Gast ::
| Hauptseite > Publikationsdatenbank > Scatter Correction based on GPU-accelerated Full Monte Carlo Simulation for Brain PET/MRI > print |
| Hauptseite > Publikationsdatenbank > Scatter Correction based on GPU-accelerated Full Monte Carlo Simulation for Brain PET/MRI > print |
| 001 | 863322 | ||
| 005 | 20210130002018.0 | ||
| 024 | 7 | _ | |a 10.1109/TMI.2019.2921872 |2 doi |
| 024 | 7 | _ | |a 0278-0062 |2 ISSN |
| 024 | 7 | _ | |a 1558-0062 |2 ISSN |
| 024 | 7 | _ | |a 1558-254X |2 ISSN |
| 024 | 7 | _ | |a 2128/25525 |2 Handle |
| 024 | 7 | _ | |a pmid:31180843 |2 pmid |
| 024 | 7 | _ | |a WOS:000506577100013 |2 WOS |
| 037 | _ | _ | |a FZJ-2019-03402 |
| 082 | _ | _ | |a 620 |
| 100 | 1 | _ | |a Ma, Bo |0 P:(DE-Juel1)169363 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Scatter Correction based on GPU-accelerated Full Monte Carlo Simulation for Brain PET/MRI |
| 260 | _ | _ | |a New York, NY |c 2020 |b IEEE |
| 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 1597733868_12991 |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 Accurate scatter correction is essential for qualitative and quantitative PET imaging. Until now, scatter correction based on Monte Carlo simulation (MCS) has been recognized as the most accurate method of scatter correction for PET. However, the major disadvantage of MCS is its long computational time, which makes it unfeasible for clinical usage. Meanwhile, single scatter simulation (SSS) is the most widely used method for scatter correction. Nevertheless, SSS has the disadvantage of limited robustness for dynamic measurements and for the measurement of large objects. In this work, a newly developed implementation of MCS using graphics processing unit (GPU) acceleration is employed, allowing full MCS-based scatter correction in clinical 3D brain PET imaging. Starting from the generation of annihilation photons to their detection in the simulated PET scanner, all relevant physical interactions and transport phenomena of the photons were simulated on GPUs. This resulted in an expected distribution of scattered events, which was subsequently used to correct the measured emission data. The accuracy of the approach was validated with simulations using GATE (Geant4 Application for Tomography Emission), and its performance was compared to SSS. The comparison of the computation time between a GPU and a single-threaded CPU showed an acceleration factor of 776 for a voxelized brain phantom study. The speedup of the MCS implemented on the GPU represents a major step toward the application of the more accurate MCS-based scatter correction for PET imaging in clinical routine |
| 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 Gaens, Michaela |0 P:(DE-Juel1)144826 |b 1 |
| 700 | 1 | _ | |a Caldeira, Liliana |0 P:(DE-Juel1)159195 |b 2 |u fzj |
| 700 | 1 | _ | |a Bert, Julian |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Lohmann, Philipp |0 P:(DE-Juel1)145110 |b 4 |u fzj |
| 700 | 1 | _ | |a Tellmann, Lutz |0 P:(DE-Juel1)131797 |b 5 |u fzj |
| 700 | 1 | _ | |a Lerche, Christoph |0 P:(DE-Juel1)164254 |b 6 |u fzj |
| 700 | 1 | _ | |a Scheins, Jurgen |0 P:(DE-Juel1)131791 |b 7 |u fzj |
| 700 | 1 | _ | |a Kops, Elena Rota |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Xu, Hancong |0 P:(DE-Juel1)168272 |b 9 |u fzj |
| 700 | 1 | _ | |a Lenz, Mirjam |0 P:(DE-Juel1)165812 |b 10 |u fzj |
| 700 | 1 | _ | |a Pietrzyk, Uwe |0 P:(DE-Juel1)131667 |b 11 |
| 700 | 1 | _ | |a Shah, N. J. |0 P:(DE-Juel1)131794 |b 12 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1109/TMI.2019.2921872 |g p. 1 - 1 |0 PERI:(DE-600)2068206-2 |n 1 |p 140-151 |t IEEE transactions on medical imaging |v 39 |y 2020 |x 1558-254X |
| 856 | 4 | _ | |y Restricted |u https://juser.fz-juelich.de/record/863322/files/08733836.pdf |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/863322/files/Post_Print_accepted_manuscript.pdf |
| 856 | 4 | _ | |y Restricted |x pdfa |u https://juser.fz-juelich.de/record/863322/files/08733836.pdf?subformat=pdfa |
| 909 | C | O | |o oai:juser.fz-juelich.de:863322 |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 0 |6 P:(DE-Juel1)169363 |
| 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 4 |6 P:(DE-Juel1)145110 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)131797 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)164254 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)131791 |
| 910 | 1 | _ | |a inm4 |0 I:(DE-HGF)0 |b 8 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 9 |6 P:(DE-Juel1)168272 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 10 |6 P:(DE-Juel1)165812 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 12 |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 2020 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1160 |2 StatID |b Current Contents - Engineering, Computing and Technology |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b IEEE T MED IMAGING : 2017 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0110 |2 StatID |b Science Citation Index |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b IEEE T MED IMAGING : 2017 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1110 |2 StatID |b Current Contents - Clinical Medicine |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-11-20170113 |k INM-11 |l Jara-Institut Quantum Information |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-4-20090406 |k INM-4 |l Physik der Medizinischen Bildgebung |x 1 |
| 920 | 1 | _ | |0 I:(DE-82)080010_20140620 |k JARA-BRAIN |l JARA-BRAIN |x 2 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-11-20170113 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-4-20090406 |
| 980 | _ | _ | |a I:(DE-82)080010_20140620 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|