Use Of Monte-Carlo Method Research Articles

SU-E-T-32: The Use of Monte Carlo Method as an Independent Dose Verification Calculation Tool for TomoTherapy

Purpose: It has been widely recognized, as stated in a ICRP 2009 report, that a secondary calculation, independent from treatment‐planning‐system (TPS), has proven to be an efficient tool for prevention of major errors in dose delivery. The purpose of this study is to develop such tool for TomoTherapy system. Methods: A Monte Carlo tool that has been validated previously, TomoPen, was modified to have better integration with the TomoTherapy software. Patient plan and CT information is DICOM exported. The Monte Carlo tool reads the plan and CT information from the dicom file and computes the dose. The Monte Carlo computed dose is compared with the TPS dose to make sure no major deviation is observed. Results: The tool has been tested on several clinical patient plans. By using a coarse dose grid, a decent dose distribution can be obtained in less than an hour on an Intel i7 CPU. The run time can be further reduced by using a computer cluster. The time takes for this secondary calculation is comparable with the standard DQA plan creation and measurement. However, the secondary calculation does not take the treatment machine time as well as physicist's time. Conclusions: A Monte Carlo based secondary calculation tool is developed to prevent major errors in TomoTherapy delivery. It is shown that it can be fitted into the current clinical workflow.

OC-0466 VIRTUAL SOURCE MODELLING OF PHOTON BEAMS FOR MONTE CARLO BASED RADIATION THERAPY TREATMENT PLANNING

OC-0466 VIRTUAL SOURCE MODELLING OF PHOTON BEAMS FOR MONTE CARLO BASED RADIATION THERAPY TREATMENT PLANNING

The encounter of interval and probabilistic approaches to structural reliability at the design point

The consideration of uncertainties in structural analysis has traditionally been made in the field of structural reliability within the framework of classical probability theory. In many cases, however, there is no sufficient probabilistic information to substantiate such an approach. In recent years several non-probabilistic methods have been proposed as alternatives. One of them is the interval analysis, oriented to estimate the range of variation of output structural variables given the ranges of the input ones. The method, however, requires the use of special arithmetics which renders its application unpractical for structural analysis based on finite elements or other numerical techniques. In this paper a Monte Carlo method that can be applied to probabilistic as well as to interval approaches to reliability analysis is proposed. It is based on the optimal properties of the classical design point of probabilistic structural reliability. In particular, it is shown that the order statistics of the output variable is concealed in a plot defined by the design point vector. This facilitates the selection of the relevant samples for either interval or probability-based analysis. The examples show that the desired interval of the response or the failure probability can be accurately estimated on such a basis. The paper also discusses the use of Monte Carlo methods for both reliability and interval analysis from the point of view of the proposed representation.

Recent Advances and Future Prospects for Monte Carlo

The history of Monte Carlo methods is closely linked to that of computers: The first known Monte Carlo program was written in 1947 for the ENIAC; a pre-release of the first Fortran compiler was used for Monte Carlo In 1957; Monte Carlo codes were adapted to vector computers in the 1980s, clusters and parallel computers in the 1990s, and teraflop systems in the 2000s. Recent advances include hierarchical parallelism, combining threaded calculations on multicore processors with message-passing among different nodes. With the advances In computmg, Monte Carlo codes have evolved with new capabilities and new ways of use. Production codes such as MCNP, MVP, MONK, TRIPOLI and SCALE are now 20-30 years old (or more) and are very rich in advanced featUres. The former 'method of last resort' has now become the first choice for many applications. Calculations are now routinely performed on office computers, not just on supercomputers. Current research and development efforts are investigating the use of Monte Carlo methods on FPGAs. GPUs, and many-core processors. Other far-reaching research is exploring ways to adapt Monte Carlo methods to future exaflop systems that may have 1M or more concurrent computational processes.

