title: Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction.
abstract: Attenuation correction is important for PET reconstruction. In PET/MR, MR intensities are not directly related to attenuation coefficients that are needed in PET imaging. The attenuation coefficient map can be derived from CT images. Therefore, prediction of CT substitutes from MR images is desired for attenuation correction in PET/MR.
  
  
  METHODS
  This study presents a patch-based method for CT prediction from MR images, generating attenuation maps for PET reconstruction. Because no global relation exists between MR and CT intensities, we propose local diffeomorphic mapping (LDM) for CT prediction. In LDM, we assume that MR and CT patches are located on 2 nonlinear manifolds, and the mapping from the MR manifold to the CT manifold approximates a diffeomorphism under a local constraint. Locality is important in LDM and is constrained by the following techniques. The first is local dictionary construction, wherein, for each patch in the testing MR image, a local search window is used to extract patches from training MR/CT pairs to construct MR and CT dictionaries. The k-nearest neighbors and an outlier detection strategy are then used to constrain the locality in MR and CT dictionaries. Second is local linear representation, wherein, local anchor embedding is used to solve MR dictionary coefficients when representing the MR testing sample. Under these local constraints, dictionary coefficients are linearly transferred from the MR manifold to the CT manifold and used to combine CT training samples to generate CT predictions.
  
  
  RESULTS
  Our dataset contains 13 healthy subjects, each with T1- and T2-weighted MR and CT brain images. This method provides CT predictions with a mean absolute error of 110.1 Hounsfield units, Pearson linear correlation of 0.82, peak signal-to-noise ratio of 24.81 dB, and Dice in bone regions of 0.84 as compared with real CTs. CT substitute-based PET reconstruction has a regression slope of 1.0084 and R2of 0.9903 compared with real CT-based PET.
  
  
  CONCLUSIONS
  In this method, no image segmentation or accurate registration is required. Our method demonstrates superior performance in CT prediction and PET reconstruction compared with competing methods.
authors.affiliation: Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.affiliation: Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Lijun
authors.initials: L
authors.lastname: Lu
authors.affiliation: Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Zhentai
authors.initials: Z
authors.lastname: Lu
authors.affiliation: Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Liming
authors.initials: L
authors.lastname: Zhong
authors.affiliation: Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Meiyan
authors.initials: M
authors.lastname: Huang
authors.affiliation: Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Yanqiu
authors.initials: Y
authors.lastname: Feng
authors.affiliation: Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China qianjinfeng08@gmail.com.
authors.forename: Qianjin
authors.initials: Q
authors.lastname: Feng
authors.affiliation: Key Laboratory of Medical Image Processing, Institute of Biomedical Engineering, Southern Medical University, Guangzhou, China.
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
mesh_terms: D001921:Brain / Q000166:cytology / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D064368:Healthy Volunteers; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D064847:Multimodal Imaging; D049268:Positron-Emission Tomography; D014057:Tomography, X-Ray Computed
keywords: CT prediction; attenuation correction; local anchor embedding; local diffeomorphic mapping; outlier detection