CN114431851A - Neural electrophysiological positive problem modeling method and device and electronic equipment - Google Patents

Neural electrophysiological positive problem modeling method and device and electronic equipment Download PDF

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CN114431851A
CN114431851A CN202210116846.6A CN202210116846A CN114431851A CN 114431851 A CN114431851 A CN 114431851A CN 202210116846 A CN202210116846 A CN 202210116846A CN 114431851 A CN114431851 A CN 114431851A
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戴亚康
刘燕
彭博
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Suzhou Institute of Biomedical Engineering and Technology of CAS
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Abstract

The invention discloses a neural electrophysiological positive problem modeling method, a device and electronic equipment, wherein the method comprises the following steps: acquiring tested MRI data of a target object, and constructing a head geometric structure model and a head surface measuring electrode distribution model according to the tested MRI data; wherein the head geometric structure model comprises position information of fontanelle tissues of the target object; constructing a source model of the target object, and determining a conduction matrix in a positive problem model according to the head geometric structure model, the head surface measurement electrode distribution model and the source model; constructing a first model according to historical tested data, constructing a second model according to the conduction matrix, and determining an error model between the first model and the second model; and constructing a third model matched with the tested MRI data based on the conduction matrix and the error model. The technical scheme provided by the application can improve the modeling precision of the positive problem model.

Description

Neural electrophysiological positive problem modeling method and device and electronic equipment
Technical Field
The invention relates to the technical field of medical imaging, in particular to a method and a device for modeling a neuroelectrophysiological positive problem and electronic equipment.
Background
The neural function imaging with high space-time resolution has important significance for infant brain science. The realization of the high-space-time-resolution nerve function imaging (namely the nerve electrophysiology source imaging) needs to construct an electroencephalogram/magnetotactic problem model. . The existing neural electrophysiological positive problem modeling methods are designed specifically for infants rarely, and most of the existing neural electrophysiological positive problem modeling methods are designed for adults. Therefore, in the prior art, how to construct an accurate model of the infant neuroelectrophysiology positive problem is a current problem.
Disclosure of Invention
In view of this, the embodiment of the invention provides a method and an apparatus for modeling a neuroelectrophysiological positive problem, and an electronic device, which can improve the modeling accuracy of a positive problem model.
The invention provides a neural electrophysiological positive problem modeling method, which comprises the following steps:
acquiring tested MRI data of a target object, and constructing a head geometric structure model and a head surface measuring electrode distribution model according to the tested MRI data; wherein the head geometric structure model comprises position information of fontanelle tissues of the target object;
constructing a source model of the target object, and determining a conduction matrix in a positive problem model according to the head geometric structure model, the head surface measurement electrode distribution model and the source model;
constructing a first model according to historical tested data, constructing a second model according to the conduction matrix, and determining an error model between the first model and the second model;
and constructing a third model matched with the tested MRI data based on the conduction matrix and the error model.
In one embodiment, said constructing a head geometry model comprises:
performing brain tissue segmentation on the target object to obtain a brain tissue segmentation result, wherein the brain tissue segmentation result comprises at least one of scalp, skull, cranial sutures, gray matter, white matter, cerebrospinal fluid, eye sockets, fontanels, dura mater, arachnoid and pia mater;
segmenting hydrogel regions matching the fontanel tissue from the subject MRI data;
identifying the skull tissue outer curved surface in the brain tissue segmentation result, and taking the region covered by the projection of the hydrogel region on the skull tissue outer curved surface as the position information of the fontanel tissue;
in one embodiment, the fontanel tissue is identified as follows:
determining the normal direction of each point in the hydrogel region, and projecting the hydrogel region to the outer curved surface of the skull tissue along the normal direction;
the skull tissue contained in the closed area of the outer curved surface of the skull covered by the projection is taken as the identified fontanel tissue.
In one embodiment, the head surface measurement electrode distribution model is constructed in the following manner:
a hydrogel region of a sensor is segmented from the subject MRI data, and a center position of the sensor is identified in conjunction with a shape and size of the sensor to construct a head surface measurement electrode distribution model from the center position.
