Condensed-history Monte-Carlo simulation for charged particles: what can it do for us?

Abstract Condensed-history (CH) Monte-Carlo (MC) groups together the vast number of individual charged-particle collisions using multiple scattering theory for elastic angular changes and stopping power for energy losses. CH codes such as EGS4 have been enormously successful in simulating the transport of electrons, for example, in radiotherapy. MC-derived values of the water-to-air stopping-power ratio, sw/air, are used in all modern codes of practice for absolute dose determination in radiotherapy clinics. MC can also directly yield the dose ratio Dmed/Ddet for a dosimeter in a medium, and Correlated Sampling has been exploited to increase the efficiency, e. g., the central electrode in an ion chamber (aluminium vs. graphite). The extremely low density of the gas in an ion chamber poses problems for CH codes. However, multiple scattering can now be combined with single scattering and is expected to finally resolve important chamber perturbation effects. An exciting application of CH MC in radiotherapy is the computation of dose distributions in patients. Currently one can achieve an uncertainty around 1% (1 SD) in mm-sized voxels in several minutes for an electron beam and in around an hour for a photon treatment plan on hardware costing less than $20,000, and thus avoid all the various approximations conventionally used to account for inhomogeneities. In the microdosimetry/track structure field, CH codes have shown that the fluence (dΦ/dE) per unit dose at low electron energies is virtually independent of incident particle energy or depth, which simply explains the negligible RBE variation.

[1]  David W. O. Rogers,et al.  Low energy electron transport with EGS , 1984 .

[2]  M. Shur,et al.  Monte Carlo simulation of electron transport in gallium nitride , 1993 .

[3]  H. Neuenschwander,et al.  A macro Monte Carlo method for electron beam dose calculations , 1992 .

[4]  Martin J. Berger,et al.  Proton Monte Carlo Transport Program PTRAN , 1993 .

[5]  David W. O. Rogers,et al.  The Monte Carlo simulation of ion chamber response to 60Co-resolution of anomalies associated with interfaces , 1985 .

[6]  Iwan Kawrakow,et al.  On the condensed history technique for electron transport , 1998 .

[7]  I. Kawrakow,et al.  3D electron dose calculation using a Voxel based Monte Carlo algorithm (VMC). , 1996, Medical physics.

[8]  T. Mackie.  6 – Applications of the Monte Carlo Method in Radiotherapy , 1990 .

[9]  G. Gagliardi,et al.  Modeling heart and lung complication data in radiation therapy of the breast , 2000 .

[10]  David W. O. Rogers,et al.  Ion chamber response and Awall correction factors in a 60Co beam by Monte Carlo simulation , 1985 .

[11]  Anders Brahme,et al.  Restricted Energy-Loss Straggling and Multiple Scattering of Electrons in Mixed Monte Carlo Procedures , 1984 .

[12]  G X Ding,et al.  Calculation of stopping-power ratios using realistic clinical electron beams. , 1995, Medical physics.

[13]  C. Ma,et al.  BEAM: a Monte Carlo code to simulate radiotherapy treatment units. , 1995, Medical physics.

[14]  D T Goodhead,et al.  Energy deposition in small cylindrical targets by ultrasoft x-rays. , 1989, Physics in medicine and biology.

[15]  C. Ma,et al.  Monte Carlo calculations of electron beam output factors for a medical linear accelerator. , 1998, Physics in medicine and biology.

[16]  Pedro Andreo,et al.  Stopping-Power Ratios for Dosimetry , 1988 .

[17]  G. Ceresoli,et al.  Use of dose-volume histograms and biophysical models to compare 2D and 3D irradiation techniques for non-small cell lung cancer. , 1999, The British journal of radiology.

[18]  P Andreo,et al.  Monte Carlo calculated stopping-power ratios, water/air, for clinical proton dosimetry (50-250 MeV). , 1997, Physics in medicine and biology.

[19]  A. Nahum,et al.  Water/air mass stopping power ratios for megavoltage photon and electron beams. , 1978, Physics in medicine and biology.

[20]  F. H. Attix,et al.  5 – Monte Carlo Techniques of Electron and Photon Transport for Radiation Dosimetry , 1990 .

[21]  D. Rogers,et al.  EGS4 code system , 1985 .

[22]  A. Ito.  Electron Track Simulation For Microdosimetry , 1988 .

[23]  Stephen M. Seltzer,et al.  An Overview of ETRAN Monte Carlo Methods , 1988 .

[24]  C. Chui,et al.  A patient-specific Monte Carlo dose-calculation method for photon beams. , 1998, Medical physics.

[25]  P. Andreo.  Depth-dose and stopping-power data for mono-energetic electron beams , 1990 .

[26]  A. Nahum,et al.  Overview of Photon and Electron Monte Carlo , 1988 .

[27]  David W. O. Rogers,et al.  Presta: The parameter reduced electron-step transport algorithm for electron monte carlo transport , 1986 .

[28]  M Udale,et al.  A Monte Carlo investigation of surface doses for broad electron beams , 1988 .

[29]  J. F. Briesmeister.  MCNP-A General Monte Carlo N-Particle Transport Code , 1993 .

[30]  A F Bielajew,et al.  The effect of strong longitudinal magnetic fields on dose deposition from electron and photon beams. , 1993, Medical physics.

[31]  J. Medin.  Studies of clinical proton dosimetry using Monte Carlo simulation and experimental techniques , 1997 .

[32]  P. Andreo.  Monte Carlo techniques in medical radiation physics. , 1991, Physics in medicine and biology.

[33]  J. Halbleib,et al.  Structure and Operation of the ITS Code System , 1988 .