Interblock processing method for brain white matter fiber bundle tracking based on riemannian manifold

Abstract

The invention discloses an interblock processing method for brain white matter fiber bundle tracking based on riemannian manifold, comprising the steps of taking a brain white matter as the riemannian manifold; discussing diffusion and anisotropy under a second Fick law and the riemannian manifold; and expressing movement of fluid in a diffusion tensor field by a Navier-Stoke equation so as to construct a partial differential equation (PDE) to describe the performance of geodesic lines between two points under the riemannian manifold, wherein an arithmetic solution of the partial differential equation is obtained by computation of a Level-Set method. The difference of sliceobrain structures among individuals is eliminated by an inverse registration method of a region of interest (ROI) in a fiber bundle tracking process, and finally, the interblock processing of the fiber bundle number is performed. The method uses the whole tensor to construct the riemannian manifold, so that the method takes full use of all information of a tensor field, solves the anti-noise problem by the PDE and the Level-Set method, and improves the robustness.

Description

technical field [0001] The invention belongs to the field of magnetic resonance diffusion tensor imaging (Diffusion Tensor Imaging, DTI), and specifically relates to a method for tracking white matter fiber bundles based on Riemannian manifolds, and then processing the number of fiber bundles between groups. Background technique [0002] Diffusion tensor imaging (DTI), a magnetic resonance technique, is currently the only technique that can non-invasively analyze and measure the anisotropic diffusion properties of water molecules in brain tissue. DTI reflects the anisotropy of water molecules by measuring the diffusion speed and state of water molecules in the order of millimeters in living tissues, so as to obtain the direction of fibrous tissues and express the microstructure of human tissues. In recent years, the research of diffusion tensor imaging has mainly focused on brain imaging, especially in the imaging of white matter fiber tracts. A Riemannian manifold is a dif...

AI Technical Summary

Problems solved by technology

The key of fiber tracking algorithm based on tensor field is to determine the current point diffusion direction, but it has a shortcoming that it cannot deal with fiber bifurcation and crossing

[0005] The method based on global energy minimization can deal with bifurcation and crossing fibers. This method increases the consideration of fiber uncertainty and randomness. By minimizing the cost function, the optimal path is sought, and the path with the minimum cost is consistent with the real path. Corresponding, but too many false positive branches were introduced during the processing, and the description of the fiber direction is not clear

[0006] Therefore, traditional white matter fiber tracking methods have the disadvantages of poor noise immunity and low robustness of fiber tract tracking.

Moreover, due to the significant differences in the brain anatomy of different people, traditional white matter fiber tracking methods will have difficulties in comparing the characteristics of fiber tracts in different people

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