Implementation of angular response function modeling in SPECT simulations with GATE

Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging

While machine learning (ML) methods may significantly improve image quality for SPECT imaging for the diagnosis and monitoring of Parkinson’s disease (PD), they require a large amount of data for training. It is often difficult to collect a large population of patient data to support the ML research, and the ground truth of lesion is also unknown. This paper leverages a generative adversarial network (GAN) to generate digital brain phantoms for training ML-based PD SPECT algorithms. A total of 594 PET 3D brain models from 155 patients (113 male and 42 female) were reviewed and 1597 2D slices containing the full or a portion of the striatum were selected. Corresponding attenuation maps were also generated based on these images. The data were then used to develop a GAN for generating 2D brain phantoms, where each phantom consisted of a radioactivity image and the corresponding attenuation map. Statistical methods including histogram, Fréchet distance, and structural similarity were used to evaluate the generator based on 10,000 generated phantoms. When the generated phantoms and training dataset were both passed to the discriminator, similar normal distributions were obtained, which indicated the discriminator was unable to distinguish the generated phantoms from the training datasets. The generated digital phantoms can be used for 2D SPECT simulation and serve as the ground truth to develop ML-based reconstruction algorithms. The cumulated experience from this work also laid the foundation for building a 3D GAN for the same application.

Advanced Monte Carlo simulations of emission tomography imaging systems with GATE

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE. Additionally, some methodological developments that are not specific for imaging but that can improve detector modeling and provide computation time gains, such as Variance Reduction Techniques and Artificial Intelligence integration, are described and discussed.

Development and validation of a full model of a four-headed neuroimaging single-photon emission computed tomography scanner

Objective

The Nucline X-Ring 4R is a four-headed gamma camera dedicated to neuroimaging. In this paper, we describe and validate a GATE (Geant4 Application for Tomographic Emission) model of the Nucline X-Ring 4R.

Materials and methods

Images produced during model simulations were compared with those acquired experimentally to confirm the model was an accurate representation of the scanner. The most commonly reported measurements used to validate a GATE model include energy resolution, spatial resolution and sensitivity. In addition to the commonly reported static imaging measures, single-photon emission computed tomography (SPECT) spatial resolution was investigated to confirm that the model produces similar SPECT images to the experimental output.

Results

The experimental full-width at half-maximum was calculated to be 12.3 keV, which corresponds to an energy resolution of 8.8%. The simulated full-width at half-maximum was measured to be 12 keV, giving an energy resolution of 8.6%. The average spatial resolutions were found to be well matched (5.69 mm – simulated and 5.64 mm – experimental). However, the sensitivity was overestimated using the GATE model (47.8 and 54.3 cps/MBq) compared with the values obtained experimentally (42.7 and 44.3 cps/MBq). Finally, the simulated SPECT spatial resolution images were found to produce qualitatively comparable results.

Conclusion

The model developed has been shown to produce similar results and images to those obtained experimentally. This model has the potential to simulate patient scans with the aim of improving patient care by optimizing scanner protocols.

Capabilities of the Monte Carlo Simulation Codes for Modeling of a Small Animal SPECT Camera

Purpose

This study aims to compare Monte Carlo-based codes' characteristics in the determination of the basic parameters of a high-resolution single photon emission computed tomography (HiReSPECT) scanner.

Methods

The geometry of this dual-head gamma camera equipped with a pixelated CsI(Na) scintillator and lead hexagonal hole collimator were accurately described in the GEANT4 Application for the Tomographic Emission (GATE), Monte Carlo N-particle extended (MCNP-X), and simulation of imaging nuclear detectors (SIMIND) codes. We implemented simulation procedures similar to the experimental test for calculation of the energy spectra, spatial resolution, and sensitivity of HiReSPECT by using ^99mTc sources.

