Wrapper for oro.pet functions

Description

Simple wrapper for getting functions from

Usage

.petWrapper(name, ...)

Arguments

Calculating SUVs for PET Using QIBA Pseudocode

Description

The standard uptake value (SUV) is calculated based on an 18F-FDG-PET acquistion using ancillary information contained in the DICOM data.

Usage

activityConcentration(pixelData, ...)

## S4 method for signature 'array'
activityConcentration(
  pixelData,
  CSV = NULL,
  seriesNumber = NULL,
  method = "qiba"
)

.activityConcentration(
  pixelData,
  CSV = NULL,
  seriesNumber = NULL,
  method = "qiba"
)

standardUptakeValue(pixelData, ...)

## S4 method for signature 'array'
standardUptakeValue(
  pixelData,
  mask = NULL,
  CSV = NULL,
  seriesNumber = NULL,
  method = c("qiba", "user"),
  prior = NULL,
  decayedDose = NULL
)

.standardUptakeValue(
  pixelData,
  mask = NULL,
  CSV = NULL,
  seriesNumber = NULL,
  method = c("qiba", "user"),
  prior = NULL,
  decayedDose = NULL
)

Arguments

Value

A list containing the following items

• SUVbwis a multidimensional array, the same dimension as pixelData, that contains the standard uptake values.

• hdris a list of DICOM header fields used in the SUV calculation.

• decayTimeis the decay time calculated from the DICOM header information.

• decayedDoseis the RadionuclideTotalDose, if taken from the DICOM header information, or the user-specified value.

• SUVbwScaleFactoris \mbox{PatientsWeight}\cdot1000/\mbox{decayedDose}.

Note

Note, for GE scanners it is common for the RescaleSlope DICOM field to vary on a slice-by-slice basis. This is taken into account if a GE scanner is detected from the Modality DICOM field. However, the InstanceNumber is used to reorder the slices so they match the incoming NIfTI file of PixelData. If this is not correct it may be necessary to manually re-order the RescaleSlope field in the CSV data frame so that the activity concentration is calculated correctly.

Author(s)

Brandon Whitcher bwhitcher@gmail.com

References

https://qibawiki.rsna.org/index.php?title=Standardized_Uptake_Value_(SUV)

See Also

dicomTable, nifti

Compartmental Models for Kinetic Parameter Estimation

Description

A selection of parametric models are provided that combine a compartmental model for tissue and empirical versions of the arterial input function or reference region time activity curve.

Usage

compartmentalModel(type)

Arguments

Details

Parametric models from the PET literature are provided to the user for kinetic parameter estimation.

Value

A function.

Author(s)

Brandon Whitcher bwhitcher@gmail.com

References

Lammertsma, A.A and Hume, S.P. (1996) Simplified reference tissue model for PET receptor studies, NeuroImage, 4, 153-158.

Wu, Y and Carson, R.E. (2002) Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging, Journal of Cerebral Blood Flow & Metabolism, 22, 1440-1452.

See Also

simplifiedReferenceTissueModel

Empirical Convolution Between an Input Function and a Single Exponential

Description

Computationally efficient method to convolve a vector of observations and a single exponential function with two parameters.

Usage

expConv(input, k1, k2)

Arguments

Details

Assuming the input function has been sampled (or interpolated) to a high temporal resolutions, say one Hertz, a simple for loop is used to perform the convolution.

Value

The vector containing the result from the convolution operation.

Author(s)

Brandon Whitcher bwhitcher@gmail.com

Estimation of the Half Maximal Inhibitory Concentration

Description

The half maximal inhibitory concentration (IC50) is a measure of the effectiveness of a compound in inhibiting biological or biochemical function. This quantitative measure indicates how much of a particular drug or other substance (inhibitor) is needed to inhibit a given biological process (or component of a process) by half.

See reference(s).

In this version of the function the maximal occupancy (rmax) is estimated automatically. This should be optional.

Usage

hillEquation(
  conc,
  occ,
  guess = c(1, 100),
  control = minpack.lm::nls.lm.control()
)

Arguments

Value

List with the following elements

• IC50Half maximal inhibitory concentration

• rmaxEstimated maximal occupancy

• IC50SEApproximate standard error for IC50

• rmaxSEApproximate standard erorr for rmax

• hessianHessian matrix from the Levenburg-Marquardt procedure

• infoReturn value from the Levenburg-Marquardt procedure

• devianceDeviance from the Levenburg-Marquardt procedure

• messageText message from the Levenburg-Marquardt procedure

Author(s)

Brandon Whitcher bwhitcher@gmail.com

References

Hill Equation IC50

See Also

nls.lm

Calculating the Lean Body Mass

Description

The lean body mass (LBM) is calculated according to the formula

1.1\cdot\mbox{weight}-128\cdot(\mbox{weight}/\mbox{height})^2

if male and

1.07\cdot\mbox{weight}-148\cdot(\mbox{weight}/\mbox{height})^2

if female.

The standard uptake value (SUV) is summarized using the hotspot method or by calculating total volume of the high values.

Usage

leanBodyMass(height, weight, gender)

hotSpotSUV(suv, radius = 10, type = "3D")

totalSUV(suv, mask, z, bg, local = TRUE)

Arguments

Value

Vector of lean body mass values in kilograms.

...

Author(s)

Brandon Whitcher bwhitcher@gmail.com

References

Sugawara, Y., K. R. Zasadny, A. W. Neuhoff, R. L. Wahl (1999) Reevaluation of the Standardized Uptake Value for FDG: Variations with Body Weight and Methods for Correction, Radiology 213: 521–525.

