000908198 001__ 908198
000908198 005__ 20220621190117.0
000908198 0247_ $$2doi$$a10.1109/ISBI.2017.7950508
000908198 037__ $$aFZJ-2022-02451
000908198 1001_ $$0P:(DE-HGF)0$$aSforazzini, Francesco$$b0
000908198 1112_ $$a2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)$$cMelbourne$$d2017-04-18 - 2017-04-21$$wAustralia
000908198 245__ $$aMR-based attenuation map re-alignment and motion correction in simultaneous brain MR-PET imaging
000908198 260__ $$c2017
000908198 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1655807735_15481
000908198 3367_ $$033$$2EndNote$$aConference Paper
000908198 3367_ $$2BibTeX$$aINPROCEEDINGS
000908198 3367_ $$2DRIVER$$aconferenceObject
000908198 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000908198 3367_ $$2ORCID$$aOTHER
000908198 520__ $$aAbstract:Head movement is a major issue in dynamic PET imaging. A simultaneous MR-PET scanner is capable of acquiring both MR and PET data concurrently, which enables opportunities to use MR information for PET motion correction. Here we propose an MR-based method to detect head motion and to correct motion artefacts during PET image reconstruction. The method is based on co-registration of multiple MR contrasts to extract motion parameters. The motion parameters are then used to guide the Multiple Acquisition Frame (MAF) algorithm to bin the PET list-mode data into multiple frames whenever significant motion occurs. Furthermore, motion parameters are used to re-align the PET attenuation u-map to each frame prior to the image reconstruction. Finally, PET images are reconstructed for each frame and combined to produce a final image. Using both phantom and in-vivo human data, we show that this method can significantly increase image quality and reduce motion artefacts.
000908198 536__ $$0G:(DE-HGF)POF4-5253$$a5253 - Neuroimaging (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000908198 588__ $$aDataset connected to CrossRef Conference
000908198 7001_ $$0P:(DE-HGF)0$$aChen, Zhaolin$$b1
000908198 7001_ $$0P:(DE-HGF)0$$aBaran, Jakub$$b2
000908198 7001_ $$0P:(DE-HGF)0$$aBradley, Jason$$b3
000908198 7001_ $$0P:(DE-HGF)0$$aCarey, Alexandra$$b4
000908198 7001_ $$0P:(DE-Juel1)131794$$aShah, N. Jon$$b5$$ufzj
000908198 7001_ $$0P:(DE-HGF)0$$aEgan, Gary$$b6
000908198 773__ $$a10.1109/ISBI.2017.7950508
000908198 909CO $$ooai:juser.fz-juelich.de:908198$$pVDB
000908198 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131794$$aForschungszentrum Jülich$$b5$$kFZJ
000908198 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5253$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
000908198 9201_ $$0I:(DE-Juel1)INM-4-20090406$$kINM-4$$lPhysik der Medizinischen Bildgebung$$x0
000908198 9201_ $$0I:(DE-Juel1)INM-11-20170113$$kINM-11$$lJara-Institut Quantum Information$$x1
000908198 9201_ $$0I:(DE-Juel1)VDB1046$$kJARA-BRAIN$$lJülich-Aachen Research Alliance - Translational Brain Medicine$$x2
000908198 980__ $$aabstract
000908198 980__ $$aVDB
000908198 980__ $$aI:(DE-Juel1)INM-4-20090406
000908198 980__ $$aI:(DE-Juel1)INM-11-20170113
000908198 980__ $$aI:(DE-Juel1)VDB1046
000908198 980__ $$aUNRESTRICTED