Brain tensor template construction method based on diffusion tensor imaging

Abstract

The present invention proposes a brain tensor template construction method based on diffusion tensor imaging, aiming to improve the accuracy and efficiency of brain tensor template construction. The implementation steps are: preprocessing the diffusion tensor image; obtaining the individual average B0 Image set, tensor image set and FA image set; determine the standard template; iteratively register the individual FA images to obtain the FA template; obtain the registration parameters of the individual FA image and the FA template; combine the average B0 image set and the tensor image set Spatial standardization; obtain the average B0 image and average tensor image set of all subjects in the standard space; obtain the brain tensor template. The present invention uses the FA template to obtain the registration parameters from the individual space to the standard space, and performs tensor redirection after acting on the tensor image, which reduces the loss of registration information, improves the accuracy of the tensor template, and at the same time requires The number of accurate times is small, which improves the efficiency of template construction.

Description

Technical field [0001] The present invention belongs to the field of image processing technology, relates to a brain image template construction method, specifically relates to a brain tensor template construction method based on diffuse tensor imaging, can be applied to the auxiliary study of brain white matter. Background [0002] The brain is made up of gray matter, white matter, and cerebrospinal fluid, of which white matter forms a major part of the central nervous system. The white matter in the brain is made up of millions of fibrous bundles that communicate with gray matter in different brain regions by transmitting action potentials to achieve coordinated operation of brain segments. Therefore, when there is an abnormality in the microstructure of white matter, it may lead to some diseases, such as schizophrenia, multiple sclerosis and so on. When studying white matter disorders in the brain, it is common to perform intergroup analysis of diffusion tensor imaging of dif...

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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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