title: Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data.
abstract: Attenuation correction (AC) of whole-body PET data in combined PET/MRI tomographs is expected to be a technical challenge. In this study, a potential solution based on a segmented attenuation map is proposed and evaluated in clinical PET/CT cases.
  
  
  METHODS
  Segmentation of the attenuation map into 4 classes (background, lungs, fat, and soft tissue) was hypothesized to be sufficient for AC purposes. The segmentation was applied to CT-based attenuation maps from (18)F-FDG PET/CT oncologic examinations of 35 patients with 52 (18)F-FDG-avid lesions in the lungs (n = 15), bones (n = 21), and neck (n = 16). The standardized uptake values (SUVs) of the lesions were determined from PET images reconstructed with nonsegmented and segmented attenuation maps, and an experienced observer interpreted both PET images with no knowledge of the attenuation map status. The feasibility of the method was also evaluated with 2 patients who underwent both PET/CT and MRI.
  
  
  RESULTS
  The use of a segmented attenuation map resulted in average SUV changes of 8% +/- 3% (mean +/- SD) for bone lesions, 4% +/- 2% for neck lesions, and 2% +/- 3% for lung lesions. The largest SUV change was 13.1%, for a lesion in the pelvic bone. There were no differences in the clinical interpretations made by the experienced observer with both types of attenuation maps.
  
  
  CONCLUSIONS
  A segmented attenuation map with 4 classes derived from CT data had only a small effect on the SUVs of (18)F-FDG-avid lesions and did not change the interpretation for any patient. This approach appears to be practical and valid for MRI-based AC.
authors.affiliation: Nuklearmedizinische Klinik und Poliklinik, Technische Universität München, Munich, Germany. a.martinez-moller@lrz.tu-muenchen.de
authors.forename: Axel
authors.initials: A
authors.lastname: Martinez-Möller
authors.forename: Michael
authors.initials: M
authors.lastname: Souvatzoglou
authors.forename: Gaspar
authors.initials: G
authors.lastname: Delso
authors.forename: Ralph A
authors.initials: RA
authors.lastname: Bundschuh
authors.forename: Christophe
authors.initials: C
authors.lastname: Chefd'hotel
authors.forename: Sibylle I
authors.initials: SI
authors.lastname: Ziegler
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
authors.forename: Markus
authors.initials: M
authors.lastname: Schwaiger
authors.forename: Stephan G
authors.initials: SG
authors.lastname: Nekolla
mesh_terms: D016477:Artifacts; D001859:Bone Neoplasms / Q000175:diagnosis*; D005240:Feasibility Studies; D006258:Head and Neck Neoplasms / Q000175:diagnosis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008175:Lung Neoplasms / Q000175:diagnosis*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D051598:Whole Body Imaging / Q000379:methods*