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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