Nothing
##
##
## Copyright (c) 2009, Brandon Whitcher and Volker Schmid
## All rights reserved.
##
## Redistribution and use in source and binary forms, with or without
## modification, are permitted provided that the following conditions are
## met:
##
## * Redistributions of source code must retain the above copyright
## notice, this list of conditions and the following disclaimer.
## * Redistributions in binary form must reproduce the above
## copyright notice, this list of conditions and the following
## disclaimer in the documentation and/or other materials provided
## with the distribution.
## * The names of the authors may not be used to endorse or promote
## products derived from this software without specific prior
## written permission.
##
## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
## "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
## LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
## A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
## HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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## OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
##
## $Id: dwi.R 332 2010-01-29 16:54:07Z bjw34032 $
##
#############################################################################
## adc.lm() = estimate ADC using Levenburg-Marquardt
#############################################################################
adc.lm <- function(signal, b, guess, control=nls.lm.control()) {
func <- function(x, y) {
S0 <- x[1]
D <- x[2]
signal <- y[[1]]
b <- y[[2]]
signal - S0 * exp(-b*D)
}
require("minpack.lm") # Levenberg-Marquart fitting
out <- nls.lm(par=guess, fn=func, control=control, y=list(signal, b))
list(S0=out$par[1], D=out$par[2], hessian=out$hessian, info=out$info,
message=out$message)
}
#############################################################################
## setGeneric("ADC.fast")
#############################################################################
setGeneric("ADC.fast", function(dwi, ...) standardGeneric("ADC.fast"))
setMethod("ADC.fast", signature(dwi="array"),
function(dwi, bvalues, dwi.mask,
control=nls.lm.control(maxiter=150),
multicore=FALSE, verbose=FALSE)
.dcemriWrapper("ADC.fast", dwi, bvalues, dwi.mask, control,
multicore, verbose))
.ADC.fast <- function(dwi, bvalues, dwi.mask,
control=nls.lm.control(maxiter=150),
multicore=FALSE, verbose=FALSE) {
if (length(dim(dwi)) != 4) { # Check dwi is a 4D array
stop("Diffusion-weighted data must be a 4D array.")
}
if (!is.logical(dwi.mask)) { # Check dyn.mask is logical
stop("Mask must be logical.")
}
nvalues <- length(bvalues)
nvoxels <- sum(dwi.mask)
if (verbose) {
cat(" Deconstructing data...", fill=TRUE)
}
dwi.mat <- matrix(dwi[dwi.mask], nvoxels)
dwi.list <- vector("list", nvoxels)
for (k in 1:nvoxels) {
dwi.list[[k]] <- dwi.mat[k,]
}
if (verbose) {
cat(" Calculating S0 and D...", fill=TRUE)
}
if (multicore && require("parallel")) {
fit.list <- mclapply(dwi.list, function(x) {
adc.lm(x, bvalues, guess=c(0.75*x[1], 0.001), control)
})
} else {
fit.list <- lapply(dwi.list, function(x) {
adc.lm(x, bvalues, guess=c(0.75*x[1], 0.001), control)
})
}
rm(dwi.list) ; gc()
S0 <- D <- list(par=rep(NA, nvoxels), error=rep(NA, nvoxels))
for (k in 1:nvoxels) {
if (fit.list[[k]]$info > 0 && fit.list[[k]]$info < 5) {
S0$par[k] <- fit.list[[k]]$S0
D$par[k] <- fit.list[[k]]$D
S0$error[k] <- sqrt(fit.list[[k]]$hessian[1,1])
D$error[k] <- sqrt(fit.list[[k]]$hessian[2,2])
} else {
S0$par[k] <- D$par[k] <- S0$error[k] <- D$error[k] <- NA
}
}
rm(fit.list) ; gc()
if (verbose) {
cat(" Reconstructing results...", fill=TRUE)
}
S0.array <- D.array <- S0error <- Derror <- array(NA, dim(dwi)[1:3])
S0.array[dwi.mask] <- S0$par
D.array[dwi.mask] <- D$par
S0error[dwi.mask] <- S0$error
Derror[dwi.mask] <- D$error
list(S0 = S0.array, D = D.array, S0.error = S0error, D.error = Derror)
}
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