Package 'cmR'

Bullseye plot

Description

Bullseye plot

Usage

bullseye(x, lim = NULL, reverse = TRUE, legend = TRUE, text = TRUE, cex = 1)

Arguments

x	vector of length 16 or 17
lim	limits of x values
reverse	boolean, reverse colors?
legend	boolean, add legend?
text	boolean, should text legend be added?
cex	cex for text legend

Value

plot

Examples

bullseye(1:16)

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Bayesian analysis of cardiovascular magnetic resonance imaging

Description

Bayesian analysis of cardiovascular magnetic resonance imaging

Usage

cmr(
  data,
  input,
  mask = NULL,
  method = "spatial",
  quantiles = c(0.25, 0.75),
  cores = parallel::detectCores()
)

Arguments

data	3D or 4D array of CMR signal
input	input function
mask	2d array of mask. Voxel with 0 or FALSE will be omitted from analysis. Default NULL: use NA values in data as mask
method	"spatial" or "local"
quantiles	quantiles used for credible interval, default: c(0.25, 0.75)
cores	number of cores for parallel computation. Spatial model only computes slices parallel, local can be parallelized on voxel level

Value

list of mbf (point estimation) and ci (credible interval)

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Spline analysis of cardiovascular magnetic resonance imaging

Description

Spline analysis of cardiovascular magnetic resonance imaging

Usage

cmr.local(data, mask, input, quantiles = c(0.25, 0.75), cores = 1)

Arguments

data	3d array of CMR signal
mask	2d array of mask. Voxel with 0 or FALSE will be omitted from analysis
input	input function
quantiles	quantiles used for credible interval, default: c(0.25, 0.75)
cores	number of cores to use in parallel computing

Value

list of mbf (point estimation) and ci (credible interval)

Examples

oldpar <- par(no.readonly = TRUE)
 library(cmR)
 data(cmrsim)
 local.mbf=local.ci=array(NA,c(30,30,3))
 for (i in 1:3){
  mask=array(NA,c(30,30))
  mask[cmrdata_sim[,,i,1]!=0]=1
  temp=cmr.local(cmrdata_sim[,,i,], mask, input_sim, cores=2)
  local.mbf[,,i]=t(as.matrix(temp$mbf))
  local.ci[,,i]=t(as.matrix(temp$ci))
 }
 par(mfrow=c(2,1))
 imageMBF(maxresp_sim, zlim=c(0,5))
 imageMBF(local.mbf, zlim=c(0,5))
 imageMBF(local.ci, zlim=c(0,0.8))
par(oldpar)

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Spatial spline analysis of cardiovascular magnetic resonance imaging

Description

Spatial spline analysis of cardiovascular magnetic resonance imaging

Usage

cmr.space(data, mask, input, quantiles = c(0.25, 0.75))

Arguments

data	3d array of CMR signal
mask	2d array of mask. Voxel with 0 or FALSE will be omitted from analysis
input	input function
quantiles	quantiles used for credible interval, default: c(0.25, 0.75)

Value

list of mbf (point estimation) and ci (credible interval)

Examples

oldpar <- par(no.readonly = TRUE)
 library(cmR)
 data(cmrsim)
 mask=array(NA,c(30,30))
 space.mbf=space.ci=array(NA,c(30,30,3))
 for (i in 1:3){
  mask=array(NA,c(30,30))
  mask[cmrdata_sim[,,i,1]!=0]=1
  temp=cmr.space(cmrdata_sim[,,i,], mask, input_sim)
  space.mbf[,,i]=t(as.matrix(temp$mbf))
  space.ci[,,i]=t(as.matrix(temp$ci))
  }
 par(mfrow=c(2,1))
 imageMBF(maxresp_sim, zlim=c(0,5))
 imageMBF(space.mbf, zlim=c(0,5))
 imageMBF(space.ci, zlim=c(0,0.8))
 par(oldpar)

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Simulated data for CMR package.

Description

This data set is provided as example for the usage of the cmR package. cmrdata_sim is a simulated CMR image.

Usage

cmrdata_sim

Format

A 4D array, 30x30 pixels for 3 slices at 30 time points.

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Plotting of (voxelwise) cardiac MBF

Description

Plotting of (voxelwise) cardiac MBF

Usage

imageMBF(img, zlim = NULL, reverse = TRUE)

Arguments

img	3d array ob MBF values
zlim	limits of MBF, default: NULL means zlim=c(0,max(img,na.rm=TRUE))
reverse	reverse color scheme

Value

plots

Examples

data(cmrsim)
imageMBF(maxresp_sim)

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Simulated data for CMR package.

Description

This data set is provided as example for the usage of the cmR package. input_sim is the simulated input function.

Usage

input_sim

Format

Vector for 30 time points.

---

Simulated data for CMR package.

Description

This data set is provided as example for the usage of the cmR package. maxresp_sim is the true maximum response used in the simulation.

Usage

maxresp_sim

Format

A 3D array, 30x30 pixels for 3 slices.

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Pseudo bullseye plot

Description

Pseudo bullseye plot

Usage

pseudobullseye(
  x,
  lim = range(x, na.rm = TRUE),
  legend = FALSE,
  text = TRUE,
  reverse = FALSE,
  center = TRUE,
  cex = 1,
  legend.width = 1
)

Arguments

x	3D array
lim	limits of x values
legend	boolean, add legend?
text	boolean, should text legend be added?
reverse	boolean, reverse colors?
center	boolean, should input x be centered before plotting
cex	cex for text legend
legend.width	Width in characters of the legend strip.

Value

plots

Examples

data(cmrsim)
pseudobullseye(maxresp_sim)

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Draw random vectors from multivariate Gaussian in canonical form

Description

Draw random vectors from multivariate Gaussian in canonical form

Usage

rmvnormcanon(n, b, P)

Arguments

n	Number of draws
b	b parameter
P	Precision matrix

Value

matrix with n columns, vector if n=1

Examples

P<-matrix(c(1,.5,.5,1),ncol=2)
b=c(2,0)
# expected value and covariance matrix
Sigma = solve(P)
mu = b%*%Sigma
# sample
x<-rmvnormcanon(1000,b,P)
mu.hat=apply(x,1,mean)
print(mu.hat-mu)
Sigma.hat=var(t(x))
print(Sigma.hat-Sigma)

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