A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging

PLoS One. 2014 Apr 11;9(4):e94592. doi: 10.1371/journal.pone.0094592. eCollection 2014.

Abstract

Purpose: The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI).

Materials and 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).

Conclusion: 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Artifacts*
  • Child
  • Child, Preschool
  • Datasets as Topic
  • Female
  • Humans
  • Image Processing, Computer-Assisted*
  • Infant
  • Infant, Newborn
  • Magnetic Resonance Imaging / methods*
  • Male
  • Motion*
  • Reproducibility of Results
  • Workflow*

Grants and funding

This work was supported by the grant from National Natural Science Foundation of China (No. 81171317), the 2011 New Century Excellent Talent Support Plan from the Ministry of Education of China (DWYXSJ11000007), the Fund for the National Clinical Key Specialty from the Ministry of Health of China, and National Basic Research Program 973 (Nos. 2011CB707903 and 2010CB732603). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.