title: A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging.
abstract: OBJECTIVE
  The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI).
  
  
  METHODS
  The proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients).
  
  
  RESULTS
  The relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05).
  
  
  CONCLUSIONS
  The proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements.
authors.affiliation: Radiology Department of the First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China; Department of Biomedical Engineering, the Key Laboratory of Biomedical Information Engineering of the Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
authors.forename: Xianjun
authors.initials: X
authors.lastname: Li
authors.affiliation: Radiology Department of the First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China; Department of Biomedical Engineering, the Key Laboratory of Biomedical Information Engineering of the Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.affiliation: Radiology Department of the First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
authors.forename: Jie
authors.initials: J
authors.lastname: Gao
authors.affiliation: Department of Biomedical Engineering, the Key Laboratory of Biomedical Information Engineering of the Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
authors.forename: Xue
authors.initials: X
authors.lastname: Luo
authors.affiliation: Radiology Department of the First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
authors.forename: Zhenyu
authors.initials: Z
authors.lastname: Zhou
authors.affiliation: Department of Biomedical Engineering, the Key Laboratory of Biomedical Information Engineering of the Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
authors.forename: Yajie
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Laboratory of Biomedical Imaging and Signal Processing, the University of Hong Kong, Hong Kong SAR, People's Republic of China.
authors.forename: Ed X
authors.initials: EX
authors.lastname: Wu
authors.affiliation: Department of Biomedical Engineering, the Key Laboratory of Biomedical Information Engineering of the Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
authors.forename: Mingxi
authors.initials: M
authors.lastname: Wan
mesh_terms: D000328:Adult; D016477:Artifacts; D002648:Child; D002675:Child, Preschool; D066264:Datasets as Topic; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007223:Infant; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009038:Motion; D015203:Reproducibility of Results; D057188:Workflow
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