Toward three-dimensional patient-specific internal dosimetry using GATE Monte Carlo technique

Karimipourfard, M; Sina, Sedigheh; Alavi, Mehrosadat

doi:10.1016/j.radphyschem.2022.110046

Cited by 8 publications

(8 citation statements)

References 28 publications

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“…The mean intensity over volume is a significant factor in justice activity variation over time, without biochemical knowledge. From a dosimetry point of view, Monte Carlo and voxel S value-based (VSV) are the main and most accurate methods used to determine the S-value in which dose maps and cumulative activity maps can be estimated according to patient image analysis [10]. There is a fact that crude knowledge and obstacle protocols above this method have hampered the progress and its clinical usage, but there is abundant room for development and analysis of the procedure.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network

Karimipourfard

Sina²,

Shoshtari

et al. 2023

Nuklearmedizin

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The cumulative activity map estimation are essential tools for patient specific dosimetry with high accuracy, which is estimated using biokinetic models instead of patient dynamic data or the number of static PET scans, owing to economical and time-consuming points of view. In the era of deep learning applications in medicine, the pix-to-pix (p2 p) GAN neural networks play a significant role in image translation between imaging modalities. In this pilot study, we extended the p2 p GAN networks to generate PET images of patients at different times according to a 60 min scan time after the injection of F-18 FDG. In this regard, the study was conducted in two sections: phantom and patient studies. In the phantom study section, the SSIM, PSNR, and MSE metric results of the generated images varied from 0.98–0.99, 31–34 and 1–2 respectively and the fine-tuned Resnet-50 network classified the different timing images with high performance. In the patient study, these values varied from 0.88–0.93, 36–41 and 1.7–2.2, respectively and the classification network classified the generated images in the true group with high accuracy. The results of phantom studies showed high values of evaluation metrics owing to ideal image quality conditions. However, in the patient study, promising results were achieved which showed that the image quality and training data number affected the network performance. This study aims to assess the feasibility of p2 p GAN network application for different timing image generation.

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Section: Introductionmentioning

confidence: 99%

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network

Karimipourfard

Sina²,

Shoshtari

et al. 2023

Nuklearmedizin

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“…The threshold for producing secondary particles was set to 2.0 mm and the inputs for the seTLE actor were set with the commands enableHybridino false, setPrimaryMultiplicity 100, and setSecondaryMultiplicity 1. Using regression, the trend curve formulas were obtained and evaluated to calculate the simulation time with relative uncertainty at 10% (Karimipourfard et al 2022). Simulations with and without VRTs were performed at 6.1 min and 1176.0 min respectively for the lutetium-177 source, and 5.4 min and 7700.0 min respectively for the radium-223 source.…”

Section: Resultsmentioning

confidence: 99%

Optimized Monte Carlo simulations for voxel-based internal dosimetry

et al. 2023

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Objective: The scientific community has considered internal dosimetry by the Monte Carlo method the gold standard. However, there is a trade-off between simulation processing time and the statistical quality of the results that makes it a challenge to obtain accurate absorbed dose values in some situations, such as dose estimation in organs affected by cross-irradiation or limited computing power. Variance reduction techniques are used to reduce computational processing time without impairing the statistical quality of the results, such as tracking energy cutoff, secondary particle production threshold, and parallelism of different types of emissions from radionuclides. Approach: In this work, GATE Monte Carlo code and its variance reduction techniques were evaluated to calculate S values of organs from the International Commission on Radiological Protection (ICRP) report 110 male phantom for the lutetium-177, iodine-131, yttrium-90, and radium-223 radionuclides. The results are compared with the data from the OpenDose collaboration. Main Results: A cutoff of 5 MeV for local electron deposition and 2.0 mm of secondary particle production range resulted in a computational efficiency increase of 7.9 and 1.05 times, respectively. Simulation of ICRP 107 spectra-based source proved to be about 5 times more efficient when compared to a decay simulation using \verb|G4RadioactiveDecay| (Geant4-based radioactive decay processes). TLE (Track Length Estimator) and seTLE (Split Exponential Track Length Estimator) techniques were used to calculate the absorbed dose of photon emissions, resulting in computational efficiency up to 29.4 and 62.5 times higher when compared to traditional simulations, respectively. In particular, the seTLE technique accelerates the simulation time by up to 1426 times, achieving a statistical uncertainty of 10\% in volumes affected by cross-irradiation. Significance: The variance reduction techniques used in this work drastically reduced the simulation time and maintained the statistical quality of the calculated absorbed dose values, proving the feasibility of the use of the Monte Carlo method in internal dosimetry under challenging situations and making it viable for clinical routine or web applications.