Monte Carlo Shielding Calculations for a Spent Fuel Transport Cask with Automated Monte Carlo Variance Reduction

For the purpose of performing reasonable shielding calculation of a spent fuel transport cask, the use of Monte Carlo methods has been proposed for solving the radiation transport problem on a detailed structure of the transport cask considering fixed neutron sources. A SMIRE (Simplified MCNP-ANISN_W Variance Reduction) system has been developed in the present study, which is possible to generate automatically the lower weight boundary of the weight window for each mesh based on the Consistent Adjoint Driven Importance Sampling (CADIS). Compared with the case of the importance based on the empirical formula, the figure of merit is increased by a factor of 25. In this system, it is possible to calculate the weight suitable for the distantly-positioned detector point from the fuel effective region to introduce the relaxation factor which relaxes the increase of the particle numbers at the boundary of weight window meshes that are generally caused by the large attenuation of adjoint flux. This system is used for a variety of the radiation transport problems as well as the transport cask.

MO-B-224-01: Clinical Implementation and Application of Monte Carlo Methods in Photon and Electron Dose Calculation - New Issues to Consider in Clinical Practice

Current advances in image guided radiation therapy have enabled very accurate delivery of radiation for the treatment of cancer. It then becomes more important that the radiationdose is calculated with utmost accuracy in patient geometry. For past two decades Monte Carlo(MC) methods have been known to be the most accurate methods available for dose calculation. However, it has been only recently that MC based commercial treatment planning systems are becoming available for routine use in the clinic. Even now radiation therapy community is struggling in adopting MCtreatment planning systems (TPS) as the only TPS in the clinic. This is either because of additional computational burden associated with the MCdose calculation or due to the lack of information available on clinical benchmarked data where the effect of MC is most significant. Although use of parallel processing and more recently GPU implementation of MC algorithms have overcome computational burden, the need for clinical use and significance of MC methods are still topics of debate. The main goal of this educational session is to familiarize clinical medical physicists with the application and implementation of Monte Carlo in routine radiotherapytreatment planning with the emphasis on the clinical significance. The continuing education session will be divided into two sections in order to specifically address implementation issues related to Monte Carlo‐based photon and electron treatment planning techniques. The following broad general topics will be addressed: 1. Introduction to Monte Carlo Methods: with its application to radiation treatment planning and commercial availability of MC based TPS 2. Commissioning and clinical implementation of MC based systems‐Beam data required for source modeling and commissioning of a MC based TPS. Use of MC in IMRT optimization and VMAT for photontreatment planning 3. Implementation, operational and physics related issues: Issues related to beam modeling, inherent statistical uncertainty in MCdose calculations, importance of CT‐to‐material conversion and dose reporting in terms of dose‐to‐medium versus dose‐to‐water. 4. Clinical significance of MC based treatment plans:Dosimetric differences of MCdose calculations compared to kernel based methods for treatment sites such as head and neck, spine, breast, lung etc and how it affects our current clinical practice in terms of new prescription and dose escalation. 5. MC as a QA tool? Use of MC method as an independent and additional QA tool. Effect of QA by calculating dose in actual patient geometry rather than water phantom. Accounting for machine delivery uncertainty by reconstructing doses from machine log files etc Learning Objectives: 1. A review of MC methods with its application to radiation treatment planning for photon and electron beams 2. To understand the clinical implementation and commissioning of MC based TPS and issues related to its routine clinical operation 3. To understand the clinical significance of MC methods for different treatment sites and how it change our current clinical practice 4. To understand the use of MC TPS as an additional and independent QA tool in routine clinical practice.

MO‐G‐BRC‐02: Patient Specific Out‐Of‐Field Dose Calculation Tool for 6MV and 18MV: Development and Validation

Purpose: A factor that greatly limits the use of Monte Carlo methods for patient specific dose calculations in research is the substantial amount of effort required to define the Monte Carlo geometry of the actual treatment and patient setup. The purpose of this study was to develop and validate computational infrastructure to automatically convert radiation field parameters and computed tomography (CT) data from Digital Imaging and Communications (DICOM)format to Monte Carlo input format, and automate a Monte Carlo based dose calculation system for external beam photon radiation therapy. Methods: Computational infrastructure was developed using DCMTK, the DICOM tool kit. The dose calculation system (ADCS) was automated using a shell script. For validation of the ADCS, infield doses calculated by the ADCS were compared with those calculated by the treatment planning software using an eight‐field, 6 MV, step‐and‐shoot intensity modulated radiation therapy plan. Doses were calculated in a water phantom, and an anthropomorphic phantom based on CT data. Results: For each field, more than 95% of the dose voxels passed 3% and 3‐mm criteria of gamma‐index analysis of 3‐D dose distributions in the water phantom. The central axis depth dose curve agreed with in 15% (1stardard deviation) to the TPS calculations in the anthropomorphic phantom for a single representative field of the RT plan. Conclusions: The ADCS can accurately extract patient‐specific radiation treatment parameters and automatically incorporate them into the Monte Carlo format and create final dose distributions compatible with commercial treatment planning systems. This automated calculation infrastructure reduces the time required to define the Monte Carlo geometry and potential for human error. This system is capable of automating and calculating doses for conventional radiation therapy and 18MV. It is also capable of calculating out‐of‐field doses (region of interest in patient anatomy can be selected prior to running ADCS).

