Type: Package
Title: Positron Emission Tomography Time-Activity Curve Analysis
Version: 0.3.1
Description: To facilitate the analysis of positron emission tomography (PET) time activity curve (TAC) data, and to encourage open science and replicability, this package supports data loading and analysis of multiple TAC file formats. Functions are available to analyze loaded TAC data for individual participants or in batches. Major functionality includes weighted TAC merging by region of interest (ROI), calculating models including standardized uptake value ratio (SUVR) and distribution volume ratio (DVR, Logan et al. 1996 <doi:10.1097/00004647-199609000-00008>), basic plotting functions and calculation of cut-off values (Aizenstein et al. 2008 <doi:10.1001/archneur.65.11.1509>). Please see the walkthrough vignette for a detailed overview of 'tacmagic' functions.
Depends: R (≥ 3.4)
License: GPL-3
URL: https://github.com/ropensci/tacmagic
BugReports: https://github.com/ropensci/tacmagic
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: graphics, grDevices, pracma, utils, R.matlab
Suggests: testthat, knitr, rmarkdown, covr, vdiffr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-12-09 02:49:29 UTC; ericbrown
Author: Eric Brown ORCID iD [aut, cre], Ariel Graff-Guerrero [dgs], Jon Clayden ORCID iD [rev] (Jon Clayden reviewed the package for ropensci, see <https://github.com/ropensci/software-review/issues/280>), Brandon Hurr ORCID iD [rev] (Brandon Hurr reviewed the package for ropensci, see <https://github.com/ropensci/software-review/issues/280>)
Maintainer: Eric Brown <eb@ericebrown.com>
Repository: CRAN
Date/Publication: 2020-12-09 10:20:02 UTC

Non-invasive reference Logan method for all ROIs in tac data

Description

This calculates the DVR using the non-invasive reference Logan method for all TACs in a supplied tac file. It uses DVR_ref_Logan.

Usage

DVR_all_ref_Logan(
  tac_data,
  ref,
  k2prime,
  t_star,
  error = 0.1,
  method = "trapz",
  ...
)

Arguments

tac_data

The time-activity curve data from tac_roi()

ref

Required – The reference region, e.g. "cerebellum"

k2prime

Required – A fixed value for k2' must be specified (e.g. 0.2)

t_star

Required – If 0, t* will be calculated using find_t_star()

error

For find_t_star()

method

Method of integration, "trapz" or "integrate"

...

When called from tm_batch, unused parameters may be supplied

Value

Data frame with calculated DVRs for all ROIs

References

Logan, J., Fowler, J. S., Volkow, N. D., Wang, G.-J., Ding, Y.-S., & Alexoff, D. L. (1996). Distribution Volume Ratios without Blood Sampling from Graphical Analysis of PET Data. Journal of Cerebral Blood Flow & Metabolism, 16(5), 834-840. https://doi.org/10.1097/00004647-199609000-00008

See Also

Other Logan plot functions: DVR_ref_Logan(), dvr(), plot.ref_Logan()

Examples

f <- system.file("extdata", "AD06.tac", package="tacmagic")
fv <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic")
AD06_tac <- load_tac(f, format="PMOD")
AD06_volume <- load_vol(fv, format="voistat")
AD06 <- tac_roi(tac=AD06_tac, volumes=AD06_volume, ROI_def=roi_ham_pib(),  
                merge=FALSE, PVC=FALSE)  

AD06_DVR <- DVR_all_ref_Logan(AD06, ref="cerebellum", k2prime=0.2, t_star=23)

Non-invasive reference Logan method

Description

This calculates the coefficient from the non-invasive Logan method, which is equal to DVR. Works for a single tac (target).

Usage

DVR_ref_Logan(
  tac_data,
  target,
  ref,
  k2prime,
  t_star,
  error = 0.1,
  method = "trapz"
)

Arguments

tac_data

The time-activity curve data from tac_roi()

target

The name of the target ROI, e.g. "frontal"

ref

The reference region, e.g. "cerebellum"

k2prime

A fixed value for k2' must be specified (e.g. 0.2)

t_star

If 0, t* will be calculated using find_t_star()

error

For find_t_star()

method

Method of integration, "trapz" or "integrate"

