Monte Carlo Simulation in Radionuclide Therapy Dosimetry

Argyrou, Maria; Lyra, Maria

doi:10.26717/bjstr.2019.15.002647

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…[17][18][19] Although DPK methods generally offer better speed to calculate dose deposited in a voxelized patient body, they use generic dose kernels simulated in a homogeneous phantom made of a material like soft tissue. 20 For dose calculation in the presence of heterogeneities, the DPK method requires the use of a kernel scaling approach. 21 However, Khan and DeWerd simulated scaled DPKs based on mass density for various tissues and found that corrected DPKs could differ by up to 25% near Bragg peak regions when compared with water.…”

Section: Introductionmentioning

confidence: 99%

“…The 3D matrix of TIAs is then convolved with a pregenerated 3D dose point kernel (DPK) or serves as input for MC code simulations 17–19 . Although DPK methods generally offer better speed to calculate dose deposited in a voxelized patient body, they use generic dose kernels simulated in a homogeneous phantom made of a material like soft tissue 20 . For dose calculation in the presence of heterogeneities, the DPK method requires the use of a kernel scaling approach 21 .…”

Section: Introductionmentioning

confidence: 99%

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Development of a stand‐alone precalculated Monte Carlo code to calculate the dose by alpha and beta emitters from the Ra‐224 decay chain

Hoseini‐Ghahfarokhi,

Kamio,

Mondor

et al. 2023

Medical Physics

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BackgroundRecent developments in alpha and beta emitting radionuclide therapy highlight the importance of developing efficient methods for patient‐specific dosimetry. Traditional tabulated methods such as Medical Internal Radiation Dose (MIRD) estimate the dose at the organ level while more recent numerical methods based on Monte Carlo (MC) simulations are able to calculate dose at the voxel level. A precalculated MC (PMC) approach was developed in this work as an alternative to time‐consuming fully simulated MC. Once the spatial distribution of alpha and beta emitters is determined using imaging and/or numerical methods, the PMC code can be used to achieve an accurate voxelized 3D distribution of the deposited energy without relying on full MC calculations.PurposeTo implement the PMC method to calculate energy deposited by alpha and beta particles emitted from the Ra‐224 decay chain.MethodsThe GEANT4 (version 10.7) MC toolkit was used to generate databases of precalculated tracks to be integrated in the PMC code as well as to benchmark its output. In this regard, energy spectra of alpha and beta particles emitted by the Ra‐224 decay chain were generated using GAMOS (version 6.2.0) and imported into GEANT4 macro files. Either alpha or beta emitting sources were defined at the center of a homogeneous phantom filled with various materials such as soft tissue, bone, and lung where particles were emitted either mono‐directionally (for database generation) or isotropically (for benchmarking). Two heterogeneous phantoms were used to demonstrate PMC code compatibility with boundary crossing events. Each precalculated database was generated step‐by‐step by storing particle track information from GEANT4 simulations followed by its integration in a PMC code developed in MATLAB. For a user‐defined number of histories, one of the tracks in a given database was selected randomly and rotated randomly to reflect an isotropic emission. Afterward, deposited energy was divided between voxels based on step length in each voxel using a ray‐tracing approach. The radial distribution of deposited energy was benchmarked against fully simulated MC calculations using GEANT4. The effect of the GEANT4 parameter StepMax on the accuracy and speed of the code was also investigated.ResultsIn the case of alpha decay, primary alpha particles show the highest contribution (>99%) in deposited energy compared to their secondary particles. In most cases, protons act as the main secondary particles in the deposition of energy. However, for a lung phantom, using a range cutoff parameter of 10 µm on primary alpha particles yields a higher contribution of secondary electrons than protons. Differences between deposited energy calculated by PMC and fully simulated MC are within 2% for all alpha and beta emitters in homogeneous and heterogeneous phantoms. Additionally, statistical uncertainties are less than 1% for voxels with doses higher than 5% of the maximum dose. Moreover, optimization of the parameter StepMax is necessary to achieve the best tradeoff between code accuracy and speed.ConclusionsThe PMC code shows good performance for dose calculations deposited by alpha and beta emitters. As a stand‐alone algorithm, it is suitable to be integrated into clinical treatment planning systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a stand‐alone precalculated Monte Carlo code to calculate the dose by alpha and beta emitters from the Ra‐224 decay chain

