AT.gamma.response: AT.gamma.response

In libamtrack: Computational Routines for Proton and Ion Radiotherapy

Description

Returns a system (detector or cells) response for given doses according to the chosen gamma response model

Usage

AT.gamma.response(d.Gy, gamma.model, gamma.parameter, lethal.event.mode)

Arguments

d.Gy	doses in Gy (array of size number.of.doses).
gamma.model	gamma response model index.
gamma.parameter	vector holding necessary parameters for the chose gamma response model (array of size 9).
lethal.event.mode	if true computation is done in lethal event mode.

Value

S	gamma responses (array of size number.of.doses)

Examples

# Show the gamma response of two Al2O3 detectors (A & B) and two protocols
# ('peak' and 'total')

# parametrized in two components (single hit/single target and two hit/single
# target)
# as measured and published by Edmund et al., NIM B 262 (2007), 261-275
require(lattice)
# Compute 100 points between 0.1 and 25 Gy
# General hit/target model
d.Gy                    <- 10^seq(from = log10(0.1), to = log10(25),
 length.out = 100)     
gamma.model		<- 2
# Probe A, 'peak'
R			<- 1
Smax			<- 0.81e6
k1			<- Smax * (R / 100)
k2			<- Smax * (1 - R / 100)
gamma.parameter.peak.A	<- c( k1 = k1, D01 = 0.36, c1 = 1, m1 = 1,
			      k2 = k2, D02 = 3.06, c2 = 2, m2 = 1,
			      0)
# Probe A, 'total'
R			<- 33
Smax			<- 6.2e6
k1			<- Smax * (R / 100)
k2			<- Smax * (1 - R / 100)
gamma.parameter.total.A	<- c( k1 = k1, D01 = 1.13, c1 = 1, m1 = 1,
			      k2 = k2, D02 = 1.77, c2 = 2, m2 = 1,
			      0)
# Probe B, 'peak'
R			<- 13 
Smax			<- 2.84e6
k1			<- Smax * (R / 100)
k2			<- Smax * (1 - R / 100)
gamma.parameter.peak.B	<- c( k1 = k1, D01 = 4.15, c1 = 1, m1 = 1,
			      k2 = k2, D02 = 5.14, c2 = 2, m2 = 1,
			      0)
# Probe B, 'total'
R			<- 44
Smax			<- 27.6e6
k1			<- Smax * (R / 100)
k2			<- Smax * (1 - R / 100)
gamma.parameter.total.B	<- c( k1 = k1, D01 = 2.90, c1 = 1, m1 = 1,
			      k2 = k2, D02 = 4.66, c2 = 2, m2 = 1,
			      0)
vecA <- AT.gamma.response( d.Gy              = d.Gy,
			   gamma.model       = gamma.model,
			   gamma.parameter   = gamma.parameter.peak.A,
                           lethal.event.mode = FALSE)$response
vecB <- AT.gamma.response( d.Gy              = d.Gy,
			   gamma.model       = gamma.model,
			   gamma.parameter   = gamma.parameter.total.A,
                           lethal.event.mode = FALSE)$response
vecC <- AT.gamma.response( d.Gy              = d.Gy,
			   gamma.model       = gamma.model,
			   gamma.parameter   = gamma.parameter.peak.B,
                           lethal.event.mode = FALSE)$response
vecD <- AT.gamma.response( d.Gy              = d.Gy,
			   gamma.model       = gamma.model,
			   gamma.parameter   = gamma.parameter.total.B,
                           lethal.event.mode = FALSE)$response
# Compose data frame
df   <- data.frame( d.Gy    = rep( d.Gy, 4), 
		    S       = c(vecA, vecB, vecC, vecD ),
		    which   = rep( c( rep("peak", length(d.Gy)),
		                      rep("total", length(d.Gy))), 2),
		    probe   = c( rep("probe A", 2 * length(d.Gy)),
				 rep("probe B", 2 * length(d.Gy))))
# Plot
xyplot(	log10(S) ~ log10(d.Gy)|probe,
        df,
        groups	= which,
	type	= 'l',
	lwd	= 2,
	ylim	= log10(c(1e3, 4e7)),
	ylab	= list(	"OSL response", cex = 1.2),
	xlim	= log10(c(0.1, 25)),
	xlab	= list(	"dose / Gy", cex = 1.2),
	scales	= list(	x = list( at     = log10(c(1,10,20)), 
                                  labels = as.character(c(1,10,20))), 
                        y = list( at     = c(4,5,6,7), 
                                  labels = 10^(c(4,5,6,7)))),
	aspect	= 2.5)

libamtrack documentation built on May 1, 2019, 6:47 p.m.