Value

Data frame with calculate DVRs for all ROIs

References

Logan, J., Fowler, J. S., Volkow, N. D., Wang, G.-J., Ding, Y.-S., & Alexoff, D. L. (1996). Distribution Volume Ratios without Blood Sampling from Graphical Analysis of PET Data. Journal of Cerebral Blood Flow & Metabolism, 16(5), 834-840. https://doi.org/10.1097/00004647-199609000-00008

See Also

Other Logan plot functions: DVR_all_ref_Logan(), dvr(), plot.ref_Logan()

Examples

f <- system.file("extdata", "AD06.tac", package="tacmagic")
fv <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic")
AD06_tac <- load_tac(f, format="PMOD")
AD06_volume <- load_vol(fv, format="voistat")
AD06 <- tac_roi(tac=AD06_tac, volumes=AD06_volume, ROI_def=roi_ham_pib(),  
                merge=FALSE, PVC=FALSE)                             
               
AD06_DVR_fr <- DVR_ref_Logan(AD06, target="frontal", ref="cerebellum",
                             k2prime=0.2, t_star=0)

Creates a tac object from a data.frame

Description

tac objects can be created from data.frame objects with 'as.tac()'. The time and activity units must be specified as arguments if not already set as attributes in the data.frame. The columns of the data frame are the regional time activity curves, with the column names the names of the ROIs.

Usage

as.tac(x, time_unit = NULL, activity_unit = NULL)

Arguments

x

data.frame with start, end time and tac data

time_unit

NULL if in data.frame or set to "seconds" or "minutes"

activity_unit

NULL if in data.frame or set to "kBq/cc", "Bq/cc", "nCi/cc"

Details

If the time_unit and activity_unit attributes are already in the data.frame, they do not need to be set again, but otherwise they will need to be specified in the input parameters.

Value

tac object

See Also

Other Loading functions: load_tac(), load_voistat(), load_vol()

Examples

manual <- data.frame(start=c(0:4), end=c(2:6), 
                     ROI1=c(10.1:14.2), ROI2=c(11:15))
manual_tac <- as.tac(manual, time_unit="minutes", activity_unit="kBq/cc")

Load (+/- merge) ROIs for batch of participants

Description

For a vector of participant IDs and correspondingly named tac files, this loads the tac files. If roi_m = T, then can also merge ROIs into larger ROIs based on the optional parameters that follow.

Usage

batch_load(
  participants,
  dir = "",
  tac_file_suffix = ".tac",
  tac_format = "PMOD",
  roi_m = FALSE,
  PVC = NULL,
  vol_file_suffix = NULL,
  vol_format = NULL,
  merge = NULL,
  ROI_def = NULL,
  tracer_dose = NULL,
  dose_unit = NULL,
  weight_kg = NULL
)

Arguments

participants

A vector of participant IDs

dir

A directory and/or file name prefix for the tac/volume files

tac_file_suffix

How participant IDs corresponds to the TAC files

tac_format

Format of tac files provided: See load_tac()

roi_m

TRUE if you want to merge atomic ROIs into larger ROIs (and if not, the following parameters are not used)

PVC

For PVC, true where the data is stored as _C in same tac file

vol_file_suffix

How participant IDs correspond to volume files

vol_format

The file format that includes volumes: See load_vol()

merge

Passes value to tac_roi(); T to also incl. original atomic ROIs

ROI_def

Object that defines combined ROIs, see ROI_definitions.R

tracer_dose

optionally, a vector of tracer doses (in the same order as participants), for SUV

dose_unit

if tracer_dose is specified, note the unit (e.g "MBq")

weight_kg

optionally, a vector of participant weights in kg, for SUV

Details

See load_tac() for specifics.

Value

A list of data.frames, each is a participant's TACs

See Also

Other Batch functions: batch_tm(), batch_voistat()

Examples

# For the working example, the participants are full filenames.
participants <- c(system.file("extdata", "AD06.tac", package="tacmagic"),
                  system.file("extdata", "AD07.tac", package="tacmagic"),
                  system.file("extdata", "AD08.tac", package="tacmagic"))

tacs <- batch_load(participants, tac_file_suffix="")

Calculate one or more models for a batch of participants

Description

For a list of tac data (from load_batch) this calculates specified models and saves in a tidy data.frame. Current model options are "SUVR", "Logan".

Usage

batch_tm(all_tacs, models, custom_model = NULL, ...)

