libamtrack-package: libamtrack package

Description Details Author(s) References See Also Examples

Description

This package is the R interface to the open-source, ANSI C library libamtrack. libamtrack provides computational routines for the prediction of detector response and relative biological efficiency in heavy charged particle beams. It is designed for research in proton and ion dosimetry and radiotherapy. libamtrack also provides many auxiliary physics routines for proton and ion beams. Please note that libamtrack is still under heavy development and so is the R interface. Function can be unstable especially when arguments are pushed out of their scope. If you experience any trouble your feed back is very appreciated: [email protected].

Details

Package: libamtrack
Version: 0.6.3 (Green Armadillo)
Date: 2015-12-12
Depends: R (>= 2.12.0)
Suggests: lattice
SystemRequirements: gsl (optinally: cernlib to enable energy loss distributions)
License: GPL (version 3 or later)

FUNCTION INDEX: Efficiency / RBE routines: These functions compute the relative efficiency / RBE of a mixed particle field according to a specific amorphous track model flavour and phyiscs

AT.run.GSM.method Grid-summation ('checkerboard') method
AT.run.IGK.method Ion-gamma-kill ('Katz') method
AT.run.CPPSC.method Compund-Poison process with successive convolutions (CPP-SC, 'SPIFF') method
AT.CPPSC.alpha.and.beta Alpha, beta for ion beams as predicted by CPP-SC method

Track-structure routines: These functions handle underlying physic used in amorphous track modeling:

AT.D.RDD.Gy Dose distribution around a particle track
AT.r.RDD.m Inverse dose distribution around a particle track
AT.max.electron.ranges.m Maximum electron range / track width
AT.gamma.response Gamma / X ray response of a system
AT.inverse.gamma.response Inverse Gamma / X ray response of a system

SPC routines: These functions provide an interface to spectral depth data:

AT.SPC.read Read in spc data from file
AT.SPC.spectrum.at.depth.step Extract spectrum at given depth step
AT.SPC.spectrum.at.depth.g.cm2 Extract spectrum at given depth (will use clostest step)
AT.SPC.tapply Applies a function to all depths of spc data

FLUKA routines: These function provide an interface to FLUKA output files:

AT.FLUKA.read.USRBIN.mesh Reads USRBIN output (Cartesian mesh, also for multiple runs).
AT.FLUKA.read.USRBIN.regs Reads USRBIN output (regions, also for multiple runs).
AT.FLUKA.read.USRTRACK Reads USRTRACK output (energy spectra, also for multiple runs).
AT.FLUKA.particle.name.to.libamtrack.particle.name Converts FLUKA particle names to libamtrack conventions.

Physics routines: These functions handle the physics of proton and ion beams needed in libamtrack. Stopping-power routines:

AT.Mass.Stopping.Power Electronic mass stopping power (data source by name)
AT.Stopping.Power Electronic stopping power (data source by name)
AT.Mass.Stopping.Power.with.no Electronic mass stopping power (data source by number)
AT.Stopping.Power.with.no Electronic stopping power (data source by number)
AT.stopping.power.ratio Computes stopping power ratios, also for mixed fields
TODO: Re-enable CSDA functions
AT.CSDA.range.g.cm2 CSDA range using PSTAR data

Mean LET / energy in mixed fields routines:

AT.fluence.weighted.LET.MeV.cm2.g Computes fluence-weighted LET
AT.dose.weighted.LET.MeV.cm2.g Computes dose-weighted LET
AT.fluence.weighted.E.MeV.u Computes fluence-weighted mean energy
AT.dose.weighted.E.MeV.u Computes dose-weighted mean energy

Dose / fluence conversions:

AT.dose.Gy.from.fluence.cm2 Compute dose(s) for given fluence(s) and particle(s)
AT.fluence.cm2.from.dose.Gy Compute fluence(s) given dose(s) and particle(s)
AT.total.D.Gy Computes total dose for a mixed field
AT.total.fluence.cm2 Computes total fluence for a mixed field

Beam related routines:

AT.beam.par.technical.to.physical For double Gaussian beam, converts FWHM and particle number into fluence and sigma width
AT.beam.par.physical.to.technical Inverse, converts fluence and sigma width into FWHM and particle number

Misc physics routines:

AT.momentum.MeV.c.u.from.E.MeV.u Momentum from kinetic energy
AT.E.MeV.u.from.momentum.MeV.c.u Kinetic energy from momentum
AT.effective.charge.from.E.MeV.u Effective charge of an ion depending on its kinetic energy
AT.max.E.transfer.MeV Max energy transfered from an ion to secondary electrons
AT.mean.number.of.tracks.contrib Mean number of tracks that desposite dose in a representative point
AT.Rutherford.SDCS Rutherford cross section of energy transferred to sec. electrons
AT.beta.from.E Relativistic beta of an ion with
AT.E.from.beta Kinetic energy for given beta
AT.gamma.from.E Relativistic gamma of an ion

Other routines:

