Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept

A time- and space-saving Monte Carlo simulation method using post-collimation generative adversarial network for dose calculation of an O-ring gantry Linac

PURPOSE

This study explores the feasibility of employing Generative Adversarial Networks (GANs) to model the RefleXion X1 Linac. The aim is to investigate the accuracy of dose simulation and assess the potential computational benefits.

METHODS

The X1 Linac is a new radiotherapy machine with a binary multi-leaf collimation (MLC) system, facilitating innovative biology-guided radiotherapy. A total of 34 GAN generators, each representing a desired MLC aperture, were developed. Each generator was trained using a phase space file generated underneath the corresponding aperture, enabling the generation of particles and serving as a beam source for Monte Carlo simulation. Dose distributions in water were simulated for each aperture using both the GAN and phase space sources. The agreement between dose distributions was evaluated. The computational time reduction from bypassing the collimation simulation and storage space savings were estimated.

RESULTS

The percentage depth dose at 10 cm, penumbra, and full-width half maximum of the GAN simulation agree with the phase space simulation, with differences of 0.4 % ± 0.2 %, 0.32 ± 0.66 mm, and 0.26 ± 0.44 mm, respectively. The gamma passing rate (1 %/1mm) for the planar dose exceeded 90 % for all apertures. The estimated time-saving for simulating an plan using 5766 beamlets was 530 CPU hours. The storage usage was reduced by a factor of 102.

CONCLUSION

The utilization of the GAN in simulating the X1 Linac demonstrated remarkable accuracy and efficiency. The reductions in both computational time and storage requirements make this approach highly valuable for future dosimetry studies and beam modeling.

MCGPU-PET: An Open-Source Real-Time Monte Carlo PET Simulator

Monte Carlo (MC) simulations are commonly used to model the emission, transmission, and/or detection of radiation in Positron Emission Tomography (PET). In this work, we introduce a new open-source MC software for PET simulation, MCGPU-PET, which has been designed to fully exploit the computing capabilities of modern GPUs to simulate the acquisition of more than 100 million coincidences per second from voxelized sources and material distributions. The new simulator is an extension of the PENELOPE-based MCGPU code previously used in cone-beam CT and mammography applications. We validated the accuracy of the accelerated code by comparing it to GATE and PeneloPET simulations achieving an agreement within 10 percent approximately. As an example application of the code for fast estimation of PET coincidences, a scan of the NEMA IQ phantom was simulated. A fully 3D sinogram with 6382 million true coincidences and 731 million scatter coincidences was generated in 54 seconds in one GPU. MCGPU-PET provides an estimation of true and scatter coincidences and spurious background (for positron-gamma emitters such as 124I) at a rate 3 orders of magnitude faster than CPU-based MC simulators. This significant speed-up enables the use of the code for accurate scatter and prompt-gamma background estimations within an iterative image reconstruction process.

Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction

Objective.Monte-Carlo simulation studies have been essential for advancing various developments in single photon emission computed tomography (SPECT) imaging, such as system design and accurate image reconstruction. Among the simulation software available, Geant4 application for tomographic emission (GATE) is one of the most used simulation toolkits in nuclear medicine, which allows building systems and attenuation phantom geometries based on the combination of idealized volumes. However, these idealized volumes are inadequate for modeling free-form shape components of such geometries. Recent GATE versions alleviate these major limitations by allowing users to import triangulated surface meshes.Approach.In this study, we describe our mesh-based simulations of a next-generation multi-pinhole SPECT system dedicated to clinical brain imaging, called AdaptiSPECT-C. To simulate realistic imaging data, we incorporated in our simulation the XCAT phantom, which provides an advanced anatomical description of the human body. An additional challenge with the AdaptiSPECT-C geometry is that the default voxelized XCAT attenuation phantom was not usable in our simulation due to intersection of objects of dissimilar materials caused by overlap of the air containing regions of the XCAT beyond the surface of the phantom and the components of the imaging system.Main results.We validated our mesh-based modeling against the one constructed by idealized volumes for a simplified single vertex configuration of AdaptiSPECT-C through simulated projection data of123I-activity distributions. We resolved the overlap conflict by creating and incorporating a mesh-based attenuation phantom following a volume hierarchy. We then evaluated our reconstructions with attenuation and scatter correction for projections obtained from simulation consisting of mesh-based modeling of the system and the attenuation phantom for brain imaging. Our approach demonstrated similar performance as the reference scheme simulated in air for uniform and clinical-like123I-IMP brain perfusion source distributions.Significance.This work enables the simulation of complex SPECT acquisitions and reconstructions for emulating realistic imaging data close to those of actual patients.

The OpenGATE ecosystem for Monte Carlo simulation in medical physics

This paper reviews the ecosystem of GATE, an open-source Monte Carlo toolkit for medical physics. Based on the shoulders of Geant4, the principal modules (geometry, physics, scorers) are described with brief descriptions of some key concepts (Volume, Actors, Digitizer). The main source code repositories are detailed together with the automated compilation and tests processes (Continuous Integration). We then described how the OpenGATE collaboration managed the collaborative development of about one hundred developers during almost 20 years. The impact of GATE on medical physics and cancer research is then summarized, and examples of a few key applications are given. Finally, future development perspectives are indicated.

Correlation between X-ray tube current exposure time and X-ray photon number in GATE

BACKGROUND

X-ray image quality relies heavily on the emitted X-ray photon number which depends on X-ray tube current and exposure time. To accurately estimate the absorbed dose in an imaging protocol, it is better to simulate the X-ray imaging with a Monte Carlo platform such as GATE (Geant4 Application for Tomographic Emission). Although input of GATE is the X-ray photon number of the simulated X-ray tube, it lacks a good way to setup the photon number for a desired X-ray tube current setting.

OBJECTIVE

To provide a method to correlate the experimental X-ray tube current exposure time and the X-ray photon number in GATE.

METHODS

The accumulated radiation dose of a micro-computed tomography (CT) X-ray tube was recorded at different current exposure times with a general-purpose ion chamber. GATE was used to model the experimental microCT imaging system and calculate the total absorbed dose (cGy) in the sensitive volume of the ion chamber with different X-ray photon numbers. Linear regression models are used to establish a correlation between the estimated X-ray photon number and the X-ray tube settings. At first, one model establishes the relationship between the experimentally measured dose and the X-ray tube setting. Then, another model establishes a relationship between the simulated dose and the X-ray number in GATE. At last, by correlating these two models, a regression model to estimate the X-ray output number from an experimental X-ray tube setting (mAs) is obtained.

RESULTS

For a typical micro-CT scan, the X-ray tube is operated at 50 kVp and 0.5 mA for a 500 ms exposure time per projection (0.25 mAs). For these X-ray imaging parameters, the X-ray number per projection is estimated to be 3.613×106 with 1.0 mm Al filter.

CONCLUSION

The findings of this work provide an approach to correlate the experimental X-ray tube current exposure time to the X-ray photon number in the GATE simulation of the X-ray tube to more accurately determine radiation dose for an imaging protocol.

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.