title: MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences.
abstract: One of the challenges in PET/MRI is the derivation of an attenuation map to correct the PET image for attenuation. Different methods have been suggested for deriving the attenuation map from an MR image. Because the low signal intensity of cortical bone on images acquired with conventional MRI sequences makes it difficult to detect this tissue type, these methods rely on some sort of anatomic precondition to predict the attenuation map, raising the question of whether these methods will be usable in the clinic when patients may exhibit anatomic abnormalities.
  
  
  METHODS
  We propose the use of the transverse relaxation rate, derived from images acquired with an ultrashort echo time sequence to classify the voxels into 1 of 3 tissue classes (bone, soft tissue, or air), without making any assumptions on patient anatomy. Each voxel is assigned a linear attenuation coefficient corresponding to its tissue class. A reference CT scan is used to determine the voxel-by-voxel accuracy of the proposed method. The overall accuracy of the MRI-based attenuation correction is evaluated using a method that takes into account the nonlocal effects of attenuation correction.
  
  
  RESULTS
  As a proof of concept, the head of a pig was used as a phantom for imaging. The new method yielded a correct tissue classification in 90% of the voxels. Five human brain PET/CT and MRI datasets were also processed, yielding slightly worse voxel-by-voxel performance, compared to a CT-derived attenuation map. The PET datasets were reconstructed using the segmented MRI attenuation map derived with the new method, and the resulting images were compared with segmented CT-based attenuation correction. An average error of around 5% was found in the brain.
  
  
  CONCLUSIONS
  The feasibility of using the transverse relaxation rate map derived from ultrashort echo time MR images for the estimation of the attenuation map was shown on phantom and clinical brain data. The results indicate that the new method, compared with CT-based attenuation correction, yields clinically acceptable errors. The proposed method does not make any assumptions about patient anatomy and could therefore also be used in cases in which anatomic abnormalities are present.
authors.affiliation: MEDISIP, Department of Electronics and Information Systems, Ghent University-IBBT-IBiTech, Ghent, Belgium. Vincent.Keereman@ugent.be
authors.forename: Vincent
authors.initials: V
authors.lastname: Keereman
authors.forename: Yves
authors.initials: Y
authors.lastname: Fierens
authors.forename: Tom
authors.initials: T
authors.lastname: Broux
authors.forename: Yves
authors.initials: Y
authors.lastname: De Deene
authors.forename: Max
authors.initials: M
authors.lastname: Lonneux
authors.forename: Stefaan
authors.initials: S
authors.lastname: Vandenberghe
mesh_terms: D000465:Algorithms; D001842:Bone and Bones / Q000000981:diagnostic imaging; D003627:Data Interpretation, Statistical; D005240:Feasibility Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000706:statistics & numerical data*; D014055:Tomography, Emission-Computed