Home > Publications database > Analytical and fast Fiber Orientation Distribution reconstruction in 3D-Polarized Light Imaging > print

001     877886
005     20210423193416.0
024 7 _ |a 10.1016/j.media.2020.101760
|2 doi
024 7 _ |a 1361-8415
|2 ISSN
024 7 _ |a 1361-8423
|2 ISSN
024 7 _ |a 1361-8431
|2 ISSN
024 7 _ |a 2128/25906
|2 Handle
024 7 _ |a pmid:32629230
|2 pmid
024 7 _ |a WOS:000567866400002
|2 WOS
024 7 _ |a altmetric:96154194
|2 altmetric
037 _ _ |a FZJ-2020-02491
082 _ _ |a 610
100 1 _ |a Alimi, Abib
|0 0000-0002-7552-4744
|b 0
|e Corresponding author
245 _ _ |a Analytical and fast Fiber Orientation Distribution reconstruction in 3D-Polarized Light Imaging
260 _ _ |a Amsterdam [u.a.]
|c 2020
|b Elsevier Science
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 1619160694_2070
|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 Three dimensional Polarized Light Imaging (3D-PLI) is an optical technique which allows mapping the spatial fiber architecture of fibrous postmortem tissues, at sub-millimeter resolutions. Here, we propose an analytical and fast approach to compute the fiber orientation distribution (FOD) from high-resolution vector data provided by 3D-PLI. The FOD is modeled as a sum of K orientations/Diracs on the unit sphere, described on a spherical harmonics basis and analytically computed using the spherical Fourier transform. Experiments are performed on rich synthetic data which simulate the geometry of the neuronal fibers and on human brain data. Results indicate the analytical FOD is computationally efficient and very fast, and has high angular precision and angular resolution. Furthermore, investigations on the right occipital lobe illustrate that our strategy of FOD computation enables the bridging of spatial scales from microscopic 3D-PLI information to macro- or mesoscopic dimensions of diffusion Magnetic Resonance Imaging (MRI), while being a means to evaluate prospective resolution limits for diffusion MRI to reconstruct region-specific white matter tracts. These results demonstrate the interest and great potential of our analytical approach.
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
|0 G:(DE-HGF)POF3-574
|c POF3-574
|f POF III
|x 0
536 _ _ |a 573 - Neuroimaging (POF3-573)
|0 G:(DE-HGF)POF3-573
|c POF3-573
|f POF III
|x 1
536 _ _ |a 571 - Connectivity and Activity (POF3-571)
|0 G:(DE-HGF)POF3-571
|c POF3-571
|f POF III
|x 2
536 _ _ |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
|0 G:(EU-Grant)785907
|c 785907
|f H2020-SGA-FETFLAG-HBP-2017
|x 3
536 _ _ |a CoBCoM - Computational Brain Connectivity Mapping (694665)
|0 G:(EU-Grant)694665
|c 694665
|f ERC-2015-AdG
|x 4
536 _ _ |a SLNS - SimLab Neuroscience (Helmholtz-SLNS)
|0 G:(DE-Juel1)Helmholtz-SLNS
|c Helmholtz-SLNS
|x 5
536 _ _ |a 3D Reconstruction of Nerve Fibers in the Human, the Monkey, the Rodent, and the Pigeon Brain (jinm11_20181101)
|0 G:(DE-Juel1)jinm11_20181101
|c jinm11_20181101
|f 3D Reconstruction of Nerve Fibers in the Human, the Monkey, the Rodent, and the Pigeon Brain
|x 6
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Deslauriers-Gauthier, Samuel
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Matuschke, Felix
|0 P:(DE-Juel1)169807
|b 2
|u fzj
700 1 _ |a Müller, Andreas
|0 P:(DE-Juel1)151332
|b 3
700 1 _ |a Muenzing, Sascha E. A.
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Axer, Markus
|0 P:(DE-Juel1)131632
|b 5
|u fzj
700 1 _ |a Deriche, Rachid
|0 0000-0002-4643-8417
|b 6
773 _ _ |a 10.1016/j.media.2020.101760
|g Vol. 65, p. 101760 -
|0 PERI:(DE-600)1497450-2
|p 101760
|t Medical image analysis
|v 65
|y 2020
|x 1361-8415
856 4 _ |u https://juser.fz-juelich.de/record/877886/files/Alimi_etal_Med%20Image%20Analy_2020_preprint.pdf
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/877886/files/Analytical_and_Fast_Fiber_Orientation_Distribution.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/877886/files/Alimi_etal_Analytical_and_fast_Fiber_Orientation_Distribution_reconstruction_authors_version.pdf
|y OpenAccess
|z StatID:(DE-HGF)0510
856 4 _ |u https://juser.fz-juelich.de/record/877886/files/Alimi_etal_Med%20Image%20Analy_2020_preprint.pdf?subformat=pdfa
|x pdfa
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/877886/files/Alimi_etal_Analytical_and_fast_Fiber_Orientation_Distribution_reconstruction_authors_version.pdf?subformat=pdfa
|x pdfa
|y OpenAccess
|z StatID:(DE-HGF)0510
909 C O |o oai:juser.fz-juelich.de:877886
|p openaire
|p open_access
|p driver
|p VDB
|p ec_fundedresources
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)169807
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)151332
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)131632
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-574
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Theory, modelling and simulation
|x 0
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
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Neuroimaging
|x 1
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-571
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Connectivity and Activity
|x 2
913 2 _ |a DE-HGF
|b Programmungebundene Forschung
|l ohne Programm
|1 G:(DE-HGF)POF4-890
|0 G:(DE-HGF)POF4-899
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-800
|4 G:(DE-HGF)POF
|v ohne Topic
|x 0
914 1 _ |y 2020
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-01-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-01-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1160
|2 StatID
|b Current Contents - Engineering, Computing and Technology
|d 2020-01-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2020-01-10
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b MED IMAGE ANAL : 2018
|d 2020-01-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-01-10
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
|d 2020-01-10
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
|d 2020-01-10
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2020-01-10
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b MED IMAGE ANAL : 2018
|d 2020-01-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-01-10
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-01-10
920 1 _ |0 I:(DE-Juel1)INM-1-20090406
|k INM-1
|l Strukturelle und funktionelle Organisation des Gehirns
|x 0
920 1 _ |0 I:(DE-82)080012_20140620
|k JARA-HPC
|l JARA - HPC
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-1-20090406
980 _ _ |a I:(DE-82)080012_20140620
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21