dcemriS4 (version 0.55)

ADC.fast: Estimate the Apparent Diffusion Coefficient (ADC)

Description

Estimation of apparent diffusion coefficient (ADC) values, using a single exponential function, is achieved through nonlinear optimization.

Usage

ADC.fast(dwi, ...)
# S4 method for array
ADC.fast(dwi, bvalues, dwi.mask,
  control = minpack.lm::nls.lm.control(maxiter = 150), multicore = FALSE,
  verbose = FALSE)
adc.lm(signal, b, guess, control = minpack.lm::nls.lm.control())

Arguments

dwi

Multidimensional array of diffusion-weighted images.

...

Additional variables defined by the method.

dwi.mask

Logical array that defines the voxels to be analyzed.

control

An optional list of control settings for nls.lm. See nls.lm.control for the names of the settable control values and their effect.

multicore

is a logical variable (default = FALSE) that allows parallel processing via parallel.

verbose

Additional information will be printed when verbose=TRUE.

signal

Signal intensity vector as a function of b-values.

b,bvalues

Diffusion weightings (b-values).

guess

Initial values of \(S_0\) and \(D\).

Value

A list structure is produced with estimates of \(S_0\), \(D\) and information about the convergence of the nonlinear optimization routine.

Details

The adc.lm function estimates parameters for a vector of observed MR signal intensities using the following relationship $$S(b) = S_0 \exp(-bD),$$ where \(S_0\) is the baseline signal intensity and \(D\) is the apparent diffusion coefficient (ADC). It requires the routine nls.lm that applies the Levenberg-Marquardt algorithm. Note, low b-values (\(<50\) or \(<100\) depending on who you read) should be avoided in the parameter estimation because they do not represent information about the diffusion of water in tissue.

The ADC.fast function rearranges the assumed multidimensional (2D or 3D) structure of the DWI data into a single matrix to take advantage of internal R functions instead of loops, and called adc.lm.

References

Buxton, R.B. (2002) Introduction to Functional Magnetic Resonance Imaging: Principles & Techniques, Cambridge University Press: Cambridge, UK.

Callahan, P.T. (2006) Principles of Nuclear Magnetic Resonance Microscopy, Clarendon Press: Oxford, UK.

Koh, D.-M. and Collins, D.J. (2007) Diffusion-Weighted MRI in the Body: Applications and Challenges in Oncology, American Journal of Roentgenology, 188, 1622-1635.

See Also

nls.lm

Examples

# NOT RUN {
S0 <- 10
b <- c(0, 50, 400, 800)  # units?
D <- 0.7e-3              # mm^2 / sec (normal white matter)

## Signal intensities based on the (simplified) Bloch-Torry equation
dwi <- function(S0, b, D) {
  S0 * exp(-b*D)
}

set.seed(1234)
signal <- array(dwi(S0, b, D) + rnorm(length(b), sd=0.15),
                c(rep(1,3), length(b)))
ADC <- ADC.fast(signal, b, array(TRUE, rep(1,3)))
unlist(ADC) # text output

par(mfrow=c(1,1)) # graphical output
plot(b, signal, xlab="b-value", ylab="Signal intensity")
lines(seq(0,800,10), dwi(S0, seq(0,800,10), D), lwd=2, col=1)
lines(seq(0,800,10), dwi(c(ADC$S0), seq(0,800,10), c(ADC$D)), lwd=2, col=2)
legend("topright", c("True","Estimated"), lwd=2, col=1:2)
# }