Standardization and Validation of Brachytherapy Seeds'' Modelling Using GATE and GGEMS Monte Carlo Toolkits

Simple Summary:& nbsp;This study used GATE and GGEMS simulation toolkits, to estimate dose distribution on Brachytherapy procedures. Specific guidelines were followed as defined by the American Association of Physicists in Medicine (AAPM) as well as by the European SocieTy for Radiotherapy and Oncology (ESTRO). Several types of brachytherapy seeds were modelled and simulated, namely Low-Dose-Rate (LDR), High-Dose-Rate (HDR), and Pulsed-Dose-Rate (PDR). The basic difference between GATE and GGEMS is that GGEMS incorporates GPU capabilities, which makes the use of Monte Carlo (MC) simulations more accessible in clinical routine, by minimizing the computational time to obtain a dose map. During the validation procedure of both codes with protocol data, differences as well as uncertainties were measured within the margins defined by the guidelines. The study concluded that MC simulations may be utilized in clinical practice, to optimize dose distribution in real time, as well as to evaluate therapeutic plans.This study aims to validate GATE and GGEMS simulation toolkits for brachytherapy applications and to provide accurate models for six commercial brachytherapy seeds, which will be freely available for research purposes. The AAPM TG-43 guidelines were used for the validation of two Low Dose Rate (LDR), three High Dose Rate (HDR), and one Pulsed Dose Rate (PDR) brachytherapy seeds. Each seed was represented as a 3D model and then simulated in GATE to produce one single Phase-Space (PHSP) per seed. To test the validity of the simulations'' outcome, referenced data (provided by the TG-43) was compared with GATE results. Next, validation of the GGEMS toolkit was achieved by comparing its outcome with the GATE MC simulations, incorporating clinical data. The simulation outcomes on the radial dose function (RDF), anisotropy function (AF), and dose rate constant (DRC) for the six commercial seeds were compared with TG-43 values. The statistical uncertainty was limited to 1% for RDF, to 6% (maximum) for AF, and to 2.7% (maximum) for the DRC. GGEMS provided a good agreement with GATE when compared in different situations: (a) Homogeneous water sphere, (b) heterogeneous CT phantom, and (c) a realistic clinical case. In addition, GGEMS has the advantage of very fast simulations. For the clinical case, where TG-186 guidelines were considered, GATE required 1 h for the simulation while GGEMS needed 162 s to reach the same statistical uncertainty. This study produced accurate models and simulations of their emitted spectrum of commonly used commercial brachytherapy seeds which are freely available to the scientific community. Furthermore, GGEMS was validated as an MC GPU based tool for brachytherapy. More research is deemed necessary for the expansion of brachytherapy seed modeling

Photon dose kernels dataset for nuclear medicine dosimetry, using the GATE Monte Carlo toolkit

Photon dose point kernels (DPKs) were generated using the GATE toolkit for different media and for radionuclides of interest in nuclear medicine. In the present work the primary photon contribution of different isotopes in different media is calculated, since this dataset is not available in the literature according to our knowledge. The generated dataset consists of photon DPKs for some of the most commonly used radionuclides in nuclear medicine, generated for different media namely water, lung and bone. A homogenous spherical phantom was used, with a point gamma source at the center, emitting isotropically. Validation of the generated dose kernels in water was performed by comparing against the dose kernels published by Furhang et al. (1996). The kernels that were generated include the following radionuclides: Cu-64, Ga-67, Ga-68, Tc-99, Pd-103, In-111, I-123, I-124, I-125, I-131, Cs-137, Sm-153, Lu-177 and were calculated, taking into account dose at all voxels of the medium, at different distances from the point source. The scored dose in each voxel comes from the primary photons of the sources, and all the subsequent interactions that are taking place. Scoring in voxels of different sizes was performed to investigate the influence of the voxel size, taking into account measured statistical uncertainty. DPKs for different radioisotopes and media can be used in 3-D internal dosimetry, by exploiting the anatomical information of each patient (e.g. CT images). When the material of each voxel is known, dose in specific organs can be calculated, without making the assumption that body is a homogeneous material consisting of water, as it is the case in most DPKs based methods. Thus, more accurate algorithms for personalized, real time dose calculation can be implemented, as it has already been suggested in the literature. © 2011 IEEE

Investigation of attenuation correction in SPECT using textural features, Monte Carlo simulations, and computational anthropomorphic models

