001     861490
005     20210130000828.0
024 7 _ |a 10.1186/s12880-018-0283-3
|2 doi
024 7 _ |a 2128/21949
|2 Handle
024 7 _ |a pmid:30400875
|2 pmid
024 7 _ |a WOS:000449349600002
|2 WOS
024 7 _ |a altmetric:50976482
|2 altmetric
037 _ _ |a FZJ-2019-01951
082 _ _ |a 610
100 1 _ |a Baran, Jakub
|0 0000-0002-4946-3837
|b 0
|e Corresponding author
245 _ _ |a Accurate hybrid template–based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications
260 _ _ |a London
|c 2018
|b BioMed Central
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1553886874_25990
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a BackgroundAttenuation correction is one of the most crucial correction factors for accurate PET data quantitation in hybrid PET/MR scanners, and computing accurate attenuation coefficient maps from MR brain acquisitions is challenging. Here, we develop a method for accurate bone and air segmentation using MR ultrashort echo time (UTE) images.MethodsMR UTE images from simultaneous MR and PET imaging of five healthy volunteers was used to generate a whole head, bone and air template image for inclusion into an improved MR derived attenuation correction map, and applied to PET image data for quantitative analysis. Bone, air and soft tissue were segmented based on Gaussian Mixture Models with probabilistic tissue maps as a priori information. We present results for two approaches for bone attenuation coefficient assignments: one using a constant attenuation correction value; and another using an estimated continuous attenuation value based on a calibration fit. Quantitative comparisons were performed to evaluate the accuracy of the reconstructed PET images, with respect to a reference image reconstructed with manually segmented attenuation maps.ResultsThe DICE coefficient analysis for the air and bone regions in the images demonstrated improvements compared to the UTE approach, and other state-of-the-art techniques. The most accurate whole brain and regional brain analyses were obtained using constant bone attenuation coefficient values.ConclusionsA novel attenuation correction method for PET data reconstruction is proposed. Analyses show improvements in the quantitative accuracy of the reconstructed PET images compared to other state-of-the-art AC methods for simultaneous PET/MR scanners. Further evaluation is needed with radiopharmaceuticals other than FDG, and in larger cohorts of participants.
536 _ _ |a 573 - Neuroimaging (POF3-573)
|0 G:(DE-HGF)POF3-573
|c POF3-573
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Chen, Zhaolin
|b 1
700 1 _ |a Sforazzini, Francesco
|b 2
700 1 _ |a Ferris, Nicholas
|b 3
700 1 _ |a Jamadar, Sharna
|b 4
700 1 _ |a Schmitt, Ben
|b 5
700 1 _ |a Faul, David
|b 6
700 1 _ |a Shah, N. J.
|0 P:(DE-Juel1)131794
|b 7
|u fzj
700 1 _ |a Cholewa, Marian
|b 8
700 1 _ |a Egan, Gary F.
|b 9
773 _ _ |a 10.1186/s12880-018-0283-3
|g Vol. 18, no. 1, p. 41
|0 PERI:(DE-600)2061975-3
|n 1
|p 41
|t BMC medical imaging
|v 18
|y 2018
|x 1471-2342
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/861490/files/s12880-018-0283-3.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/861490/files/s12880-018-0283-3.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:861490
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 7
|6 P:(DE-Juel1)131794
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-573
|2 G:(DE-HGF)POF3-500
|v Neuroimaging
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2019
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b BMC MED IMAGING : 2017
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Open peer review
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-4-20090406
|k INM-4
|l Physik der Medizinischen Bildgebung
|x 0
920 1 _ |0 I:(DE-82)080010_20140620
|k JARA-BRAIN
|l JARA-BRAIN
|x 1
920 1 _ |0 I:(DE-Juel1)INM-11-20170113
|k INM-11
|l Jara-Institut Quantum Information
|x 2
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-4-20090406
980 _ _ |a I:(DE-82)080010_20140620
980 _ _ |a I:(DE-Juel1)INM-11-20170113
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21