{"Affiliation":[{"label":"Affiliation","value":"Science, Faculty of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."},{"label":"Affiliation","value":"Other UBC","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."},{"label":"Affiliation","value":"Non UBC","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."},{"label":"Affiliation","value":"Physics and Astronomy, Department of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."}],"AggregatedSourceRepository":[{"label":"Aggregated Source Repository","value":"DSpace","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","classmap":"ore:Aggregation","property":"edm:dataProvider"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","explain":"A Europeana Data Model Property; The name or identifier of the organization who contributes data indirectly to an aggregation service (e.g. Europeana)"}],"Citation":[{"label":"Citation","value":"EJNMMI Physics. 2021 Feb 26;8(1):20","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#identifierCitation","classmap":"oc:PublicationDescription","property":"oc:identifierCitation"},"iri":"https:\/\/open.library.ubc.ca\/terms#identifierCitation","explain":"UBC Open Collections Metadata Components; Local Field; Indicates a bibliographic reference for the resource if it has been previously published."}],"Contributor":[{"label":"Contributor","value":"Djavad Mowafaghian Centre for Brain Health. Pacific Parkinson's Research Centre","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/contributor","classmap":"dpla:SourceResource","property":"dcterms:contributor"},"iri":"http:\/\/purl.org\/dc\/terms\/contributor","explain":"A Dublin Core Terms Property; An entity responsible for making contributions to the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Contributor","value":"Djavad Mowafaghian Centre for Brain Health. MS\/MRI Research Group","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/contributor","classmap":"dpla:SourceResource","property":"dcterms:contributor"},"iri":"http:\/\/purl.org\/dc\/terms\/contributor","explain":"A Dublin Core Terms Property; An entity responsible for making contributions to the resource.; Examples of a Contributor include a person, an organization, or a service."}],"CopyrightHolder":[{"label":"Copyright Holder","value":"The Author(s)","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#rightsCopyright","classmap":"oc:PublicationDescription","property":"oc:rightsCopyright"},"iri":"https:\/\/open.library.ubc.ca\/terms#rightsCopyright","explain":"UBC Open Collections Metadata Components; Local Field; Refers to the publisher or author who holds the copyright."}],"Creator":[{"label":"Creator","value":"Mannheim, Julia G.","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Cheng, Ju-Chieh (Kevin)","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Vafai, Nasim","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Shahinfard, Elham","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"English, Carolyn","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"McKenzie, Jessamyn","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Zhang, Jing","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Barlow, Laura","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Creator","value":"Sossi, Vesna","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."}],"DateAvailable":[{"label":"Date Available","value":"2021-02-26T22:39:32Z","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"edm:WebResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"DateIssued":[{"label":"Date Issued","value":"2021-02-26","attrs":{"lang":"","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"oc:SourceResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"Description":[{"label":"Description","value":"Background:\r\n                The Siemens high-resolution research tomograph (HRRT - a dedicated brain PET scanner) is to this day one of the highest resolution PET scanners; thus, it can serve as useful benchmark when evaluating performance of newer scanners. Here, we report results from a cross-validation study between the HRRT and the whole-body GE SIGNA PET\/MR focusing on brain imaging.\r\n                Phantom data were acquired to determine recovery coefficients (RCs), % background variability (%BG), and image voxel noise (%). Cross-validation studies were performed with six healthy volunteers using [11C]DTBZ, [11C]raclopride, and [18F]FDG. Line profiles, regional time-activity curves, regional non-displaceable binding potentials (BPND) for [11C]DTBZ and [11C]raclopride scans, and radioactivity ratios for [18F]FDG scans were calculated and compared between the HRRT and the SIGNA PET\/MR.\r\n              \r\n              \r\n                Results:\r\n                Phantom data showed that the PET\/MR images reconstructed with an ordered subset expectation maximization (OSEM) algorithm with time-of-flight (TOF) and TOF + point spread function (PSF) + filter revealed similar RCs for the hot spheres compared to those obtained on the HRRT reconstructed with an ordinary Poisson-OSEM algorithm with PSF and PSF + filter. The PET\/MR TOF + PSF reconstruction revealed the highest RCs for all hot spheres. Image voxel noise of the PET\/MR system was significantly lower. Line profiles revealed excellent spatial agreement between the two systems. BPND values revealed variability of less than 10% for the [11C]DTBZ scans and 19% for [11C]raclopride (based on one subject only). Mean [18F]FDG ratios to pons showed less than 12% differences.\r\n              \r\n              \r\n                Conclusions:\r\n                These results demonstrated comparable performances of the two systems in terms of RCs with lower voxel-level noise (%) present in the PET\/MR system. Comparison of in vivo human data confirmed the comparability of the two systems. The whole-body GE SIGNA PET\/MR system is well suited for high-resolution brain imaging as no significant performance degradation was found compared to that of the reference standard HRRT.","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/description","classmap":"dpla:SourceResource","property":"dcterms:description"},"iri":"http:\/\/purl.org\/dc\/terms\/description","explain":"A Dublin Core Terms Property; An account of the resource.; Description may include but is not limited to: an abstract, a table of contents, a graphical representation, or a free-text account of the resource."