A sequential Monte Carlo approach to design for population pharmacokinetics studies

Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of phase III clinical trials where the derivation of sampling windows is required, along with the optimal sampling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions artificially constructed via an annealed utility. The algorithm derives a catalogue of highly efficient designs which, not only contain the optimal, but can also be used to derive sampling windows. We demonstrate our approach by designing a hypothetical phase III clinical trial.

Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to generalised non-linear models for discrete data. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. We also consider a flexible parametric model for the stimulus-response relationship together with a newly developed hybrid design utility that can produce more robust estimates of the target stimulus in the presence of substantial model and parameter uncertainty. The algorithm is applied to hypothetical clinical trial or bioassay scenarios. In the discussion, potential generalisations of the algorithm are suggested to possibly extend its applicability to a wide variety of scenarios

A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design

Here we present a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time Bayesian sequential design problem in the presence of model uncertainty where discrete data are encountered. Our focus is on adaptive design for model discrimination but the methodology is applicable if one has a different design objective such as parameter estimation or prediction. An SMC algorithm is run in parallel for each model and the algorithm relies on a convenient estimator of the evidence of each model which is essentially a function of importance sampling weights. Other methods for this task such as quadrature, often used in design, suffer from the curse of dimensionality. Approximating posterior model probabilities in this way allows us to use model discrimination utility functions derived from information theory that were previously difficult to compute except for conjugate models. A major benefit of the algorithm is that it requires very little problem specific tuning. We demonstrate the methodology on three applications, including discriminating between models for decline in motor neuron numbers in patients suffering from neurological diseases such as Motor Neuron disease.

Monte Carlo modeling of a 4 mm conical collimator for a Novalis Tx Linear Accelerator

The work presented in this poster outlines the steps taken to model a 4 mm conical collimator (BrainLab, Germany) on a Novalis Tx linear accelerator (Varian, Palo Alto, USA) capable of producing a 6MV photon beam for treatment of Stereotactic Radiosurgery (SRS) patients. The verification of this model was performed by measurements in liquid water and in virtual water. The measurements involved scanning depth dose and profiles in a water tank plus measurement of output factors in virtual water using Gafchromic® EBT3 film.

Experimental evaluation of MCDTK, the Monte Carlo DICOM Tool-Kit

The Monte Carlo DICOM Tool-Kit (MCDTK) is a software suite designed for treatment plan dose verification, using the BEAMnrc and DOSXYZnrc Monte Carlo codes. MCDTK converts DICOM-format treatment plan information into Monte Carlo input files and compares the results of Monte Carlo treatment simulations with conventional treatment planning dose calculations. In this study, a treatment is planned using a commercial treatment planning system, delivered to a pelvis phantom containing ten thermoluminescent dosimeters and simulated using BEAMnrc and DOSXYZnrc using inputs derived from MCDTK. The dosimetric accuracy of the Monte Carlo data is then evaluated via comparisons with the dose distribution obtained from the treatment planning system as well as the in-phantom point dose measurements. The simulated beam arrangement produced by MCDTK is found to be in geometric agreement with the planned treatment. An isodose display generated from the Monte Carlo data by MCDTK shows general agreement with the isodose display obtained from the treatment planning system, except for small regions around density heterogeneities in the phantom, where the pencil-beam dose calculation performed by the treatment planning systemis likely to be less accurate. All point dose measurements agree with the Monte Carlo data obtained using MCDTK, within confidence limits, and all except one of these point dose measurements show closer agreement with theMonte Carlo data than with the doses calculated by the treatment planning system. This study provides a simple demonstration of the geometric and dosimetric accuracy ofMonte Carlo simulations based on information from MCDTK.

A sequential Monte Carlo approach to the sequential design for discriminating between rival continuous data models

Here we present a sequential Monte Carlo approach to Bayesian sequential design for the incorporation of model uncertainty. The methodology is demonstrated through the development and implementation of two model discrimination utilities; mutual information and total separation, but it can also be applied more generally if one has different experimental aims. A sequential Monte Carlo algorithm is run for each rival model (in parallel), and provides a convenient estimate of the marginal likelihood (of each model) given the data, which can be used for model comparison and in the evaluation of utility functions. A major benefit of this approach is that it requires very little problem specific tuning and is also computationally efficient when compared to full Markov chain Monte Carlo approaches. This research is motivated by applications in drug development and chemical engineering.

The development of a Monte Carlo system to verify radiotherapy treatment dose calculations

Recent advances in the planning and delivery of radiotherapy treatments have resulted in improvements in the accuracy and precision with which therapeutic radiation can be administered. As the complexity of the treatments increases it becomes more difficult to predict the dose distribution in the patient accurately. Monte Carlo methods have the potential to improve the accuracy of the dose calculations and are increasingly being recognised as the “gold standard” for predicting dose deposition in the patient. In this study, software has been developed that enables the transfer of treatment plan information from the treatment planning system to a Monte Carlo dose calculation engine. A database of commissioned linear accelerator models (Elekta Precise and Varian 2100CD at various energies) has been developed using the EGSnrc/BEAMnrc Monte Carlo suite. Planned beam descriptions and CT images can be exported from the treatment planning system using the DICOM framework. The information in these files is combined with an appropriate linear accelerator model to allow the accurate calculation of the radiation field incident on a modelled patient geometry. The Monte Carlo dose calculation results are combined according to the monitor units specified in the exported plan. The result is a 3D dose distribution that could be used to verify treatment planning system calculations. The software, MCDTK (Monte Carlo Dicom ToolKit), has been developed in the Java programming language and produces BEAMnrc and DOSXYZnrc input files, ready for submission on a high-performance computing cluster. The code has been tested with the Eclipse (Varian Medical Systems), Oncentra MasterPlan (Nucletron B.V.) and Pinnacle3 (Philips Medical Systems) planning systems. In this study the software was validated against measurements in homogenous and heterogeneous phantoms. Monte Carlo models are commissioned through comparison with quality assurance measurements made using a large square field incident on a homogenous volume of water. This study aims to provide a valuable confirmation that Monte Carlo calculations match experimental measurements for complex fields and heterogeneous media.

