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Method for joint visualization of UKF fiber tracking data

A data and fiber technology, applied in the field of fiber tracking data visualization in magnetic resonance imaging, can solve problems such as restricting the depth and breadth of fiber tracking data analysis, improve visualization analysis efficiency, avoid switching problems and alignment problems, and improve analysis depth. and breadth of effects

Active Publication Date: 2017-12-12
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

However, in practical applications, the measurement data derived from the diffusion tensor are up to a dozen
This situation severely limits the depth and breadth of analysis of fiber tracking data

Method used

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  • Method for joint visualization of UKF fiber tracking data
  • Method for joint visualization of UKF fiber tracking data
  • Method for joint visualization of UKF fiber tracking data

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Experimental program
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Embodiment

[0041] A method for joint visualization of UKF fiber tracking data comprising the steps of:

[0042] Step 1. Input the UKF fiber tracking dataset, which includes fiber track data, diffusion tensor data and tensor measure data.

[0043] The present invention uses a simple corpus callosum nerve fiber data set as an implementation example. The data set is calculated according to the UKF fiber tracing algorithm, including 7 nerve fiber trajectories passing through the corpus callosum of the brain, and the diffusion tensor data and tensor measurement data associated with the sample points on these fiber trajectories, where each sample Each point has two simplified diffusion tensors D 1 and D 2 , with D 1 and D 2 The associated tensor measurement data are: Fractional Anisotropy (FA), Trace (TR), Major Eigenvalue (Major), Minor Eigenvalue (Minor), Volume Ratio (VR), General Anisotropy (GA), Linear anisotropy (CL), Planar anisotropy (CP), Spherical anisotropy (CS), etc.

[0044]...

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Abstract

The present invention relates to a method for joint visualization of UKF fiber tracking data, and belongs to the field of visualization of fiber tracking data in magnetic resonance imaging. In the method, fiber tracking data (including fiber trajectory data, dispersion tensor data and tensor measurement data) obtained by a UKF fiber tracking algorithm is integrated under a unified framework and visualized at the same time. According to the method, a new prism tensor icon is established, and by using a shape, a size and a direction of the prism tensor icon, the dispersion tensor data is visualized, and by using a color of a prism surface, many tensor measurement data is visualized, and then the prism tensor icon is connected serially to a fiber trajectory curve, so as to realize joint visualization of UKF fiber tracking data in a same view. The switching and alignment problems among different data views are avoided, the visualized analysis efficiency of data can be effectively increased, and the analysis depth and breadth of UKF fiber tracking data are improved.

Description

technical field [0001] The invention relates to a multi-attribute data visualization method, in particular to a visualization method for nerve fiber tracking data including one tensor attribute and multiple scalar attributes, and belongs to the field of fiber tracking data visualization in magnetic resonance imaging. Background technique [0002] Tractography based on magnetic resonance diffusion tensor imaging (DTI) and diffusion weighted imaging (DWI) is an important method for exploring the structure of living nerve fibers in the field of neuroscience and clinical application , It is widely used in the study of central nervous system histomorphology and pathology. For this reason, researchers in related fields have proposed many fiber tracking algorithms, such as the streamline tracking algorithm (S.Mori, B.Crain, V.Chacko, P.Van Zijl. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging .Annals of Neurology,45(2),265-269,1999) and ...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06T15/00
Inventor 张文耀赵稳宁建国
Owner BEIJING INSTITUTE OF TECHNOLOGYGY