ma1 | R Documentation |
Function to fit the MA1 of Ichise et al (2002) to data.
ma1(
t_tac,
tac,
input,
tstarIncludedFrames,
weights = NULL,
inpshift = 0,
vB = 0,
dur = NULL,
frameStartEnd = NULL
)
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 |
tstarIncludedFrames |
The number of frames to be used in the regression model, i.e. the number of frames for which
the function is linear after pseudo-equilibrium is reached. This is a count from the end of the measurement, so a value of
10 means that last 10 frames will be used. This value can be estimated using |
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 set to 0. This can be fitted using 1TCM or 2TCM. |
vB |
Optional. The blood volume fraction. If not specified, this will be ignored and assumed to be 0 will be corrected for prior to parameter estimation using the following equation:
|
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. |
A list with a data frame of the fitted parameters out$par
, the model fit object out$fit
,
a dataframe containing the TACs of the data out$tacs
, a dataframe containing the fitted values out$fitvals
,
the blood input data frame after time shifting input
, a vector of the weights out$weights
,
the inpshift value used inpshift
, the specified vB value out$vB
and the specified tstarIncludedFrames
value out$tstarIncludedFrames
.
Granville J Matheson, mathesong@gmail.com
Ichise M, Toyama H, Innis RB, Carson RE. Strategies to improve neuroreceptor parameter estimation by linear regression analysis. Journal of Cerebral Blood Flow & Metabolism. 2002 Oct 1;22(10):1271-81.
data(pbr28)
t_tac <- pbr28$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
)
fit1 <- ma1(t_tac, tac, input, 10, weights)
fit2 <- ma1(t_tac, tac, input, 10, weights, inpshift = 0.1, vB = 0.05)
fit3 <- ma1(t_tac, tac, input, 10, weights, inpshift = 0.1, vB = 0.05, dur = dur)
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