Quantify spheres using concentrations

Parameters

run_parallele: Boolean ( input )
do_AimsRoiFeatures: Boolean ( input )
PET_mean: Phantom PET Mean ( input )
sph_rois: Phantom ROI Sph ( input )
sph_roi_in_Noise: Phantom ROI Sph inNoise ( input )
CT: Phantom CT ( optional, input )
center: String ( input )
Pets_mean: ListOf( Phantom PET Mean ) ( input )
CT2PetMeans: ListOf( CT_to_PETMean ) ( input )
theoric_values: Phantom Theoric Values ( optional, output )
bkg_preparedConcentration: Float ( input )
prepared concentration in background unit is bequerel
bkg_H_M_pre: String ( input )
bkg_residualConcentration: Float ( input )
residual concentration in background unit is bequerel
bkg_H_M_res: String ( input )
sph_preparedConcentration: Float ( input )
prepared concentration in spheres unit is bequerel
sph_H_M_pre: String ( input )
sph_residualConcentration: Float ( input )
residual concentration in spheres unit is bequerel
sph_H_M_res: String ( input )
H_M_acq: String ( input )
periode_isotope: Float ( input )
stat: Phantom Activity Stats ( output )
stat_in_noise: Phantom Noise Stats ( output )
result: Phantom Result ( output )
stat_all: ListOf( Phantom Activity Stats ) ( output )
stat_in_noise_all: ListOf( Phantom Noise Stats ) ( output )
result_all: ListOf( Phantom Result ) ( output )

Technical information

Toolbox : Nuclear Imaging

User level : 1

Identifier : jzkQuantifySph_fromConcentration

File name : brainvisa/toolboxes/nuclearimaging/processes/jaszczak/jzkQuantifySph_fromConcentration.py

Supported file formats :

PET_mean :
NIFTI-1 image, NIFTI-1 image
sph_rois :
NIFTI-1 image, NIFTI-1 image
sph_roi_in_Noise :
NIFTI-1 image, NIFTI-1 image
CT :
NIFTI-1 image, NIFTI-1 image
Pets_mean :
NIFTI-1 image, NIFTI-1 image
CT2PetMeans :
Transformation matrix, Transformation matrix
theoric_values :
CSV file, CSV file
stat :
CSV file, CSV file
stat_in_noise :
CSV file, CSV file
result :
CSV file, CSV file
stat_all :
CSV file, CSV file
stat_in_noise_all :
CSV file, CSV file
result_all :
CSV file, CSV file