Brain age prediction method based on automatic identification of fiber bundles

A technology of automatic identification and prediction method, which is applied in the fields of medical image processing and machine learning, and can solve the problems of inability to accurately locate the fiber characteristics of fiber bundles and other problems.

Pending Publication Date: 2022-08-05
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] In order to overcome the shortcomings of the existing methods of extracting white matter features based on voxels to predict brain age that cannot accurately locate fiber bundles that

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  • Brain age prediction method based on automatic identification of fiber bundles
  • Brain age prediction method based on automatic identification of fiber bundles
  • Brain age prediction method based on automatic identification of fiber bundles

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[0037] specific implementation

[0038] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further explained below with reference to the specific implementation and the accompanying drawings.

[0039] refer to figure 1 , a brain age prediction method based on automatic identification of fiber tracts, which can effectively predict brain age through diffusion tensor imaging data, find age-sensitive fiber tracts, and judge the health of the brain, including the following steps:

[0040] Step 1: Preprocessing of magnetic resonance diffusion tensor imaging images, the process is as follows:

[0041] Since the subject's breathing, head movement and noise of the machine will affect the image quality during the MRI scan process, it is necessary to use the FSL tool to de-noise the image, head Motion Correction, Eddy Current Correction, Distortion Correction, and EPI Correction to rule out potential artif...

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Abstract

The invention discloses a brain age prediction method based on automatic identification of fiber bundles, and aims to solve the problems that fiber bundles sensitive to age change cannot be accurately positioned and fiber characteristics are averaged in an existing method for predicting brain age by extracting white matter characteristics based on voxels. A whole-brain fiber bundle graph is obtained from a diffusion tensor imaging image through a fiber tracking algorithm, fiber bundles with anatomical significance are automatically segmented and recognized according to a white matter graph of the brain, diffusion indexes are quantified along the fiber bundles, finally, the diffusion indexes serve as features to be input into a brain age prediction model, and the accuracy of the prediction model is tested. According to the method, tensor values are extracted through a fiber bundle automatic identification method to serve as white matter features, subtle changes of a white matter fiber bundle microstructure in the aging process can be better reflected by constructing a brain age prediction model, and fiber bundles with sensitive changes in the aging process are found out.

Description

technical field [0001] The invention relates to the fields of medical image processing and machine learning, in particular to a brain age prediction method based on automatic clustering and labeling of fiber bundles. Background technique [0002] As a non-invasive in vivo method, magnetic resonance imaging (MRI) technology has been widely used in neuroscience research, and it has played an important role in obtaining clinical nerve fiber structure information and assisting in understanding the functional connections between different areas of the brain. great effect. The brain has very good plasticity. Throughout the human life cycle, the structure of the brain will undergo certain changes due to factors such as physiological development, cognitive activities, and diseases. The Brain Age Prediction Framework allows us to assess an individual's brain health, and an individual's brain can be considered healthy if the predicted brain age is within the predicted normal variatio...

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Application Information

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IPC IPC(8): A61B5/055G06K9/62G06T7/00A61B5/00
CPCA61B5/055A61B5/0042A61B5/4064A61B5/7267G06T7/0012G06T2207/10088G06T2207/30016G06T2207/20081G06F18/214
Inventor 冯远静黎锦雯章诚哲陆星州王佳凤赵昶辰
Owner ZHEJIANG UNIV OF TECH
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