CN108961259A - Cerebral function area opposite side localization method based on tranquillization state functional MRI - Google Patents
Cerebral function area opposite side localization method based on tranquillization state functional MRI Download PDFInfo
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Abstract
The invention belongs to Medical Image Processing and application field, it is related to carrying out brain supplementary motor area the technology of functional localization more particularly to a kind of method using tranquillization state Functional magnetic resonance imaging automatic positioning supplementary motor area.The present invention is automatically positioned supplementary motor area using tranquillization state Functional magnetic resonance imaging, the high accuracy positioning of supplementary motor area is realized using machine learning algorithm, and algorithm validity and reliability demonstration are carried out in multiple data, fully consider the clinical state of Patients with Brain Tumors, only need by the minimum participation of locating personnel, it can be not only used for the brain synkinesia Orientation of functions of Healthy People and patient with brain tumors, this method can overcome the magnetic resonance location technology of task based access control normal form often can only limited activation to brain domain, the shortcomings that task execution degree of especially Patients with Brain Tumors can not meet clinical needs very well when generally poor.
Description
Technical field
The invention belongs to Medical Image Processing and application field, the method for being related to positioning cerebral function area is specifically related to
And a kind of automatic positioning using tranquillization state Functional magnetic resonance imaging from healthy half brain (strong side) to impaired half brain (Ipsilateral)
Method.More particularly to a kind of cerebral function area opposite side localization method based on tranquillization state functional MRI.This method can overcome base
In task normal form magnetic resonance location technology to brain domain often can only limited activation, especially Patients with Brain Tumors task execution
The shortcomings that degree can not meet clinical needs very well when universal poor.
Background technique
It is reported that glioma is the most common central nerve neuroma.Statistics display, in China, glioma year
Disease incidence is 3-6 people/100,000 people, and year, death toll was up to 30,000 people.Currently, encephalic operation treatment is most straight in clinical intervention measure
It connects, the treatment means of effective glioma.Clinical practice shows, for the tumour that brain language, motor area nearby occur,
It is impaired that its excision of performing the operation very likely results in the language of patient, motor function, or even aphasia, hemiplegia occurs etc. and seriously affect and is postoperative
The case where quality of life.In recent years, it by being introduced into the advanced technologies means such as stimulus of direct current in art, neuronavigation system, performs the operation
Success rate significantly improve, still, since the structure and function of brain are complicated, individual difference is obvious, and tumour growth causes in addition
Deformation and compensatory brain function phenomena such as, cause the language of tumor vicinity is accurately positioned, motor area seems very difficult,
Seriously constrain the optimization selection performed the operation between tumor resection and function and protecting.
Brain domain be located in the operative treatment of the common central nerve tumor such as glioma play it is very crucial
Effect.If encephalic operation causes language and movement etc., brain functions are badly damaged, it is likely that lead to aphasia or hemiplegia, by tight shadow
Ring the postoperative life quality of patient.Since cerebral function is sufficiently complex, the difference function such as language, movement, emotion, memory, sensory perception
Can, function is totally different, may there is entirely different organizational form in the brain, causes difficulty to volume infarct cerebral.For brain
Tumor patient, tumour growth leads to brain structure deformation occurs or even functional areas recombination etc., and band is accurately positioned to preoperative brain function
Carry out lot of challenges, seriously constrains encephalic operation further increasing in terms of resection rate and disability rate.
About Orientation of functions, (functional is mainly tested using preoperative task state functional MRI at present
Magnetic resonance imaging, fMRI), it is knocked by finger or the simple tasks normal forms such as picture name activates
The movement of brain, language related brain areas are completed the individuation confirmation of functional areas, are then carried out in neuronavigation system to functional areas
Mapping, to go out the approximate spatial locations of functional areas for doctor identification in operative space.In the course of surgery, by being called out in art
The method waken up in conjunction with stimulus of direct current carries out more detailed mark to the functional areas of tumor vicinity, thus in tumor resection
The damage to functional areas is reduced as far as possible.Clinical experience shows to carry out hand except the range of the fMRI functional areas 1-2cm positioned
Art can be very good functional section；When distance is less than 1.5cm, need using stimulus of direct current in art in fMRI functional localization
On the basis of, with the physical location of higher precision confirmation functional areas.But practice display, single task role normal form is for brain function
The activation in energy area is limited；By taking motor area as an example, supplementary motor area is difficult to be activated with the task that finger has rhythm to knock,
The state that the patient even having has been in motor function or cognitive function is badly damaged, can not cooperate task normal form at all,
Therefore, the problems such as brain domain positioning of task based access control state is activated imperfect and task execution to spend functional areas by task
Limitation, cannot still fully meet clinical needs.