In one embodiment, constructing the source model of the target object comprises:
segmenting the cerebral cortex and carrying out surface reconstruction on the segmented cerebral cortex;
based on the surface reconstruction result of the cerebral cortex, the vertex/center of gravity of each triangular patch on the surface is set as an electric dipole.
In one embodiment, constructing the head geometry model comprises:
performing brain tissue segmentation on the target object, and performing skull suture segmentation on the brain tissue segmentation result;
mapping the skull suture segmentation result to the outer curved surface of the skull to obtain a mapping curve of the skull suture on the outer curved surface of the skull;
calculating the position information of fontanel tissues on the skull by combining the standard statistical data of fontanels and the mapping curve of the cranial sutures on the outer curved surface of the skull;
and constructing a head geometric structure model according to the brain tissue segmentation result and the position information of the fontanelle tissue.
In one embodiment, the error model is determined as follows:
generating posterior probability density distribution with a source model as a condition, and converting the posterior probability density distribution into a probability distribution model conforming to Gaussian distribution; the probability distribution model comprises a depth coefficient of a dipole source;
and obtaining the probability distribution model by calculating the depth coefficient of the dipole source, and representing the error model by the probability distribution model.
In another aspect, the present invention provides a device for modeling a neuroelectrophysiological positive problem, the device comprising:
the model building unit is used for obtaining tested MRI data of a target object and building a head geometric structure model and a head surface measuring electrode distribution model according to the tested MRI data; wherein the head geometric structure model comprises position information of fontanelle tissues of the target object;
the conduction matrix determining unit is used for constructing a source model of the target object and determining a conduction matrix in the positive problem model according to the head geometric structure model, the head surface measurement electrode distribution model and the source model;
the error determination unit is used for constructing a first model according to historical tested data, constructing a second model according to the conduction matrix and determining an error model between the first model and the second model;
and the model construction unit is used for constructing a third model matched with the tested MRI data based on the conduction matrix and the error model.
In another aspect, the present invention further provides an electronic device, which includes a memory and a processor, wherein the memory is used for storing a computer program, and the computer program is executed by the processor to realize the above-mentioned method for modeling a neuro-electrophysiological positive problem.
In another aspect, the present invention further provides a computer-readable storage medium for storing a computer program, which when executed by a processor, implements the above-mentioned method for modeling a neuroelectrophysiological positive problem.
According to the technical scheme provided by the application, the personalized real brain geometric model, the head surface electrode distribution model and the conduction matrix determined by the source model are established based on the tested MRI of the target object, and the modeling precision of the positive problem model is improved by combining the error model determined by the conduction matrix.
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The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 shows a schematic representation of the steps of the neuroelectrophysiological positive problem modeling method of the present invention;
FIG. 2 is a functional block diagram of a neuroelectrophysiological positive problem modeling apparatus in an embodiment of the present invention;
fig. 3 shows a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention are described clearly and completely below, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The brain electricity (noninvasive collection outside the scalp)/brain magnetism has the advantages of no radiation, no invasion, high time resolution, accessibility to bed and the like, is particularly suitable for the analysis of the brain function of the infant, and is an important means for the brain science of the infant. However, the spatial resolution of the brain electricity/brain magnetism is undeniably low, and the independent use of the brain electricity/brain magnetism for auxiliary diagnosis has certain limitations. Therefore, in order to solve the problem, high spatial resolution neuroimaging means such as MR, CT and the like and electroencephalogram/magnetoencephalography high time resolution neuroimaging means are often combined, so that high space-time resolution neuroimaging is realized. The above process may be referred to as neuroelectrophysiological source imaging, and its principle is: on the basis of constructing a real head model based on the structural image, the electrical activity of an electrophysiological source of the cerebral cortex is calculated in an inversion mode from the potential/magnetic flux generated by the nerve activity of an organism observed by the scalp, so that the nerve function imaging with high space-time resolution is completed. The imaging premise is to construct an electroencephalogram/magnetic positive problem model, namely a model for calculating the distribution of surface potential/magnetic flux of the head based on a source model, a head geometric model, a head surface measurement electrode distribution model and the electric/magnetic permeability of each brain tissue. Therefore, the accurate construction of the electroencephalogram/magnetotactic problem model is the central importance of realizing the high-space-time-resolution nerve function imaging (namely, the neuroelectrophysiological source imaging).
