000908204 001__ 908204
000908204 005__ 20220621190117.0
000908204 0247_ $$2doi$$a10.1109/NSSMIC.2018.8824431
000908204 037__ $$aFZJ-2022-02455
000908204 1001_ $$0P:(DE-Juel1)168272$$aXu, Hancong$$b0
000908204 1112_ $$a2018 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)$$cSydney$$d2018-11-10 - 2018-11-17$$wAustralia
000908204 245__ $$aRESampling between Projection SpACEs (RESPACE) using Bayse’ Theorem
000908204 260__ $$c2018
000908204 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1655804407_15481
000908204 3367_ $$033$$2EndNote$$aConference Paper
000908204 3367_ $$2BibTeX$$aINPROCEEDINGS
000908204 3367_ $$2DRIVER$$aconferenceObject
000908204 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000908204 3367_ $$2ORCID$$aOTHER
000908204 520__ $$aAbstract:In order to simplify the Point-Spread-Function (PSF) reconstruction framework, resolution modelling can be decoupled from the iterative reconstruction process by an additional data resampling step previous to reconstruction. We call the proposed algorithm RESampling between Projection spACEs (RESPACE). In this abstract, RESPACE is applied to resample the simulated projection data to 2D Generic Cylinder Model (GCM) projection data structure, which will be used for reconstruction afterwards. Theoretically, the proposed algorithm merges pre-calculated detection probability information and prior information into the resampled projection data by applying Bayes' Theorem. In contrast to conventional projection data handling, RESPACE can make the iterative reconstruction isolated from any detection model or PSF modelling, ensuring the closed structure of normal non-PSF iterative algorithms. In this study, we implemented a 2D-PET simulation Monte Carlo framework, which has the same geometrical property as Siemens BrainPET transverse structure. Conventional MLEM, MLEM-PSF (both image space and projection space with shift-invariant kernel) and RESPACE are implemented and investigated. As figures of merit, Bias-Resolution curves demonstrate that RESPACE could achieve similar resolution and even better bias suppression performance as the PSF method with a shift-invariant kernel. Moreover, no significant visual difference is observed between images from PSF and RESPACE reconstruction. These results demonstrate that RESAPCE offers equivalent performance as the shift-invariant PSF method and this approach is an alternative resolution modelling method independent from the iterative reconstruction algorithm.
000908204 536__ $$0G:(DE-HGF)POF4-5253$$a5253 - Neuroimaging (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000908204 588__ $$aDataset connected to CrossRef Conference
000908204 7001_ $$0P:(DE-Juel1)131791$$aScheins, Jurgen$$b1$$ufzj
000908204 7001_ $$0P:(DE-Juel1)164254$$aLerche, Christoph$$b2$$ufzj
000908204 7001_ $$0P:(DE-Juel1)131794$$aShah, N. J.$$b3$$ufzj
000908204 773__ $$a10.1109/NSSMIC.2018.8824431
000908204 909CO $$ooai:juser.fz-juelich.de:908204$$pVDB
000908204 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131791$$aForschungszentrum Jülich$$b1$$kFZJ
000908204 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)164254$$aForschungszentrum Jülich$$b2$$kFZJ
000908204 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131794$$aForschungszentrum Jülich$$b3$$kFZJ
000908204 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$$x0
000908204 9201_ $$0I:(DE-Juel1)INM-4-20090406$$kINM-4$$lPhysik der Medizinischen Bildgebung$$x0
000908204 9201_ $$0I:(DE-Juel1)INM-11-20170113$$kINM-11$$lJara-Institut Quantum Information$$x1
000908204 9201_ $$0I:(DE-Juel1)VDB1046$$kJARA-BRAIN$$lJülich-Aachen Research Alliance - Translational Brain Medicine$$x2
000908204 980__ $$aabstract
000908204 980__ $$aVDB
000908204 980__ $$aI:(DE-Juel1)INM-4-20090406
000908204 980__ $$aI:(DE-Juel1)INM-11-20170113
000908204 980__ $$aI:(DE-Juel1)VDB1046
000908204 980__ $$aUNRESTRICTED