blmod_feng: Blood Model: Feng

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

Blood Model: Feng

Description

Fit a Feng model to AIF data. This model fits the conventional Feng model to AIF data. Note: this model is pretty terrible for just about all tracers except for FDG. The convolved Feng model, fengconv is usually much more effective.

Usage

blmod_feng(
  time,
  activity,
  Method = NULL,
  weights = NULL,
  fit_t0 = TRUE,
  lower = NULL,
  upper = NULL,
  start = NULL,
  multstart_lower = NULL,
  multstart_upper = NULL,
  multstart_iter = 100,
  Method_weights = TRUE,
  taper_weights = TRUE,
  weightscheme = 2,
  check_startpars = FALSE,
  expdecay_props = c(1/60, 0.1)
)

Arguments

time	The time of each measurement in seconds
activity	The radioactivity of each measurement
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 150% of the starting parameters).
start	Optional. The starting parameters for the fit. If left as NULL, they will be selected using blmod_exp_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.
Method_weights	If no weights provided, should the weights be divided by discrete and continuous samples equally (i.e. with more continuous samples, the continuous samples each get less weight). Default is TRUE.
taper_weights	If no weights provided, should the weights be tapered to gradually trade off between the continuous and discrete samples after the peak?
weightscheme	If no weights provided, which weighting scheme should be used before accommodating Method_divide and taper_weights? 1 represents a uniform weighting before accommodating Method_divide and taper_weights. 2 represents time/AIF as used by Columbia PET Centre. Default is 2.
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_feng.

References

Wang, Xinmin, and Dagan Feng. "A study on physiological parameter estimation accuracy for tracer kinetic modeling with positron emission tomography (pet)." 1992 American Control Conference. IEEE, 1992. Feng, D., Z. Wang, and S. C. Huang. "Tracer plasma time-activity curves in circulatory system for positron emission tomography kinetic modeling studies." IFAC Proceedings Volumes 26.2 (1993): 175-178.

Examples

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

mathesong/kinfitr documentation built on Nov. 27, 2024, 1:38 a.m.