No abstract

Simulation of biological effects of ionizing radiation at the DNA scale requires not only the modeling of direct damages induced on DNA by the incident radiation and by secondary particles but also the modeling of indirect effects of radiolytic products resulting from liquid water radiolysis. They can provoke single, double strand breaks and base damage by reacting with DNA. The Geant4 Monte Carlo toolkit is currently being extended for the simulation of biological damages of ionizing radiation at the DNA scale in the framework of the "Geant4-DNA" project. Physics models for the modeling of direct effects are already available in Geant4. In the present paper, an approach for the modeling of radiation chemistry in pure liquid water within Geant4 is presented. In particular, this modeling includes Brownian motion and chemical reactions between molecules following water radiolysis. First results on time-dependent radiochemical yields from 1 picosecond up to 1 microsecond after irradiation are compared to published data and discussed.

International audienceGold nanoparticles have been reported as a possible radio-sensitizer agent in radiation therapy due totheir ability to increase energy deposition and subsequent direct damage to cells and DNA within theirlocal vicinity. Moreover, this increase in energy deposition also results in an increase of the radiochemicalyields. In this work we present, for the first time, an in silico investigation, based on the general purposeMonte Carlo simulation toolkit Geant4, into energy deposition and radical species production around aspherical gold nanoparticle 50 nm in diameter via proton irradiation. Simulations were preformed forincident proton energies ranging from 2 to 170 MeV, which are of interest for clinical proton therapy

Monte Carlo simulation (MCS) plays a key role in medical applications, especially for emission tomography and radiotherapy. However MCS is also associated with long calculation times that prevent its use in routine clinical practice. Recently, graphics processing units (GPU) became in many domains a low cost alternative for the acquisition of high computational power. The objective of this work was to develop an efficient framework for the implementation of MCS on GPU architectures. Geant4 was chosen as the MCS engine given the large variety of physics processes available for targeting different medical imaging and radiotherapy applications. In addition, Geant4 is the MCS engine behind GATE which is actually the most popular medical applications' simulation platform. We propose the definition of a global strategy and associated structures for such a GPU based simulation implementation. Different photon and electron physics effects are resolved on the fly directly on GPU without any approximations with respect to Geant4. Validations have shown equivalence in the underlying photon and electron physics processes between the Geant4 and the GPU codes with a speedup factor of 80-90. More clinically realistic simulations in emission and transmission imaging led to acceleration factors of 400-800 respectively compared to corresponding GATE simulations.

In single photon emission computed tomography (SPECT) with parallel hole collimation, image reconstruction is usually performed as a set of bidimensional (2D) analytical or iterative reconstructions. This approach ignores the tridimensional (3D) nature of scatter and detector response function that affects the detected signal. To deal with the 3D nature of the image formation process, iterative reconstruction can be used by considering a 3D projector modelling the 3D spread of photons. In this paper, we investigate the value of using accurate Monte Carlo simulations to determine the 3D projector used in a fully 3D Monte Carlo (F3DMC) reconstruction approach. Given the 3D projector modelling all physical effects affecting the imaging process, the reconstruction problem is solved using the maximum likelihood expectation maximization (MLEM) algorithm. To validate the concept, three data sets were simulated and F3DMC was compared with two other 3D reconstruction strategies using analytical corrections for attenuation, scatter and camera point spread function. Results suggest that F3DMC improves spatial resolution, relative and absolute quantitation and signal-to-noise ratio. The practical feasibility of the approach on real data sets is discussed.

No abstract

This work presents a Monte Carlo study of energy depositions due to protons, alpha particles and carbon ions of the same linear-energy-transfer (LET) in liquid water. The corresponding track structures were generated using the Geant4-DNA toolkit, and the energy deposition spatial distributions were analyzed using an adapted version of the DBSCAN clustering algorithm. Combining the Geant4 simulations and the clustering algorithm it was possible to compare the quality of the different radiation types. The ratios of clustered and single energy depositions are shown versus particle LET and frequency-mean lineal energies. The estimated effect of these types of radiation on biological tissues is then discussed by comparing the results obtained for different particles with the same LET.

This study presents new parameters for proton ionisation cross sections in guanine, adenine, thymine, and cytosine based upon the semi-empirical Rudd model. The same model was used to find differential electron cross sections considering a speed scaling procedure. To accelerate computation, the total electron cross sections were obtained using the binary-encounter-Bethe approximation instead of the integrated Rudd formula. The cross sections were implemented in the Geant4 simulation toolkit as Geant4-DNA processes, and simulations were carried out measuring protons lineal energies in spherical micrometric volumes filled with water, adenine, thymine, guanine, and cytosine. Large differences were seen in the lineal energies evaluated for the different materials, with the lineal energy measured in guanine being sometimes twice that of water. This suggests that the cross sections developed here should be considered in biological simulations where cellular substructures are modelled, in contrast to the current approach which approximates these volumes as consisting of liquid water.