}],"DigitalResourceOriginalRecord":[{"label":"Digital Resource Original Record","value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/77408?expand=metadata","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","classmap":"ore:Aggregation","property":"edm:aggregatedCHO"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","explain":"A Europeana Data Model Property; The identifier of the source object, e.g. the Mona Lisa itself. This could be a full linked open date URI or an internal identifier"}],"FullText":[{"label":"Full Text","value":"ORIGINAL RESEARCH Open AccessCross-validation study between the HRRTand the PET component of the SIGNA PET\/MRI system with focus on neuroimagingJulia G. Mannheim1,2,3*\u2020 , Ju-Chieh (Kevin) Cheng1,4\u2020, Nasim Vafai4, Elham Shahinfard4, Carolyn English4,Jessamyn McKenzie5, Jing Zhang6, Laura Barlow7 and Vesna Sossi1* Correspondence: julia.mannheim@med.uni-tuebingen.de\u2020Julia G. Mannheim and Ju-Chieh(Kevin) Cheng contributed equallyto this work.1Department of Physics andAstronomy, University of BritishColumbia, Vancouver, BritishColumbia, Canada2Werner Siemens Imaging Center,Department of Preclinical Imagingand Radiopharmacy, Eberhard-KarlsUniversity Tuebingen, Tuebingen,GermanyFull list of author information isavailable at the end of the articleAbstractBackground: The Siemens high-resolution research tomograph (HRRT - a dedicatedbrain PET scanner) is to this day one of the highest resolution PET scanners; thus, itcan serve as useful benchmark when evaluating performance of newer scanners.Here, we report results from a cross-validation study between the HRRT and thewhole-body GE SIGNA PET\/MR focusing on brain imaging.Phantom data were acquired to determine recovery coefficients (RCs), % backgroundvariability (%BG), and image voxel noise (%). Cross-validation studies were performedwith six healthy volunteers using [11C]DTBZ, [11C]raclopride, and [18F]FDG. Lineprofiles, regional time-activity curves, regional non-displaceable binding potentials(BPND) for [11C]DTBZ and [11C]raclopride scans, and radioactivity ratios for [18F]FDGscans were calculated and compared between the HRRT and the SIGNA PET\/MR.Results: Phantom data showed that the PET\/MR images reconstructed with anordered subset expectation maximization (OSEM) algorithm with time-of-flight (TOF)and TOF + point spread function (PSF) + filter revealed similar RCs for the hotspheres compared to those obtained on the HRRT reconstructed with an ordinaryPoisson-OSEM algorithm with PSF and PSF + filter. The PET\/MR TOF + PSFreconstruction revealed the highest RCs for all hot spheres. Image voxel noise of thePET\/MR system was significantly lower. Line profiles revealed excellent spatialagreement between the two systems. BPND values revealed variability of less than10% for the [11C]DTBZ scans and 19% for [11C]raclopride (based on one subject only).Mean [18F]FDG ratios to pons showed less than 12% differences.Conclusions: These results demonstrated comparable performances of the twosystems in terms of RCs with lower voxel-level noise (%) present in the PET\/MRsystem. Comparison of in vivo human data confirmed the comparability of the twosystems. The whole-body GE SIGNA PET\/MR system is well suited for high-resolutionbrain imaging as no significant performance degradation was found compared tothat of the reference standard HRRT.Keywords: Cross-validation study, HRRT, PET\/MR, Recovery coefficients, Bindingpotentials\u00a9 The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, whichpermits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a creditline to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted bystatutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view acopy of this licence, visit http:\/\/creativecommons.org\/licenses\/by\/4.0\/.EJNMMI PhysicsMannheim et al. EJNMMI Physics            (2021) 8:20 https:\/\/doi.org\/10.1186\/s40658-020-00349-0BackgroundPositron emission tomography (PET) is one of the most sensitive non-invasive in vivoimaging techniques [1] and has demonstrated its tremendous impact in clinical and re-search studies [2\u20135].The high resolution research tomograph (HRRT, CTI PET Systems, Knoxville, TN, USA),introduced in the late 1990s\/early 2000s, is to this day arguably one of the highest resolutionPET scanners for human brain imaging [6]. The double layer of cerium-doped lutetiumoxyorthosilicate (LSO) and cerium-doped lutetium-yttrium oxyorthosilicate (LYSO) crystals,which enable photon depth-of-interaction (DOI) detection, results in a spatial resolution of(~ 2.5mm)3 at 1 cm tangential offset from center of the field of view (cFOV) and fairly uni-form across the FOV [7]; the scanner can thus serve as a very useful benchmark whenevaluating performance of newer scanners. Given that the HRRT is no longer commerciallyavailable, most dedicated brain imaging sites consider acquiring newer PET systems. Eventhough in most cases dedicated brain PET scanners would be preferred for cost, and poten-tially better sensitivity and resolution performance, the choice is generally practically limitedto whole-body hybrid systems, i.e., whole-body PET systems in combination with computedtomography (CT) or magnetic resonance (MR) imaging, as standalone dedicated PET sys-tems such as the HRRT are no longer available or common.The GE SIGNA PET\/MR system (GE Healthcare, Chicago, IL, USA) is the first whole-body hybrid PET\/MR system based on silicon photomultipliers (SiPMs) with time-of-flight(TOF) capabilities. The PET detectors are integrated in the MR bore allowing whole-bodysimultaneous acquisition of PET and MR [8] with high PET detection stability [9].The aim of this work was to perform a cross-validation study between the HRRT andthe SIGNA PET\/MR beyond the National Electrical Manufacturers Association(NEMA) evaluation to provide a direct and clinically relevant comparison with particu-lar focus on brain imaging. While the benefits of simultaneous PET\/MR imaging forneurological applications have been discussed elsewhere [10, 11], the present study fo-cuses on the comparison of the image quality obtained with TOF data acquired on thePET\/MR (default acquisition mode) and the non-TOF data acquired on the HRRT (byhardware default).In a first step, phantom data were acquired to determine contrast recovery coefficients(RCs) and percent background variability (%BG), as well as percent voxel-level noise (%)for both systems. Cross-validation studies were then performed with six healthy volun-teers scanned on both systems with commonly used PET tracers: [11C]dihydrotetrabena-zine ([11C]DTBZ), a marker for the vesicular monoamine transporter 2 (VMAT2), theglucose analog [18F]-fluorodeoxyglucose ([18F]FDG) and [11C]raclopride, a D2\/3 receptorantagonist. The choice of tracers was dictated by the requirement to examine cases ofwidespread tracer distribution ([18F]FDG) and tracers with more localized distributionand a wide range of count-rates ([11C]DTBZ and [11C]raclopride).MethodsSystems descriptionThe HRRT system is based on a dual-layer design of 2.44 \u00d7 2.44 \u00d7 10mm3 LSO\/LYSOcrystals enabling DOI encoding and coupled to photomultiplier tubes (PMTs). TheFOV spans 25 cm in the axial and 35 cm in the transaxial direction [7, 12].Mannheim et al. EJNMMI Physics            (2021) 8:20 Page 2 of 22The PET component of the SIGNA PET\/MRI is based on SiPMs coupled to 4.0 \u00d7 5.3\u00d7 25mm3 lutetium-based scintillation crystals, which enable the use of TOF informa-tion during data reconstruction (timing resolution < 400 ps) [8]. The detector ring,spanning an axial FOV of 25 cm and a transaxial FOV of 60 cm [8, 13], is fully enclosedin the MRI scanner. The MR component is equipped with a radiofrequency transmitbody coil embedded in a static 3 T magnet. All MR subject data used in this study wereacquired with the GE head neck unit 12 channel coil. Table 1 lists a detailed compari-son of both systems\u2019 specifications and reported performance parameters.Phantom studiesRCs and %BG variability were determined using a cylindrical phantom (referred to ascontrast phantom, Flanged Jaszczak ECT Phantom, Data Spectrum Corporation, Dur-ham, NC, USA) scanned on the HRRT and on the PET\/MR to enable a direct compari-son, as