Biomedical subjects

J Sempau

Publications and source records attributed to J Sempau.

6 recordsLinked to original sources

PubMed2006

Configuration of the electron transport algorithm of PENELOPE to simulate ion chambers.

The stability of the electron transport algorithm implemented in the Monte Carlo code PENELOPE with respect to variations of its step length is analysed in the context of the simulation of ion chambers used in photon and electron dosimetry. More precisely, the degree of violation of the Fano theorem is quantified (to the 0.1% level) as a function of the simulation parameters that determine the step size. To meet the premises of the theorem, we define an infinite graphite phantom with a cavity delimited by two parallel planes (i.e., a slab) and filled with a 'gas' that has the same composition as graphite but a mass density a thousand-fold smaller. The cavity walls and the gas have identical cross sections, including the density effect associated with inelastic collisions. Electrons with initial kinetic energies equal to 0.01, 0.1, 1, 10 or 20 MeV are generated in the wall and in the gas with a uniform intensity per unit mass. Two configurations, motivated by the design of pancake- and thimble-type chambers, are considered, namely, with the initial direction of emission perpendicular or parallel to the gas-wall interface. This version of the Fano test avoids the need of photon regeneration and the calculation of photon energy absorption coefficients, two ingredients that are common to some alternative definitions of equivalent tests. In order to reduce the number of variables in the analysis, a global new simulation parameter, called the speedup parameter (a), is introduced. It is shown that setting a = 0.2, corresponding to values of the usual PENELOPE parameters of C1 = C2 = 0.02 and values of WCC and WCR that depend on the initial and absorption energies, is appropriate for maximum tolerances of the order of 0.2% with respect to an analogue, i.e., interaction-by-interaction, simulation of the same problem. The precise values of WCC and WCR do not seem to be critical to achieve this level of accuracy. The step-size dependence of the absorbed dose is explained in the light of the properties of PENELOPE's transport mechanics. This work is intended to help users to adopt an optimal configuration that guarantees both a high-accuracy calculation of the absorbed dose and a reasonably short computing time.

Algorithms↗

PubMed2004

Study of the point spread function (PSF) for 123I SPECT imaging using Monte Carlo simulation.

The iterative reconstruction algorithms employed in brain single-photon emission computed tomography (SPECT) allow some quantitative parameters of the image to be improved. These algorithms require accurate modelling of the so-called point spread function (PSF). Nowadays, most in vivo neurotransmitter SPECT studies employ pharmaceuticals radiolabelled with 123I. In addition to an intense line at 159 keV, the decay scheme of this radioisotope includes some higher energy gammas which may have a non-negligible contribution to the PSF. The aim of this work is to study this contribution for two low-energy high-resolution collimator configurations, namely, the parallel and the fan beam. The transport of radiation through the material system is simulated with the Monte Carlo code PENELOPE. We have developed a main program that deals with the intricacies associated with tracking photon trajectories through the geometry of the collimator and detection systems. The simulated PSFs are partly validated with a set of experimental measurements that use the 511 keV annihilation photons emitted by a 18F source. Sensitivity and spatial resolution have been studied, showing that a significant fraction of the detection events in the energy window centred at 159 keV (up to approximately 49% for the parallel collimator) are originated by higher energy gamma rays, which contribute to the spatial profile of the PSF mostly outside the 'geometrical' region dominated by the low-energy photons. Therefore, these high-energy counts are to be considered as noise, a fact that should be taken into account when modelling PSFs for reconstruction algorithms. We also show that the fan beam collimator gives higher signal-to-noise ratios than the parallel collimator for all the source positions analysed.

Fluorine Radioisotopes↗

PubMed2001

Monte Carlo simulation of electron beams from an accelerator head using PENELOPE.

