lin2tcm_inpshiftProfile: Profile the inpshift using the linearised 2TCM

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

Profile the inpshift using the linearised 2TCM

Description

Function to fit the linearised 2TCM function with several different delay values to find the optimal delay.

Usage

lin2tcm_inpshiftProfile(
  t_tac,
  tac,
  input,
  weights = NULL,
  vB = NULL,
  dur = NULL,
  frameStartEnd = NULL,
  inpshift_vals = NULL
)

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.
vB	Optional. The blood volume fraction. If not specified, this will be fitted. If specified as a number (e.g. 0.05 for 5 will be used.
dur	Optional. Numeric vector of the time durations of the frames. If not included, the integrals will be calculated using trapezoidal integration.
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.
inpshift_vals	Optional. The values of the inpshift to assess with the grid. By default, a grid between -1 and 1 with spacing of 0.01 will be used.

Value

A plot with the residual weighted sums of squares for each value of the input shift

Author(s)

Granville J Matheson, [e-mail]

References

Oikonen, V (2003). Multilinear solution for 4-compartment model: I. Tissue compartments in series. Gjedde A, Wong DF 1990. Modeling neuroreceptor binding of radioligands in vivo. In: Quantitative imaging: neuroreceptors, neurotransmitters, and enzymes. (Eds. Frost JJ, Wagner HM Jr). Raven Press, 51-79.

Examples

data(pbr28)

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

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
)

lin2tcm_inpshiftProfile(t_tac, tac, input, weights,
   inpshift_vals = seq(-0.5, 0.5, length.out=101))
lin2tcm_inpshiftProfile(t_tac, tac, input, dur = dur)
lin2tcm_inpshiftProfile(t_tac, tac, input, vB=0.05,
  frameStartEnd = c(1,15))

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