the NEMA recommended phantom does not fit in the HRRT FOV. However, toenable a comparison with other systems, the NEMA recommended image quality phan-tom was additionally scanned on the PET\/MR. Results can be found in the supplemen-tary data.The contrast phantom contained 6 fillable hollow spheres with inner diameters (IDs)of 9.9, 12.4, 15.4, 19.8, 24.8, and 31.3 mm. The two largest spheres were filled withwater; the four smaller spheres and the background (volume 6000 ml) were filled with18F activity in a 3.88:1 and 3.91:1 contrast ratio for the PET\/MR and HRRT, respect-ively (total activity: ~ 29MBq). No background activity was placed outside the FOV.List-mode data were acquired and histogrammed into one frame with activity concen-tration and counts matched between scanners.A transmission scan using a rotating 137Cs source was performed on the HRRT tocorrect for attenuation. HRRT data were reconstructed using an ordinary Poisson-ordered subset expectation maximization (OP-OSEM) algorithm [14] with a 256 \u00d7 256\u00d7 207 matrix resulting in a reconstructed voxel size of 1.22 \u00d7 1.22 \u00d7 1.22 mm3. SixteenTable 1 System specification and performance parameters of the Siemens HRRT and GE SIGNAPET\/MR systemHRRT SIGNA PET\/MRCrystal material LSO\/LYSO Lutetium basedCrystal dimensions [mm3] 2.44 \u00d7 2.44 \u00d7 10 4.0 \u00d7 5.3 \u00d7 25Number of crystals 119,808 20,160Detector PMTs Si-PMFOV (axial vs. transaxial) [cm] 25.2 x 31.2 25 \u00d7 60Reported resolution (radial \u00d7 tangential \u00d7axial) [mm]2.3 \u00d7 2.3 \u00d7 2.5a[7, 12]3.48 \u00d7 3.43 \u00d7 4.67b [8]Reported sensitivity 2.9%c [12] 23.3 cps\/kBqd [8]Special characteristics DOI correction TOF (timing resolution < 400 ps),simultaneous PET\/MRIaAt 1 cm tangential offset to the center of the FOV (reconstructed with a 3D ordinary Poisson-ordered subsetexpectation-maximization algorithm (OP-OSEM))bAt 1 cm tangential offset to the center of the FOV (reconstructed with a TOF OSEM algorithm with filter)cAt the center FOV based on the NEMA 2001 evaluation protocol; note that measured activity was normalized by the linesource length in the scanner FOV (25 cm), rather than its entire length (70 cm)dAt the center FOV based on the NEMA 2012 evaluation protocol. Sensitivity of 23.3 cps\/kBq is equivalent to 2.3%. Whenexpressed in the same units as the sensitivity measured for the HRRT, the GE SIGNA sensitivity is estimated to be 6.5%Mannheim et al. EJNMMI Physics            (2021) 8:20 Page 3 of 22subsets and 6 iterations were used for reconstructions without post-filtering (this willbe denoted as \u201cnative\u201d) and with a 2-mm FWHM Gaussian filter (standard in-house re-construction), and 10 iterations for reconstructions with PSF [15, 16] and with PSF anda 2-mm FWHM Gaussian filter, respectively (see Table 2).In order to correct the data acquired on the PET\/MR for attenuation, the transmis-sion maps acquired on the HRRT were resliced and co-registered to the non-attenuation corrected PET\/MR images. The manufacturer\u2019s attenuation map of thePET\/MR coil was then co-registered and integrated into the resliced HRRT attenuationmap.PET\/MR data were reconstructed using a TOF-OSEM algorithm with 28 subsets, 2iterations, and a 128 \u00d7 128 \u00d7 89 matrix resulting in a reconstructed voxel size of 2.781\u00d7 2.781 \u00d7 2.780 mm3 (reconstructed in-plane FOV: 35.6 cm, GE recommendation forphantom data). Additionally, the data were reconstructed using TOF-OSEM + Gauss-ian post-filtering with a 3.5 mm full width at half maximum (FWHM) filter in all threedimensions (TOF + filter), TOF-OSEM + resolution modeling with point spread func-tion (PSF, TOF + PSF) (PSF correction was implemented by the manufacturer follow-ing the approach from [17]) and TOF-OSEM + PSF + Gaussian post-filtering with a3.5-mm FWHM filter in all three dimensions (TOF + PSF + filter, Table 3). Manufac-turer supplied corrections for decay, random and scattered coincidences, normalization,and dead time were applied. PET\/MR phantom data were also reconstructed withoutTOF information (woTOF) using the same parameter settings as previously de-scribed. Different sized post filters between the HRRT and PET\/MR were applied toachieve noise reduction without significantly degrading the resolution; i.e., the FWHMof the filter is in each case slightly smaller than the smallest dimension of the detectorcrystal.Phantom data analysisData analysis was performed by placing regions of interest (ROI) using the softwarepackage PMOD (version 3.602 & 4.005, PMOD Technologies Ltd., Zurich,Switzerland). A single slice ROI equal to the size of the actual inner sphere diameterwas placed in the transaxial plane in which the sphere was most visible (according toTable 2 Reconstruction parameters used for the HRRT phantom and healthy subject scansHRRT Matrix size Voxel size[mm3]Iterations Subsets PSF correction FilterPhantomdataHealthysubjectdataNative 256 \u00d7 256 \u00d72071.22 \u00d7 1.22 \u00d71.221 up to 10 6 16 None NoneFilter 256 \u00d7 256 \u00d72071.22 \u00d7 1.22 \u00d71.221 up to 10 6 16 None 2mm FWHMgaussian in all3 dimensionsPSF 256 \u00d7 256 \u00d72071.22 \u00d7 1.22 \u00d71.221 up to 10 10 16 Yes (see referenceto PSF correction[15])NonePSF + filter 256 \u00d7 256 \u00d72071.22 \u00d7 1.22 \u00d71.221 up to 10 10 16 Yes (see referenceto PSF correction[15])2 mm FWHMgaussian inall 3 dimensionsMannheim et al. EJNMMI Physics            (2021) 8:20 Page 4 of 22Table3ReconstructionparametersusedforthePET\/MRphantomandhealthysubjectscansPET\/MRMatrixsizeIn-planeFOV[cm]Voxelsize[mm3]IterationsSubsetsPSFcorrectionFilterPhantomdataHealthysubjectdataPhantomdataHealthysubjectdataPhantomdataHealthysubjectdataPhantomdataHealthysubjectdataTOF128\u00d7128\u00d789256\u00d7256\u00d78935.62.781\u00d72.781\u00d72.7801.391\u00d71.391\u00d72.7801upto10428NoneNoneNoneTOF+filter128\u00d7128\u00d789256\u00d7256\u00d78935.62.781\u00d72.781\u00d72.7801.391\u00d71.391\u00d72.7801upto10428None3.5mmFWHMgaussianinall3dimensions3.5mmGaussiantransaxialand3-pointaxialconvolutionfilterTOF+PSF128\u00d7128\u00d789256\u00d7256\u00d78935.62.781\u00d72.781\u00d72.7801.391\u00d71.391\u00d72.7801upto10428Yes(PSFcorrectionwasimplementedfollowingtheapproachfrom[17])NoneNoneTOF+PSF+filter128\u00d7128\u00d789256\u00d7256\u00d78935.62.781\u00d72.781\u00d72.7801.391\u00d71.391\u00d72.7801upto10428Yes(PSFcorrectionwasimplementedfollowingtheapproachfrom[17])3.5mmFWHMgaussianinall3dimensions3.5mmGaussiantransaxialand3-pointaxialconvolutionfilterMannheim et al. EJNMMI Physics            (2021) 8:20 Page 5 of 22the NEMA recommendations) [18]. Furthermore, a spherical VOI with the same diam-eter as the sphere size was placed on the volumetric sphere image. For the background,12 ROIs with sizes corresponding to those placed on the spheres were drawn on thesame plane where the single slice ROI was placed and at an axial offset of +\/\u2212 1 cmand +\/\u2212 2 cm, resulting in a total number of 60 background ROIs for each sphere, re-spectively. Single slice background ROIs were not extended to spherical VOIs.RCs and %BG variability were determined according to the standardized NEMA NU2-2007 protocol [18] using the following formulas:RChot \u00bcChot sphere.Cbackground sphere\u0002 \u0003\u2212 1Ahot.Abackground\u0002 \u0003\u2212 1\u0002 100% \u00f01\u00deRCcold \u00bc 