However, the existing modeling methods for the positive neuroelectrophysiology problem are not designed specifically for infants, and most of the existing modeling methods are designed for adults. According to the existing research, the infant has special brain structure tissue fontanels (strictly speaking fontanels include anterior fontanels and posterior fontanels, generally the posterior fontanels are closed shortly after the newborn, therefore, the anterior fontanels are mainly focused on), and the method for modeling and solving the neuroelectrophysiological positive problem of the adult without considering the fontanels is directly applied to the infant to generate certain errors. Therefore, a neural electrophysiological positive problem modeling and solving method for infants is provided.
As mentioned previously, neuroelectrophysiology source imaging includes two key links, neuroelectrophysiology positive problem modeling and solving. The modeling of the neuroelectrophysiology positive problem is the premise of imaging of the neuroelectrophysiology source, and the error of the positive problem model can directly influence the source imaging precision. Existing positive problem modeling methods can be broadly classified into two categories depending on whether real brain geometry is used: the first type is a spherical or ellipsoidal model without a priori real brain geometry; the second category is personalized positive problem models with a real head geometry prior. Obviously, the model accuracy of the second class of methods is much higher than the first class. Theoretically, the more accurate the real head geometry prior model, the more accurate the positive problem model. However, the actual head geometric structure prior modeling is calculated based on the head structure imaging and segmentation modeling results, that is, the accuracy of the head structure imaging and the segmentation modeling directly affects the accuracy of the individual head structure prior modeling, and can also be said to directly affect the accuracy of the neural electrophysiological positive problem modeling. For the brain structure imaging and the segmentation modeling of the infant, a more accurate result can be obtained by combining CT and MRI together. However, because CT is somewhat radioactive, it is not possible to perform a radioactive CT for the infant. For this reason, in most cases, MRI is currently used only for brain structure imaging and segmentation modeling of infants. However, the contrast of imaging of the infant special brain tissue fontanelle by the existing MRI is extremely low, and the segmentation modeling of the infant fontanelle cannot be realized by the existing MRI segmentation modeling method, and the segmentation modeling of the infant fontanelle cannot be realized by hand. Therefore, the accuracy of constructing the real head geometric model of the infant based on MRI is limited by the impossibility of dividing fontanels.
In some application scenarios, such as when examining the brain of an infant, a neuro-electrophysiological positive problem model for infants is needed because CT has radiation that is not suitable for infants, and because the resolution of magnetic resonance imaging (MRI for short) is low due to the presence of fontanels.
In view of this, the present application proposes a method for modeling a neuro-electrophysiology positive problem, wherein an expression of the positive problem model may be represented as Y ═ AX + E, where Y is a modeling result, a is a conduction matrix in the constructed infant neuro-electrophysiology positive problem model, X is a source model in the constructed infant neuro-electrophysiology positive problem model, and E is an error model in the constructed infant neuro-electrophysiology positive problem model.
Referring to fig. 1, a method for modeling a neuroelectrophysiology positive problem according to an embodiment of the present application may include the following steps.
S1: acquiring tested MRI data of a target object, and constructing a head geometric structure model and a head surface measuring electrode distribution model according to the tested MRI data; wherein the geometric model of the head comprises the position information of the fontanelle tissue of the target object. The target object refers to the head of the tested infant.
S3: and constructing a source model of the target object, and determining a conduction matrix in the positive problem model according to the head geometric structure model, the head surface measurement electrode distribution model and the source model.
S5: and constructing a first model according to historical tested data, constructing a second model according to the conduction matrix, and determining an error model between the first model and the second model.
