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Post-processing noise elimination method for performing ICA analysis of plural f MRI data

A data and complex technology, applied in the field of ICA analysis of complex fMRI data, can solve problems such as brain information loss and data discarding, and achieve the effect of solving data discarding and brain information loss

Active Publication Date: 2014-08-13
DALIAN UNIV OF TECH
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  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to cancel the phase denoising pretreatment before ICA, and instead provide a method for effectively denoising the components of the spatially activated brain regions after ICA, so as to ensure that ICA can be used for both single and multi-test subjects. Analysis of fMRI data to solve the problem of data discarding and brain information loss caused by denoising preprocessing
For this reason, the present invention uses the source phase image for denoising

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  • Post-processing noise elimination method for performing ICA analysis of plural f MRI data
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  • Post-processing noise elimination method for performing ICA analysis of plural f MRI data

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specific Embodiment 1

[0016] Specific embodiment 1: Assume that the multiple fMRI data of a single subject collected under the existing exercise stimulation, the number of scans of the whole brain of the subject is 165. ICA is performed on the complex fMRI data to obtain task-related spatially activated brain area components containing a large amount of high-amplitude noise. The specific steps for post-processing and denoising are as follows: figure 1 shown.

[0017] In the first step, the number of independent components of the complex fMRI data of a single subject was estimated according to the information theory criterion, and the result was 30. The PCA (principle component analysis) method was used to reduce the complex fMRI data from 165 dimensions to 30 dimensions.

[0018] The second step is to use the complex EBM (entropy bound minimization) algorithm to perform ICA on the complex fMRI data after PCA dimensionality reduction, and obtain 30 complex ICA estimation components, from which the t...

specific Embodiment 2

[0024]Specific Example 2: For the complex fMRI data of 16 subjects collected under the existing exercise stimulation, the number of scans of the whole brain of each subject is 165. ICA was performed on the complex fMRI data of 16 subjects respectively, and the specific steps to obtain the group average task-related spatial activation brain area components of the final denoising were as follows: figure 2 shown.

[0025] In the first step, PCA was used to reduce the complex fMRI data of 16 subjects from 165 dimensions to 30 dimensions.

[0026] The second step is to use the complex EBM algorithm to perform ICA on the complex fMRI data of 16 subjects after PCA dimensionality reduction, and select the task-related spatial activation brain area components of each subject These components contain a large number of high-amplitude noise voxels.

[0027] In the third step, the phase correction method based on the time course component is selected for the Perform phase correction ...

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Abstract

The invention provides a post-processing noise elimination method for performing ICA analysis of plural fMRI data, and belongs to the field of plural fMRI data analysis. As for phase-free ambiguous space activation encephalic region components (please see the representation in the specification), firstly, a phase value range of a phase image (please see the representation in the specification) of the component is -pi to pi, the -pi is not included, voxel corresponding to the phase value within the range from -pi / 4 to pi / 4 is defined as the activation voxel, so that a single-tested-set noise elimination mask and a multi-tested-set average noise elimination mask are built and used for a single test (please see the representation in the specification) and the set average component, the voxel with small amplitude is removed, and a final noise elimination result is obtained. The method can guarantee that the ICA performs analysis of plural fMRI data, and further solves the problems of data loss and brain information loss caused by pre-processing noise elimination. As for the plural fMRI data collected under activity stimulation, only 26 independent components can be estimated through the preprocessing noise eliminating method, and 49 independent components can be estimated through the method.

Description

technical field [0001] The invention relates to ICA analysis of complex fMRI data, in particular to a post-processing denoising method for ICA analysis of complex fMRI data. Background technique [0002] Brain function research is an important and difficult point worldwide, and functional magnetic resonance imaging (fMRI) has become one of the important means of brain function research due to its non-invasive and high spatial resolution advantages. By adopting model-driven analysis methods such as GLM (general linear model), or data-driven analysis methods such as independent component analysis (ICA), one can extract rich information about spatially activated brain regions from fMRI data. [0003] fMRI data is complex and includes magnitude data and phase data. However, most fMRI analysis methods only analyze the magnitude data and completely discard the phase data. The reason for this is that phase data is noisier than magnitude data and its properties are unknown, making...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 林秋华于谋川龚晓峰丛丰裕
Owner DALIAN UNIV OF TECH