1 \u2212 Ccold sphereCbackground sphere\u0002 \u0003\u0002 100% \u00f02\u00de%BG variability \u00bc SDsphereCbackground sphere\u0002 100% \u00f03\u00deSDsphere \u00bcffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXKk\u00bc1Cbackground sphere;k \u2212 Cbackground sphere\u0005 \u00062\u0007K \u2212 1\u00f0 \u00devuut ;K \u00bc 60 \u00f04\u00dewhere Chot _ sphere, Ccold _ sphere, and Cbackground _ sphere correspond to the mean concen-tration measured in ROIs placed on the images of the hot and cold sphere and on thebackground for the respective spheres. Ahot and Abackground correspond to the true activ-ity concentration measured with a well-counter for the hot spheres and background,respectively.For the contrast phantom data, the coefficient of variation between voxel valueswithin uniform background regions was used as a measure of voxel noise (%) in the re-constructed images according to the following formula:voxel noise %\u00f0 \u00de \u00bc SDbackground sphereCbackground sphere\u0002 100% \u00f05\u00dewhere SDbackground _ sphere describes the standard deviation measured in ROIs placedon the background for the respective spheres.The voxel noise (%) was determined for frames with a relatively high number ofprompts (~ 135 million), and a low number of prompts (~ 12 million) to mimic countstatistics encountered during typical human 11C scans. High count data were analyzedTable 4 Injected activity of the respective tracer for each subject at start of the acquisitionTracer Subject ID HRRT [MBq] SIGNA PET\/MR [MBq][11C]DTBZ Subject 1 341.3 326.8Subject 2 356.1 339.2[18F]FDG Subject 3 187.3 183.2Subject 4 51.3 188.3Subject 5 181.4 173.6[11C]raclopride Subject 6 359.6 378.7Mannheim et al. EJNMMI Physics            (2021) 8:20 Page 6 of 22based on a single realization while low count data were based on five replicates. Datawere reconstructed with up to 10 iterations using the reconstruction parameters as spe-cified above.Human scansCross-validation studies were performed with six healthy volunteers (age 63.5 \u00b1 15.18years). The Clinical Research Ethics Board of the University of British Columbia ap-proved the study, and informed written consent was provided by all participatingsubjects.Two healthy volunteers were scanned using [11C]DTBZ, three with [18F]FDG, andone with [11C]raclopride on both scanners. The two scans of each subject were per-formed within 1.5 months except for one [11C]DTBZ subject whose time interval be-tween the two scans was 8 months; this was not considered to be a confound asVMAT2 binding shows a negligible age dependence over this time frame [19]. Subjectsscanned with [18F]FDG fasted for at least 6 h before tracer injection.On the HRRT, the subjects were positioned using external lasers aligning the gantrywith the inferior orbital-external metal line, and custom-fitted thermoplastic maskswere applied to minimize head movement. Subject positioning on the SIGNA PET\/MRscanner was performed based on an MRI localizer sequence. Intravenous administra-tion of the tracers (see Table 4 for detailed information on injected activity amounts)over 60 s were performed using an infusion pump (Harvard Instruments, Southnatick,MA, USA; one volunteer scanned with [18F]FDG was manually injected due to morefragile veins of this subject). Injected activity between scanners was matched for eachsubject, respectively, with the exception of one scan, where the injected activity waslower for the HRRT scan due to technical reasons (Table 4). List-mode data were ac-quired for 60 min and histogrammed into 16 frames for [11C]DTBZ and [11C]raclopride(4 \u00d7 60 s, 3 \u00d7 120 s, 8 \u00d7 300 s, 1 \u00d7 600 s) and into 17 frames for [18F]FDG (4 \u00d7 60 s, 3 \u00d7120 s, 10 \u00d7 300 s).For the HRRT, a transmission scan with a 137Cs source was performed before tracerinjection. Reconstruction of the HRRT data was performed using OP-OSEM with 16subsets and 6 iterations (see Table 2). Corrections for decay, dead-time, random, atten-uated and scattered coincidences, and detector normalization were applied. Recon-structed images were post-processed with a 2.0-mm FHWM Gaussian filter (standardin-house reconstruction).For the PET\/MR, a zero echo time (ZTE) approach was utilized to correct for attenu-ation (ZTE MRAC) with sequence parameters: echo time (TE) = 0.016 ms, repetitiontime (TR) = 399.564 ms, matrix size = 110 \u00d7 100 \u00d7 116, voxel size = 2.4 \u00d7 2.4 \u00d7 2.4mm3, flip angle (FA) = 0.8\u00b0, number of excitations (NEX) = 4, acquisition time = 42 s[20]. PET\/MR data were reconstructed using TOF-OSEM with 28 subsets and 4 itera-tions, a matrix size of 256 \u00d7 256 resulting in a reconstructed voxel size of 1.391 \u00d7 1.391\u00d7 2.780 mm3 (see Table 3). Based on the results of the phantom study, the post-processing parameters chosen for the human data included the manufacturer supplied3.5 mm Gaussian transaxial filter as well as a 3-point axial convolution filter and PSFresolution modeling. All manufacturer-provided corrections were applied (dead-time,decay, random, attenuation, and scattered coincidences, normalization).Mannheim et al. EJNMMI Physics            (2021) 8:20 Page 7 of 22Reconstructed dynamic PET images were frame-to-frame realigned based on a rigid-body transformation (Statistical Parametric Mapping (SPM), version 12, WellcomeTrust Centre for Neuroimaging, University College London, UK) to correct for poten-tial motion during the scan. Anatomical MR images were co-registered and resampledto the mean PET images using SPM. MR sequence parameters were: 3D brain volumeimaging (BRAVO) sequence, TE = 2.984 ms, TR = 7.948 ms, matrix size = 256 \u00d7 256 \u00d7162, voxel size = 1 \u00d7 1 \u00d7 1mm3, FA = 12\u00b0, NEX = 1, acquisition time = 6:13 min forhealthy volunteer 1-4; magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence, TE = 3.168 ms, TR = 8.412 ms, MPRAGE TR = 2488 ms, matrix size =256 \u00d7 256 \u00d7 164, voxel size = 1 \u00d7 1 \u00d7 1mm3, FA = 8\u00b0, NEX = 1, and acquisition time =7:39 min for healthy volunteer 5-6. Predefined target and reference ROIs based on theMontreal Neurological Institute (MNI) template were eroded and applied to the PETimages by using the inverse transformation of the co-registered MR images to the MNIspace.For [11C]DTBZ and [11C]raclopride scans, non-displaceable binding potentials (BPND)in the caudate (left and right) and for three putamen regions covering the entire lengthwere calculated using the Logan graphical analysis [21] with the occipital cortex andcerebellum, respectively, as reference regions.