blmod_fengconv: Blood Model: FengConv

In mathesong/kinfitr: Kinetic Modelling of PET Time Activity Curves

Blood Model: FengConv

Description

Fits a Feng model, convolved with an infusion injection to AIF data. This model is significantly more effective than the original Feng model in the vast majority of applications. The infusion duration can be a little bit tricky to fit. I recommend trying to find out the approximate duration of the injection and including this as a fixed parameter for most stable performance.

Usage

blmod_fengconv(
  time,
  activity,
  inftime = NULL,
  Method = NULL,
  weights = NULL,
  fit_t0 = TRUE,
  lower = NULL,
  upper = NULL,
  start = NULL,
  multstart_lower = NULL,
  multstart_upper = NULL,
  multstart_iter = 500,
  taper_weights = TRUE,
  check_startpars = FALSE,
  expdecay_props = c(0, 0.5)
)

Arguments

time	The time of each measurement in seconds
activity	The radioactivity of each measurement
inftime	The infusion time during which the tracer is administered. It is recommended to provide this value if known. Leaving this argument empty will result in the inftime being fitted. Alternatively, two values can be specified, e.g. inftime=c(25, 35) to fit the infusion time with specified upper and lower limits if the known infusion time is approximate.
Method	Optional. The method of collection, i.e. "Discrete" or "Continuous"
weights	Optional. Weights of each measurement.
fit_t0	Should time point zero be fitted? If not, it is set to 0. Default is TRUE.
lower	Optional. The lower limits of the fit. If left as NULL, they will be given reasonable defaults (mostly 10% of the starting parameters).
upper	Optional. The upper limits of the fit. If left as NULL, they will be given reasonable defaults (mostly 20x the starting parameters).
start	Optional. The starting parameters for the fit. If left as NULL, they will be selected using blmod_feng_startpars.
multstart_lower	Optional. The lower limits of the starting parameters.
multstart_upper	Optional. The upper limits of the starting parameters.
multstart_iter	The number of fits to perform with different starting parameters. If set to 1, then the starting parameters will be used for a single fit.
taper_weights	Should the weights be tapered to gradually trade off between the continuous and discrete samples after the peak?
check_startpars	Optional. Return only the starting parameters. Useful for debugging fits which do not work.
expdecay_props	What proportions of the decay should be used for choosing starting parameters for the exponential decay. Defaults to 0 and 0.5, i.e. the latter two exponentials, B, C, beta, gamma are estimated using halfway to the end of the decay, and the beginning to halfway through the decay. The first parameters, A and alpha, are estimated from the ascent.

Value

A model fit including all of the individual parameters, fit details, and model fit object of class blood_fengconv.

References

Wong, Koon-Pong, Sung-Cheng Huang, and Michael J. Fulham. "Evaluation of an input function model that incorporates the injection schedule in FDG-PET studies." 2006 IEEE Nuclear Science Symposium Conference Record. Vol. 4. IEEE, 2006. Tonietto, M. (2012). Methods and models for the characterization of arterial input function in dynamic PET studies. Master thesis.

Examples

blooddata <- pbr28$blooddata[[1]]
blooddata <- bd_blood_dispcor(blooddata)
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_fengconv(aif$time,
                           aif$aif,
                           Method = aif$Method,
                           multstart_iter = 1,
                           inftime = 22)

mathesong/kinfitr documentation built on Jan. 15, 2024, 11:07 p.m.