S7: and constructing a third model matched with the tested MRI data based on the conduction matrix and the error model.
The construction of the conductive matrix provided in one embodiment of the present application comprises the following steps:
and S21, acquiring the tested MRI data of the target object. The method specifically comprises the following steps:
determining the shape, size and number of sensors to be used through an electroencephalogram application instruction;
preparing a developing gel which is consistent with the fontanelle and the shape and the size of an electroencephalogram sensor placed on the head surface of an infant;
specifically, the hair of the infant is removed for skin preparation; marking areas of bregma and the sensor of the baby by using a non-toxic and easily removable color pen; the desired hydrogel can be prepared by recording the mark with a camera or the like and extracting the shape and size of the bregma and the sensor based on a computer vision method.
After the prepared hydrogel is placed at a corresponding position according to the shape and the size, MRI scanning is carried out on the target object.
S23: constructing a head geometric structure model, which specifically comprises the following steps:
in this embodiment, the target object is subjected to brain tissue segmentation based on existing methods/software (e.g., fieldtrip, convincing brain, etc.) to obtain brain tissue segmentation results (e.g., scalp, skull, sutures, gray matter, white matter, cerebrospinal fluid, orbit, fontanel, dura, arachnoid, pia, etc.), and then the internal and external surfaces of the brain tissue are reconstructed.
Since the hydrogel of the bregma part is not on the surface of the scalp, the imaging result can not be exactly matched with the actual result of the head. Therefore, the area covered by the projection of the hydrogel area on the outer curved surface of the skull tissue is used as the position information of the fontanel tissue.
Specifically, a hydrogel area matched with the fontanel tissue is segmented from the tested MRI data;
determining the normal direction of each point in the hydrogel region, and projecting the hydrogel region to the outer curved surface of the skull tissue along the normal direction;
and identifying the skull tissue outer curved surface in the brain tissue segmentation result, and taking the region covered by the projection of the hydrogel region on the skull tissue outer curved surface as the position information of the fontanel tissue.
And according to the brain tissue segmentation result and the position information of the fontanelle tissue, completing the construction of a head geometric structure model.
S25: the construction of the head surface measurement electrode distribution model specifically comprises the following steps:
and (3) segmenting a hydrogel area of the sensor from the tested MRI data, and identifying the central position of the sensor by combining the shape and the size of the sensor, so as to obtain the electrode distribution of the position of the sensor, and further constructing a head surface measuring electrode distribution model.
S27: a source model of the target object is constructed.
The source model is a neuron discharge model, the cerebral cortex is segmented based on software such as Kangrui brain, and the segmented cerebral cortex is subjected to surface reconstruction;
based on the surface reconstruction result of the cerebral cortex, the vertex/center of gravity of each triangular patch on the surface is set as an electric dipole.
S29: based on the head geometric model, the head surface measurement electrode distribution model, the source model and the electrical/magnetic permeability of each brain tissue, the conduction matrix construction in the positive problem is completed by using calculation methods such as finite elements/boundary elements and the like according to an electromagnetic field theory.
In another embodiment of the present application, a conductive matrix is constructed comprising the steps of:
s31: the target object is obtained by MRI test conventionally, such as directly carrying out MRI scan on the target object.
S33: and constructing a head geometric structure model.
Based on the obtained MRI, performing brain tissue segmentation on the target object, then performing skull suture segmentation, and mapping the skull suture segmentation result to the outer curved surface of the skull to obtain a mapping curve of the skull suture on the outer curved surface of the skull;
according to the standard statistical data obtained by the statistical values corresponding to the fontanelle sizes of different months ages of a large number of infants historically, the fontanelle approximate size of the target object and the mapping curve of the cranial sutures on the external curved surface of the skull are calculated, and the position information of fontanelle tissues on the skull is identified.
S35: and (5) constructing a head surface measurement electrode distribution model.
Obtained by standard sensor distribution and MRI registration methods.
S37: a source model of the target object is constructed.