[18F]FDG binding ratios were determined based on the last 30 min of each datasetusing the pons as reference region for the same striatal ROIs used in the evaluations of[11C]DTBZ and [11C]raclopride and additional regions including the cerebellum, leftand right medial front gyrus, medulla, midbrain and occipital cortex to reflect the wide-spread [18F]FDG distribution across the brain.Regional time activity curves (TACs) were determined for all subjects for two targetregions (left caudate and left putamen 1) and the respective reference region (DTBZ:occipital cortex; FDG: pons; raclopride: cerebellum). Radial profiles of a single voxelwidth were placed on the images averaged over the entire acquisition to determine po-tential spatial variations between the HRRT and PET\/MR data. The activity concentra-tions along the profiles were normalized to the injected activity.Fig. 1 Representative images of the contrast phantom data for different reconstruction parameters for thetwo systems. Color scales of the images were normalized based on the average background valuemultiplied by the respective sphere to background ratioMannheim et al. EJNMMI Physics            (2021) 8:20 Page 8 of 22ResultsRCs and %BG variabilityFigure 1 shows representative images of the contrast phantom data for different recon-struction parameters. RCs and %BG variability as a function of sphere diameter for differ-ent reconstruction settings are depicted in Fig. 2 and listed in Tables 5 and 6.Supplementary Figure 1 and 2 and Supplementary Table 1 depict the corresponding re-sults for the image quality phantom according to NEMA recommendations. As expected,Fig. 2 RCs and %BG variability as a function of sphere diameter for the contrast phantom (PET\/MR: RCs (a), %BGvariability (b); HRRT: RCs (c), %BG variability (d)). PET\/MR data were reconstructed with TOF, TOF with filter, TOF withPSF, and TOF with PSF and filter. Two iterations and 28 subsets were used, respectively (GE recommendation forphantom data). HRRT data were reconstructed without any filters (native), with a 2-mm Gaussian filter (standard in-house reconstruction for human data) using 6 iterations and 16 subsets, respectively, with PSF, and with PSFcorrection and 2-mm filter using 10 iterations and 16 subsets, respectively. Note the gap between spheres marks coldvs. hot spheresTable 5 RCs for the contrast phantom scanned on the PET\/MR and HRRTSpheres9.9mm 12.4 mm 15.4mm 19.8 mm 24.8mm 31.3 mmPET\/MR ContrastphantomwTOF 49.0 51.9 67.7 78.5 79.5 81.4wTOF + filter 40.9 44.8 60.8 72.6 77.9 79.9wTOF + PSF 61.1 60.9 72.9 82.6 80.4 81.9wTOF + PSF +filter50.2 52.6 66.7 77.2 78.7 80.5HRRT ContrastphantomNative 44.5 50.1 59.1 68.4 79.3 80.4Filter 40.4 48.7 59.6 69.4 76.8 79.3PSF 54.4 56.9 66.9 71.6 84.5 85.9PSF + filter 49.5 54.9 65.6 73.4 82.5 85.0Mannheim et al. EJNMMI Physics            (2021) 8:20 Page 9 of 22Table 6 %BG variability for the contrast phantom scanned on the PET\/MR and HRRTSpheres9.9mm12.4mm15.4mm19.8mm24.8mm31.3mmPET\/MRContrastphantomwTOF 4.7 3.0 3.0 2.7 2.5 2.9wTOF + filter 3.6 2.5 2.6 2.4 2.4 2.6wTOF + PSF 3.6 2.8 2.4 2.5 2.7 2.7wTOF + PSF +filter3.0 2.4 2.2 2.3 2.5 2.5HRRT ContrastphantomNative 5.0 4.6 3.5 5.5 3.9 2.7Filter 3.8 4.1 3.1 3.2 2.6 1.6PSF 5.5 5.7 4.5 5.1 4.0 2.5PSF + filter 4.6 5.2 4.1 3.6 2.8 1.8Fig. 3 PET\/MR RCs (%) as a function of voxel noise (%) for two different sphere sizes and for a high andlow count frame for the contrast phantom (19.8 mm sphere: high count frame (a), low count frame (b); 9.9mm sphere: high count frame (c), low count frame (d)). The high count frame corresponds to ~ 135 millionprompts within the frame, the low count frame to ~ 12 million prompts. Note the difference in x-axis limits.PET\/MR data were reconstructed with TOF, TOF with filter, TOF with PSF, and TOF with PSF and filter using28 subsets, respectively. High count data were analyzed based on a single realization while low count datawere based on five replicates. Each point represents an OSEM iteration, and the number of iterationsincreases from left to right. The dotted line represents the HRRT standard in-house reconstruction (2-mmGaussian filter) used for human data reconstructionMannheim et al. EJNMMI Physics            (2021) 8:20 Page 10 of 22the highest RCs for all hot spheres of the contrast phantom scanned on the PET\/MR wereobtained from the TOF + PSF reconstruction, while the lowest RCs were obtained by ap-plying post-filtering without PSF correction (Fig. 2a). The cold spheres revealed similarRCs for all 4 evaluated reconstruction settings. %BG variability was highest for TOF onlyreconstruction and lowest for TOF + PSF + filter reconstruction for most of the spheres(Fig. 2b).For the contrast phantom scanned on the HRRT (Fig. 2c), similar RCs were obtainedfrom the PSF reconstruction with and without filter for all spheres except for the smal-lest one (PSF: 54.4%; PSF + filter: 49.5%). Lowest RCs for the four hot spheres were ob-served from the native and the 2-mm Gaussian post filter reconstruction. As expected,the relatively narrow post filter had the biggest impact on the RC of the smallestsphere. The filtered reconstruction yielded lowest %BG variability values for all spheres(Fig. 2d).In direct comparison of the contrast phantom data, both the PET\/MR TOF onlyand TOF + PSF + filter reconstructions revealed similar RCs for the hot spherescompared to both the HRRT PSF and PSF + filter reconstructions (Fig. 2 andFig. 4 HRRT RCs (%) as a function of voxel noise (%) for two different sphere sizes and for a high and lowcount frame for the contrast phantom (19.8 mm sphere: high count frame (a), low count frame (b); 9.9 mmsphere: high count frame (c), low count frame (d)). The high count frame corresponds to ~ 135 millionprompts within the frame, the low count frame to ~ 12 million prompts. Note the difference in x-axis limits.HRRT data were reconstructed without any filters (native), with a 2-mm Gaussian filter (standard in-housereconstruction for human data), with PSF, and with PSF correction and 2-mm filter using 16 subsets,respectively. High count data were analyzed based on a single realization while low count data were basedon five replicates. Each point represents an OSEM iteration, and the number of iterations increases from leftto right. The dotted line represents the PET\/MR reconstruction (wTOF, PSF, 3.5-mm Gaussian filter) used forhuman data reconstructionMannheim et al. EJNMMI Physics            (2021) 8:20 Page 11 of 22Tables 5 and 6), except for the smallest sphere for the HRRT PSF reconstruc-tion (deviations ~ 10% between scanners). The PET\/MR TOF + filter reconstruc-tion revealed similar RC values compared to the HRRT filter only reconstruction.RCs of the PET\/MR decreased for all sphere sizes when the TOF information wasnot used (woTOF, Supplementary Figure 3 and Supplementary Table 2).RCs of all hot spheres decreased for the spherical VOI analysis in comparison to thesingle slice ROI analysis (except for the largest hot sphere for the HRRT PSF recon-struction, Supplementary Figure 4).RC and voxel-level noise (%)RCs versus voxel noise (%) comparison between various reconstruction parameters,two different sphere sizes, and count statistics for the contrast phantom scanned on thePET\/MR and the HRRT is illustrated in Figs. 3 and 4, respectively. A systematic in-crease in voxel noise (%) was observed for the low count frame in comparison to thehigh count frame. The highest noise levels for the PET\/MR were obtained for the TOFonly reconstruction, whereas the lowest noise levels were obtained when using TOFwith filter and TOF with PSF and filtering. The HRRT native reconstruction revealedFig. 