Arguments

all_tacs

A list by participant, of tac data (load_batch())

models

A vector of names of the models to calculate

custom_model

A function that can be run like other models (advanced)

...

The arguments that get passed to the specified models/custom model, many are required; please check with model desired.

Details

For further details about how the models are calculated, see the individual functions that they rely on. "SUVR" uses suvr(), "Logan" uses DVR_all_ref_Logan().

Value

A table of SUVR values for the specified ROIs for all participants

See Also

Other Batch functions: batch_load(), batch_voistat()

Examples

participants <- c(system.file("extdata", "AD06.tac", package="tacmagic"),
                  system.file("extdata", "AD07.tac", package="tacmagic"),
                  system.file("extdata", "AD08.tac", package="tacmagic"))

tacs <- batch_load(participants, tac_file_suffix="")

# Keeps only the ROIs without partial-volume correction (PMOD convention)
tacs <- lapply(tacs, split_pvc, FALSE)

batch <- batch_tm(tacs, models=c("SUVR", "Logan"), ref="Cerebellum_r",
                  SUVR_def=c(3000,3300,3600), k2prime=0.2, t_star=23)

Obtain values from voistat files (using load_voistat() for a batch.

Description

For a vector of participant IDs and correspondingly named .voistat files, this extracts the value from the files for the specified ROIs. participants can also be a vector of filenames, in which case set dir="" and filesuffix="", as in the example.

Usage

batch_voistat(
  participants,
  ROI_def,
  dir = "",
  filesuffix = ".voistat",
  varname = "VALUE"
)

Arguments

participants

A vector of participant IDs

ROI_def

Object that defines combined ROIs, see ROI_definitions.R

dir

Directory and/or filename prefix of the files

filesuffix

Optional filename characters between ID and ".voistat"

varname

The name of the variable being extracted, e.g. "SRTM"

Details

See load_voistat() for specifics.

Value

A table of values for the specified ROIs for all participants

See Also

Other Batch functions: batch_load(), batch_tm()

Examples

participants <- c(system.file("extdata", "AD06_BPnd_BPnd_Logan.voistat", 
                              package="tacmagic"),
                   system.file("extdata", "AD07_BPnd_BPnd_Logan.voistat", 
                               package="tacmagic"),
                   system.file("extdata", "AD08_BPnd_BPnd_Logan.voistat", 
                               package="tacmagic"))

batchtest <- batch_voistat(participants=participants, ROI_def=roi_ham_pib(), 
                           dir="", filesuffix="", varname="Logan")

Convert radioactivity units

Description

Change the radioactivity units of a tac or numeric object to the specified desired units (e.g. Bq, kBq, MBq, nCi, uCi, mCi, Ci). For convenience, if the unit is per volume ("x/cc" or "x/mL"), the "/cc" part is ignored for the conversion.

Usage

change_units(x, to_unit, from_unit)

Arguments

x

time-activity curve or numeric object

to_unit

the desired unit (e.g. "kBq")

from_unit

not used for tac object (it is in the tac object), but for numeric objects, must be specified (e.g. "nCi")

Value

the converted object, same type as x

Examples

f <- system.file("extdata", "AD06.tac", package="tacmagic")
AD06_tac <- load_tac(f, format="PMOD")
AD06_tac_nCicc <- change_units(AD06_tac, to_unit = "nCi/cc")

change_units(5, to_unit = "kBq", from_unit = "nCi")
change_units(0.185, to_unit = "nCi", from_unit = "kBq")

Cutoff value calculation using method described in Aizenstein et al. 2008

Description

See the reference below and the tacmagic walkthrough vignette. Aizenstein et al. (2008) proposed a standardized method of calculating Pittsburgh Compound B (PIB) cutoff values to classify participants as PIB+ or PIB-. They used the distribution volume ratio (DVR) from several ROIs associated with amyloid deposition. The steps are summarized below. cutoff_aiz() implements 1-3, returning cutoff values for each ROI. It can be used to dichotomize participants, with pos_anyroi().

Usage

cutoff_aiz(modelstats, ROIs)

Arguments

modelstats

SUVR or DVR data for group of participants from batch_tm()

ROIs

list of variables (ROIs) to use for cutoff detection

Details

1. Remove outliers from a group of cognitively normal individuals. An outlier is defined as having any ROI with DVR > upper inner fence of that ROI (= 3rd quartile + (1.5 * IQR). 2. Iterate step 1 as needed until there are no more outlying participants. 3. From this subset of the group with outliers removed, the cutoff value for each ROI is set as the upper inner fence. 4. For all participants, if there is any ROI above the cutoff for that region, then the participant is deemed to be PIB+.