PurposeTo present and evaluate a new methodology to investigate the effect of attenuation correction (AC) in single-photon emission computed tomography (SPECT) using textural features analysis, Monte Carlo techniques, and a computational anthropomorphic model.Materials and methodsThe GATE Monte Carlo toolkit was used to simulate SPECT experiments using the XCAT computational anthropomorphic model, filled with a realistic biodistribution of Tc-99m-N-DBODC. The simulated gamma camera was the Siemens ECAM Dual-Head, equipped with a parallel hole lead collimator, with an image resolution of 3.54x3.54mm(2). Thirty-six equispaced camera positions, spanning a full 360 degrees arc, were simulated. Projections were calculated after applying a 20% energy window or after eliminating all scattered photons. The activity of the radioisotope was reconstructed using the MLEM algorithm. Photon attenuation was accounted for by calculating the radiological pathlength in a perpendicular line from the center of each voxel to the gamma camera. Twenty-two textural features were calculated on each slice, with and without AC, using 16 and 64 gray levels. A mask was used to identify only those pixels that belonged to each organ.ResultsTwelve of the 22 features showed almost no dependence on AC, irrespective of the organ involved. In both the heart and the liver, the mean and SD were the features most affected by AC. In the liver, six features were affected by AC only on some slices. Depending on the slice, skewness decreased by 22-34% with AC, kurtosis by 35-50%, long-run emphasis mean by 71-91%, and long-run emphasis range by 62-95%. In contrast, gray-level non-uniformity mean increased by 78-218% compared with the value without AC and run percentage mean by 51-159%. These results were not affected by the number of gray levels (16 vs. 64) or the data used for reconstruction: with the energy window or without scattered photons.ConclusionThe mean and SD were the main features affected by AC. In the heart, no other feature was affected. In the liver, other features were affected, but the effect was slice dependent. The number of gray levels did not affect the results. Copyright (C) 2015 Wolters Kluwer Health, Inc. All rights reserved

Optimization of a gamma imaging probe for axillary sentinel lymph mapping

Sentinel lymph node (SLN) mapping is a technique for assessing whether early-stage invasive breast cancer has metastasized, thus determining prognosis and treatment options. SLN identification is achieved using the blue-dye and radioactive colloids techniques, which are sometimes combined with lymphoscintigraphy. Furthermore, intra-operative gamma acoustic probes, as well as gamma imaging probes are used during surgery. The purpose of this study is the construction of a gamma probe for sentinel lymph node imaging and its optimization in terms of sensitivity with respect to spatial resolution. The reference probe has small field of view (2.5 x 2.5 cm(2)) and is based on a position sensitive photomultiplier tube (PSPMT) coupled to a pixellated CsI(Tl) scintillator. Following experimental validation, we simulated the system using the GATE Monte Carlo toolkit (GATE v6.1) and modeled various collimator geometries, in order to evaluate their performance and propose the optimal configuration. The constraints of the proposed gamma imaging probe are i) sensitivity close to 2 cps/kBq and ii) spatial resolution equal to 6 mm at 2 cm source-to-collimator distance and similar to 10mm at 5 cm. An integrated structure that achieves those requirements is a tungsten collimator with 2 x 2 mm(2)square holes, 16 mm thickness, 0.15 mm septa, where each CsI(Tl) 2 x 2 x 5 mm(3) crystal pixel is placed inside the collimator

Evaluation of an imaging gamma probe based on R8900U-00-C12 PSPMT

In this study we present the construction and the evaluation of a gamma probe based on a R8900U-00-C12 position sensitive photomultiplier tube coupled to a pixelated CsI(Tl) crystal array with 2mm x 2mm x 3mm crystal elements and a general purpose parallel collimator. Sensitivity, energy resolution and spatial resolution were measured under 140keV irradiation, using Tc 99m. Spatial resolution was found equal to 2.4mm at zero source to collimator distance, while the sensitivity was 120cps/MBq and the energy resolution equal to 16%. Following its construction and experimental validation, the probe was simulated in GATE toolkit (version 6.0). Simulation studies were carried towards the determination of the optimal collimator that will show best compromise between high sensitivity and spatial resolution. © 2011 IEEE

Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences

Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated, it may lead to cirrhosis, hepatic carcinoma and death. Ultrasound (US) shear wave elastography (SWE) is a relatively new, popular, non-invasive technique among radiologists. Although many studies have been published validating the SWE technique either in a clinical setting, or by applying machine learning on SWE elastograms, minimal work has been done on comparing the performance of popular pre-trained deep learning networks on CLD assessment. Currently available literature reports suggest technical advancements on specific deep learning structures, with specific inputs and usually on a limited CLD fibrosis stage class group, with limited comparison on competitive deep learning schemes fed with different input types. The aim of the present study is to compare some popular deep learning pre-trained networks using temporally stable and full elastograms, with or without augmentation as well as propose suitable deep learning schemes for CLD diagnosis and progress assessment. 200 liver biopsy validated patients with CLD, underwent US SWE examination. Four images from the same liver area were saved to extract elastograms and processed to exclude areas that were temporally unstable. Then, full and temporally stable masked elastograms for each patient were separately fed into GoogLeNet, AlexNet, VGG16, ResNet50 and DenseNet201 with and without augmentation. The networks were tested for differentiation of CLD stages in seven classification schemes over 30 repetitions using liver biopsy as the reference. All networks achieved maximum mean accuracies ranging from 87.2%-97.4% and area under the receiver operating characteristic curves (AUCs) ranging from 0.979-0.990 while the radiologists had AUCs ranging from 0.800-0.870. ResNet50 and DenseNet201 had better average performance than the other networks. The use of the temporal stability mask led to improved performance on about 50% of inputs and network combinations while augmentation led to lower performance for all networks. These findings can provide potential networks with higher accuracy and better setting in the CLD diagnosis and progress assessment. A larger data set would help identify the best network and settings for CLD assessment in clinical practice. © 2020 Institute of Physics and Engineering in Medicine

Automated DICOM metadata and volumetric anatomical information extraction for radiation dosimetry

Patient-specific dosimetry calculations based on simulation techniques have as a prerequisite the modeling of the modality system and the creation of voxelized phantoms. This procedure requires the knowledge of scanning parameters and patients’ information included in a DICOM file as well as image segmentation. However, the extraction of this information is complicated and time-consuming. The objective of this study was to develop a simple graphical user interface (GUI) to (i) automatically extract metadata from every slice image of a DICOM file in a single query and (ii) interactively specify the regions of interest (ROI) without explicit access to the radiology information system. The user-friendly application developed in Matlab environment. The user can select a series of DICOM files and manage their text and graphical data. The metadata are automatically formatted and presented to the user as a Microsoft Excel file. The volumetric maps are formed by interactively specifying the ROIs and by assigning a specific value in every ROI. The result is stored in DICOM format, for data and trend analysis. The developed GUI is easy, fast and and constitutes a very useful tool for individualized dosimetry. One of the future goals is to incorporate a remote access to a PACS server functionality

Ionizing Radiation and Complex DNA Damage: Quantifying the Radiobiological Damage Using Monte Carlo Simulations

Ionizing radiation is a common tool in medical procedures. Monte Carlo (MC) techniques are widely used when dosimetry is the matter of investigation. The scientific community has invested, over the last 20 years, a lot of effort into improving the knowledge of radiation biology. The present article aims to summarize the understanding of the field of DNA damage response (DDR) to ionizing radiation by providing an overview on MC simulation studies that try to explain several aspects of radiation biology. The need for accurate techniques for the quantification of DNA damage is crucial, as it becomes a clinical need to evaluate the outcome of various applications including both low- and high-energy radiation medical procedures. Understanding DNA repair processes would improve radiation therapy procedures. Monte Carlo simulations are a promising tool in radiobiology studies, as there are clear prospects for more advanced tools that could be used in multidisciplinary studies, in the fields of physics, medicine, biology and chemistry. Still, lot of effort is needed to evolve MC simulation tools and apply them in multiscale studies starting from small DNA segments and reaching a population of cells

Pediatric personalized CT-dosimetry Monte Carlo simulations, using computational phantoms

The last 40 years Monte Carlo (MC) simulations serve as a “gold standard” tool for a wide range of applications in the field of medical physics and tend to be essential in daily clinical practice. Regarding diagnostic imaging applications, such as computed tomography (CT), the assessment of deposited energy is of high interest, so as to better analyze the risks and the benefits of the procedure. The last few years a big effort is done towards personalized dosimetry, especially in pediatric applications. In the present study the GATE toolkit was used and computational pediatric phantoms have been modeled for the assessment of CT examinations dosimetry. The pediatric models used come from the XCAT and IT’IS series. The X-ray spectrum of a Brightspeed CT scanner was simulated and validated with experimental data. Specifically, a DCT-10 ionization chamber was irradiated twice using 120 kVp with 100 mAs and 200 mAs, for 1 sec in 1 central axial slice (thickness = 10mm). The absorbed dose was measured in air resulting in differences lower than 4% between the experimental and simulated data. The simulations were acquired using similar to 10(10) number of primaries in order to achieve low statistical uncertainties. Dose maps were also saved for quantification of the absorbed dose in several children critical organs during CT acquisition