See Also

standardUptakeValue

leanBodyMass

Examples

library(oro.pet)
n <- 11
h <- seq(200, 150, length=n)
w <- seq(80, 120, length=n)
cbind(h, w, leanBodyMass(h, w, "male"), leanBodyMass(h, w, "female"))

The Multilinear Reference Tissue Model

Description

The multilinear reference tissue model (MRTM) estimates the binding potential from an observed time activity curve without the need for arterial sampling. Instead, a second time activity curve must be provided from a suitable reference region where there is negligible binding.

Usage

multilinearReferenceTissueModel(
  tac,
  ref,
  time,
  tstar,
  MRTM2 = TRUE,
  k2prime = NULL
)

Arguments

Details

See the references.

The numeric integration required to construct the design matrix is performed by interpolating the time activity curves, both for the tissue and reference region, to one-second resolution and then performing the cumsum operation on them.

Given the nonlinear relationship between binding potential and the regression parameters, the deltamethod is used to approximate its standard error.

Value

Author(s)

Brandon Whitcher bwhitcher@gmail.com

References

Ichise, M., Ballinger, J.R., Golan, H., Vines, D., Luong, A., Tsai, S. and Kung, H.F. (1996) Noninvasive quantification of dopamine D2 receptors with iodine-123-IBF SPECT, Journal of Nuclear Medicine, 37, 513-520.

Ichise, M., Liow, J.-S., Lu, J.-Q., Takano, A., Model, K., Toyama, H., Suhara, T., Suzuki, K., Innis, R.B., Carson, R.E. (2003) Linearized reference tissue parametric imaging methods: Application to [11C]DASB positron emission tomography studies of the serotonin transporter in human brain, Journal of Cerebral Blood Flow & Metabolism, 23, 1096-1112.

See Also

cumsum, deltamethod

Compute Drug Occupancy with Approximate Standard Errors

Description

Receptor occupancy is calculated from posititron emission tomography (PET) data as the treatment-induced relative change in the concentration of available (not occupied) receptors.

Usage

occupancy(base, drug, baseSE = NULL, drugSE = NULL, base.drug.corr = 0)

Arguments

Details

Occupancy is calculated using the straightforward and well-known formula. If the standard errors for the two binding potentials are provided, then the delta method is used to approximate the standard error for the estimate of occupancy.

Value

Author(s)

Brandon Whitcher bwhitcher@gmail.com

References

Cunningham VJ, Rabiner EA, Slifstein M, Laruelle M (2010). Measuring drug occupancy in the absence of a reference region: the Lassen plot re-visited, Journal of Cerebral Blood Flow & Metababolism, 30, 46-50.

Passchier J, Gee A, Willemsen A, Vaalburg W, van Waarde A (2002). Measuring drug-related receptor occupancy with positron emission tomography, Methods, 27, 278-286.

See Also

deltamethod

Plot Baseline Versus Post-Treatment Binding Potentials

Description

Inspired by the Lassen plot (Cunningham et al., 2010) this is a straightforward graphical summary of pre-treatment versus post-treatment binding potentials for a single subject across multiple brain regions.

Usage

plotBindingPotential(
  base,
  drug,
  lty45 = 2,
  lty = 1,
  lwd45 = 2,
  lwd = 3,
  col45 = "darkgrey",
  col = "orange",
  pch = 1,
  cex = 1,
  xlim = range(0, base, 0.5),
  ylim = range(0, drug, 0.5),
  xlab = expression(BP[ND]^{
     Base
 }),
  ylab = expression(BP[ND]^{
     Drug
 }),
  ...
)

Arguments

Details

See the reference below.

Value

A plot is shown, NULL is returned

Author(s)

Brandon Whitcher bwhitcher@gmail.com

References

Cunningham VJ, Rabiner EA, Slifstein M, Laruelle M (2010). Measuring drug occupancy in the absence of a reference region: the Lassen plot re-visited, Journal of Cerebral Blood Flow & Metababolism, 30, 46-50.

See Also

par, plot

The Simplified Reference Tissue Model

Description

The simplified reference tissue model (SRTM) estimates the binding potential from an observed time activity curve without the need for aterial sampling. It assumes a one-tissue compartment model to describe the influx and efflux in the tissue region of interest and the reference region.

Usage

simplifiedReferenceTissueModel(
  tac,
  ref,
  time,
  SRTM2 = TRUE,
  k2prime = NULL,
  guess = c(R1 = 0.5, k2 = 0.01),
  control = minpack.lm::nls.lm.control()
)

Arguments

Details

See the references.

The model has been parameterized in the manner of Wu and Carson (2002). That is, the nonlinear regression estimates R1, k2 and k'2 for the three-parameter model (SRTM) and R1 and k2 for the two-parameter model (SRTM2).

The convolution is performed after interpolating the time activity curves, both for the tissue and the reference region, to one-second resolution then downsampling them back to the original sampling rate.

Value

Author(s)

Brandon Whitcher b.whitcher@gmail.com

References

Lammertsma, A.A. and Hume, S.P. (1996) Simplified reference tissue model for PET receptor studies, NeuroImage, 4, 153-158.

Wu, Y. and Carson, R.E. (2002) Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging, Journal of Cerebral Blood Flow & Metabolism, 22, 1440-1452.

See Also

deltamethod, expConv, nls.lm