Paper Info
Title
Brain tissue selection procedures for image derived input functions derived using independent components analysis.
Abstract
Absolute quantification of positron emission tomography (PET) data requires invasive blood sampling in order to obtain the arterial input function (AIF). This procedure involves considerable costs and risks. A less invasive approach is to estimate the AIF directly from images, known as an image derived input function (IDIF). One promising method, EPICA, extracts IDIF by applying independent components analysis (ICA) on dynamic PET data from the entire brain. EPICA requires exclusion of non-brain voxels from the PET images, which is achieved by using a brain mask prior to ICA. Including the entire brain in the mask may degrade the performance of ICA due to noise, artifacts and confounding information. We applied EPICA to 3 [(18)F]FDG and 3 [(11)C]WAY data sets and investigated if altering the brain mask by including or excluding tissue structures improves EPICA performance. EPICA applied to whole brain data yields poor performance but with the appropriate brain mask IDIF curves approximate the AIF well. Different tissue structures are important for different radiotracers suggesting that the kinetics of the radiotracer and its diffusion characteristics in the brain influence IDIF estimation with ICA.
Year
DOI
Venue
2012
10.1109/EMBC.2012.6347358
EMBC
Keywords
Field
DocType
nonbrain voxels,aif,artifacts,radioactive tracers,[11c]way data sets,image derived input functions,noise,dynamic pet data,mri,blood vessels,idif,diffusion characteristics,image denoising,independent component analysis,radiotracers,input function,invasive blood sampling,data acquisition,biomedical mri,confounding information,positron emission tomography,arterial input function,pet,brain,[18f]fdg data sets,tissue structures,ica,brain tissue selection procedures,independent components analysis,arterial blood sampling,brain mask,medical image processing,blood,algorithms
Nuclear medicine,Voxel,Data set,Computer science,Arterial input function,Artificial intelligence,Positron emission tomography,Input function,Brain tissue,Computer vision,Pattern recognition,Independent component analysis,Blood sampling
Conference
Volume
ISSN
ISBN
2012
1557-170X
978-1-4577-1787-1
Citations 
PageRank 
References 
0
0.34
2
Authors
5
Name
Order
Citations
PageRank
Arthur Mikhno183.24
Francesca Zanderigo2236.97
Mika Naganawa300.34
Andrew F. Laine474783.01
Ramin V Parsey544318.18