The source model is a neuron discharge model, the cerebral cortex is segmented based on software such as Kangrui brain, and the segmented cerebral cortex is subjected to surface reconstruction;
based on the surface reconstruction result of the cerebral cortex, the vertex/center of gravity of each triangular patch on the surface is set as an electric dipole.
S39: based on the head geometric model, the head surface measurement electrode distribution model, the source model and the electrical/magnetic permeability of each brain tissue, the conduction matrix construction in the positive problem is completed by using calculation methods such as finite elements/boundary elements and the like according to an electromagnetic field theory.
The construction of the error model in the infant neuroelectrophysiology positive problem model specifically comprises the following steps:
specifically, in practical applications, a first model may be constructed based on historical data based on existing infant CT and MRI data, with the expression: where e is the error caused by the measurement
Y＝A6X+e
And constructing a second model based on the data obtained by the conduction matrix, wherein the expression is as follows:
Y′＝A5X+e
constructing a third model matched with the tested MRI data based on the conduction matrix and an error model between the first model and the second model, wherein the expression of the third model is as follows: (wherein the sum of the model-induced error and the measurement error is E ═ E + E)
Y″＝A5X+(Y-A5X)+e＝AX5+ε+e＝AX5+E (1)
Determination of the error model E:
the modeling of E can be considered as a posterior probability density distribution P (E | X) conditioned on X.
The posterior probability density distribution can be converted into a probability distribution model conforming to Gaussian distribution;
let P (e) and P (ε | X) obey a Gaussian distribution, respectively, i.e.
P(e)～N(e*,σe)，P(ε|X)～N(ε*|X,σε|X)
To solve for epsilon*|XAnd σε|XConstruction of
Figure BDA0003496693950000091
Z obeys a Gaussian distribution, i.e., P (Z) N (Z)*,σz) And is and
Figure BDA0003496693950000092
wherein the content of the first and second substances,
Figure BDA0003496693950000093
can obtain
Figure BDA0003496693950000094
Figure BDA0003496693950000095
Based on equation (1) and the probability distribution model p (x), the above values can be solved based on using a monte carlo simulation method. The probability distribution model p (x) described above may be constructed based on the nature of the source. For example, the L12 norm may be used to solve for the prior distribution, i.e.
Figure BDA0003496693950000101
Wherein, | | Xi||2Representing the intensity of the ith dipole source, alpha representing the probability distribution coefficient of the source intensity, wiIs the depth coefficient of the ith dipole source, and the solving method is as follows:
[w1…wM]＝diag(AT(AAT)-1A)
referring to fig. 2, the present application further provides a device for modeling a neuroelectrophysiological positive problem, the device comprising:
the model building unit is used for obtaining tested MRI data of a target object and building a head geometric structure model and a head surface measuring electrode distribution model according to the tested MRI data; wherein the geometric brain structure model comprises position information of fontanelle tissues of the target object;
the conduction matrix determining unit is used for constructing a source model of the target object and determining a conduction matrix in the positive problem model according to the head geometric structure model, the head surface measurement electrode distribution model and the source model;
the error determination unit is used for constructing a first model according to historical tested data, constructing a second model according to the conduction matrix and determining an error model between the first model and the second model;
and the model construction unit is used for constructing a third model matched with the tested MRI data based on the conduction matrix and the error model.
Referring to fig. 3, the present application further provides an electronic device comprising a memory and a processor, the memory being used for storing a computer program, and the computer program, when executed by the processor, implementing the method for modeling a neuro-electrophysiological positive problem.
The present application also provides a computer-readable storage medium for storing a computer program which, when executed by a processor, implements a method of modeling a neuroelectrophysiological positive problem.
