title: An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: application to simultaneous PET/MR brain imaging.
abstract: We present an approach for head MR-based attenuation correction (AC) based on the Statistical Parametric Mapping 8 (SPM8) software, which combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (μ maps) from MR data in integrated PET/MR scanners.
  
  
  METHODS
  Coregistered anatomic MR and CT images of 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray matter, white matter, cerebrospinal fluid, bone, soft tissue, and air), which were then nonrigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomic MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients to be used for AC of PET data. The method was validated on 16 new subjects with brain tumors (n = 12) or mild cognitive impairment (n = 4) who underwent CT and PET/MR scans. The μ maps and corresponding reconstructed PET images were compared with those obtained using the gold standard CT-based approach and the Dixon-based method available on the Biograph mMR scanner. Relative change (RC) images were generated in each case, and voxel- and region-of-interest-based analyses were performed.
  
  
  RESULTS
  The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain linear attenuation coefficients (RC, 1.38% ± 4.52%) compared with the gold standard. Similar results (RC, 1.86% ± 4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and region-of-interest-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87% ± 5.0% and 2.74% ± 2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0% ± 10.25% and 9.38% ± 4.97%, respectively). Areas closer to the skull showed the largest improvement.
  
  
  CONCLUSIONS
  We have presented an SPM8-based approach for deriving the head μ map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and requires only the morphologic data acquired with a single MR sequence. The method is accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks.
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts.
authors.forename: David
authors.initials: D
authors.lastname: Izquierdo-Garcia
authors.affiliation: Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
authors.forename: Adam E
authors.initials: AE
authors.lastname: Hansen
authors.affiliation: Department of Nuclear Medicine, Technische Universität München, Munich, Germany.
authors.forename: Stefan
authors.initials: S
authors.lastname: Förster
authors.affiliation: Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
authors.forename: Didier
authors.initials: D
authors.lastname: Benoit
authors.affiliation: Department of Nuclear Medicine, Technische Universität München, Munich, Germany.
authors.forename: Sylvia
authors.initials: S
authors.lastname: Schachoff
authors.affiliation: Department of Nuclear Medicine, Technische Universität München, Munich, Germany.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Fürst
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts Department of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts; and.
authors.forename: Kevin T
authors.initials: KT
authors.lastname: Chen
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts Department of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts; and Program in Biophysics, Harvard University, Cambridge, Massachusetts.
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Chonde
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts ccatana@nmr.mgh.harvard.edu.
authors.forename: Ciprian
authors.initials: C
authors.lastname: Catana
mesh_terms: D000465:Algorithms; D001842:Bone and Bones / Q000000981:diagnostic imaging; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D003072:Cognition Disorders / Q000000981:diagnostic imaging / Q000473:pathology; D005909:Glioblastoma / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D059906:Neuroimaging; D049268:Positron-Emission Tomography; D015203:Reproducibility of Results; D012886:Skull / Q000000981:diagnostic imaging
keywords: attenuation correction; integrated PET/MRI; segmentation; template