Prediction from the One‐Way Error Components Model with AR(1) Disturbances

ABSTRACTIn this paper we extend the works of Baillie and Baltagi (1999, in Analysis of Panels and Limited Dependent Variables Models, Hsiao C et al. (eds). Cambridge University Press: Cambridge, UK; 255–267) and generalize certain results from the Baltagi and Li (1992, Journal of Forecasting 11: 561–567) paper accounting for AR(1) errors in the disturbance term. In particular, we derive six predictors for the one‐way error components model, as well as their associated asymptotic mean squared error of multi‐step prediction in the presence of AR(1) errors in the disturbance term. In addition, we also provide both theoretical and simulation evidence as to the relative efficiency of our alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods and indicates that the ordinary optimal predictor performs well for various accuracy criteria. Copyright © 2011 John Wiley & Sons, Ltd.

Stochastic bifurcations in Duffing-van der Pol oscillator with Lévy stable noise

This paper aims to investigate the influence of Lévy stable noise on a bistable Duffing-van der Pol oscillator. We obtain the stationary probability density function of amplitude for the Duffing-van der Pol oscillator by use of Monte Carlo method, and analyze the influences of the noise intensity and the stability index on the stationary probability density. Stochastic bifurcations are further discussed though a qualitative change of the stationary probability distribution, which indicates that not only system parameters and noise intensity can be treated as bifurcation parameters, but also the change of the stability index will induce stochastic bifurcations.

Random Lattices for Option Pricing Problems in Finance

While the use of Monte Carlo methods is well established for pricing derivatives, this paper focuses on a random-lattice approach, also known in the literature as the stochastic-mesh method. The method is reviewed here. We show that the method may be refined with an ad-hoc bias correction, that suitably adjusts these models for accuracy. The paper presents experimental results, related analysis, and a set of applications, demonstrating easy applicability to popular choices for option pricing stochastic processes. The flexibility and ease of implementation of this approach, as seen from the examples, suggests that this approach has wide practical applicability.

Prediction from the regression model with two-way error components

In this paper we extend the Baillie and Baltagi (1999) paper (Prediction from the regression model with one-way error components. In Analysis of Panels and Limited Dependent Variables Models, Hsiao C, Lahiri K, Lee LF, Pesaran H (eds). Cambridge University Press, Cambridge, UK). In particular, we derive six predictors for the two-way error components model, as well as their associated asymptotic mean squared error (AMSE) of multi-step prediction. In addition, we also provide both theoretical and simulation evidence as to the relative efficiency of our six alternative predictors. The adequacy of the prediction AMSE formula is also investigated by the use of Monte Carlo methods which indicate that the ordinary optimal predictors perform well for various accuracy criteria. Copyright © 2010 John Wiley & Sons, Ltd.

TH‐D‐BRB‐02: Improving the Accuracy of Monte Carlo Spread‐Out Bragg Peak Treatment Head Models Using Beam Current Modulation Optimization

Purpose: For challenging clinical cases Monte Carlo methods can help to verify the accuracy of proton beam planning results based on the pencil beam algorithm. However the use of Monte Carlo methods as a verification tool requires an accurate model of the proton fields generated in the treatment head. This paper presents a novel approach to improve upon the achievable accuracy of Monte Carlo models of proton beam treatment heads. We will also demonstrate the usefulness of this approach by presenting commissioning results of two treatment head models at our facility. Methods and Materials: The achievable accuracy of our treatment head model was examined by quantifying the impact of uncertainties in material and beam parameters on SOPB dose distributions. A novel approach to improve this accuracy was developed by optimizing the beam current modulation function defined in our Monte Carlo model to produce clinically acceptable SOBP dose distributions. Finally this method was utilized for the commissioning two models of SOBP proton beam treatment heads at our facility. Results: Uncertainty in material density led to the largest impact on the SOBP dose distributions (less than 4%). The impact of mean ionization potential uncertainties was smaller (less than 2%). We did not detect a statistically significant influence of clinically relevant beam energy spreads and spot sizes on the SOBP dose distribution. The optimization of our beam current modulation functions allowed us to fully commission two treatment head models at our facility. Conclusion: Several proton beam therapy centers are now operational or being planned and constructed which promises the future need for Monte Carlo modeling of proton beam therapy treatment heads. The methodology and results presented in this paper will help accommodate the commissioning of new Monte Carlo simulated treatment heads.

