title: MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation- and atlas-based methods.
abstract: PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR).
  
  
  METHODS
  Eleven patients each underwent a whole-body PET/CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard.
  
  
  RESULTS
  In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% ± 10.2% and 7.7% ± 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% ± 7.9% for the segmentation method and 5.7% ± 4.7% for the AT&PR method.
  
  
  CONCLUSIONS
  The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.
authors.affiliation: Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Hofmann
authors.forename: Ilja
authors.initials: I
authors.lastname: Bezrukov
authors.forename: Frederic
authors.initials: F
authors.lastname: Mantlik
authors.forename: Philip
authors.initials: P
authors.lastname: Aschoff
authors.forename: Florian
authors.initials: F
authors.lastname: Steinke
authors.forename: Thomas
authors.initials: T
authors.lastname: Beyer
authors.forename: Bernd J
authors.initials: BJ
authors.lastname: Pichler
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schölkopf
mesh_terms: D000368:Aged; D000465:Algorithms; D001271:Atlases as Topic; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008670:Metals; D008875:Middle Aged; D009369:Neoplasms / Q000000981:diagnostic imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D019736:Prostheses and Implants; D019275:Radiopharmaceuticals; D051598:Whole Body Imaging / Q000379:methods*
chemical_list: D008670:Metals; D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18