twotcm: Two Tissue Compartment Model

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

Two Tissue Compartment Model

Description

Function to fit the Two Tissue Compartment Model to data.

Usage

twotcm(
  t_tac,
  tac,
  input,
  weights = NULL,
  inpshift = NULL,
  vB = NULL,
  frameStartEnd = NULL,
  K1.start = 0.1,
  K1.lower = 1e-04,
  K1.upper = 1,
  k2.start = 0.1,
  k2.lower = 1e-04,
  k2.upper = 0.5,
  k3.start = 0.1,
  k3.lower = 1e-04,
  k3.upper = 0.5,
  k4.start = 0.1,
  k4.lower = 1e-04,
  k4.upper = 0.5,
  inpshift.start = 0,
  inpshift.lower = -0.5,
  inpshift.upper = 0.5,
  vB.start = 0.05,
  vB.lower = 0.01,
  vB.upper = 0.1,
  multstart_iter = 1,
  multstart_lower = NULL,
  multstart_upper = NULL,
  printvals = F
)

Arguments

t_tac	Numeric vector of times for each frame in minutes. We use the time halfway through the frame as well as a zero. If a time zero frame is not included, it will be added.
tac	Numeric vector of radioactivity concentrations in the target tissue for each frame. We include zero at time zero: if not included, it is added.
input	Data frame containing the blood, plasma, and parent fraction concentrations over time. This can be generated using the blood_interp function.
weights	Optional. Numeric vector of the weights assigned to each frame in the fitting. We include zero at time zero: if not included, it is added. If not specified, uniform weights will be used.
inpshift	Optional. The number of minutes by which to shift the timing of the input data frame forwards or backwards. If not specified, this will be fitted, however this takes longer to compute. Recommended to perform once on a large ROI for each measurement, and to specify this value for the remainder of the regions. Or perhaps even to use a simpler model, such as 1TCM.
vB	Optional. The blood volume fraction. If not specified, this will be fitted. Recommended to perform once on a large ROI for each measurement, and to specify this value for the remainder of the regions.
frameStartEnd	Optional: This allows one to specify the beginning and final frame to use for modelling, e.g. c(1,20). This is to assess time stability.
K1.start	Optional. Starting parameter for fitting of K1. Default is 0.1.
K1.lower	Optional. Lower bound for the fitting of K1. Default is 0.0001.
K1.upper	Optional. Upper bound for the fitting of K1. Default is 1.
k2.start	Optional. Starting parameter for fitting of k2. Default is 0.1.
k2.lower	Optional. Lower bound for the fitting of k2. Default is 0.0001.
k2.upper	Optional. Upper bound for the fitting of k2. Default is 0.5.
k3.start	Optional. Starting parameter for fitting of k3. Default is 0.1.
k3.lower	Optional. Lower bound for the fitting of k3. Default is 0.0001.
k3.upper	Optional. Upper bound for the fitting of k3. Default is 0.5.
k4.start	Optional. Starting parameter for fitting of k4. Default is 0.1.
k4.lower	Optional. Lower bound for the fitting of k4. Default is 0.0001.
k4.upper	Optional. Upper bound for the fitting of k4. Default is 0.5.
inpshift.start	Optional. Starting parameter for fitting of inpshift. Default is 0.
inpshift.lower	Optional. Lower bound for the fitting of inpshift. Default is -0.5.
inpshift.upper	Optional. Upper bound for the fitting of inpshift. Default is 0.5.
vB.start	Optional. Starting parameter for fitting of vB. Default is 0.05.
vB.lower	Optional. Lower bound for the fitting of vB. Default is 0.01.
vB.upper	Optional. Upper bound for the fitting of vB. Default is 0.1.
multstart_iter	Number of iterations for starting parameters. Default is 1. For more information, see nls_multstart. If specified as 1 for any parameters, the original starting value will be used, and the multstart_lower and multstart_upper values ignored.
multstart_lower	Optional. Lower bounds for starting parameters. Defaults to the lower bounds. Named list of whichever parameters' starting bounds should be altered.
multstart_upper	Optional. Upper bounds for starting parameters. Defaults to the upper bounds. Named list of whichever parameters' starting bounds should be altered.
printvals	Optional. This displays the parameter values for each iteration of the model. This is useful for debugging and changing starting values and upper and lower bounds for parameters.

Value

A list with a data frame of the fitted parameters out$par, their percentage standard errors (scaled so that 1 represents 100%) out$par.se, the model fit object out$fit, a dataframe containing the TACs both of the data and the fitted values out$tacs, the blood input data frame after time shifting input, a vector of the weights out$weights, a logical of whether the inpshift was fitted inpshift_fitted and a logical of whether the vB was fitted vB.

Author(s)

Granville J Matheson, [e-mail]

Examples

data(pbr28)

t_tac <- pbr28$tacs[[2]]$Times / 60
tac <- pbr28$tacs[[2]]$FC
weights <- pbr28$tacs[[2]]$Weights

input <- blood_interp(
  pbr28$procblood[[2]]$Time / 60, pbr28$procblood[[2]]$Cbl_dispcorr,
  pbr28$procblood[[2]]$Time / 60, pbr28$procblood[[2]]$Cpl_metabcorr,
  t_parentfrac = 1, parentfrac = 1
)

fit1 <- twotcm(t_tac, tac, input, weights)
fit2 <- twotcm(t_tac, tac, input, weights, inpshift = 0.1, vB = 0.05)

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