The Monte Carlo code PENELOPE has been used to simulate electron beams from a Siemens Mevatron KDS linac with nominal energies of 6, 12 and 18 MeV. Owing to its accuracy, which stems from that of the underlying physical interaction models, PENELOPE is suitable for simulating problems of interest to the medical physics community. It includes a geometry package that allows the definition of complex quadric geometries, such as those of irradiation instruments, in a straightforward manner. Dose distributions in water simulated with PENELOPE agree well with experimental measurements using a silicon detector and a monitoring ionization chamber. Insertion of a lead slab in the incident beam at the surface of the water phantom produces sharp variations in the dose distributions, which are correctly reproduced by the simulation code. Results from PENELOPE are also compared with those of equivalent simulations with the EGS4-based user codes BEAM and DOSXYZ. Angular and energy distributions of electrons and photons in the phase-space plane (at the downstream end of the applicator) obtained from both simulation codes are similar, although significant differences do appear in some cases. These differences, however, are shown to have a negligible effect on the calculated dose distributions. Various practical aspects of the simulations, such as the calculation of statistical uncertainties and the effect of the 'latent' variance in the phase-space file, are discussed in detail.

Dose-Response Relationship, Radiation↗

PubMed2000

Towards the elimination of Monte Carlo statistical fluctuation from dose volume histograms for radiotherapy treatment planning.

The Monte Carlo calculation of dose for radiotherapy treatment planning purposes introduces unavoidable statistical noise into the prediction of dose in a given volume element (voxel). When the doses in these voxels are summed to produce dose volume histograms (DVHs), this noise translates into a broadening of differential DVHs and correspondingly flatter DVHs. A brute force approach would entail calculating dose for long periods of time--enough to ensure that the DVHs had converged. In this paper we introduce an approach for deconvolving the statistical noise from DVHs, thereby obtaining estimates for converged DVHs obtained about 100 times faster than the brute force approach described above. There are two important implications of this work: (a) decisions based upon DVHs may be made much more economically using the new approach and (b) inverse treatment planning or optimization methods may employ Monte Carlo dose calculations at all stages of the iterative procedure since the prohibitive cost of Monte Carlo calculations at the intermediate calculation steps can be practically eliminated.

Algorithms↗

PubMed2000

DPM, a fast, accurate Monte Carlo code optimized for photon and electron radiotherapy treatment planning dose calculations.

A new Monte Carlo (MC) algorithm, the 'dose planning method' (DPM), and its associated computer program for simulating the transport of electrons and photons in radiotherapy class problems employing primary electron beams, is presented. DPM is intended to be a high accuracy MC alternative to the current generation of treatment planning codes which rely on analytical algorithms based on an approximate solution of the photon/electron Boltzmann transport equation. For primary electron beams, DPM is capable of computing 3D dose distributions (in 1 mm3 voxels) which agree to within 1% in dose maximum with widely used and exhaustively benchmarked general-purpose public-domain MC codes in only a fraction of the CPU time. A representative problem, the simulation of 1 million 10 MeV electrons impinging upon a water phantom of 128(3) voxels of 1 mm on a side, can be performed by DPM in roughly 3 min on a modern desktop workstation. DPM achieves this performance by employing transport mechanics and electron multiple scattering distribution functions which have been derived to permit long transport steps (of the order of 5 mm) which can cross heterogeneity boundaries. The underlying algorithm is a 'mixed' class simulation scheme, with differential cross sections for hard inelastic collisions and bremsstrahlung events described in an approximate manner to simplify their sampling. The continuous energy loss approximation is employed for energy losses below some predefined thresholds, and photon transport (including Compton, photoelectric absorption and pair production) is simulated in an analogue manner. The delta-scattering method (Woodcock tracking) is adopted to minimize the computational costs of transporting photons across voxels.

Algorithms↗

PubMed1999

Practical aspects of Monte Carlo simulation of charged particle transport: mixed algorithms and variance reduction techniques.

This paper is concerned with the practical implementation of Monte Carlo simulation methods for charged particle transport. The emphasis is on light particles (electrons and positrons) because of the larger scattering and energy straggling effects. Differential cross sections (DCS) for the various interaction mechanisms are described. As the average number of interactions along the particle track increases with the initial energy, detailed simulation becomes unfeasible at high energies. We can then rely on mixed simulation algorithms: hard events (i.e. individual interactions with angular deflection or energy loss larger than given cutoff values) are sampled from the DCS whereas soft events are simulated by means of a multiple scattering approach. Too frequently, the statistical uncertainty of analogue simulation (i.e. strict simulation of the physical interaction process) is found to be so large that results are meaningless. This problem can be partially solved by applying simple variance reduction techniques.

Algorithms↗