TH-C-201B-04: Methods to Account for Imaging Doses from Diagnostic MDCT or Kilovoltage CBCT in Prostate Treatment Planning: Monte Carlo Studies Using Scanner Models and Patient-Specific Anatomy

Purpose: MDCT and kilovoltage CBCT are increasingly used in IGRT. AAPM TG‐75 has made a series of recommendations on imagingdoses. Recent studies have focused on an inclusion of imagingdoses using a radiation treatment planning (RTP) system. This paper describes the use of Monte Carlo methods to calculate imagingdose for a prostate IGRTtreatment case. Methods: An IMRT treatment plan per RTOG 0126 for a prostate carcinoma was used involving 28 initial fractions and a boost in 16 fractions. Constraints to rectum bladder and femoral heads were enforced. A total of 40 imaging procedures were considered involving a MDCT or a KVCBCT scanner that is operated at 250 mAs. A GE LightSpeed 16‐MDCT scanner and a Varian On‐Board Imager (OBI) were modeled with parameters reported in the literature. Planning CT images were used to construct a patient phantom within the MCNPX simulation environment. OARs and background voxels were categorixed into six tissue types. Results: For a total of 40 scans rectum received 69.7 cGy and 68.2 cGy from MDCT and KVCBCT respectively. The bladder received slightly greater doses 73.3 cGy and 69.3 cGy while the femoral heads received much higher doses 161.9 cGy and 125.7 cGy respectively. To investigate the impact MDCT imagingdoses are added to those from the original treatment plans and dose‐volume histograms evaluated. Among notable findings: the rectum dose after adding the imagingdoses may increase to or above the maximum acceptable level. Conclusion:Imagingdoses can reach the level that may require the adjustment of original planning in order to still satisfy the constraints. RTP systems may not be suitable for low‐energy X‐rays especially for bones and lungs. The data are expected to be useful to the newly formed AAPM TG‐180.

Dosimetric measurements and Monte Carlo simulation for achieving uniform surface dose in pulsed electron beam irradiation facility

A prototype pulsed electron beam irradiation facility for radiation processing of food and medical products is being commissioned at our centre in Indore, India. Analysis of surface dose and uniformity for a pulsed beam facility is of crucial importance because it is influenced by various operating parameters such as beam current, pulse repetition rate (PRR), scanning current profile and frequency, scanning width and product conveying speed. A large number of experiments are required to determine the harmonized setting of these operating parameters for achieving uniform dose. Since there is no readily available tool to set these parameters, use of Monte Carlo methods and computational tools can prove to be the most viable and time saving technique to support the assessment of the dose distribution. In the present study, Monte Carlo code, MCNP, is used to simulate the transport of 10 MeV electron beam through various mediums coming into the beam path and generate an equivalent dose profile in a polystyrene phantom for stationary state. These results have been verified with experimentally measured dose profile, showing that results are in good agreement within 4%. The Monte Carlo simulation further has been used to optimize the overlapping between the successive pulses of a scan to achieve ±5% dose uniformity along the scanning direction. A mathematical model, which uses the stationary state data, is developed to include the effect of conveyor speed. The algorithm of the model is discussed and the results are compared with the experimentally measured values, which show that the agreement is better than 15%. Finally, harmonized setting for operating parameters of the accelerator are derived to deliver uniform surface dose in the range of 1–13 kGy/pass.