Hoseini‐Ghahfarokhi,

Kamio,

Mondor

et al. 2023

Medical Physics

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show abstract

“…In the late 1990s, based on the sequential importance sampling (SIS) [ 22 , 23 ], Gordon proposed a particle filter (PF) algorithm [ 24 , 25 ] by combining the resampling technique with Monte Carlo importance sampling. This algorithm is an optimal regression algorithm, combining Monte Carlo thought [ 26 ] and recursive Bayesian filtering [ 27 ], and it has a good estimation effect when dealing with nonlinear/non-Gaussian systems [ 28 , 29 , 30 ]. However, particle degradation and particle shortage occur during particle sampling, which seriously affects the accuracy of the PF.…”

Section: Introductionmentioning

confidence: 99%

Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Deng

2021

Sensors

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Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

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“…In recent years scientists use simulation methods to overcome such situations. One of the most powerful methods used in radiation calculations is Monte Carlo (MC) simulations, which are commonly used in medical radiation physics (17)(18)(19). This is a theoretical model in which physical quantities are determined by simulating the transport of X-ray photons.…”

Section: Introductionmentioning

confidence: 99%

Impact of Eye and Breast Shielding on Organ Doses During Cervical Spine Radiography: Design and Validation of MIRD Computational Phantom

Elshami

Tekın

Issa

et al. 2021

Front. Public Health

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Purpose: The study aimed to design and validate computational phantoms (MIRD) using the MCNPX code to assess the impact of shielding on organ doses.Method: To validate the optimized phantom, the obtained results were compared with experimental results. The validation of the optimized MIRD phantom was provided by using the results of a previous anthropomorphic phantom study. MIRD phantom was designed by considering the parameters used in the anthropomorphic phantom study. A test simulation was performed to compare the dose reduction percentages (%) between the experimental anthropomorphic phantom study and the MCNPX-MIRD phantom. The simulation was performed twice, with and without shielding materials, using the same number and locations of the detector.Results: The absorbed dose amounts were directly extracted from the required organ and tissue cell parts of output files. Dose reduction percentages between the simulation with shielding and simulation without shielding were compared. The highest dose reduction was noted in the thymus (95%) and breasts (88%). The obtained dose reduction percentages between the anthropomorphic phantom study and the MCNPX-MIRD phantom were highly consistent and correlated values with experimental anthropomorphic data. Both methods showed Relative Difference (%) ranges between 0.88 and 2.22. Moreover, the MCNPX-MIRD optimized phantom provides detailed dose analysis for target and non-target organs and can be used to assess the efficiency of shielding in radiological examination.Conclusion: Shielding breasts and eyes during cervical radiography reduced the radiation dose to many organs. The decision to not shield patients should be based on research evidence as this approach does not apply to all cases.

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Monte Carlo Simulation in Radionuclide Therapy Dosimetry

Abstract: Monte Carlo [MC] method is a modeling tool, capable of achieving a close adherence to reality, concerning the analysis of complex systems. Generally is a method for estimation of the solution of mathematical problems by means of random numbers

Cited by 4 publications

References 27 publications

Development of a stand‐alone precalculated Monte Carlo code to calculate the dose by alpha and beta emitters from the Ra‐224 decay chain

Development of a stand‐alone precalculated Monte Carlo code to calculate the dose by alpha and beta emitters from the Ra‐224 decay chain

Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Impact of Eye and Breast Shielding on Organ Doses During Cervical Spine Radiography: Design and Validation of MIRD Computational Phantom

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