)F-FDG PET images indistinguishable from real scans can be simulated using activity maps with spatial resolution higher than that used in routine clinical applications.

Realistic and Efficient Modeling of Radiotracer Heterogeneity in Monte Carlo Simulations of PET Images With Tumors

“…However, it also has limitations, namely the finite spatial resolution of the input activity map, the associated noise and possible artifacts. If no artifact is present in the images used as the input data to the simulations, the trade-off between noise and spatial resolution can be well controlled, as shown in another study [28]. In [28], we show that the spatial resolution of the input activity map should be as high as possible, regardless of the noise level, as the noise in the input image does not propagate into the simulated data.…”

“…If no artifact is present in the images used as the input data to the simulations, the trade-off between noise and spatial resolution can be well controlled, as shown in another study [28]. In [28], we show that the spatial resolution of the input activity map should be as high as possible, regardless of the noise level, as the noise in the input image does not propagate into the simulated data. We therefore used an activity map of the HP corresponding to a highly iterated (100 iterations and 16 subsets) image.…”

A Numerical Study of Different Types of Collimators for a High-Resolution Preclinical CdTe Pixelated Semiconductor SPECT System

“…GATE is a widely used Monte Carlo simulation platform, with general-purpose code Geant4 and advanced open-source software developed by the international OpenGATE collaboration in 2001 [14]. The accuracy, usefulness, and effectiveness of this platform have been confirmed in many studies [14][15][16][17]. In this study, we used GATE version 6.…”

The Impact of Image Reconstruction Bias on PET/CT 90Y Dosimetry After Radioembolization

“…Each phantom background SD was determined following the method described by Stute et al (22) using the mean SD from 10 regions of interest consisting of 45 voxels placed at random at least 2 pixel lengths apart and 3 pixel lengths from the border and syringe VOIs (11).…”