Value

Cutoff values for each ROI based on the above method

References

Aizenstein HJ, Nebes RD, Saxton JA, et al. 2008. Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch Neurol 65: 1509-1517.

See Also

Other Cutoff functions: pos_anyroi()

Examples

cutoff_aiz(fake_DVR, c("ROI1_DVR", "ROI2_DVR", "ROI3_DVR", "ROI4_DVR"))

Distribution volume ratio (DVR) for one or more ROIs

Description

This calculates the DVR using the non-invasive reference Logan method for all TACs in a supplied tac file. It uses DVR_ref_Logan if a target ROI is specified, otherwise will calculate DVR for all ROIs with DVR_ref_all_Logan()

Usage

dvr(
  tac,
  model = "logan",
  target = NULL,
  ref,
  k2prime,
  t_star,
  error = 0.1,
  method = "trapz"
)

Arguments

tac

The time-activity curve data from load_tac() or tac_roi()

model

Only model currently available is "logan"

target

Optional - otherwise will calculate DVR for all regions

ref

Required – The reference region, e.g. "cerebellum"

k2prime

Required – A fixed value for k2' must be specified (e.g. 0.2)

t_star

Required – If 0, t* will be calculated using find_t_star()

error

For find_t_star()

method

Method of integration, "trapz" or "integrate"

Details

For other model parameters, directly call DVR_ref_Logan().

Value

Data frame with calculated DVRs

References

Logan, J., Fowler, J. S., Volkow, N. D., Wang, G.-J., Ding, Y.-S., & Alexoff, D. L. (1996). Distribution Volume Ratios without Blood Sampling from Graphical Analysis of PET Data. Journal of Cerebral Blood Flow & Metabolism, 16(5), 834-840. https://doi.org/10.1097/00004647-199609000-00008

See Also

Other Logan plot functions: DVR_all_ref_Logan(), DVR_ref_Logan(), plot.ref_Logan()

Examples

f <- system.file("extdata", "AD06.tac", package="tacmagic")
fv <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic")
AD06_tac <- load_tac(f, format="PMOD")
AD06_volume <- load_vol(fv, format="voistat")
AD06 <- tac_roi(tac=AD06_tac, volumes=AD06_volume, ROI_def=roi_ham_pib(),  
                merge=FALSE, PVC=FALSE)  

AD06_DVRs <- dvr(AD06, ref="cerebellum", k2prime=0.2, t_star=23)

AD06_DVR <- dvr(AD06, target="frontal", ref="cerebellum", 
             k2prime=0.2, t_star=23)

Fake DVR data for vignette and package testing

Description

A fake dataset of 50 simulated participants in the format that the function tm_batch() would be expected to produce with the "Logan" model specified. The data itself was generated as follows:

#higher <- matrix(rnorm(40, 1.9, 0.6), ncol=4, nrow=10)
#lower <- matrix(rnorm(160, 1.3, 0.3), ncol=4, nrow=40)
#fake_data <- as.data.frame(rbind(higher, lower))
#row.names(fake_data) <- paste0("p", 1:50)
#colnames(fake_data) <- c("ROI1_DVR", "ROI2_DVR", "ROI3_DVR", "ROI4_DVR")
#save(fake_data, "fake_DVR.Rda")

Usage

fake_DVR

Format

A data frame with 50 rows and 4 variables representing ROIs

Loads TAC from file for use by other functions (default is PMOD .tac format)

Description

This is the main function for loading an individual participant's TAC data. The minimal required information within the supplied files is the start and stop times and a time unit (either seconds or minutes), as well as the activity values for 1 or more ROIs, and units for activity. The currently supported formats (with the corresponding format argument), include:

Usage

load_tac(
  filename,
  format = "PMOD",
  acqtimes = NULL,
  time_unit = NULL,
  activity_unit = NULL
)

Arguments

filename

(e.g. "participant01.tac")

format

A character string, with options listed above (e.g. "PMOD")

acqtimes

Filename for a .acqtimes file (as in PMOD), required for format="voistat"

time_unit

NULL if in file (e.g. PMOD .tac), or set to "seconds" or "minutes" if not in file or to override file

activity_unit

NULL if in file (e.g. PMOD .tac), or set to "kBq/cc", "Bq/cc", "nCi/cc"

