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Eddy-current-induced distortion correction using maximum reconciled mutual information in diffusion MR imaging

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

In diffusion tensor imaging, a large number of diffusion-weighted (DW) images with different diffusion gradient directions are attained during scanning. However, subjects’ involuntary head movements and eddy current effect related to large diffusion-sensitizing gradients will cause distortions of DW images. Therefore, for tracking accurately white matter structures and tractography, the distortions have to be realigned before model fitting. Currently, traditional methods use maximum mutual information (MMI) or normalized mutual information (NMI) as similarity measure for DW images registration. These information measures are defined by Shannon entropy. The image entropy is able to embody the global information complexity but ignore the local information complexity caused by heterogeneous intensity contrasts in DW images, making registration algorithm early converge.

Method

To overcome the above problem, we present maximum reconciled mutual information (MRMI) combining both global information and local information as the similarity measure of the registration algorithm framework.

Result

(i) In comparison with traditional methods, under our proposed MRMI method, the border of DW image is more anastomotic with the b0 image, and the fitted fractional anisotropy (FA) map after registration is closer to the true brain boundary. (ii) By quantitative analysis of registration results, our method has a significant advantage over others in terms of NMI between b0 image and the aligned DW images.

Conclusion

The results suggest that there is a high-level matching in space between the b0 image and the DW images aligned by the MRMI method, raising the registration robustness and accuracy compared to the traditional DW registration methods. It may provide a better option for the existing diffusion image registration tools (e.g., FMRIB Software Library) and commonly multimodal medical image registration.

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Funding

This work was funded by the National Natural Science Foundation of China (Grant Number: 61773256 and 61472247).

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Authors and Affiliations

  1. College of Physical Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Junling Liang & Shujun Zhao

  2. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Junling Liang, Pengcheng Sun, Xinyu Chai & Heng Li

  3. School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China

    Liqing Di

  4. Xuanwu Hospital of Capital Medical University, Beijing, 100053, China

    Jingjuan Wang

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  1. Junling Liang
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Correspondence to Xinyu Chai or Heng Li.

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This article does not contain any studies with animals performed by any of the authors. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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11548_2018_1901_MOESM1_ESM.gif

An example for dynamically rendering differences inside the brain volume. For the sake of visually observing in detail, we randomly select one direction DW image from one subject, which embodies three sections displayed from left to right. The red lines are sketched in the same spatial location of corresponding images under different methods. (GIF 605 kb)

More examples for dynamically rendering differences inside the brain volume. (GIF 8527 kb)

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Liang, J., Zhao, S., Di, L. et al. Eddy-current-induced distortion correction using maximum reconciled mutual information in diffusion MR imaging. Int J CARS 14, 463–472 (2019). https://doi.org/10.1007/s11548-018-01901-1

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