AT.particle.name.from.particle.no Converts particle index numbers into particle names
AT.particle.no.from.particle.name Converts particle names into particle index numbers
AT.particle.no.from.Z.and.A Returns particle index number for given mass and atomic number
AT.A.from.particle.no Returns mass number for given particle number
AT.Z.from.particle.no Returns atomic number for given particle number
AT.nuclear.spin.from.particle.no Returns nuclear spin for given particle number
AT.electron.density.m3 Returns electron density from average Z and A
AT.electron.density.m3.from.material.no Returns electron density for given material
AT.material.name.from.material.no Converts material index numbers into material names
AT.material.no.from.material.name Converts material names into material index numbers
AT.electron.density.m3.from.composition Computes electron density from material composition
AT.average.A.from.composition Computes average mass number from material composition
AT.average.Z.from.composition Computes average atomic number from material composition
AT.effective.Z.from.composition Computes effective atomic number from material composition
AT.I.eV.from.composition Computes average I value from material composition
AT.set.user.material Sets properties of user defined material (CAVE: only valid until library is freed)
AT.set.user.material.from.composition Sets properties of user defined material from elemental composition (CAVE: only valid until library is freed)
AT.get.materials.data Returns properties of pre-defined materials

Helper routines shipped with libamtrack:

AT.add.leading.zeros Adds leading zeros to a character string representing a number
AT.add.trailing.zeros Adds trailing zeros to a character string representing a number

Author(s)

C2R autoconversion developed by: Felix Klein [email protected] Package maintainer: Steffen Greilich [email protected]

References

Greilich, Grzanka, Bassler, Andersen and Jakel, Amorphous track models: A numerical comparison study, doi:10.1016/j.radmeas.2010.05.039

See Also

http://libamtrack.dkfz.org

Examples

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##############################################################################
############################ 1. LET ##########################################
cat("Compute the LET (in keV/um) of a 270 MeV/u carbon ion in Aluminum\n")
cat("using the PSTAR stopping power data:\n")
AT.Stopping.Power( E.MeV.u               = 270,
                   particle.no           =
 AT.particle.no.from.particle.name("12C"),
                   material.no           =
 AT.material.no.from.material.name("Aluminum"),
                   stopping.power.source = "PSTAR")
                  
cat("... and in water:\n")
AT.Stopping.Power( E.MeV.u               = 270,
                   particle.no           =
 AT.particle.no.from.particle.name("12C"),
                   material.no           =
 AT.material.no.from.material.name("Water, Liquid"),
                   stopping.power.source = "PSTAR")
###############################################################################

# 2. DOSE AROUND A TRACK 
cat
("Compare the Geiss parametrization for protons and Carbon at different energies:\n"
)
df    <-    expand.grid(    E.MeV.u           = 10^seq(0, 3, length.out = 4), 
      # from 1 to 1000 MeV/u in 4 steps
                            particle.no       = c(1001,6012),                 
      # protons and carbons
                            r.m               = 10^seq(-9, -2, length.out =
 100),   # from 1 nm to 1 cm in 100 steps
                            material.no       = 2,                            
      # Aluminium Oxide
                            rdd.model         = 3,                            
      # Geiss parametrization
                            rdd.parameter     = 5e-8,                         
      # Fixed core size of 50 nm
                            er.model          = 4,                            
      # Geiss track width parametrization
                            D.Gy              = 0)                            
      # For later use
ii                   <-  df$particle.no == 1001                               
      # Add particle names
df$particle.name     <-  "Carbon-12"
df$particle.name[ii] <-  "Protons"
for (i in 1:nrow(df)){                                                        
      # Loop through particles/energies
    df$D.Gy[i]    <-    AT.D.RDD.Gy(     r.m              = df$r.m[i],
                                         E.MeV.u          = df$E.MeV.u[i],
                                         particle.no      = df$particle.no[i],
                                         material.no      = df$material.no[i],
                                         rdd.model        = df$rdd.model[i],
                                         rdd.parameter    =
 df$rdd.parameter[i],
                                         er.model         = df$er.model[i],
                                         stopping.power.source.no            
 = 2)[[1]]                           # use PSTAR data
}
lattice::xyplot( log10(D.Gy) ~ log10(r.m)|particle.name,                      
               # Plot
                 df,
                 type      = 'l',
                 groups    = E.MeV.u,
                 auto.key  = TRUE)[c(2,1)]
        
#####################################################################
#################### 3. DETECTOR EFFICIENCY #########################
cat("Compute the relative efficiency of Alanine in 10 MeV protons\n")
cat("Waligorskis version of the Katz' model\n")
AT.run.IGK.method( particle.no                          = 1001,               
         # namely protons with
                   E.MeV.u                              = 10,                 
         # 10 MeV/u
                   fluence.cm2.or.dose.Gy               = c(-1.0),            
         # delivering 1 Gy
                   material.no                          = 5,                  
         # i.e. Alanine
                   rdd.model                            = 4,                  
         
# Katz parametrization of radial dose distribution with simplified extended
# targets
                   rdd.parameter                        = c(5e-8,1e-10),      
         # with 50 nm target size and 1e-10 dose minimum
                   er.model                             = 2,                  
         # Butts&Katz parametrization of track radius
                   gamma.model                          = 2,                  
         # Use general target/hit model but here...
                   gamma.parameters                     = c(1,500,1,1,0),     
         # ...as exponential saturation with characteristic dose 500 Gy
                   saturation.cross.section.factor      = 1.4,                
         # factor to take 'brush' around track into account
                   write.output                         = TRUE,               
         # write a log file
                   stopping.power.source.no             = 2)                  
         # use PSTAR data

libamtrack documentation built on May 29, 2017, 9:28 a.m.