Value

tac object

See Also

Other Loading functions: as.tac(), load_voistat(), load_vol()

Examples

f_raw_tac <- system.file("extdata", "AD06.tac", package="tacmagic") 
tac <- load_tac(f_raw_tac)

Reads PMOD .voistat files and optionally merges volume-weighted ROIs

Description

PMOD can produce .voistat files with the average model values by ROI for its voxelwise binding potential (BPnd) models, such as Logan, SRTM, etc. This function reads the .voistat file and returns a data.frame with the ROI as rows and the model value as the column. Optionally, the ROIs can be combined into larger ROIs if ROI_def is specified, just as with TAC loading.

Usage

load_voistat(filename, ROI_def = NULL, model = "VALUE")

Arguments

filename

(e.g. participant_logan.voistat)

ROI_def

Optional ROI definitions to combine ROIs (e.g. roi_ham_pib())

model

A string to name the variable being extracted, e.g. "Logan_DVR"

Value

data.frame with loaded model data in specified combined weighted ROIs

See Also

Other Loading functions: as.tac(), load_tac(), load_vol()

Examples

f <- system.file("extdata", "AD06_BPnd_BPnd_Logan.voistat", 
                 package="tacmagic")
vs <- load_voistat(f, ROI_def=roi_ham_pib(), model="Logan")

Loads ROI volumes from file for use by other functions

Description

Loads ROI volumes from file for use by other functions

Usage

load_vol(filename, format = "voistat")

Arguments

filename

(e.g. participant.voistat)

format

(default is the TAC .voistat format from PMOD, also accepts "DFT and "BPndPaste")

Value

data.frame with loaded TAC data

See Also

Other Loading functions: as.tac(), load_tac(), load_voistat()

Examples

f_raw_vol <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic")

vol <- load_vol(f_raw_vol)

Non-invasive reference Logan plot

Description

This plots the non-invasive Logan plot.

Usage

## S3 method for class 'ref_Logan'
plot(x, ...)

Arguments

x

Reference Logan model data object from DVR_ref_Logan()

...

Additional parameters than can be passed to plotting function

Value

No return f <- system.file("extdata", "AD06.tac", package="tacmagic") fv <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic") AD06_tac <- load_tac(f, format="PMOD") AD06_volume <- load_vol(fv, format="voistat") AD06 <- tac_roi(tac=AD06_tac, volumes=AD06_volume, ROI_def=roi_ham_pib(), merge=FALSE, PVC=FALSE) AD06_DVR_fr <- DVR_ref_Logan(AD06, target="frontal", ref="cerebellum", k2prime=0.2, t_star=0) plot(AD06_DVR_fr)

See Also

Other Logan plot functions: DVR_all_ref_Logan(), DVR_ref_Logan(), dvr()

Plots time activity curves from 1 or 2 participants or groups.

Description

Plots time activity curves from 1 or 2 participants or groups.

Usage

## S3 method for class 'tac'
plot(
  x,
  tac2 = NULL,
  ROIs,
  ymax = 25,
  time = "minutes",
  title = "",
  colors = rainbow,
  ...
)

Arguments

x

A tac object containing time-activity curves to plot, e.g. from tac_roi() or load_tac()

tac2

An optional, second TAC, to plot for comparison

ROIs

A vector of ROIs to plot, names matching the TAC headers

ymax

The maximum value on the y-axis

time

"seconds" or "minutes" depending on desired x-axis, converts tac

title

A title for the plot

colors

If null, rainbow palette is used, otherwise another palette can be specified (heat.colors, terrain.colors, topo.colors, cm.colors

...

