Diffusion tensor imaging white matter fiber clustering method

Abstract

The invention provides a diffusion tensor imaging white matter fiber clustering method. The method includes the steps of pre-processing original rsf magnetic resonance imaging (MRI) data and the original diffusion tensor imaging (DTI) data, registering the pre-processed rsf MRI data into a DTI space, respectively conducting fiber tracking and brain tissue segmentation on the pre-processed DTI data, conducting fiber projection on white matter fiber which can not reach grey matter or exceed a grey matter surface in a white matter fiber obtained by DTI, then calculating functional similarities among the white matter fiber, obtaining a matrix of the similarities and clustering by adopting an affine spread clustering algorithm. Fiber bundle has functional independence, accuracy without need to relay on of a genetic linkage map and require complicated registration.

Description

technical field [0001] The invention relates to a method for clustering brain white matter fibers, which utilizes human brain diffusion tensor imaging (DTI) data and resting-state functional nuclear magnetic resonance (rsfMRI) data for clustering, so that the clustered fiber bundles have functions Meaning, belongs to the field of image processing. Background technique [0002] Clustering of white matter fibers in the brain is a critical step in performing tract-based qualitative analysis of white matter. Among them, white matter fibers are processed by diffusion tensor imaging. In order to obtain meaningful information from diffusion tensor imaging that can be compared between people, a large number of white matter fiber trajectories obtained from diffusion tensor imaging need to be segmented It is a meaningful fiber bundle (it should be noted that since the white matter fibers are generated through DTI data processing, the white matter fibers here are actually different fr...

AI Technical Summary

Problems solved by technology

Although these two methods have their own advantages, the functional significance of their classification results cannot be explained

Images