title: Whole-Body PET/MR Imaging: Quantitative Evaluation of a Novel Model-Based MR Attenuation Correction Method Including Bone.
abstract: In routine whole-body PET/MR hybrid imaging, attenuation correction (AC) is usually performed by segmentation methods based on a Dixon MR sequence providing up to 4 different tissue classes. Because of the lack of bone information with the Dixon-based MR sequence, bone is currently considered as soft tissue. Thus, the aim of this study was to evaluate a novel model-based AC method that considers bone in whole-body PET/MR imaging.
  
  
  METHODS
  The new method ("Model") is based on a regular 4-compartment segmentation from a Dixon sequence ("Dixon"). Bone information is added using a model-based bone segmentation algorithm, which includes a set of prealigned MR image and bone mask pairs for each major body bone individually. Model was quantitatively evaluated on 20 patients who underwent whole-body PET/MR imaging. As a standard of reference, CT-based μ-maps were generated for each patient individually by nonrigid registration to the MR images based on PET/CT data. This step allowed for a quantitative comparison of all μ-maps based on a single PET emission raw dataset of the PET/MR system. Volumes of interest were drawn on normal tissue, soft-tissue lesions, and bone lesions; standardized uptake values were quantitatively compared.
  
  
  RESULTS
  In soft-tissue regions with background uptake, the average bias of SUVs in background volumes of interest was 2.4% ± 2.5% and 2.7% ± 2.7% for Dixon and Model, respectively, compared with CT-based AC. For bony tissue, the -25.5% ± 7.9% underestimation observed with Dixon was reduced to -4.9% ± 6.7% with Model. In bone lesions, the average underestimation was -7.4% ± 5.3% and -2.9% ± 5.8% for Dixon and Model, respectively. For soft-tissue lesions, the biases were 5.1% ± 5.1% for Dixon and 5.2% ± 5.2% for Model.
  
  
  CONCLUSIONS
  The novel MR-based AC method for whole-body PET/MR imaging, combining Dixon-based soft-tissue segmentation and model-based bone estimation, improves PET quantification in whole-body hybrid PET/MR imaging, especially in bony tissue and nearby soft tissue.
authors.affiliation: Institute of Medical Physics, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany daniel.paulus@imp.uni-erlangen.de.
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Paulus
authors.affiliation: Institute of Medical Physics, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany Erwin L. Hahn Institute for MR Imaging, University Duisburg-Essen, Essen, Germany High Field and Hybrid MR Imaging, University Hospital Essen, Essen, Germany.
authors.forename: Harald H
authors.initials: HH
authors.lastname: Quick
authors.affiliation: Siemens AG Healthcare, Erlangen, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Geppert
authors.affiliation: Siemens AG Healthcare, Erlangen, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Fenchel
authors.affiliation: Siemens AG Healthcare, Malvern, Pennsylvania.
authors.forename: Yiqiang
authors.initials: Y
authors.lastname: Zhan
authors.affiliation: Siemens AG Healthcare, Malvern, Pennsylvania.
authors.forename: Gerardo
authors.initials: G
authors.lastname: Hermosillo
authors.affiliation: Siemens AG Healthcare, New York, New York.
authors.forename: David
authors.initials: D
authors.lastname: Faul
authors.affiliation: Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York; and Center for Advanced Imaging Innovation and Research (CAI2R), New York, New York.
authors.forename: Fernando
authors.initials: F
authors.lastname: Boada
authors.affiliation: Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York; and.
authors.forename: Kent P
authors.initials: KP
authors.lastname: Friedman
authors.affiliation: Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York; and Center for Advanced Imaging Innovation and Research (CAI2R), New York, New York.
authors.forename: Thomas
authors.initials: T
authors.lastname: Koesters
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001842:Bone and Bones / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D019788:Fluorodeoxyglucose F18 / Q000737:chemistry; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D064847:Multimodal Imaging; D049268:Positron-Emission Tomography; D019275:Radiopharmaceuticals / Q000737:chemistry; D014057:Tomography, X-Ray Computed; D051598:Whole Body Imaging
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
keywords: MR-based attenuation correction; PET/MR hybrid imaging; attenuation correction of bone; model-based attenuation correction