Additional arguments

Value

Creates a plot

See Also

Other tac functions: save_tac(), split_pvc(), tac_roi()

Examples

# f_raw_tac and f_raw_vol are the filenames of PMOD-generated files
f_raw_tac <- system.file("extdata", "AD06.tac", package="tacmagic") 
f_raw_vol <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic")

tac <- load_tac(f_raw_tac)
vol <- load_vol(f_raw_vol)
AD06_tac_nc <- tac_roi(tac, vol, roi_ham_full(), merge=FALSE, PVC=FALSE)
plot(AD06_tac_nc, ROIs=c("frontal", "cerebellum"), title="Example Plot")

Dichotomize participants based on ROI cutoff values

Description

Aizenstein et al. (2008) proposed a standardized method of calculating PIB+ cutoff values to classify participants as PIB+ or PIB-. They used the DVR from 7 ROIs associated with amyloid deposition. This function takes the ROI-based cutoff values, e.g. from cutoff_aiz(), and returns a table specifying which participants are positive, i.e. which have at least one ROI greater than the cutoff.

Usage

pos_anyroi(modelstats, cutoff)

Arguments

modelstats

SUVR or DVR data for group of participants from batch_tm()

cutoff

cutoffs for ROIs as from cutoff_aiz()

Value

data.frame of participants and positive/negative status

References

Aizenstein HJ, Nebes RD, Saxton JA, et al. 2008. Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch Neurol 65: 1509-1517.

See Also

Other Cutoff functions: cutoff_aiz()

Return a list of merged ROIs made up of the atomic ROIs in the AAL atlas. The groupings for major lobes are as described in the PMOD documentation.

Description

Return a list of merged ROIs made up of the atomic ROIs in the AAL atlas. The groupings for major lobes are as described in the PMOD documentation.

Usage

roi_AAL_full()

Value

A list of lists, where each list is an ROI (e.g.) frontal lobe that specifies the atomic ROIs from the atlas that make it up.

References

PMOD Groupings of the AAL Atlas.

Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, F. Crivello, O. Étard, N. Delcroix, B. Mazoyer, and M. Joliot. NeuroImage 2002, 15 :273-289 <doi:10.1006/nimg.2001.0978>

See Also

Other ROI definitions: roi_ham_full(), roi_ham_pib(), roi_ham_stand()

Examples

roi_AAL_full()

Return a list of larger ROIs made up of the ROIs in the Hammer's atlas.

Description

This includes the cortical regions of roi_ham_stand() but also other regions. It can be modified to suit the user's needs.

Usage

roi_ham_full()

Value

A list of lists, where each list is an ROI (e.g.) frontal lobe that specifies the atomic ROIs from the atlas that make it up.

References

Hammers, Alexander, Richard Allom, Matthias J. Koepp, Samantha L. Free, Ralph Myers, Louis Lemieux, Tejal N. Mitchell, David J. Brooks, and John S. Duncan. 2003. Three-dimensional Maximum Probability Atlas of the Human Brain, with Particular Reference to the Temporal Lobe. Human Brain Mapping 19 (4): 224-247. doi:10.1002/hbm.10123

See Also

Other ROI definitions: roi_AAL_full(), roi_ham_pib(), roi_ham_stand()

Examples

roi_ham_full()

Return a list of merged ROIs made up of atomic ROIs in the Hammer's atlas.

Description

This includes the ROIs from roi_ham_full and also the PIB cortical composite ROI as defined in the PMOD documentation and as widely used in PIB studies. See PMOD Neuro Tool (PNEURO) (Version 4.0) documentation.

Usage

roi_ham_pib()

Value

A list of lists, where each list is an ROI (e.g.) frontal lobe that specifies the atomic ROIs from the atlas that make it up.

References

Hammers, Alexander, Richard Allom, Matthias J. Koepp, Samantha L. Free, Ralph Myers, Louis Lemieux, Tejal N. Mitchell, David J. Brooks, and John S. Duncan. 2003. Three-dimensional Maximum Probability Atlas of the Human Brain, with Particular Reference to the Temporal Lobe. Human Brain Mapping 19 (4): 224-247. doi:10.1002/hbm.10123

See Also

Other ROI definitions: roi_AAL_full(), roi_ham_full(), roi_ham_stand()

Examples

roi_ham_pib()

Return a list of merged ROIs made up of the atomic ROIs in the Hammer's atlas.

Description

Return a list of merged ROIs made up of the atomic ROIs in the Hammer's atlas.

Usage

roi_ham_stand()

Value

A list of lists, where each list is an ROI (e.g.) frontal lobe that specifies the atomic ROIs from the atlas that make it up.

