001     872864
005     20220930130228.0
024 7 _ |a 10.3389/fphys.2019.01617
|2 doi
024 7 _ |a 2128/24105
|2 Handle
024 7 _ |a altmetric:74892229
|2 altmetric
024 7 _ |a pmid:32063864
|2 pmid
024 7 _ |a WOS:000514299600001
|2 WOS
037 _ _ |a FZJ-2020-00330
082 _ _ |a 610
100 1 _ |0 P:(DE-Juel1)171331
|a He, Xuan
|b 0
245 _ _ |a Image-Derived Input Functions for Quantification of A1 Adenosine Receptors Availability in Mice Brains Using PET and [18F]CPFPX
260 _ _ |a Lausanne
|b Frontiers Research Foundation
|c 2020
336 7 _ |2 DRIVER
|a article
336 7 _ |2 DataCite
|a Output Types/Journal article
336 7 _ |0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
|a Journal Article
|b journal
|m journal
|s 1580388991_17363
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 ORCID
|a JOURNAL_ARTICLE
336 7 _ |0 0
|2 EndNote
|a Journal Article
520 _ _ |a In vivo imaging for the A1 adenosine receptors (A1ARs) with positron emission tomography (PET) using 8-cyclopentyl-3-(3-[18F]fluoropropyl)-1-propylxanthine ([18F]CPFPX) has become an important tool for studying physiologic processes quantitatively in mice. However, the measurement of arterial input functions (AIFs) on mice is a method with restricted applicability because of the small total blood volume and the related difficulties in withdrawing blood. Therefore, the aim of this study was to extract an appropriate [18F]CPFPX image-derived input function (IDIF) from dynamic PET images of mice.In this study five mice were scanned with [18F]CPFPX for 60 min. Arterial blood samples (n=7 per animal) were collected from the femoral artery and corrected for metabolites. To generate IDIFs, three different approaches were selected: (A) volume of interest (VOI) placed over the heart (cube, 10mm); (B) VOI set over abdominal vena cava/aorta region with a cuboid (5 × 5 × 15mm); and (C) with 1 × 1 × 1mm voxels on 5 consecutive slices. A calculated scaling factor (α) was used to correct for partial volume effect, the method of obtaining the total metabolite correction of [18F]CPFPX for IDIFs was developed. Three IDIFs were validated by comparison with AIF. Validation included: visual performance; computing area under the curve (AUC) ratios (IDIF / AIF) of whole-blood curves and parent curves; in addition, the mean distribution volume (VT) ratios (IDIF / AIF) of A1ARs calculated by Logan plot and two-tissue compartment model (2TCM).Compared with the AIF, the IDIF with VOI over heart showed the best performance among the three IDIFs after scaling by 1.77 (α) in terms of visual analysis, AUC ratios (IDIF / AIF, whole-blood AUC ratio 1.03 ± 0.06, parent curve AUC ratio 1.01 ± 0.10) and VT ratios (IDIF / AIF; Logan VT ratio 1.00 ± 0.17, 2TCM VT ratio 1.00 ± 0.13) evaluation. The A1ARs distribution of average parametric images was in good accordance to autoradiography of the same mice brains.The proposed study provides evidence that IDIF with VOI over heart can replace AIF effectively for quantification of A1ARs by using PET and [18F]CPFPX in mice brains.