In order to overcome above-mentioned difficulties of the existing technology, the quasi- one kind that provides of present inventor is " based on tranquillization state
Cerebral function area opposite side localization method ": this method malformation, the function integrity etc. common for brain tumor patients Ipsilateral brain
Feature is proposed suitable for a variety of brain domains of full brain from by the lesser strong side of effects of tumors based on tranquillization state brain function
The opposite side localization method of connection mode.
Summary of the invention
The purpose of the present invention is provide a kind of based on tranquillization state functional MRI data to overcome defect in the prior art
Brain domain opposite side localization method, this method is able to achieve the accurate positioning of main brain domain, is suitable for Healthy People and brain is swollen
The volume infarct cerebral of the cerebral diseases patient such as tumor.Tranquillization state fMRI technology of the invention is for task state fMRI technology, no
Only do not limited by single task role normal form, can than more fully observing the activity condition of the brain functions network such as language, movement,
And it can be applied to the low patient of cognitive impairment task fitness.Brain domain positioning based on tranquillization state of the invention
Technology has a good application prospect.
In order to achieve the above purpose, the brain zone function opposite side localization method of the invention based on tranquillization state magnetic resonance uses
Following technical solution:
The division of function sub-district is carried out to brain area using the tranquillization state function connects of full brain, later for each function
Area, Training Support Vector Machines (SVM, support vector machine) classifier, by establishing it to each function sub-district
Specific half brain tranquillization state function connects of opposite side, and the training classifier in Healthy People big data, it is final to realize to each function
The positioning of sub-district.
Specifically, the present invention passes through following methods and step:
1. obtaining brain domain patients with gliomas 5 minutes tranquillization state functional images using tranquillization state functional MRI technology
And high-precision structure image, multinomial standardized pretreatment: scanning slice time adjustment is taken, the dynamic correction of head is mapped to standard
Change space, removes trend term, bandpass filtering and Scrubbing；Later to the brain data pre-processed according to Montreal mind
Through research establishment propose AAL (automated anatomical labelling) template, be to brain Preliminary division
45 brain areas (each 45 of bilateral symmetry)；
2. the division of function sub-district is carried out to each brain area using the tranquillization state function connects of full brain voxel level, in full brain
Function zoning obtains 218 function sub-districts (as shown in Figure 1)；In the embodiment of the present invention, to each brain area, this is calculated
The tranquillization state signal of all voxels of brain area calculates separately the related coefficient of itself and the remaining 88 brain area average signals of full brain later,
After obtaining correlation matrix, chooses suitable λ opt and 0-1ization is carried out to matrix:
Classified later using LM algorithm (a minimum network linking algorithm based on local property), to obtain
The sub-zone dividing of each brain area；Here, the λ opt that the present invention chooses is obtained by 50 grouping crosschecks, specifically
, the present invention passes through normalized mutual information
To make NMI (X, Y) maximum λ, division result the most stable is obtained, so that the body in each sub-regions
It is known as almost the same tranquillization state function chain feature (as shown in Figure 2)；
3. being directed to each function sub-district, Training Support Vector Machines (SVM, support vector machine) classifier；
In the embodiment of the present invention, for each brain subregion, all bodies within the scope of this region and its surrounding 6mm are focused on
Voxel inside objective function area is labeled as 1 by element, the output of trained classifier, and by the voxel mark on objective function area periphery
It is denoted as 0, then, the input feature vector of the classifier is the tranquillization state brain function connection mode of half brain of opposite side, is defined as follows: sentencing
Whether some voxel that breaks belongs to specified functional areas, needs to calculate the half brain tranquillization state function connects of specific opposite side of the voxel, this
The function connects of a little specificity are by voxel inside and outside the given functional areas of comparison to the tranquillization state brain function of all voxels of half brain of opposite side
It can connection (as shown in Figure 3) that provides: calculate separately the average signal of the inside and outside two groups of voxels in functional areas in of the invention, while to
Half brain computing function bonding strength of opposite side carries out comparison among groups t- inspection and identifies after multiple alignment corrects with significant group
Between difference brain area cluster, find the inside and outside opposite side brain function with significant difference in these functional areas and be connected to classifier
Input feature vector；Therefore, by establishing its half brain tranquillization state function connects of specific opposite side to each function sub-district, and in health
Training classifier in National People's Congress's data, the final positioning realized to each function sub-district；
4. each function sub-district positioning result of AAL template is merged, the functional localization map to 45 brain areas is obtained.