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“…The 3D S‐factor map was generated using a MATLAB script considering labeled CT images of the organs. The statistical uncertainties of the dose rates were estimated to be less than 0.1% at the voxel level 29,30 …”

Section: Methodsmentioning

confidence: 97%

“…The statistical uncertainties of the dose rates were estimated to be less than 0.1% at the voxel level. 29,30 To evaluate the impact of the segmentation method, the GATE MC simulator was employed. The two aforementioned DL approaches were compared with manual segmentation.…”

Section: Impact Of DL Methods On the Organ's S-valuementioning

confidence: 99%

“…Similarly, the corresponding CT image was considered as the voxelized phantom to account for the anatomical details of each patient during the MC modeling. Some key details of MC simulation such as the physics model, dealing with voxelized sources and phantoms in GATE, and the scoring of the desired quantities are previously published by some of the authors 29 . The S‐value for the bladder, liver, lungs, kidneys, stomach, heart's wall, and spleen were finally investigated and compared.…”

Section: Methodsmentioning

confidence: 99%

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Impact of deep learning‐based multiorgan segmentation methods on patient‐specific internal dosimetry in PET/CT imaging: A comparative study

Karimipourfard,

Sina,

Mahani

et al. 2024

J Applied Clin Med Phys

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PurposeAccurate and fast multiorgan segmentation is essential in image‐based internal dosimetry in nuclear medicine. While conventional manual PET image segmentation is widely used, it suffers from both being time‐consuming as well as subject to human error. This study exploited 2D and 3D deep learning (DL) models. Key organs in the trunk of the body were segmented and then used as a reference for networks.MethodsThe pre‐trained p2p‐U‐Net‐GAN and HighRes3D architectures were fine‐tuned with PET‐only images as inputs. Additionally, the HighRes3D model was alternatively trained with PET/CT images. Evaluation metrics such as sensitivity (SEN), specificity (SPC), intersection over union (IoU), and Dice scores were considered to assess the performance of the networks. The impact of DL‐assisted PET image segmentation methods was further assessed using the Monte Carlo (MC)‐derived S‐values to be used for internal dosimetry.ResultsA fair comparison with manual low‐dose CT‐aided segmentation of the PET images was also conducted. Although both 2D and 3D models performed well, the HighRes3D offers superior performance with Dice scores higher than 0.90. Key evaluation metrics such as SEN, SPC, and IoU vary between 0.89–0.93, 0.98–0.99, and 0.87–0.89 intervals, respectively, indicating the encouraging performance of the models. The percentage differences between the manual and DL segmentation methods in the calculated S‐values varied between 0.1% and 6% with a maximum attributed to the stomach.ConclusionThe findings prove while the incorporation of anatomical information provided by the CT data offers superior performance in terms of Dice score, the performance of HighRes3D remains comparable without the extra CT channel. It is concluded that both proposed DL‐based methods provide automated and fast segmentation of whole‐body PET/CT images with promising evaluation metrics. Between them, the HighRes3D is more pronounced by providing better performance and can therefore be the method of choice for 18F‐FDG‐PET image segmentation.

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Toward three-dimensional patient-specific internal dosimetry using GATE Monte Carlo technique

Cited by 8 publications

References 28 publications

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network

Optimized Monte Carlo simulations for voxel-based internal dosimetry

Impact of deep learning‐based multiorgan segmentation methods on patient‐specific internal dosimetry in PET/CT imaging: A comparative study

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