References

Hammers, Alexander, Richard Allom, Matthias J. Koepp, Samantha L. Free, Ralph Myers, Louis Lemieux, Tejal N. Mitchell, David J. Brooks, and John S. Duncan. 2003. Three-dimensional Maximum Probability Atlas of the Human Brain, with Particular Reference to the Temporal Lobe. Human Brain Mapping 19 (4): 224-247. doi:10.1002/hbm.10123

See Also

Other ROI definitions: roi_AAL_full(), roi_ham_full(), roi_ham_pib()

Examples

roi_ham_stand()

Save a tac object as a .tac file

Description

Saves a tac object, created by load_tac(), tac_roi() or manually, and saves it as a PMOD-formatted tac file. Using the .tac extension in the file name is recommended.

Usage

save_tac(tac, outfile)

Arguments

tac

The time-activity curve data, e.g. from load_tac() or tac_roi()

outfile

The output filename

Value

Does not return an object, only saves a file

See Also

Other tac functions: plot.tac(), split_pvc(), tac_roi()

Subset PMOD tacs with or without PVC

Description

When partial volume correction (PVC) is used in PMOD, the saved tac files have ROIs with and without PVC. When loaded with load_tac()) it may be desirable to keep only either the PVC or non-PVC tacs. This returns a tac object that is a subset of the input tac object with only the PVC or non-PVC tacs. This relies on PMOD's convention of labelling tac columns with "_C".

Usage

split_pvc(tac, PVC = TRUE)

Arguments

tac

The time-activity curve data from loading function (PMOD)

PVC

If TRUE, includes columns with "_C", if FALSE, ones without "_C"

Value

Time-activity curve object

See Also

Other tac functions: plot.tac(), save_tac(), tac_roi()

Examples

# f_raw_tac and f_raw_vol are the filenames of PMOD-generated files
f_raw_tac <- system.file("extdata", "AD06.tac", package="tacmagic") 

tac <- load_tac(f_raw_tac)
tac_pvc <- split_pvc(tac, TRUE)
tac_nc <- split_pvc(tac, FALSE)

Calculate average SUV over time window, or maximum SUV

Description

Calculate the standardized uptake value (SUV) from a tac object, the participant's weight, and the tracer dose. These values may be in the tac object or manually supplied. The weight must be in kg, and the tracer units must be specified. The dose is converted to MBq, the tac is converted to kBq/cc, and the final SUV units are thus in g/cc. Aside from the tac object, the remaining parameters should be left NULL if the required data is in the tac object attributes (as can be done with batch_load()).

Usage

suv(tac, SUV_def, dose = NULL, dose_unit = NULL, weight_kg = NULL, ...)

Arguments

tac

time-activity curve object (decay-corrected)

SUV_def

vector of start times for window for SUV weighted average, or alternatively, "max" for the maximum ROI SUV value

dose

the injected tracer dose

dose_unit

unit of tracer dose (e.g. "MBq", "kBq", "mCi"...)

weight_kg

the participant's weight in kg

...

When called from tm_batch, unused parameters may be supplied

Value

table of SUV values

See Also

Other SUV functions: tac_suv()

Examples

f <- system.file("extdata", "AD06.tac", package="tacmagic")
fv <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic")
AD06_tac <- load_tac(f, format="PMOD")
AD06_volume <- load_vol(fv, format="voistat")
AD06 <- tac_roi(tac=AD06_tac, volumes=AD06_volume, ROI_def=roi_ham_pib(),
                merge=FALSE, PVC=FALSE)
# dose and weight are fabricated for the example
AD06_suvmax <- suv(AD06, "max", dose = 9.0, dose_unit = "mCi",
                     weight_kg = 70)
AD06_suv <- suv(AD06, c(3000, 3300, 3600), dose = 9.0, dose_unit = "mCi",
                  weight_kg = 70)

Calculate weighted SUVRs for specified regions of interest

Description

Calculate the standardized uptake value ratio (SUVR) for all ROIs in the provided tac data, using the specified reference region.

Usage

suvr(tac, SUVR_def, ref, ...)

Arguments

tac

The time-activity curve data from tac_roi()

SUVR_def

a vector of start times for window to be used in SUVR

ref

a string, e.g. "cerebellum", to specify reference region

...