More specifically, the cerebral function area opposite side localization method of the invention based on tranquillization state functional MRI comprising
Step:
1) complete 218 sub-district map of brain is established using tranquillization state data by the big data sample of Healthy People；
2) svm classifier is respectively trained for each sub-district in 218 sub-district maps by the big data sample of Healthy People
Device；
3) the tranquillization state functional image for the brain domain patients with gliomas that will acquire and high-precision structure image carry out pre-
Processing, comprising: scanning slice time adjustment, the dynamic correction of head are mapped to standardised space, remove trend term, bandpass filtering and
Scrubbing；While registration, it is removed for standardized influence by tumour MASK, tumor imaging will be not affected by
Healthy side brain is mapped to normed space；
4) for each voxel of Ipsilateral target area, it is related to the multiple characteristic area signals in opposite side to calculate separately it
Coefficient, in this, as the input of support vector machines (SVM) classifier, whether output then belongs to for each voxel of target area
This brain sub-district；
5) it finally, by the result split of all positioning, maps back individual space and is formed to entire Ipsilateral cerebral function area
Positioning result.
In step 1) of the present invention, while the function connects of the voxel in computing function area Yu full brain remaining 88 brain area, it obtains
Carry out binaryzation to it after calculating its similar matrix N to connection matrix M and classify, by maximize mutual information come
The division result stable to one；
In the present invention, when to similar matrix N binaryzation, cross-checked using 50 groupings, it is mutual by normalized
Information,
It obtains so that NMI (X, Y) maximum λ, carries out binaryzation to similar matrix N with this:
In step 1) of the present invention, obtained 218 sub-district map of full brain.
In step 2) of the present invention, it is defined target area: to each brain area, with the brain area position of AAL Template Location
Centered on, the regions of 2 voxels (i.e. 6mm) is expanded outward using this as target area, it is intended to by functional areas from wherein marking off
Come；
In step 2) of the present invention, each brain subregion and surrounding voxel are calculated on training set to half brain of opposite side
The function connects of each voxel carry out comparison among groups t- inspection and identify after multiple alignment corrects with significant group difference
Brain area cluster；
In step 2) of the present invention, calculated again with the voxel of target area with the average signal of voxel in the cluster that finds
Function connects are as feature, for each sub-district one support vector machines (SVM) classifier of training.
In step 3) of the present invention, by T2 image, tumor section is drawn manually on each tomographic image, and in registration
In the process, the weight of this part of standards is all set as 0, removes tumour for standardized influence.
In step 4) of the present invention, for each voxel of Ipsilateral target area, itself and the multiple features in opposite side are calculated separately
The related coefficient of regional signal, in this, as the input of support vector machines (SVM) classifier, output is then each of target area
Whether a voxel belongs to some specific brain domain.
In step 5) of the present invention, the functional localization result on normed space is mapped back into individual space, by 45 function
The positioning one by one of energy brain area draws volume infarct cerebral map in half brain of Ipsilateral.
The beneficial effects of the present invention are:
1. this method breaks through the anatomical landmarks limitation of nuclear magnetic resonance image, can be on cortex or infracortical various functions
Brain area is positioned.
2. this method breaches the brain function network positions limitation low in local cerebral Orientation of functions precision, list is realized
The high accuracy positioning of a brain area.
3. being subjected to this method reduce the requirement of the operative cooperation degree of patient with brain tumors suitable for movement or cognitive function
The patient pressed harder against to tumour carries out preoperative volume infarct cerebral.
4. the data and same subject multiple measurement data in the acquisition of different websites demonstrate the reliability of algorithm.
5. the Orientation of functions technology that this method is realized, can comprehensively mark the functional areas around tumour, tie
Neuronavigation system is closed, conceptual design and the implementation, assessment of encephalic operation are beneficial to, to preferably protect brain function, more
Thorough tumor resection.
Detailed description of the invention
Fig. 1,218 sub-district maps of full brain domain.
Fig. 2, the sub-area division process of full brain function brain area.
Fig. 3, the feature brain area schematic diagram for supplementary motor area positioning.
Fig. 4, the function map that half volume infarct cerebral of glioma Ipsilateral obtains.
