Cerebral cortex thickness estimation method based on three-dimensional Laplace operator

Abstract

Cerebral cortex thickness estimation in brain magnetic resonance imaging (MRI) is an important technical means for researching brain development and neurodegenerative diseases in neuroimaging. The invention provides a cerebral cortex thickness estimation algorithm based on a three-dimensional Laplace operator, which can accurately capture geometrical morphological characteristics in brain nuclearmagnetic resonance imaging. The method comprises the following steps: firstly, starting from the elimination of a cross overlapping region generated on the surface of a gray matter layer and the surface of a white matter layer, constructing a tetrahedral mesh which reflects the inherent geometrical characteristics of the brain and is matched with MRI; secondly, constructing a three-dimensional Laplace operator by utilizing a geometric constraint relationship of a tetrahedral mesh, and calculating the distribution of a cerebral cortex internal temperature field under a Diels boundary by utilizing a finite element method; then, determining a local isothermal surface, obtaining the gradient line direction of the isothermal surface in the temperature field through a calculation geometry method, and a tetrahedral mesh unit where internal points on a gradient line are located is rapidly locked through a half-half surface data storage structure; and finally obtaining the thickness characteristic information of the cerebral cortex according to the direction and the step length of each gradient line by combining the set gradient step length. According to the method, the morphological structure detection capability of the cerebral cortex can be effectively improved by constructing a high-quality cerebral cortex tetrahedral mesh and determining a high-precision temperature field gradientline.

Description

technical field

[0001] The invention belongs to the field of computer application technology, relates to MRI cerebral cortex morphology change detection technology, and is used for early diagnosis of neurodegenerative diseases such as AD. Background technique

[0002] Alzheimer's disease (AD) is a degenerative neurological disease with insidious onset, manifested by generalized dementia such as memory impairment, aphasia, apraxia, agnostic spatial skill impairment, executive dysfunction, and personality and behavioral changes. characteristics, seriously endangering the healthy development of the elderly. In order to better treat and prevent AD diseases, it is necessary to accurately diagnose mild cognitive impairment (MCI) who are easily transformed into AD diseases. With the aggravation of AD symptoms, the corresponding changes in cerebral cortex morphology show thinning of the cortical thickness. Therefore, by estimating the thickness information of the overall cerebral c...

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