When called from tm_batch, unused parameters may be supplied

Value

A data.frame of SUVR values for the specified ROIs

See Also

Other SUVR functions: suvr_auc()

Examples

f <- system.file("extdata", "AD06.tac", package="tacmagic")
fv <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic")
AD06_tac <- load_tac(f, format="PMOD")
AD06_volume <- load_vol(fv, format="voistat")
AD06 <- tac_roi(tac=AD06_tac, volumes=AD06_volume, ROI_def=roi_ham_pib(),  
                merge=FALSE, PVC=FALSE)

AD06_SUVR <- suvr(AD06, SUVR_def=c(3000,3300,3600), ref="cerebellum")

Calculate SUVRs for regions of interest with AUC from mid-frame times

Description

Calculate the standardized uptake value ratio (SUVR) for all ROIs in the provided tac data, using the specified reference region. This is an alternate to suvr() which should provide very similar values.

Usage

suvr_auc(tac, SUVR_def, ref, ...)

Arguments

tac

The time-activity curve data from tac_roi()

SUVR_def

a vector of start times for window to be used in SUVR

ref

is a string, e.g. "cerebellum", to specify reference region

...

When called from tm_batch, unused parameters may be supplied

Value

A data.frame of SUVR values for the specified ROIs #' f <- system.file("extdata", "AD06.tac", package="tacmagic") fv <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic") AD06_tac <- load_tac(f, format="PMOD") AD06_volume <- load_vol(fv, format="voistat") AD06 <- tac_roi(tac=AD06_tac, volumes=AD06_volume, ROI_def=roi_ham_pib(), merge=FALSE, PVC=FALSE)

AD06_SUVR <- suvr_auc(AD06, SUVR_def=c(3000,3300,3600), ref="cerebellum")

See Also

Other SUVR functions: suvr()

Calculate weighted time-activity curves for specified regions of interest

Description

Calculate weighted time-activity curves for specified regions of interest

Usage

tac_roi(tac, volumes, ROI_def, merge, PVC)

Arguments

tac

The time-activity curve data from loading function

volumes

The ROI volume data from loading function

ROI_def

The definition of ROIs by combining smaller ROIs from TAC file

merge

If TRUE, includes the original ROIs in the output data

PVC

If TRUE, appends "_C" to ROI name header (as in PMOD TAC files)

Value

Time-activity curves for the specified ROIs

See Also

Other tac functions: plot.tac(), save_tac(), split_pvc()

Examples

# f_raw_tac and f_raw_vol are the filenames of PMOD-generated files
f_raw_tac <- system.file("extdata", "AD06.tac", package="tacmagic") 
f_raw_vol <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic")

tac <- load_tac(f_raw_tac)
vol <- load_vol(f_raw_vol)
AD06_tac_nc <- tac_roi(tac, vol, roi_ham_full(), merge=FALSE, PVC=FALSE)

Calculate SUV from TAC

Description

Calculate the standardized uptake value (SUV) time-activity curve from a tac object, the participant's weight, and the tracer dose. The weight must be in kg, and the tracer dose must be specified. The dose is converted to MBq, the tac is converted to kBq/cc, and the final SUV units are thus in g/cc. Aside from the tac object, the remaining parameters should be left NULL if the required data is in the tac object attributes (as can be done with batch_load().

Usage

tac_suv(tac, dose = NULL, dose_unit = NULL, weight_kg = NULL)

Arguments

tac

time-activity curve object (decay-corrected)

dose

the injected tracer dose

dose_unit

unit of tracer dose (e.g. "MBq", "kBq", "mCi"...)

weight_kg

the participant's weight in kg

Value

tac object with SUV values

See Also

Other SUV functions: suv()

Examples

f <- system.file("extdata", "AD06.tac", package="tacmagic")
fv <- system.file("extdata", "AD06_TAC.voistat", package="tacmagic")
AD06_tac <- load_tac(f, format="PMOD")
AD06_volume <- load_vol(fv, format="voistat")
AD06 <- tac_roi(tac=AD06_tac, volumes=AD06_volume, ROI_def=roi_ham_pib(),
                merge=FALSE, PVC=FALSE)
# dose and weight are fabricated for the example
AD06_suv <- tac_suv(AD06, dose = 9.0, dose_unit = "mCi", weight_kg = 70)

tacmagic: PET Analysis in R

Description

The main features of tacmagic are to load PET time activity curve (tac) data from multiple formats, merge ROIs weighted for volume, calculate binding potential models including SUVR and DVR, basic plotting, and calculation of cut-off values. Please see the walkthrough vignette for a detailed overview.