001     858926
005     20210130000136.0
024 7 _ |a 10.1039/9781788013062-00162
|2 doi
037 _ _ |a FZJ-2018-07762
100 1 _ |a Scheins, Jürgen
|0 P:(DE-Juel1)131791
|b 0
|e Corresponding author
245 _ _ |a CHAPTER 7. PET Quantification
260 _ _ |a Cambridge
|c 2018
|b Royal Society of Chemistry
295 1 0 |a Hybrid MR-PET Imaging / Shah, N Jon (Editor)
300 _ _ |a 162 - 182
336 7 _ |a BOOK_CHAPTER
|2 ORCID
336 7 _ |a Book Section
|0 7
|2 EndNote
336 7 _ |a bookPart
|2 DRIVER
336 7 _ |a INBOOK
|2 BibTeX
336 7 _ |a Output Types/Book chapter
|2 DataCite
336 7 _ |a Contribution to a book
|b contb
|m contb
|0 PUB:(DE-HGF)7
|s 1553865154_25990
|2 PUB:(DE-HGF)
490 0 _ |a New Developments in NMR
520 _ _ |a A major benefit of the three-dimensional (3D) PET imaging technique in neuroscience, as well as in clinical applications, is that it offers the possibility of dynamically quantifying metabolic processes with a sensitivity of up to 10−12 mol L−1 for the tracer concentration. However, all positron emission tomographs provide biased data with complex dependencies, which means that to obtain quantitative activity distributions in 3D, it is necessary to make several corrections. For example, inhomogeneous detector efficiencies, photon attenuation, Compton scattering, and random coincidences need to be corrected. Furthermore, dynamic imaging represents a challenge, because a high temporal resolution requires short acquisition time frames with rather poor statistics of recorded events from the radioactive decay. Apart from the necessary corrections, the applied reconstruction method has an important impact on the achievable image quality in PET. In this respect, iterative reconstruction methods are becoming the state-of-the-art techniques as they offer superior image quality when compared to analytical methods. Although iterative reconstruction is associated with higher computational demand, the higher calculation effort can be moderated by using a range of optimisation strategies and has been further helped by the remarkable boost in computational resources over the last two decades.
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 Book Series
700 1 _ |a Kops, E. Rota
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Caldeira, L.
|0 P:(DE-Juel1)159195
|b 2
700 1 _ |a Ma, B.
|0 P:(DE-Juel1)169363
|b 3
773 _ _ |a 10.1039/9781788013062-00162
787 0 _ |a Shah, N Jon
|d Cambridge : Royal Society of Chemistry, 2018
|i RelatedTo
|0 FZJ-2018-02194
|r
|t Hybrid MR-PET Imaging: Systems, Methods and Applications
856 4 _ |u https://juser.fz-juelich.de/record/858926/files/9781788013062-00162.pdf
|y Restricted
856 4 _ |u https://juser.fz-juelich.de/record/858926/files/9781788013062-00162.pdf?subformat=pdfa
|x pdfa
|y Restricted
909 C O |o oai:juser.fz-juelich.de:858926
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)131791
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)159195
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)169363
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
920 1 _ |0 I:(DE-Juel1)INM-4-20090406
|k INM-4
|l Physik der Medizinischen Bildgebung
|x 0
920 1 _ |0 I:(DE-Juel1)INM-11-20170113
|k INM-11
|l Jara-Institut Quantum Information
|x 1
920 1 _ |0 I:(DE-82)080010_20140620
|k JARA-BRAIN
|l JARA-BRAIN
|x 2
980 _ _ |a contb
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-4-20090406
980 _ _ |a I:(DE-Juel1)INM-11-20170113
980 _ _ |a I:(DE-82)080010_20140620
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21