CN103006220A - Method for detecting brain function communicated area based on signal sparse approximation - Google Patents
Method for detecting brain function communicated area based on signal sparse approximation Download PDFInfo
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Abstract
The invention discloses a method for detecting a brain function communicated area based on signal sparse approximation. The method comprises the following steps: 1, performing wavelet packet decomposition on each time point data in a functional magnetic resonance signal to obtain a wavelet tree on original time point data; 2, carrying out sparsity measurement on nodes in the wavelet tree, and selecting the node with strongest sparsity in the wavelet tree to form an effective sparse approximation data set on original functional magnetic resonance data; and 3, carrying out hybrid matrix optimization on the formed sparse approximation data set by adopting independent content analysis, and reconstructing a source signal in a functional area in combination with an original functional magnetic resonance mixed signal to finish accurate positioning and detection of the functional area. According to the method, sparse approximation of original mixed data is obtained by utilizing the more general supposed sparsity of the functional magnetic resonance signal, and the source signal is separated through the independent content analysis and the signal reconstruction, so that the purpose of accurately positioning the brain function communicated area is achieved.
Description
Technical field
The present invention relates to a kind of human brain function connected region detection method based on Functional magnetic resonance imaging, be specifically related to a kind of based on accurate location and the detection method of the sparse approximate blind source signal separation method of functional MRI mixed signal to the human brain function connected region.
Background technology
Functional magnetic resonance imaging is a kind of Novel magnetic resonance imaging technique that begins to rise the nineties in 20th century.This technology combines function, dissection and image three aspects: information, study the systematic study that combines with function for traditional mr techniques from single morphosis strong technical support is provided, simultaneously for providing favourable technical guarantee to diagnosis of research, brain section disease and the mental illness of human brain function connected region, neuro-cognitive etc.Utilizing Functional magnetic resonance imaging to carry out in the research of human brain function connected region, the method for processing effectively, accurately the higher-dimension MR data has played vital effect to the research of human brain function connected region.Yet current Data Management Analysis method although can finish to a certain extent the detection of functional area, is all deposited more deficiency and defective, and the accuracy of its Orientation of functions is also treated further raising.For example, method of fuzzy cluster analysis is limited by the restriction that number is estimated in iteration speed, Fuzzy Exponential and functional areas; Independent component analysis is limited by the separate hypothesis of stronger functional areas source signal fully, has limited the detection in Functional connectivity zone.
Therefore, existing human brain function connected region detection technique is also waited to further develop and improve.
Summary of the invention
The invention provides a kind of based on the sparse approximate brain function connected region detection method of signal, it is sparse approximate by the functional MRI mixed signal is carried out, obtain sparse approximate data collection, adopt again Independent Component Analysis to carry out that brain function signal is blind to be cut apart, realize more accurately detection and localization brain function connected region.
For achieving the above object, the invention provides and a kind ofly be characterized in based on the sparse approximate brain function connected region detection method of signal, the method includes the steps of:
In the above-mentioned steps 1, the time point data are carried out respectively three layers of one dimension WAVELET PACKET DECOMPOSITION.
The present invention is a kind of to be compared based on the sparse approximate brain function connected region detection method of signal and the Data Management Analysis method of prior art, its advantage is, the present invention is by the sparse property of the more general hypothesis of at first utilizing functional magnetic resonance signal, obtain the sparse approximate of former blended data, again by independent component analysis and signal reconstruction to realize separating of source signal, thereby reach the purpose of accurate location brain function connected region, be conducive to the research to aspects such as brain science, neuroscience and brain section diseases.
Description of drawings
Fig. 1 is a kind of method flow diagram based on the sparse approximate brain function connected region detection method of signal of the present invention;
Fig. 2 is a kind of flow chart based on the sparse approximate brain function connected region detection method of signal of the present invention.
The specific embodiment
Below in conjunction with accompanying drawing, further specify specific embodiments of the invention.
Such as Fig. 1 and in conjunction with shown in Figure 2, the present invention discloses a kind of based on the sparse approximate brain function connected region detection method of signal, and the method includes the steps of:
Step 2.1, one are the sparse property tolerance device of the wavelet tree node of a distance metric norm based on normed linear space of structure (lp norm), sparse property to each wavelet tree node is measured, obtain one about the sparse property amount vector Q of each node, it satisfies, and wherein its expression is about the wavelet packet tree of former each time point of functional MRI data the
lThe common wavelet coefficient vector that forms of individual wavelet tree node, and n represents the length of vector.
Step 2.2, it is two according to sparse property size, selects the wavelet tree node of sparse property maximum to form a kind of effectively sparse approximate collection about former mixed signal, thereby has improved reconstruct and the stationkeeping ability of brain function signal.
Although content of the present invention has been done detailed introduction by above preferred embodiment, will be appreciated that above-mentioned description should not be considered to limitation of the present invention.After those skilled in the art have read foregoing, for multiple modification of the present invention with to substitute all will be apparent.Therefore, protection scope of the present invention should be limited to the appended claims.
Claims (2)
1. one kind based on the sparse approximate brain function connected region detection method of signal, it is characterized in that, the method includes the steps of:
Step 1, each the time point data in the functional magnetic resonance signal are carried out WAVELET PACKET DECOMPOSITION, obtain the wavelet tree about former time point data;
Step 2, the node in the wavelet tree is carried out sparse property tolerance, and select the strongest wavelet tree node of sparse property, so that form effective sparse property approximate data collection about former functional MRI data;
Step 3, the sparse property approximate data centralized procurement that forms is carried out the hybrid matrix optimizing with independent component analysis, and carry out the reconstruct of functional areas source signal in conjunction with former functional MRI mixed signal, to finish accurate location and the detection of functional area.
2. as claimed in claim 1ly it is characterized in that based on the sparse approximate brain function connected region detection method of signal, in the described step 1, the time point data are carried out respectively three layers of one dimension WAVELET PACKET DECOMPOSITION.
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CN103211597A (en) * | 2013-04-27 | 2013-07-24 | 上海海事大学 | Resting brain function connected region detecting method based on affine clustering |
CN103961103A (en) * | 2014-05-07 | 2014-08-06 | 大连理工大学 | Method for performing phase correction on ICA estimation components of plural fMRI data |
CN104850863A (en) * | 2015-05-29 | 2015-08-19 | 上海海事大学 | Human brain functional activity state classification method |
CN108197661A (en) * | 2018-01-19 | 2018-06-22 | 上海海事大学 | Cognitive activities state classification system and method based on voxel level brain function information |
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Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
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CN103211597A (en) * | 2013-04-27 | 2013-07-24 | 上海海事大学 | Resting brain function connected region detecting method based on affine clustering |
CN103211597B (en) * | 2013-04-27 | 2014-12-17 | 上海海事大学 | Resting brain function connected region detecting method based on affine clustering |
CN103961103A (en) * | 2014-05-07 | 2014-08-06 | 大连理工大学 | Method for performing phase correction on ICA estimation components of plural fMRI data |
CN103961103B (en) * | 2014-05-07 | 2015-12-30 | 大连理工大学 | A kind of ICA to plural fMRI data is estimated the method being divided into line phase and correcting |
CN104850863A (en) * | 2015-05-29 | 2015-08-19 | 上海海事大学 | Human brain functional activity state classification method |
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CN108197661A (en) * | 2018-01-19 | 2018-06-22 | 上海海事大学 | Cognitive activities state classification system and method based on voxel level brain function information |
CN108197661B (en) * | 2018-01-19 | 2022-03-01 | 上海海事大学 | Cognitive activity state classification system and method based on voxel horizontal brain function information |
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