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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Technical Note: Clustering-based motion compensation scheme for multishot diffusion tensor imaging.

Zhongbiao Xu1,2, Feng Huang3, Zhigang Wu3

  • 1School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.

Medical Physics
|October 12, 2018
PubMed
Summary

A new clustered image reconstruction using image-space sampling function (CIRIS) method effectively corrects large-scale motion in multishot diffusion-weighted imaging (DWI). This improves image quality and diffusion tensor imaging (DTI) accuracy for high-resolution scans.

Keywords:
clusteringdiffusion tensor imagingdiffusion-weighted imagingmotion correctionmultishot echo-planar imaging

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Biomedical Engineering

Background:

  • Multishot diffusion-weighted imaging (DWI) is susceptible to artifacts from large-scale motion.
  • Existing image reconstruction methods like IRIS struggle with significant motion, limiting image quality and accuracy.
  • Accurate diffusion tensor imaging (DTI) is crucial for understanding brain structure and function.

Purpose of the Study:

  • To extend the image reconstruction using image-space sampling function (IRIS) method to address large-scale motion in multishot DWI.
  • To develop a novel algorithm capable of correcting for substantial in-plane motion during DWI acquisition.
  • To enhance the accuracy of DTI estimations in the presence of motion artifacts.

Main Methods:

  • Proposed a clustered IRIS (CIRIS) algorithm to group shots with minimal intracluster motion and reconstruct them using IRIS.
  • Registered and combined reconstructed cluster images via weighted averaging to correct for intercluster motion-induced voxel mismatch.
  • For DTI, treated motion-induced direction changes as additional diffusion directions and incorporated motion-corrupted data rejection.

Main Results:

  • CIRIS significantly reduced motion-induced blurring and artifacts in DWI compared to standard IRIS.
  • The method provided more accurate DTI estimations when large-scale motion was present.
  • Experiments using simulated and in vivo datasets validated the effectiveness of CIRIS.

Conclusions:

  • CIRIS offers a novel and effective approach for correcting large-scale in-plane motion in multishot DWI.
  • The algorithm is expected to significantly benefit the practical application of high-resolution diffusion imaging.
  • This advancement holds promise for improved diagnostic capabilities in neuroimaging.