5 RCs (%) as a function of voxel noise (%) for all sphere sizes for a high and low count frame for thecontrast phantom (PET\/MR (a), HRRT (b)). The high count frame corresponds to ~ 135 million promptswithin the frame, the low count frame to ~ 12 million prompts. Note the difference in x-axis limits. Eachpoint represents an OSEM iteration, and the number of iterations increases from left to right. PET\/MR datawere reconstructed with TOF + PSF + filter; HRRT data were reconstructed with filter only (standard in-house reconstruction for human data). High count data were analyzed based on a single realization whilelow count data were based on five replicates. Solid lines represent the hot spheres, dotted lines thecold spheresMannheim et al. EJNMMI Physics            (2021) 8:20 Page 12 of 22the highest voxel noise (%), while the lowest voxel noise (%) was determined for bothfiltered reconstructions.Figure 5 shows the RCs (%) versus voxel noise (%) for all sphere sizes based on thereconstruction parameters used for the human data. Increased voxel noise (%) for theHRRT compared to the PET\/MR was found for the high and low count frame. For thePET\/MR, the trajectory of RCs versus voxel noise (%) reached a relatively stable valuefrom iteration 4 on for most of the hot spheres; reconstructing with more iterations in-creased noise but not RCs significantly.Based on the phantom results, the best trade-off in terms of RCs, %BG variability,and voxel noise (%) for the PET\/MR was achieved with the reconstruction setting ofTOF + PSF + filter and 4 iterations (lowest %BG variability, voxel noise in % vs RCs foralmost all spheres converged along with high RCs due to PSF correction), which wasconsequently used for the reconstruction of all human data.Human scansFigure 6 depicts representative images of the mean [11C]DTBZ, [18F]FDG, and[11C]raclopride tracer distribution for each tracer. Figure 7 illustrates regional time-activity curves for each subject. A voxel-wise correlation of the HRRT to PET\/MR ac-tivity concentration for each subject can be found in Supplementary Figure 5.Fig. 6 Representative images of the mean [11C]DTBZ, [18F]FDG, and [11C]raclopride tracer distribution foreach subject. Color scales of the images were adjusted based on SUV values for each tracer separately witha common maximum between scanners. PET\/MR data were reconstructed with TOF with PSF and filterusing 4 iterations and 28 subsets; HRRT data were reconstructed using 6 iterations and 16 subsets and post-processed with a 2.0 mm FHWM Gaussian filter (standard in-house reconstruction). Note: Subject 4 wasinjected with a lower amount of [18F]FDG activity for the HRRT scan compared to the other scans; hence,noise characteristics differ accordinglyMannheim et al. EJNMMI Physics            (2021) 8:20 Page 13 of 22Line profilesLine profiles show excellent spatial agreement between the images obtained with thetwo scanners (Fig. 8). [11C]DTBZ and [11C]raclopride profiles reveal partial magnitudedifferences, while [18F]FDG profiles were almost identical.[11C]DTBZThe estimated BPND values obtained on the two scanners were found to be in goodagreement and deviations were below 10% except for the left caudate for both subjects,the right anterior putamen for subject 1 and the right posterior putamen for subject 2(Fig. 9a and b).[18F]FDGOverall, the ratios between different regions and pons were comparable between scan-ners (Fig. 9c and d). Deviations larger than 10% were detected for the anterior left andright putamen and left and right medial front gyrus for subject 3 and subject 5, respect-ively, and for the midbrain for subject 3 and the right putamen 2, as well as the medullafor subject 5.[11C]racloprideBPND values revealed deviations below 10% except for the left caudate and the rightposterior putamen (Fig. 9e and f).Fig. 7 Regional time activity curves for each subject for two target regions (left caudate and left putamen1) and the reference region ([11C]DTBZ: occipital cortex, [18F]FDG: pons, [11C]raclopride: cerebellum) for allsubjects. Note that the offset in time activity curves between the PET\/MR and HRRT is due to differentacquisition start triggering mechanisms (HRRT: manual acquisition start; PET\/MR: acquisition start based oncounts). Solid lines represent the PET\/MR data, dotted lines the HRRT dataMannheim et al. EJNMMI Physics            (2021) 8:20 Page 14 of 22Figure 9g displays mean HRRT to PET\/MR parameters of all subjects for each tracer,respectively. Mean [11C]DTBZ ratios revealed deviations below 10% for all investigatedregions. The same was detected for the mean [18F]FDG ratios except for the left anter-ior putamen (10.4%) and left and right medial front gyrus (11.9% and 11.1%, respect-ively). [11C]raclopride ratios were determined based on a single subject.DiscussionCross-validation studies between the HRRT and the SIGNA PET\/MR system were per-formed to assess the comparability with a specific focus on brain imaging.For phantom studies, different PET\/MR reconstruction parameters were tested to evalu-ate those most suitable for in vivo studies, though the scope of the study was not to neces-sarily perform a full optimization of the reconstruction and post-processing parameters, butrather to evaluate parameters that can be routinely used for human data acquisition.As expected, the highest RCs were observed from PSF reconstructions for both PET\/MR and HRRT data (Fig. 2) as has been reported by multiple studies for clinical PETsystems [8, 16, 22\u201325]. However, since resolution modeling with PSF can cause edgeartifacts (Gibbs artifacts), PSF reconstruction is not necessarily always the most quanti-tatively accurate method and it needs to be carefully evaluated [16, 22, 26, 27]. %BGFig. 8 Line profiles ([11C]DTBZ, [18F]FDG, [11C]raclopride) of mean PET images. Profiles were normalized tothe injected activity of each subject. Position of line profiles along the FOV (solid lines: typical targetregions; dotted lines: typical reference regions) are illustrated on a representative slice of the mean[11C]DTBZ, [18F]FDG and [11C]raclopride imageMannheim et al. EJNMMI Physics            (2021) 8:20 Page 15 of 22variability was lowest when applying post-filtering for both scanners and this concur-rently resulted in a decrease in RCs (Fig. 2).Reconstruction with TOF only and with TOF, PSF correction and filter, revealedsimilar RCs for the PET\/MR. This is likely due to fact that the improvement in RCsdue the resolution modeling is reversed due to the post-processing filter with theFig. 9 BPND of [11C]DTBZ (a) and [11C]raclopride (e) scans and ratios to reference region for [18F]FDG (c)scans. Calculated ratios of HRRT to PET\/MR scans are depicted for [11C]DTBZ (b), [18F]FDG (d), and[11C]raclopride (f). Mean ratios of HRRT to PET\/MR values for each tracer are depicted in (g). Dotted red linerepresents the ratio of 1Mannheim et al. EJNMMI Physics            (2021) 8:20 Page 16 of 22chosen width. The net result was noise reduction without altering RCs. Applying a dif-ferent filter width would likely yield differences in RCs between TOF only and TOF,PSF correction + filter.The HRRT %BG variability was slightly higher compared to the PET\/MR for most ofthe spheres (Fig. 2b and d). Voxel noise (%) of HRRT images was significantly higher(Fig. 5). As the HRRT uses smaller detector crystals compared to the PET\/MR and thereconstructed voxel size was also smaller, higher noise levels both on a regional andvoxel-level are expected due to the lower measured counts per detector pair.Determined voxel noise (%) for the HRRT (Fig. 5) is comparable to already publishedresults [28] within the limits of small differences in the phantom used and methodo-logical approaches.While the HRRT clearly outperforms the PET\/MR in terms of intrinsic spatial