Note that TARQUIN and LCModel require these packages to be installed, and the functions set_tqn_cmd and set_lcm_cmd (respectively) need to be used to specify the location of these software packages.

fit_mrs(
  metab,
  basis = NULL,
  method = "ABFIT",
  w_ref = NULL,
  opts = NULL,
  parallel = FALSE,
  cl = NULL,
  time = TRUE,
  progress = "text",
  extra = NULL
)

Arguments

metab

metabolite data.

basis

basis class object or character vector to basis file in LCModel .basis format.

method

'ABFIT' (default), 'VARPRO', 'VARPRO_3P', 'TARQUIN' or 'LCMODEL'.

w_ref

water reference data for concentration scaling (optional).

opts

options to pass to the analysis method.

parallel

perform analyses in parallel (TRUE or FALSE).

cl

a parallel socket cluster required to run analyses in parallel. Eg, cl <- parallel::makeCluster(4).

time

measure the time taken for the analysis to complete (TRUE or FALSE).

progress

option is passed to plyr::alply function to display a progress bar during fitting. Default value is "text", set to "none" to disable.

extra

an optional data frame to provide additional variables for use in subsequent analysis steps, eg id or grouping variables.

Value

MRS analysis object.

Details

Fitting approaches described in the following references: ABfit Wilson, M. Adaptive baseline fitting for 1H MR spectroscopy analysis. Magn Reson Med 2012;85:13-29.

VARPRO van der Veen JW, de Beer R, Luyten PR, van Ormondt D. Accurate quantification of in vivo 31P NMR signals using the variable projection method and prior knowledge. Magn Reson Med 1988;6:92-98.

TARQUIN Wilson, M., Reynolds, G., Kauppinen, R. A., Arvanitis, T. N. & Peet, A. C. A constrained least-squares approach to the automated quantitation of in vivo 1H magnetic resonance spectroscopy data. Magn Reson Med 2011;65:1-12.

LCModel Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 1993;30:672-679.

Examples

fname <- system.file("extdata", "philips_spar_sdat_WS.SDAT", package =
"spant")
svs <- read_mrs(fname)
if (FALSE) { # \dontrun{
basis <- sim_basis_1h_brain_press(svs)
fit_result <- fit_mrs(svs, basis)
} # }