Eddy-current-induced distortion correction using maximum reconciled mutual information in diffusion MR imaging
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.
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.
(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.
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.
KeywordsDiffusion MR Diffusion-weighted imaging Registration Mutual information Reconcile entropy
This work was funded by the National Natural Science Foundation of China (Grant Number: 61773256 and 61472247).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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.
Informed consent was obtained from all individual participants included in the study.
- 9.Negwer C, Beurskens E, Sollmann N, Maurer S, Ille S, Giglhuber K, Kirschke JS, Ringel F, Meyer B, Krieg SM (2018) Loss of subcortical language pathways correlates with surgery-related aphasia in brain tumor patients: an investigation via rTMS-based DTI fiber tracking. World Neurosurg. https://doi.org/10.1016/j.wneu.2017.12.163 Google Scholar
- 19.Zhuang J, Hrabe J, Kangarlu A, Xu D, Bansal R, Branch CA, Peterson BS (2006) Correction of eddy-current distortions in diffusion tensor images using the known directions and strengths of diffusion gradients. J Magn Reson Imaging JMRI 24(5):1188–1193. https://doi.org/10.1002/jmri.20727 CrossRefGoogle Scholar
- 20.Yu B, Alexander DC (2008) Model-based registration to correct for motion between acquisitions in diffusion MR imaging. In: IEEE international symposium on biomedical imaging: from nano to macro, pp 947–950. https://doi.org/10.1109/ISBI.2008.4541154
- 21.Yao XF, Song ZJ (2011) Deformable registration for geometric distortion correction of diffusion tensor imaging. In: International conference on computer analysis of images and patterns, pp 545–553. https://doi.org/10.1007/978-3-642-23672-3_66
- 26.Hellier P, Barillot C (2000) Multimodal non-rigid warping for correction of distortions in functional MRI. In: International conference on medical image computing and computer-assisted intervention, vol 1935, pp 512–520. https://doi.org/10.1007/978-3-540-40899-4_52
- 27.Castellanos P, del Angel PL, Medina V (2001) Deformation of MR images using a local linear transformation. In: Medical imaging 2001: image processing, vol 4322, pp 909–917. https://doi.org/10.1117/12.430963
- 28.Hermosillo G and Faugeras O (2001) Dense image matching with global and local statistical criteria: a variational approach. In: Conference on proceedings of the 2001 IEEE computer society. https://doi.org/10.1109/CVPR.2001.990458
- 31.Teukolsky SA, Flannery BP, Press W, Vetterling W (1992) Numerical recipes in C. SMR, 693. https://doi.org/10.2307/3619708