reso-lution and resolution uniformity, the PET\/MR TOF capability leads to significantly im-proved signal-to-noise ratios [29, 30]. It has been reported, that TOF reconstructionresults in higher RCs at matched noise levels with faster convergence when comparingto woTOF data [31\u201333]. This is in line with our results (Fig. 5). Furthermore, whencomparing RCs reconstructed with and without TOF, all spheres (Supplementary Fig-ure 3 and Supplementary Table 2), revealed a decrease in RCs when no TOF informa-tion was used for reconstruction, as previously reported [30, 34, 35]. The cold spheresexhibited a huge decrease in RCs when TOF information was not utilized for recon-struction, which is in line with published results [8, 36]. Furthermore, comparing thewoTOF PET\/MR data to the HRRT data revealed lower RCs for all PET\/MR recon-structions for the two smallest hot spheres due to the lower spatial resolution of thePET\/MR system (Supplementary Figure 3 and Supplementary Table 2). Hence, the useof TOF information for reconstruction contributes to partially off-set the intrinsicallyhigher spatial resolution of the HRRT system.RCs were determined according to the NEMA protocol and are based on a singleslice ROI; the single slice ROI shall be positioned on the image slice with the high-est sphere activity concentration [18]. However, this might be prone to inaccur-acies, as the center of the sphere might be between two adjacent slices (axial slicethickness PET\/MR: 2.78 mm; HRRT: 1.22 mm). A VOI-based analysis method cov-ering the entire sphere was proposed as a more robust analysis method [37]. De-termining the recovery with a VOI-based method revealed lower RCs for all hotspheres for the PET\/MR and HRRT (except for the largest hot sphere PSF recon-struction), though changes in RCs due to the analysis method were larger for thePET\/MR (Supplementary Figure 4). Our results are in line with results from theliterature [37], demonstrating that the analysis method clearly has a significant im-pact on the determination of the RCs.Within the limitations discussed above (different reconstructed voxel sizes betweenscanners, spatial resolution of the scanners and reconstruction parameters (TOF vs.woTOF, etc.)), a direct comparison of the phantom data between the HRRT and PET\/MR revealed comparable RCs, %BG variability, though voxel noise (%) differed signifi-cantly. The best trade-off (lowest %BG variability, voxel noise in % vs RCs for almostall spheres was converged along with high RCs due to PSF correction) between RCs,%BG variability and voxel noise (%) for the PET\/MR was found with PSF correctionand filtering. Hence, this reconstruction paradigm was used for reconstruction of allMannheim et al. EJNMMI Physics            (2021) 8:20 Page 17 of 22subject scans and compared to results from our own standard HRRT reconstructionsetting used for patient scans (2 mm Gaussian filtering).TACs of the HRRT subject scans revealed higher noise than the PET\/MR TACs, dueto the smaller crystals, as well as smaller reconstructed voxel sizes (Fig. 7), consistentwith the phantom data. No systematic differences between line profiles were detected(Fig. 8), indicating that differences in profiles are likely due to scan-to-scan or biologicalvariations. This also indicates that the quantitative data correction algorithms imple-mented on each scanner do not contribute to significant acquisition and scanner spe-cific biases.The variation of the [11C]DTBZ BPND was found to be within 10% (Fig. 9g). Althoughthe time interval between both scans for subject 1 was 8 months, no influence of theextended time interval was detected, which is in line with literature reporting thatVMAT2 binding shows a negligible age dependence over this time frame [19].Similar differences as for [11C]DTBZ were found for [18F]FDG ratios for most of theinvestigated regions, which were spanning a wider fraction of the brain. The largest de-viation was detected for the medial front gyrus possibly due to the differences in spatialresolution uniformity across the FOV [8, 12]. Considering also the fact that calculateddeviations are based on 3 subjects only, we can conclude that both systems revealedvery similar [18F]FDG ratios.In general, a trend towards larger differences between both systems for the posteriorputamen was observed. This region is affected most by the partial volume effect, as it isthe smallest region investigated in this study [38]. As the HRRT has a higher spatialresolution, differences in BPND, as well as ratios, could be expected to be the largest forthis region.Comparison of the acquired healthy subject data might be biased due to different at-tenuation correction approaches used for the HRRT and PET\/MR data. HRRT subjectdata were corrected for attenuation using a 137Cs transmission scan approach, whereasthe PET\/MR subject data were corrected based on a ZTE MRI scan of the respectivescan. Transmission scans are in general more sensitive to noise, as the signal to noiseratio solely depends on the acquisition duration of the transmission scan and the activ-ity of the used transmission source. Furthermore, Sousa et al. determined higher linearattenuation coefficients in brain imaging for ZTE attenuation maps compared to 68Geattenuation maps. For the cerebellum and posterior cortical regions, a higher correl-ation and improved precision in standard uptake values were detected when ZTE at-tenuation maps were used compared to 68Ge transmission scans [39]. Multiple studieshave shown that this is due to an improved estimation of the temporal bone in ZTEmaps [20, 39, 40]. However, based on the evaluated line profiles as shown in Fig. 8, nosignificant and systematic impact due to different attenuation correction approacheswas determined.Smaller voxel sizes were chosen for the reconstruction of PET\/MR subjects\u2019 scanscompared to phantom data to enable the VOI position based on a finer pixel grid.Voxel size was determined to have no impact on the RCs (deviation of RCs between128 and 256 matrix size was below 1.6%).Several studies have reported that variability (instrumentation and biological based)in test-retest scans of multiple applications is approximately 10% or more [41\u201344].Given that the number of subjects in our study was relatively low, coupled with theMannheim et al. EJNMMI Physics            (2021) 8:20 Page 18 of 22outcomes of the phantom studies, it is reasonable to expect that the estimate of vari-ability between same-subject scans performed on the two scanners may even decreasewhen increasing the number of subjects.It is of interest to note that our results most likely are applicable to a compari-son between the HRRT and a GE PET\/CT system, as both the GE PET\/MR andPET\/CT systems are based on a similar detector design and reconstruction algo-rithms [45]. Especially the 5-ring configuration of the PET\/CT system with thesame axial FOV as the PET\/MR will most likely demonstrate similar in vivo imagequality parameters [45].ConclusionsThis work demonstrates that the whole-body hybrid SIGNA PET\/MR system is wellsuited for high-resolution human brain imaging and that the scanner performance in aclinical setting is quite comparable to that of the HRRT system, which is still arguablythe human scanner with the highest intrinsic resolution. The HRRT outperforms thePET\/MR in terms