Experimental design methodology for hardware-in-the-loop missile performance simulations

Hardware-in-the-loop (HWIL) simulation can be a cost effective and validated way to assess the performance of missiles. Current experimental practise for HWIL simulations favours the use of Monte Carlo methods. There are often a significant number of interrelated factors that may affect the outcome of a missile/target engagement. Proper consideration of all factors can lead to a large test matrix with associated large resource demands. There is a need to develop a methodology to reduce the number of simulations required and still achieve a statistically significant result. In this regard, the selection of the sample size or the number of replications required for each experimental condition based on scientific methods is important. This paper discusses ways to establish sample size and confidence levels for the results. The paper also discusses factorial design applied to HWIL simulations; in particular, how factorial design aids the analysis of factor effects and interactions.

MedLinac2: a GEANT4 based software package for radiotherapy.

Dose distribution evaluation in oncological radiotherapy treatments is an outstanding problem that requires sophisticated computing technologies to optimize the clinical results (i.e. increase the dose to the tumour and reduce the dose to the healthy tissues). Nowdays, dose calculation algorithms based on the Monte Carlo method are generally regarded as the most accurate tools for radiotherapy. The flexibility of the GEANT4 (GEometry ANd Tracking) Monte Carlo particle transport simulation code allows a wide range of applications, from high-energy to medical physics. In order to disseminate and encourage the use of Monte Carlo method in oncological radiotherapy, a software package based on the GEANT4 Monte Carlo toolkit has been developed. The developed package (MedLinac2) allows to simulate in an adequate flexible way a linear accelerator for radiotherapy and to evaluate the dose distributions.

System reliability analysis by enhanced Monte Carlo simulation

The main focus of this paper is on the development of a Monte Carlo based method for estimating the reliability of structural systems. The use of Monte Carlo methods for system reliability analysis has several attractive features, the most important being that the failure criterion is usually relatively easy to check almost irrespective of the complexity of the system. The flip side of such methods is the amount of computational efforts that may be involved. However, by reformulating the reliability problem to depend on a parameter and exploiting the regularity of the failure probability as a function of this parameter, it is shown that a substantial reduction of the computational efforts involved can be obtained.

Monte Carlo modelling of the influence of boron microdistribution on BNCT microdosimetry

The ion transport Monte Carlo code SRIM has been used to calculate single event lineal energy spectra for the products of the boron-neutron capture reaction in a water-based medium. The event spectra have been benchmarked against spectra measured with a boron-loaded tissue-equivalent proportional counter (TEPC). Agreement is excellent and supports the use of Monte Carlo methods in understanding the influence of boron delivery on the effectiveness of boron-neutron capture therapy (BNCT).

The use of Monte Carlo method in significance tests of Ripley's function outcome or how to avoid false discovery of nonrandom spatial structure of tree stand

Metoda Monte Carlo w badaniu istotności wyników funkcji Ripleya, czyli jak się ustrzec fałszywego stwierdzenia nielosowości struktury przestrzennej drzewostanu

Image-Based Monte Carlo Modeling of Ca2+ sparks in Ventricular Myocytes

The geometry of a dyadic calcium release unit (CRU) plays a critical role in regulating intracellular Ca2+ spark generation and furthermore the excitation-contraction (E-C) coupling in ventricular myocytes. A number of computational models have showed how the geometries of T-tubules and junctional sarcoplasmic reticulum (jSR) and distributions of L-type Ca2+ channels (LCCs) and ryanodine receptors (RyRs) could affect local Ca2+ signaling, but almost all of them were based on simple domain geometries such as rectangular or cylindrical shapes. For this reason, incorporating image-based realistic geometric models into mathematical simulation is timely and expected to provide more accurate simulation of many biological processes, in particular, the Ca2+ spark formation in ventricular myocytes.The present study has two goals. First, we adopt a chain of image and geometric processing approaches to construct realistic 3D models (represented by high-quality surface and volumetric meshes) of both T-tubules and junctional SR from electron microscopy images of adult mouse cardiomyocytes. The distributions of LCCs and RyRs are obtained in two ways: random assignment and image-based localization (for RyRs only). The second goal of the present study is the use of Monte Carlo methods to model the randomness of release and diffusion of individual Ca2+ ions within a narrow dyadic cleft. To this end, the M-Cell software package, combined with the realistic geometries obtained in the first goal, is used to simulate how Ca2+ sparks are generated in a single dyad (or CRU) and how they change temporally and spatially with different geometries of membrane structures and distributions of channeling proteins.