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.

Radiotherapy Monte Carlo simulation using cloud computing technology

Cloud computing allows for vast computational resources to be leveraged quickly and easily in bursts as and when required. Here we describe a technique that allows for Monte Carlo radiotherapy dose calculations to be performed using GEANT4 and executed in the cloud, with relative simulation cost and completion time evaluated as a function of machine count. As expected, simulation completion time decreases as 1=n for n parallel machines, and relative simulation cost is found to be optimal where n is a factor of the total simulation time in hours. Using the technique, we demonstrate the potential usefulness of cloud computing as a solution for rapid Monte Carlo simulation for radiotherapy dose calculation without the need for dedicated local computer hardware as a proof of principal. Funding source Cancer Australia (Department of Health and Ageing) Research Grant 614217

Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Ridall et al. (2007) implement a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However we find that the approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. Here we focus on improved inference by marginalising over latent variables to create the likelihood. In particular we explore how this can improve the RJMCMC mixing and investigate alternative approaches that utilise the likelihood (e.g. DIC (Spiegelhalter et al., 2002)). For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. We provide a tractable and accurate approximation for this quantity and also investigate simulation approaches incorporated into RJMCMC using results of Andrieu and Roberts (2009).

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.

Faster Monte Carlo simulation of radiotherapy geometries

Using Monte Carlo simulation for radiotherapy dose calculation can provide more accurate results when compared to the analytical methods usually found in modern treatment planning systems, especially in regions with a high degree of inhomogeneity. These more accurate results acquired using Monte Carlo simulation however, often require orders of magnitude more calculation time so as to attain high precision, thereby reducing its utility within the clinical environment. This work aims to improve the utility of Monte Carlo simulation within the clinical environment by developing techniques which enable faster Monte Carlo simulation of radiotherapy geometries. This is achieved principally through the use new high performance computing environments and simpler alternative, yet equivalent representations of complex geometries. Firstly the use of cloud computing technology and it application to radiotherapy dose calculation is demonstrated. As with other super-computer like environments, the time to complete a simulation decreases as 1=n with increasing n cloud based computers performing the calculation in parallel. Unlike traditional super computer infrastructure however, there is no initial outlay of cost, only modest ongoing usage fees; the simulations described in the following are performed using this cloud computing technology. The definition of geometry within the chosen Monte Carlo simulation environment - Geometry & Tracking 4 (GEANT4) in this case - is also addressed in this work. At the simulation implementation level, a new computer aided design interface is presented for use with GEANT4 enabling direct coupling between manufactured parts and their equivalent in the simulation environment, which is of particular importance when defining linear accelerator treatment head geometry. Further, a new technique for navigating tessellated or meshed geometries is described, allowing for up to 3 orders of magnitude performance improvement with the use of tetrahedral meshes in place of complex triangular surface meshes. The technique has application in the definition of both mechanical parts in a geometry as well as patient geometry. Static patient CT datasets like those found in typical radiotherapy treatment plans are often very large and present a significant performance penalty on a Monte Carlo simulation. By extracting the regions of interest in a radiotherapy treatment plan, and representing them in a mesh based form similar to those used in computer aided design, the above mentioned optimisation techniques can be used so as to reduce the time required to navigation the patient geometry in the simulation environment. Results presented in this work show that these equivalent yet much simplified patient geometry representations enable significant performance improvements over simulations that consider raw CT datasets alone. Furthermore, this mesh based representation allows for direct manipulation of the geometry enabling motion augmentation for time dependant dose calculation for example. Finally, an experimental dosimetry technique is described which allows the validation of time dependant Monte Carlo simulation, like the ones made possible by the afore mentioned patient geometry definition. A bespoke organic plastic scintillator dose rate meter is embedded in a gel dosimeter thereby enabling simultaneous 3D dose distribution and dose rate measurement. This work demonstrates the effectiveness of applying alternative and equivalent geometry definitions to complex geometries for the purposes of Monte Carlo simulation performance improvement. Additionally, these alternative geometry definitions allow for manipulations to be performed on otherwise static and rigid geometry.

Calculation of dose kernels for radiotherapy applications using the GEANT 4 Monte Carlo toolkit

Dose kernels may be used to calculate dose distributions in radiotherapy (as described by Ahnesjo et al., 1999). Their calculation requires use of Monte Carlo methods, usually by forcing interactions to occur at a point. The Geant4 Monte Carlo toolkit provides a capability to force interactions to occur in a particular volume. We have modified this capability and created a Geant4 application to calculate dose kernels in cartesian, cylindrical, and spherical scoring systems. The simulation considers monoenergetic photons incident at the origin of a 3 m x 3 x 9 3 m water volume. Photons interact via compton, photo-electric, pair production, and rayleigh scattering. By default, Geant4 models photon interactions by sampling a physical interaction length (PIL) for each process. The process returning the smallest PIL is then considered to occur. In order to force the interaction to occur within a given length, L_FIL, we scale each PIL according to the formula: PIL_forced = L_FIL 9 (1 - exp(-PIL/PILo)) where PILo is a constant. This ensures that the process occurs within L_FIL, whilst correctly modelling the relative probability of each process. Dose kernels were produced for an incident photon energy of 0.1, 1.0, and 10.0 MeV. In order to benchmark the code, dose kernels were also calculated using the EGSnrc Edknrc user code. Identical scoring systems were used; namely, the collapsed cone approach of the Edknrc code. Relative dose difference images were then produced. Preliminary results demonstrate the ability of the Geant4 application to reproduce the shape of the dose kernels; median relative dose differences of 12.6, 5.75, and 12.6 % were found for an incident photon energy of 0.1, 1.0, and 10.0 MeV respectively.

Blocking layer optimisation of poly(3-hexylthiopene)based solid state dye sensitized solar cells

The optimisation study of the fabrication of a compact TiO2 blocking layer (via Spray Pyrolysis Deposition) for poly (3-hexylthiopene) (P3HT) for Solid State Dye Sensitized Solar Cells (SDSCs) is reported. We used a novel spray TiO2 precursor solution composition obtained by adding acetylacetone to a conventional formulation (Diisopropoxytitanium bis (acetylacetonate) in ethanol). By Scanning Electron Microscopy a TiO2 layer with compact morphology and thickness of around 100 nmis shown. Through a Tafel plot analysis an enhancement of the device diode-like behaviour induced by the acetylacetone blocking layer respect to the conventional one is observed. Significantly, the device fabricatedwith the acetylacetone blocking layer shows an overall increment of the cell performance with respect to the cellwith the conventional one (DJsc/Jsc = +13.8%, DFF/FF = +39.7%, DPCE/PCE = +55.6%). A conversion efficiency optimumis found for 15 successive spray cycles where the diode-like behaviour of the acetylacetone blocking layer is more effective. Over three batches of cells (fabricated with P3HT and dye D35) an average conversion efficiency value of 3.9% (under a class A sun simulator with 1 sun A.M. 1.5 illumination conditions) was measured. From the best cell we fabricated a conversion efficiency value of 4.5% was extracted. This represents a significant increment with respect to previously reported values for P3HT/dye D35 based SDSCs.

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.