of spatial resolution, but exhibits a higher voxel noise (%) due to itssmaller crystals and smaller reconstructed voxel sizes and lack of TOF capability; thePET\/MR TOF capability indeed contributed to off-set to some degree the higher spatialresolution of the HRRT in terms of overall image quality.BPND of [11C]DTBZ subjects, as well as ratios of [18F]FDG scans, revealed a variabilitybetween both scans of less than 12%, which is in the range of typical test-retest variabil-ity. From a clinical point of view and based on our results, we expect the two scannersto provide similar results in regard to brain imaging. The main difference between thetwo scanners is the attenuation correction approach with the HRRT scanner using atransmission source scan, whereas the PET\/MR is based on a ZTE MRI scan. Based onour results, this was not a confound in comparing the two scanners. A significant clearadvantage of the PET\/MR however remains the fact that the simultaneous imaging ap-proach of PET and MR enables the acquisition of multiple parameters with the brain inthe same physiological state, in addition to providing an anatomical reference requiredfor several image analysis approaches.Supplementary InformationThe online version contains supplementary material available at https:\/\/doi.org\/10.1186\/s40658-020-00349-0.Additional file 1: Supplementary Figure 1: Representative images of the image quality phantom data fordifferent reconstruction parameters. Color scales of the images were normalized based on the average backgroundvalue multiplied by the respective sphere to background ratio. Supplementary Figure 2: RCs (a) and %BGvariability (b) as a function of sphere diameter for the image quality phantom. PET\/MR data were reconstructedwith TOF, TOF with filter, TOF with PSF, and TOF with PSF and filter. 2 iterations and 28 subsets were used,respectively (GE recommendation for phantom data). Note the gap between spheres marks cold vs. hot spheres.Supplementary Figure 3: RCs (%) of the PET\/MR (a) as a function of sphere diameter for the contrast phantomwith (solid lines) and without TOF (dotted lines). Comparison of RCs (b) without TOF of the PET\/MR (solid lines) tothe HRRT (dotted lines). Note the gap between spheres marks cold vs. hot spheres. Supplementary Figure 4: RCs(%) (PET\/MR (a), HRRT (b)) versus sphere diameter for the contrast phantom. Data were analyzed with a single sliceROI (solid lines), the standard NEMA analysis method, and with a spherical VOI matching the physical spherediameter (dotted lines). Note the gap between spheres marks cold vs. hot spheres. Supplementary Figure 5:Voxel-wise correlation of the HRRT to PET\/MR activity concentration for each subject ([11C]DTBZ, [18F]FDG,[11C]raclopride). The black line indicates the identity line, the red dotted line displays the linear regression of thevalues with the corresponding R2. Supplementary Table 1: RCs (a) and %BG variability (b) for the image qualityphantom scanned on the PET\/MR. Supplementary Table 2: RCs for the contrast phantom scanned on the PET\/MR and HRRT. PET\/MR data were reconstructed with and without TOF information.Mannheim et al. EJNMMI Physics            (2021) 8:20 Page 19 of 22Abbreviations[11C]DTBZ: [11C]dihydrotetrabenazine; [18F]FDG: [18F]-fluorodeoxyglucose; %BG: Percent background variability;BRAVO: Brain volume imaging; BPND: Non-displaceable binding potentials; cFOV: Center of the field of view;CT: Computed tomography; DOI: Depth-of-interaction; FA: Flip angle; FBP: Filtered backprojection; FWHM: Full width athalf maximum; HRRT: High-resolution research tomograph; ID: Inner diameter; LSO: Lutetium oxyorthosilicate;LYSO: Lutetium-yttrium oxyorthosilicate; MNI: Montreal Neurological Institute; MPRAGE: Magnetization-prepared rapidacquisition gradient echo; MRI: Magnetic resonance imaging; NEMA: National Electrical Manufacturers Association;NEX: Number of excitations; OP-OSEM: Ordinary Poisson ordered subset expectation maximization; PET: Positronemission tomography; PMT: Photomultiplier tube; PSF: Point spread function; RC: Recovery coefficient; ROI: Regions ofinterest; SiPM: Silicon photomultiplier; SPM: Statistical Parametric Mapping; TE: Echo time; TOF: Time-of-flight;TR: Repetition time; VMAT2: Vesicular monoamine transporter 2; ZTE: Zero echo timeAcknowledgementsThe authors would like to thank the UBC PET and MR scanning team and the TRIUMF radio-chemistry production staff.All volunteer subjects, who generously donated their time to this research, are also most gratefully acknowledged.Authors\u2019 contributionsJGM, JCC, and VS designed the study. JGM and JCC performed phantom data acquisition. ES, CE, JNM, JZ, and LBprepared and performed subjects\u2019 scans. JGM analyzed phantom data, as well as subject data in cooperation with NV.JGM drafted the manuscript. All authors have been involved in critically revising the manuscript. All authors read andapproved the final manuscript.FundingThis study was partially funded through Natural Sciences and Engineering Research Council grant (240670-13) and aCanadian Foundation for Innovation grant. TRIUMF is funded by the National Research Council Canada. No otherpotential conflict of interest relevant to this article was reported.Availability of data and materialsThe phantom data are available upon request. The human data are not publicly available due to subjectconfidentiality.Ethics approval and consent to participateThe Clinical Research Ethics Board of the University of British Columbia approved the study.Consent for publicationInformed written consent was provided by all participating subjects.Competing interestsThe authors declare that they have no competing interests.Author details1Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada. 2WernerSiemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard-Karls University Tuebingen,Tuebingen, Germany. 3Cluster of Excellence iFIT (EXC 2180) \u201cImage Guided and Functionally Instructed TumorTherapies\u201d, University of Tuebingen, Tuebingen, Germany. 4Pacific Parkinson\u2019s Research Centre, University of BritishColumbia, Vancouver, British Columbia, Canada. 5Djavad Mowafaghian Centre for Brain Health, Pacific Parkinson\u2019sResearch Centre, University of British Columbia & Vancouver Coastal Health, Vancouver, British Columbia, Canada.6Global MR Applications & Workflow, GE Healthcare Canada, Vancouver, British Columbia, Canada. 7UBC MRI ResearchCentre, University of British Columbia, Vancouver, British Columbia, Canada.Received: 10 June 2020 Accepted: 16 December 2020References1. 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Pan T, Einstein SA, Kappadath SC, Grogg KS, Lois Gomez C, Alessio AM, et al. Performance evaluation of the 5-Ring GEDiscovery MI PET\/CT system using the national electrical manufacturers association NU 2-2012 Standard. Med Phys.2019;46(7):3025\u201333.Publisher\u2019s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Mannheim et al. EJNMMI Physics            (2021) 8:20 Page 22 of 22","attrs":{"lang":"en","ns":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","classmap":"oc:AnnotationContainer"},"iri":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","explain":"Simple Knowledge Organisation System; Notes are used to provide information relating to SKOS concepts. 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