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Diffusion Imaging in the Rat Cervical Spinal Cord
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A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging.

Xianjun Li1, Jian Yang1, Jie Gao2

  • 1Radiology 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.

Plos One
|April 15, 2014
PubMed
Summary
This summary is machine-generated.

This study developed a post-processing workflow to improve motion-corrupted diffusion kurtosis imaging (DKI) data. The method enhances image quality and measurement precision for clinical applications in subjects with involuntary movements.

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Diffusion Kurtosis Imaging (DKI)

Background:

  • Motion artifacts significantly degrade the quality of diffusion kurtosis imaging (DKI) datasets.
  • Existing post-processing methods may not be sufficiently robust for handling motion corruption in DKI.
  • Accurate DKI parameter estimation is crucial for clinical applications, especially in populations prone to movement.

Purpose of the Study:

  • To develop and validate a robust post-processing workflow for motion-corrupted DKI datasets.
  • To improve image quality and measurement precision in DKI data affected by motion.
  • To establish a reliable method for clinical application of DKI in subjects with involuntary movements.

Main Methods:

  • A multi-step workflow including brain extraction, rigid registration, distortion correction, artifact rejection using local Pearson correlation coefficient (LPCC), spatial smoothing, and tensor estimation.
  • Comparison of LPCC with conventional correlation coefficient for motion artifact detection.
  • Evaluation of the influence of rejected artifacts on parameter estimation using mean square error (MSE) and noise variance.

Main Results:

  • LPCC demonstrated higher sensitivity in detecting motion artifacts compared to the conventional correlation coefficient (p<0.05).
  • Rejected artifacts had less influence on parameter precision than noise, as indicated by MSEs being smaller than noise variance.
  • The proposed workflow significantly improved image quality and reduced measurement biases in motion-corrupted DKI datasets (p<0.05).

Conclusions:

  • The developed post-processing workflow is reliable for enhancing image quality and measurement precision in motion-corrupted DKI datasets.
  • This workflow offers an effective solution for clinical implementation of DKI, particularly in pediatric subjects and patients with essential tremor.
  • The method ensures the clinical practicality of DKI by addressing challenges posed by involuntary subject movements.