Specific embodiment
Embodiment 1
1, pass through the big data sample of Healthy People first, using tranquillization state data, establishes full brain area map and training SVM
Classifier:
1) multinomial standardized pretreatment: scanning slice time adjustment is taken to fMRI data, the dynamic correction of head is mapped to standard
Change space, removes trend term, bandpass filtering and Scrubbing.Later to the brain data pre-processed according to Montreal mind
Through research establishment propose AAL (automated anatomical labelling) template, be to brain Preliminary division
45 brain areas (each 45 of bilateral symmetry)；
2) it to each brain area, is finely divided using the function connects of itself and remaining 88 brain areas of full brain, is assisted with left side
For motor area: left side SMA shares 666 voxels, calculates separately the phase relation of itself and the remaining 88 brain area average signals of full brain
Number, obtains correlation matrix M666×88, the similitude between voxel two-by-two is then calculated wherein, similar matrix LN is obtained666×666,
Wherein lnij=corr (lmi, lmj).After obtaining similar matrix, chooses suitable λ opt and 0-1ization is carried out to matrix:
Later, classified using LM algorithm (a minimum network linking algorithm based on local property), thus
To the sub-zone dividing of each brain area, here, the λ opt of selection be obtained by 50 groupings crosscheck, specifically,
Pass through normalized mutual information
It obtains so that NMI (X, Y) maximum λ, to obtain division result the most stable: it is 3 sons that left side SMA, which is divided,
Region.Work more than repeating, is finely divided in full brain area domain, obtains 218 sub-district maps of full brain domain；
3) for each brain sub-district, the training SVM classifier in the big data of Healthy People；Specifically, with left side
For Pre-SMA: all voxels within the scope of the Pre-SMA and its surrounding 6mm of concern left side calculate separately Pre-SMA inside and outside two
The average signal of group voxel, while half brain computing function bonding strength of side to the right, carry out comparison among groups t- inspection, through multiple ratio
After correction, on right side, half brain finds the region that the opposite side brain function inside and outside Pre-SMA with significant difference connects: with
Three regions based on Lingual_R, Frontal_Mid_R, Angular_R, with each voxel and three of these three regions
Input feature vector of the average signal as classifier, output are labeled as the body on the periphery 1, Pre-SMA then with the voxel inside Pre-SMA
Element label is to train SVM classifier with this, realize the positioning to Pre-SMA；Work more than repeating is distinguished in full brain area domain
Training SVM classifier obtains full the brain domain characteristic spectrum of totally 218 sub-districts and corresponding SVM classifier.
2, later, model progress clinical research is tried out:
1) brain domain patients with gliomas 5 minutes tranquillization state functional images are obtained using tranquillization state functional MRI technology
And high-precision structure image, multinomial pretreatment: scanning slice time adjustment is taken, the dynamic correction of head is mapped to standardised space, goes
Trend term, bandpass filtering and Scrubbing, the multinomial pretreatment are the fMRI data prediction stream of series of standards
Journey；
2) while registration, it is removed for standardized influence by tumour MASK, patient on probation is not affected by swollen
The healthy side brain (strong side) of tumor image is mapped to normed space, specifically, being drawn manually on each tomographic image by T2 image
Tumor section out, and during registration, the weight of this part of standards is all set as 0, removes tumour for standard
The influence of change；
3) for each voxel of trier's Ipsilateral target area, itself and the multiple characteristic area signals in opposite side are calculated separately
Related coefficient, in this, as the input of support vector machines (SVM) classifier, output is then for each voxel of target area
It is no to belong to some specific brain domain；Then, the functional localization result on normed space is mapped back into individual space, by right
The positioning one by one of 45 function brain areas draws volume infarct cerebral map (as shown in Figure 3) in half brain of trier's Ipsilateral.
Research result on trial shows that the present invention is calculated special by tranquillization state functional image and high-precision structure image
Half brain tranquillization state function connects of property opposite side, are able to achieve the positioning to complete 45 functional areas of brain, this method can be used for operation plan system
It is fixed, the damage to functional areas can be reduced as far as possible in tumor resection.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to restrict the invention, all in essence of the invention
Any modifications, equivalent replacements, and improvements etc. done within mind and principle, should all be included in the protection scope of the present invention.