Results

The energy resolutions simulated by SIMIND, MCNP-X, and GATE were 17.53, 19.24, and 18.26%, respectively, while it was calculated at 19.15% in experimental test. The average spatial resolutions of the HiReSPECT camera at 2.5 cm from the collimator surface simulated by SIMIND, MCNP-X, and GATE were 3.18, 2.9, and 2.62 mm, respectively, while this parameter was reported at 2.82 mm in the experiment test. The sensitivities simulated by SIMIND, MCNP-X, and GATE were 1.44, 1.27, and 1.38 cps/μCi, respectively, on the collimator surface.

Conclusions

Comparison between simulation and experimental results showed that among these MC codes, GATE enabled to accurately model realistic SPECT system and electromagnetic physical processes, but it required more time and hardware facilities to run simulations. SIMIND was the most flexible and user-friendly code to simulate a SPECT camera, but it had limitations in defining the non-conventional imaging device. The most important characteristics like time and speed of simulation, preciseness of results, and user-friendliness should be considered during simulations.

Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images

Background

Full Monte Carlo (MC)-based SPECT reconstructions have a strong potential for correcting for image degrading factors, but the reconstruction times are long. The objective of this study was to develop a highly parallel Monte Carlo code for fast, ordered subset expectation maximum (OSEM) reconstructions of SPECT/CT images. The MC code was written in the Compute Unified Device Architecture language for a computer with four graphics processing units (GPUs) (GeForce GTX Titan X, Nvidia, USA). This enabled simulations of parallel photon emissions from the voxels matrix (128³ or 256³). Each computed tomography (CT) number was converted to attenuation coefficients for photo absorption, coherent scattering, and incoherent scattering. For photon scattering, the deflection angle was determined by the differential scattering cross sections. An angular response function was developed and used to model the accepted angles for photon interaction with the crystal, and a detector scattering kernel was used for modeling the photon scattering in the detector. Predefined energy and spatial resolution kernels for the crystal were used. The MC code was implemented in the OSEM reconstruction of clinical and phantom ¹⁷⁷Lu SPECT/CT images. The Jaszczak image quality phantom was used to evaluate the performance of the MC reconstruction in comparison with attenuated corrected (AC) OSEM reconstructions and attenuated corrected OSEM reconstructions with resolution recovery corrections (RRC).

Result

The performance of the MC code was 3200 million photons/s. The required number of photons emitted per voxel to obtain a sufficiently low noise level in the simulated image was 200 for a 128³ voxel matrix. With this number of emitted photons/voxel, the MC-based OSEM reconstruction with ten subsets was performed within 20 s/iteration. The images converged after around six iterations. Therefore, the reconstruction time was around 3 min. The activity recovery for the spheres in the Jaszczak phantom was clearly improved with MC-based OSEM reconstruction, e.g., the activity recovery was 88% for the largest sphere, while it was 66% for AC-OSEM and 79% for RRC-OSEM.

Conclusion

The GPU-based MC code generated an MC-based SPECT/CT reconstruction within a few minutes, and reconstructed patient images of ¹⁷⁷Lu-DOTATATE treatments revealed clearly improved resolution and contrast.

A detector response function design in pinhole SPECT including geometrical calibration

Clinical single photon emission computed tomography (SPECT) equipped with pinhole collimators have a magnification factor that results in high spatial resolution images for small animal imaging. Using Monte Carlo simulations to model the acquisition process and the propagation of the photons from their point of emission to their detection point then integrating the model into an iterative reconstruction algorithm improves the signal-to-noise ratio, the contrast and the spatial resolution in the reconstructed images. However, pinhole SPECT systems are known to be very sensitive to geometrical misalignments. Geometrical misalignments are defined as the radial or axial shift of the collimator pinhole and/or twist and tilt of the detector heads and are introduced in the system each time the collimation device is changed (pinhole to parallel holes or vice versa). In this work, we present a flexible detector response function table (DRFT) design that takes into account the geometrical misalignments and avoids performing new Monte Carlo simulations for each exam in order to calculate a geometrical study-dependent system matrix. The utilization of the DRFT for the calculation of the system matrix speeds up its computation time by two orders of magnitude making it acceptable for preclinical and clinical applications.

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

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.