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A method for phase correction of the ICA estimated component of complex fMRI data

A phase correction and data technology, which is applied in medical science, sensors, diagnostic recording/measurement, etc., can solve the problems of amplitude ambiguity, sign ambiguity inspection error, difference, etc., and achieve the effect of avoiding the influence of errors

Active Publication Date: 2015-12-30
DALIAN UNIV OF TECH
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Problems solved by technology

The second is the amplitude ambiguity (scaling ambiguity), that is, the amplitude of the ICA estimated signal may be different from the amplitude of the source signal
At this time, if the above-mentioned maximization The principle of real part energy is concentrated in the high-amplitude voxels of the positive semi-axis of the real part of the complex number domain, which may contain a large number of noise voxels, which will cause the rotation angle Estimation error and sign ambiguity test error
Thus, high-amplitude noisy voxels severely impact the performance of phase correction methods based on the composition of spatially activated brain regions

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  • A method for phase correction of the ICA estimated component of complex fMRI data
  • A method for phase correction of the ICA estimated component of complex fMRI data
  • A method for phase correction of the ICA estimated component of complex fMRI data

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[0016] A specific embodiment of the present invention will be described in detail below in conjunction with the technical solution and FIG. 1 .

[0017] Assuming that the multiple fMRI data of a single subject collected under the existing exercise stimulation, the number of scans for the whole brain of the subject is 165. The specific steps of performing ICA on the complex fMRI data to obtain task-related components and perform phase correction are shown in the attached figure.

[0018] The first step is to perform PCA (principle component analysis) dimensionality reduction on the complex fMRI data of a single subject. According to the information theory criterion, the number of independent components was estimated to be 30, so PCA was used to reduce the complex fMRI data from 165 dimensions to 30 dimensions.

[0019] In the second step, ICA is performed on the complex fMRI data after PCA dimensionality reduction using the complex EBM (entropyboundminimization) algorithm, and 3...

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Abstract

The invention provides a method for performing phase correction on ICA estimation components of plural fMRI data and belongs to the field of analysis of plural fMRI data. Based on the time process component (please see the formula in the specification) estimated by ICA, the phase angle theta[k] is estimated, preliminary phase correction is performed on the ICA estimation components (please see the formulae in the specification) with phase ambiguity, and primary phase correction signals (please see the formulae in the specification) are obtained; the primary phase correction signals (please see the formulae in the specification) are subjected to symbol ambiguity detection and removal through prior information which can be easily acquired and a correlation coefficient method. Because one ICA estimation component (please see the formula in the specification) has larger non-annular degree than the other ICA estimation component (please see the formula in the specification), error influences caused by high-amplitude noise voxel can be avoided. Due to the utilization of the prior information, the symbol ambiguity can be accurately detected and eliminated. When ICA estimation components, acquired under movement stimulation, of 16 pieces of tested plural fMRI data are subjected to phase correction, the accuracy of the method based on the ICA estimation component (please see the formula in the specification) is only 81.25 percent, but the accuracy rate of the method is 100%, and a guarantee is provided for ICA of multiple tested plural fMRI data.

Description

technical field [0001] The invention relates to an ICA analysis method of complex fMRI data, in particular to a method for phase correction of ICA estimation components of complex fMRI data. Background technique [0002] Independent component analysis (ICA) is a data-driven analysis method, also known as blind source separation (BSS). ICA can estimate the maximum independent source signal and its mixing parameters from the mixed signal without any prior information, and has been widely used in speech, image, biomedical signal processing and other fields. Due to people's limited understanding of the brain, since 1998, ICA has been highly valued and effectively applied in the analysis of brain functional magnetic resonance imaging (fMRI) data. The ability to maximize the independent spatial activations of brain area components (spatial activations, that is, source signals) and their time course components (timecourses, that is, mixed parameters), and then provide a basis for ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/055
Inventor 林秋华于谋川龚晓峰丛丰裕
Owner DALIAN UNIV OF TECH
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