Claims (10)
1. the cerebral function area opposite side localization method based on tranquillization state functional MRI, which is characterized in that itself comprising steps of
1) complete 218 sub-district map of brain is established using tranquillization state data by the big data sample of Healthy People；
2) SVM classifier is respectively trained for each sub-district in 218 sub-district maps by the big data sample of Healthy People；
3) the tranquillization state functional image for the brain domain patients with gliomas that will acquire and high-precision structure image, are located in advance
Reason, comprising: scanning slice time adjustment, the dynamic correction of head are mapped to standardised space, remove trend term, bandpass filtering and
Scrubbing；While registration, it is removed for standardized influence by tumour MASK, tumor imaging will be not affected by
Healthy side brain is mapped to normed space；
4) for each voxel of Ipsilateral target area, the phase relation of itself and the multiple characteristic area signals in opposite side is calculated separately
Number, in this, as the input of support vector machines (SVM) classifier, whether output then belongs to this for each voxel of target area
A brain sub-district；
5) it finally, by the result split of all positioning, maps back individual space and forms positioning to entire Ipsilateral cerebral function area
As a result.
2. the cerebral function area opposite side localization method according to claim 1 based on tranquillization state functional MRI, feature exist
In, in the step 1), while the function connects of the voxel in computing function area Yu full brain remaining 88 brain area, it is connected
Matrix M carries out binaryzation to it and classifies after calculating its similar matrix N, obtains one surely by maximizing mutual information
Fixed division result.
3. the cerebral function area opposite side localization method as described in claim 2 based on tranquillization state functional MRI, feature exist
In, wherein when to similar matrix N binaryzation, is cross-checked using 50 groupings, pass through normalized mutual information
It obtains so that NMI (X, Y) maximum λ, carries out binaryzation to similar matrix N with this:
4. the cerebral function area opposite side localization method according to claim 1 based on tranquillization state functional MRI, feature exist
In obtaining complete 218 sub-district map of brain in the step 1).
5. the cerebral function area opposite side localization method according to claim 1 based on tranquillization state functional MRI, feature exist
In being defined target area in the step 2): to each brain area, during the brain area position with AAL Template Location is
The heart expands the regions of 2 voxels (i.e. 6mm) using this as target area outward, it is intended to by functional areas from wherein marking off.
6. the cerebral function area opposite side localization method according to claim 1 based on tranquillization state functional MRI, feature exist
In, in step 2), calculated on training set each brain subregion and surrounding voxel to opposite side half brain each voxel
Function connects carry out comparison among groups t- inspection and identify the brain area with significant group difference after multiple alignment corrects
cluster。
7. the cerebral function area opposite side localization method according to claim 1 based on tranquillization state functional MRI, feature exist
In in step 2), the voxel computing function with the average signal of voxel in the cluster that finds again with target area is connected to
Feature, for each sub-district one support vector machines (SVM) classifier of training.
8. the cerebral function area opposite side localization method according to claim 1 based on tranquillization state functional MRI, feature exist
In in step 3), by T2 image, tumor section being drawn manually on each tomographic image, and during registration, by this
A part of standardized weight is all set as 0, removes tumour for standardized influence.
9. the cerebral function area opposite side localization method according to claim 1 based on tranquillization state functional MRI, feature exist
In for each voxel of Ipsilateral target area, calculating separately the phase of itself and the multiple characteristic area signals in opposite side in step 4)
Relationship number, in this, as the input of support vector machines (SVM) classifier, whether output then belongs to for each voxel of target area
In some specific brain domain.
10. the cerebral function area opposite side localization method according to claim 1 based on tranquillization state functional MRI, feature exist
In, in step 5), the functional localization result on normed space is mapped back into individual space, by 45 function brain areas one by one
Positioning draws volume infarct cerebral map in half brain of Ipsilateral.
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| FENGPING ZHU 等: ""Connectivity-Based Functional Parcellation and Localization of the Human Supplementary Motor Area Based on Resting-State Functional Magnetic Resting Imaging and Its Utility in Brain Tumor Surgery"", 《CLINICAL NEUROSURGERY》 * |
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| CN114187227A (en) * | 2021-09-13 | 2022-03-15 | 北京银河方圆科技有限公司 | Method and device for determining functional area of brain tumor affected area |
| CN114708263A (en) * | 2022-06-06 | 2022-07-05 | 中国科学院自动化研究所 | Individual brain functional region positioning method, device, equipment and storage medium |
| CN116522210A (en) * | 2023-07-03 | 2023-08-01 | 中国医学科学院生物医学工程研究所 | Motor imagery electroencephalogram signal classification method based on brain network difference analysis |
| CN116522210B (en) * | 2023-07-03 | 2023-09-01 | 中国医学科学院生物医学工程研究所 | Motor imagery electroencephalogram signal classification method based on brain network difference analysis |
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