000130791 001__ 130791
000130791 005__ 20240228145553.0
000130791 0247_ $$2doi$$a10.1186/s40658-017-0177-4
000130791 0247_ $$2pmid$$apmid:28251575
000130791 0247_ $$2pmc$$apmc:PMC5332322
000130791 037__ $$aDKFZ-2017-05869
000130791 041__ $$aeng
000130791 082__ $$a530
000130791 1001_ $$00000-0002-4712-8074$$aHeußer, Thorsten$$b0$$eFirst author
000130791 245__ $$aMLAA-based attenuation correction of flexible hardware components in hybrid PET/MR imaging.
000130791 260__ $$aBerlin$$bSpringer Open$$c2017
000130791 3367_ $$2DRIVER$$aarticle
000130791 3367_ $$2DataCite$$aOutput Types/Journal article
000130791 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1525780102_10339
000130791 3367_ $$2BibTeX$$aARTICLE
000130791 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000130791 3367_ $$00$$2EndNote$$aJournal Article
000130791 520__ $$aAccurate PET quantification demands attenuation correction (AC) for both patient and hardware attenuation of the 511 keV annihilation photons. In hybrid PET/MR imaging, AC for stationary hardware components such as patient table and MR head coil is straightforward, employing CT-derived attenuation templates. AC for flexible hardware components such as MR-safe headphones and MR radiofrequency (RF) surface coils is more challenging. Registration-based approaches, aligning CT-based attenuation templates with the current patient position, have been proposed but are not used in clinical routine. Ignoring headphone or RF coil attenuation has been shown to result in regional activity underestimation values of up to 18%. We propose to employ the maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm to estimate the attenuation of flexible hardware components. Starting with an initial attenuation map not including flexible hardware components, the attenuation update of MLAA is applied outside the body outline only, allowing to estimate hardware attenuation without modifying the patient attenuation map. Appropriate prior expectations on the attenuation coefficients are incorporated into MLAA. The proposed method is investigated for non-TOF PET phantom and (18)F-FDG patient data acquired with a clinical PET/MR device, using headphones or RF surface coils as flexible hardware components.Although MLAA cannot recover the exact physical shape of the hardware attenuation maps, the overall attenuation of the hardware components is accurately estimated. Therefore, the proposed algorithm significantly improves PET quantification. Using the phantom data, local activity underestimation when neglecting hardware attenuation was reduced from up to 25% to less than 3% under- or overestimation as compared to reference scans without hardware present or to CT-derived AC. For the patient data, we found an average activity underestimation of 7.9% evaluated in the full brain and of 6.1% for the abdominal region comparing the uncorrected case with MLAA.MLAA is able to provide accurate estimations of the attenuation of flexible hardware components and can therefore be used to significantly improve PET quantification. The proposed approach can be readily incorporated into clinical workflow.
000130791 536__ $$0G:(DE-HGF)POF3-315$$a315 - Imaging and radiooncology (POF3-315)$$cPOF3-315$$fPOF III$$x0
000130791 588__ $$aDataset connected to CrossRef, PubMed,
000130791 7001_ $$0P:(DE-He78)65dc5d2a03aac87b199cba2986986d05$$aRank, Christopher$$b1$$udkfz
000130791 7001_ $$0P:(DE-HGF)0$$aBerker, Yannick$$b2
000130791 7001_ $$0P:(DE-He78)c420f6efccb409e1a287be027501a74c$$aFreitag, Martin$$b3$$udkfz
000130791 7001_ $$0P:(DE-He78)f288a8f92f092ddb41d52b1aeb915323$$aKachelriess, Marc$$b4$$eLast author$$udkfz
000130791 773__ $$0PERI:(DE-600)2768912-8$$a10.1186/s40658-017-0177-4$$gVol. 4, no. 1, p. 12$$n1$$p12$$tEJNMMI Physics$$v4$$x2197-7364$$y2017
000130791 909CO $$ooai:inrepo02.dkfz.de:130791$$pVDB
000130791 9101_ $$0I:(DE-588b)2036810-0$$60000-0002-4712-8074$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ
000130791 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)65dc5d2a03aac87b199cba2986986d05$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000130791 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-HGF)0$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000130791 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)c420f6efccb409e1a287be027501a74c$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ
000130791 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)f288a8f92f092ddb41d52b1aeb915323$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ
000130791 9131_ $$0G:(DE-HGF)POF3-315$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vImaging and radiooncology$$x0
000130791 9141_ $$y2017
000130791 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000130791 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000130791 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal
000130791 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ
000130791 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ
000130791 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000130791 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index
000130791 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000130791 9201_ $$0I:(DE-He78)E020-20160331$$kE020$$lMedizinische Physik in der Radiologie$$x0
000130791 9201_ $$0I:(DE-He78)E025-20160331$$kE025$$lRadiologie_Legacy_$$x1
000130791 9201_ $$0I:(DE-He78)E010-20160331$$kE010$$lRadiologie$$x2
000130791 980__ $$ajournal
000130791 980__ $$aVDB
000130791 980__ $$aI:(DE-He78)E020-20160331
000130791 980__ $$aI:(DE-He78)E025-20160331
000130791 980__ $$aI:(DE-He78)E010-20160331
000130791 980__ $$aUNRESTRICTED