A Robust Post-Processing Workflow for Datasets with Motion Artifacts in Diffusion Kurtosis Imaging

Xianjun Li, Jian Yang, Jie Gao, Xue Luo, Zhenyu Zhou, Yajie Hu, Ed X Wu and Mingxi Wan

PLOS ONE, 2014, vol. 9, issue 4, 1-9

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

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0094592

DOI: 10.1371/journal.pone.0094592

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