Brain tensor template construction method based on diffusion tensor imaging

Abstract

The invention provides a brain tensor template construction method based on diffusion tensor imaging, and aims to improve the accuracy and efficiency of brain tensor template construction. The methodcomprises the steps: carrying out the preprocessing of a diffusion tensor image; obtaining an individual average B0 image set, a tensor image set and an FA image set; determining a standard template;performing iterative registration on the individual FA image to obtain an FA template; obtaining registration parameters of the individual FA image and the FA template; performing spatial standardization on the average B0 image set and the tensor image set; obtaining an average B0 image and an average tensor image set of all tested objects in a standard space; obtaining brain tensor template. According to the method, the registration parameters from the individual space to the standard space are obtained by utilizing the FA template, and tensor redirection is performed after the registration parameters act on the tensor image, so the loss of registration information is reduced, the accuracy of the tensor template is improved, the required registration frequency is low, and the template construction efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for constructing a brain image template, in particular to a method for constructing a brain tensor template based on diffusion tensor imaging, which is applicable to auxiliary research on brain white matter. Background technique [0002] The brain is composed of gray matter, white matter, and cerebrospinal fluid, with white matter making up the majority of the central nervous system. The white matter in the brain is composed of millions of fiber bundles that communicate with the gray matter in different brain regions by transmitting action potentials to achieve coordinated operation between brain regions. Therefore, when white matter microstructure is abnormal, it may lead to some diseases, such as schizophrenia and multiple sclerosis. When studying brain white matter diseases, it is usually a group analysis of the diffusion tensor imaging (DTI) of different indi...

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Problems solved by technology

The shortcomings of this method are: the transformation parameters obtained by averaging and merging the registration parameters may lose part of the image transformation information, resulting in insufficient accuracy of the image obtained by standardizing the corresponding DTI image using the transformation parameters. Moreover, after normalizing the DTI images, the tensor direction correction is not performed, resulting in insufficient accuracy of the tensor template generated on average for the normalized DTI images. At the same time, in order to obtain all the registration parameters from the selected image to the rest of the images, Number of subjects × (number of subjects - 1) registration operations are required, which is heavy workload, time-consuming and inefficient

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