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000012015 0247_ $$2pmid$$apmid:20871138
000012015 0247_ $$2DOI$$a10.1088/0031-9155/55/20/006
000012015 0247_ $$2WOS$$aWOS:000282599000006
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000012015 037__ $$aPreJuSER-12015
000012015 041__ $$aeng
000012015 082__ $$a570
000012015 084__ $$2WoS$$aEngineering, Biomedical
000012015 084__ $$2WoS$$aRadiology, Nuclear Medicine & Medical Imaging
000012015 1001_ $$0P:(DE-Juel1)VDB95135$$aLoukiala, A.$$b0$$uFZJ
000012015 245__ $$aGap-filling methods for 3D PlanTIS data
000012015 260__ $$aBristol$$bIOP Publ.$$c2010
000012015 300__ $$a6125 - 6139
000012015 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
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000012015 3367_ $$00$$2EndNote$$aJournal Article
000012015 3367_ $$2BibTeX$$aARTICLE
000012015 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000012015 3367_ $$2DRIVER$$aarticle
000012015 440_0 $$011061$$aPhysics in Medicine and Biology$$v55$$x0031-9155$$y55
000012015 500__ $$aThe authors thank Dr Gerhard Roeb and Marco Dautzenberg for assistance with the phantom studies. This work was supported by the Academy of Finland (application number 129657, Finnish Programme for Centres of Excellence in Research 2006-2011) and by the Graduate School in Electronics, Telecommunication and Automation (GETA), Finland. This work was partly funded by Forschungszentrum Julich, Germany.
000012015 520__ $$aThe range of positron emitters and their labeled compounds have led to high-resolution PET scanners becoming widely used, not only in clinical and pre-clinical studies but also in plant studies. A high-resolution PET scanner, plant tomographic imaging system (PlanTIS), was designed to study metabolic and physiological functions of plants noninvasively. The gantry of the PlanTIS scanner has detector-free regions. Even when the gantry of the PlanTIS is rotated during the scan, these regions result in missing sinogram bins in the acquired data. Missing data need to be estimated prior to the analytical image reconstructions in order to avoid artifacts in the final reconstructed images. In this study, we propose three gap-filling methods for estimation of the unique gaps existing in the 3D PlanTIS sinogram data. The 3D sinogram data were gap-filled either by linear interpolation in the transaxial planes or by the bicubic interpolation method (proposed for the ECAT high-resolution research tomograph) in the transradial planes or by the inpainting method in the transangular planes. Each gap-filling method independently compensates for slices in one of three orthogonal sinogram planes (transaxial, transradial and transangular planes). A 3D numerical Shepp-Logan phantom and the NEMA image quality phantom were used to evaluate the methods. The gap-filled sinograms were reconstructed using the analytical 3D reprojection (3DRP) method. The NEMA phantom sinograms were also reconstructed by the iterative reconstruction method, ordered subsets maximum a posteriori one step late (OSMAPOSL), to compare the results of gap filling followed by 3DRP with the results of OSMAPOSL reconstruction without gap filling. The three methods were evaluated quantitatively (by mean square error and coefficients of variation) over the selected regions of the 3D numerical Shepp-Logan phantom at eight different Poisson noise levels. Moreover, the NEMA phantom scan data were used in visual assessments of the methods. We observed that all methods improved the reconstructed images both quantitatively and visually. Therefore, the proposed gap-filling methods followed by the analytical 3DRP are alternative for the reconstructions of not only the 3D PlanTIS data, but also other PET scanner data of the ClearPET family.
000012015 536__ $$0G:(DE-Juel1)FUEK407$$2G:(DE-HGF)$$aTerrestrische Umwelt$$cP24$$x0
000012015 588__ $$aDataset connected to Web of Science, Pubmed
000012015 650_2 $$2MeSH$$aImaging, Three-Dimensional: methods
000012015 650_2 $$2MeSH$$aPhantoms, Imaging
000012015 650_2 $$2MeSH$$aPlant Physiological Processes
000012015 650_2 $$2MeSH$$aPlants: metabolism
000012015 650_2 $$2MeSH$$aPositron-Emission Tomography
000012015 650_2 $$2MeSH$$aTomography: methods
000012015 650_7 $$2WoSType$$aJ
000012015 7001_ $$0P:(DE-Juel1)VDB95136$$aTuna, U.$$b1$$uFZJ
000012015 7001_ $$0P:(DE-Juel1)VDB72428$$aBeer, S.$$b2$$uFZJ
000012015 7001_ $$0P:(DE-Juel1)129336$$aJahnke, S.$$b3$$uFZJ
000012015 7001_ $$0P:(DE-Juel1)VDB95137$$aRuotsalainen, U.$$b4$$uFZJ
000012015 773__ $$0PERI:(DE-600)1473501-5$$a10.1088/0031-9155/55/20/006$$gVol. 55, p. 6125 - 6139$$p6125 - 6139$$q55<6125 - 6139$$tPhysics in medicine and biology$$v55$$x0031-9155$$y2010
000012015 8567_ $$uhttp://dx.doi.org/10.1088/0031-9155/55/20/006
000012015 909CO $$ooai:juser.fz-juelich.de:12015$$pVDB
000012015 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed
000012015 9141_ $$y2010
000012015 9131_ $$0G:(DE-Juel1)FUEK407$$aDE-HGF$$bErde und Umwelt$$kP24$$lTerrestrische Umwelt$$vTerrestrische Umwelt$$x0
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000012015 9201_ $$0I:(DE-Juel1)ICG-3-20090406$$d31.10.2010$$gICG$$kICG-3$$lPhytosphäre$$x1
000012015 9201_ $$0I:(DE-Juel1)ZEL-20090406$$gZEL$$kZEL$$lZentralinstitut für Elektronik$$x0
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