536 _ _ |0 G:(DE-HGF)POF3-573
|a 573 - Neuroimaging (POF3-573)
|c POF3-573
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |0 P:(DE-Juel1)131711
|a Wedekind, Franziska
|b 1
700 1 _ |0 P:(DE-Juel1)131691
|a Kroll, Tina
|b 2
700 1 _ |0 P:(DE-Juel1)131712
|a Oskamp, Angela
|b 3
700 1 _ |0 P:(DE-Juel1)133864
|a Beer, Simone
|b 4
700 1 _ |0 P:(DE-Juel1)177611
|a Drzezga, Alexander
|b 5
700 1 _ |0 P:(DE-Juel1)131818
|a Ermert, Johannes
|b 6
|u fzj
700 1 _ |0 P:(DE-Juel1)166419
|a Neumaier, Bernd
|b 7
700 1 _ |0 P:(DE-Juel1)131672
|a Bauer, Andreas
|b 8
700 1 _ |0 P:(DE-Juel1)131679
|a Elmenhorst, David
|b 9
|e Corresponding author
773 _ _ |0 PERI:(DE-600)2564217-0
|a 10.3389/fphys.2019.01617
|g Vol. 10, p. 1617
|p 1617
|t Frontiers in physiology
|v 10
|x 1664-042X
|y 2020
856 4 _ |u https://juser.fz-juelich.de/record/872864/files/fphys-10-01617.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/872864/files/fphys-10-01617.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:872864
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)171331
|a Forschungszentrum Jülich
|b 0
|k FZJ
910 1 _ |0 I:(DE-HGF)0
|6 P:(DE-Juel1)171331
|a 2Department of Neurophysiology, Institute of Zoology (Bio-II), RWTH Aachen University, Aachen, Germany
|b 0
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)131711
|a Forschungszentrum Jülich
|b 1
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)131691
|a Forschungszentrum Jülich
|b 2
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)131712
|a Forschungszentrum Jülich
|b 3
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)133864
|a Forschungszentrum Jülich
|b 4
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)177611
|a Forschungszentrum Jülich
|b 5
|k FZJ
910 1 _ |0 I:(DE-HGF)0
|6 P:(DE-Juel1)177611
|a Department of Nuclear Medicine, University Hospital of Cologne, Cologne, Germany
|b 5
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)131818
|a Forschungszentrum Jülich
|b 6
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)166419
|a Forschungszentrum Jülich
|b 7
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)131672
|a Forschungszentrum Jülich
|b 8
|k FZJ
910 1 _ |0 I:(DE-HGF)0
|6 P:(DE-Juel1)131672
|a Neurological Department, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
|b 8
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)131679
|a Forschungszentrum Jülich
|b 9
|k FZJ
910 1 _ |0 I:(DE-HGF)0
|6 P:(DE-Juel1)131679
|a Division of Medical Psychology, University of Bonn, Bonn, Germany
|b 9
913 1 _ |0 G:(DE-HGF)POF3-573
|1 G:(DE-HGF)POF3-570
|2 G:(DE-HGF)POF3-500
|a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|v Neuroimaging
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2020
915 _ _ |0 StatID:(DE-HGF)0200
|2 StatID
|a DBCoverage
|b SCOPUS
915 _ _ |0 StatID:(DE-HGF)1050
|2 StatID
|a DBCoverage
|b BIOSIS Previews
915 _ _ |0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
|a Creative Commons Attribution CC BY 4.0
915 _ _ |0 StatID:(DE-HGF)0100
|2 StatID
|a JCR
|b FRONT PHYSIOL : 2017
915 _ _ |0 StatID:(DE-HGF)0501
|2 StatID
|a DBCoverage
|b DOAJ Seal
915 _ _ |0 StatID:(DE-HGF)0500
|2 StatID
|a DBCoverage
|b DOAJ
915 _ _ |0 StatID:(DE-HGF)0111
|2 StatID
|a WoS
|b Science Citation Index Expanded
915 _ _ |0 StatID:(DE-HGF)0150
|2 StatID
|a DBCoverage
|b Web of Science Core Collection
915 _ _ |0 StatID:(DE-HGF)9900
|2 StatID
|a IF < 5
915 _ _ |0 StatID:(DE-HGF)0510
|2 StatID
|a OpenAccess
915 _ _ |0 StatID:(DE-HGF)0030
|2 StatID
|a Peer Review
|b DOAJ : Blind peer review
915 _ _ |0 StatID:(DE-HGF)0310
|2 StatID
|a DBCoverage
|b NCBI Molecular Biology Database
915 _ _ |0 StatID:(DE-HGF)0300
|2 StatID
|a DBCoverage
|b Medline
915 _ _ |0 StatID:(DE-HGF)0320
|2 StatID
|a DBCoverage
|b PubMed Central
915 _ _ |0 StatID:(DE-HGF)0199
|2 StatID
|a DBCoverage
|b Clarivate Analytics Master Journal List
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-2-20090406
|k INM-2
|l Molekulare Organisation des Gehirns
|x 0
920 1 _ |0 I:(DE-Juel1)INM-5-20090406
|k INM-5
|l Nuklearchemie
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-2-20090406
980 _ _ |a I:(DE-Juel1)INM-5-20090406
980 _ _ |a APC
980 1 _ |a APC
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21