According to the technical scheme provided by the application, the personalized real brain geometric model, the head surface electrode distribution model and the conduction matrix determined by the source model are established based on the tested MRI of the target object, and the modeling precision of the positive problem model is improved by combining the error model determined by the conduction matrix.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims (10)

1. A method of modeling a neuroelectrophysiological positive problem, the method comprising:
acquiring tested MRI data of a target object, and constructing a head geometric structure model and a head surface measuring electrode distribution model according to the tested MRI data; wherein the head geometric structure model comprises position information of fontanelle tissues of the target object;
constructing a source model of the target object, and determining a conduction matrix in a positive problem model according to the head geometric structure model, the head surface measurement electrode distribution model and the source model;
constructing a first model according to historical tested data, constructing a second model according to the conduction matrix, and determining an error model between the first model and the second model;
and constructing a third model matched with the tested MRI data based on the conduction matrix and the error model.
2. The method of claim 1, wherein constructing the brain geometry model comprises:
performing brain tissue segmentation on the target object to obtain a brain tissue segmentation result; the brain tissue segmentation result comprises at least one of scalp, skull, cranial suture, gray matter, white matter, cerebrospinal fluid, orbit, fontanel, dura mater, arachnoid and pia mater;
segmenting hydrogel regions matching the fontanel tissue from the subject MRI data;
identifying the skull tissue outer curved surface in the brain tissue segmentation result, and taking the region covered by the projection of the hydrogel region on the skull tissue outer curved surface as the position information of the fontanel tissue;
and constructing a head geometric structure model according to the brain tissue segmentation result and the position information of the fontanelle tissue.
3. The method according to claim 2, characterized in that the fontanel tissue is identified in the following manner:
determining the normal direction of each point in the hydrogel region, and projecting the hydrogel region to the outer curved surface of the skull tissue along the normal direction;
the skull tissue contained in the closed area of the outer curved surface of the skull covered by the projection is taken as the identified fontanel tissue.
4. The method of claim 1, wherein the head surface measurement electrode distribution model is constructed in the following manner:
the hydrogel area of the sensor is segmented from the tested MRI data, and the central position of the sensor is identified by combining the shape and the size of the sensor, so that a head surface measuring electrode distribution model is constructed according to the central position.
5. The method of claim 1, wherein constructing the source model of the target object comprises:
segmenting the cerebral cortex and carrying out surface reconstruction on the segmented cerebral cortex;
based on the surface reconstruction result of the cerebral cortex, the vertex/center of gravity of each triangular patch on the surface is set as an electric dipole.
6. The method of claim 1, wherein constructing the brain geometry model comprises:
performing brain tissue segmentation on the target object, and performing skull suture segmentation on the brain tissue segmentation result;
mapping the skull suture segmentation result to the outer curved surface of the skull to obtain a mapping curve of the skull suture on the outer curved surface of the skull;
calculating the position information of fontanel tissues on the skull by combining the standard statistical data of fontanels and the mapping curve of the cranial sutures on the outer curved surface of the skull;
and constructing a head geometric structure model according to the brain tissue segmentation result and the position information of the fontanelle tissue.
7. The method of claim 1, wherein the error model is determined as follows:
generating posterior probability density distribution with a source model as a condition, and converting the posterior probability density distribution into a probability distribution model conforming to Gaussian distribution; the probability distribution model comprises a depth coefficient of a dipole source;
and obtaining the probability distribution model by calculating the depth coefficient of the dipole source, and representing the error model by the probability distribution model.
8. A neuroelectrophysiological positive problem modeling apparatus, characterized in that the apparatus comprises:
the model building unit is used for obtaining tested MRI data of a target object and building a head geometric structure model and a head surface measuring electrode distribution model according to the tested MRI data; wherein the head geometric structure model comprises position information of fontanelle tissues of the target object;
the conduction matrix determining unit is used for constructing a source model of the target object and determining a conduction matrix in a positive problem model according to the head geometric structure model, the head surface measurement electrode distribution model and the source model;
the error determination unit is used for constructing a first model according to historical tested data, constructing a second model according to the conduction matrix and determining an error model between the first model and the second model;
and the model construction unit is used for constructing a third model matched with the tested MRI data based on the conduction matrix and the error model.
9. An electronic device, characterized in that the electronic device comprises a memory for storing a computer program which, when executed by the processor, implements the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program which, when executed by a processor, implements the method of any one of claims 1 to 7.
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