Multiple scattering in deep inelastic neutron scattering : Monte Carlo simulations and experiments at the ISIS eVS inverse geometry spectrometer

A procedure for the evaluation of multiple scattering contributions is described, for deep inelastic neutron scattering (DINS) studies using an inverse geometry time-of-flight spectrometer. The accuracy of a Monte Carlo code DINSMS, used to calculate the multiple scattering, is tested by comparison with analytic expressions and with experimental data collected from polythene, polycrystalline graphite and tin samples. It is shown that the Monte Carlo code gives an accurate representation of the measured data and can therefore be used to reliably correct DINS data.

Monte Carlo-based diode design for correction-less small field dosimetry

Due to their small collecting volume diodes are commonly used in small field dosimetry. However the relative sensitivity of a diode increases with decreasing small field size. Conversely, small air gaps have been shown to cause a significant decrease in the sensitivity of a detector as the field size is decreased. Therefore this study uses Monte Carlo simulations to look at introducing air upstream to diodes such that they measure with a constant sensitivity across all field sizes in small field dosimetry. Varying thicknesses of air were introduced onto the upstream end of two commercial diodes (PTW 60016 photon diode and PTW 60017 electron diode), as well as a theoretical unenclosed silicon chip using field sizes as small as 5 mm × 5 mm . The metric D_(w,Q)/D_(Det,Q) used in this study represents the ratio of the dose to a point of water to the dose to the diode active volume, for a particular field size and location. The optimal thickness of air required to provide a constant sensitivity across all small field sizes was found by plotting D_(w,Q)/D_(Det,Q) as a function of introduced air gap size for various field sizes, and finding the intersection point of these plots. That is, the point at which D_(w,Q)/D_(Det,Q) was constant for all field sizes was found. The optimal thickness of air was calculated to be 3.3 mm, 1.15 mm and 0.10 mm for the photon diode, electron diode and unenclosed silicon chip respectively. The variation in these results was due to the different design of each detector. When calculated with the new diode design incorporating the upstream air gap, k_(Q_clin 〖,Q〗_msr)^(f_clin 〖,f〗_msr ) was equal to unity to within statistical uncertainty (0.5 %) for all three diodes. Cross-axis profile measurements were also improved with the new detector design. The upstream air gap could be implanted on the commercial diodes via a cap consisting of the air cavity surrounded by water equivalent material. The results for the unclosed silicon chip show that an ideal small field dosimetry diode could be created by using a silicon chip with a small amount of air above it.

Monte Carlo verification of intensity modulated radiotherapy treatments using EPIDS

Purpose: Electronic Portal Imaging Devices (EPIDs) are available with most linear accelerators (Amonuk, 2002), the current technology being amorphous silicon flat panel imagers. EPIDs are currently used routinely in patient positioning before radiotherapy treatments. There has been an increasing interest in using EPID technology tor dosimetric verification of radiotherapy treatments (van Elmpt, 2008). A straightforward technique involves the EPID panel being used to measure the fluence exiting the patient during a treatment which is then compared to a prediction of the fluence based on the treatment plan. However, there are a number of significant limitations which exist in this Method: Resulting in a limited proliferation ot this technique in a clinical environment. In this paper, we aim to present a technique of simulating IMRT fields using Monte Carlo to predict the dose in an EPID which can then be compared to the measured dose in the EPID. Materials: Measurements were made using an iView GT flat panel a-SI EPfD mounted on an Elekta Synergy linear accelerator. The images from the EPID were acquired using the XIS software (Heimann Imaging Systems). Monte Carlo simulations were performed using the BEAMnrc and DOSXVZnrc user codes. The IMRT fieids to be delivered were taken from the treatment planning system in DICOMRT format and converted into BEAMnrc and DOSXYZnrc input files using an in-house application (Crowe, 2009). Additionally. all image processing and analysis was performed using another in-house application written using the Interactive Data Language (IDL) (In Visual Information Systems). Comparison between the measured and Monte Carlo EPID images was performed using a gamma analysis (Low, 1998) incorporating dose and distance to agreement criteria. Results: The fluence maps recorded by the EPID were found to provide good agreement between measured and simulated data. Figure 1 shows an example of measured and simulated IMRT dose images and profiles in the x and y directions. "A technique for the quantitative evaluation of dose distributions", Med Phys, 25(5) May 1998 S. Crowe, 1. Kairn, A. Fielding, "The Development of a Monte Carlo system to verify Radiotherapy treatment dose calculations", Radiotherapy & Oncology, Volume 92, Supplement 1, August 2009, Pages S71-S71.