CN115005802A - Method, system and device for positioning onset part of brain network disease - Google Patents
Method, system and device for positioning onset part of brain network disease Download PDFInfo
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Abstract
The embodiment of the specification provides a method, a system and a device for positioning an onset part of a brain network disease, wherein the method comprises the following steps: projecting PET metabolic data of a subject to the same structural space, wherein the subject comprises in particular: brain network disease patients and healthy people; in the same structure space, eliminating the morphological difference among the subjects, and carrying out the same structure registration among the subjects; specifically extracting PET (positron emission tomography) metabolic data of gray matter based on the registration coordinates between the subjects in the same structural space, and performing statistical analysis to obtain PET metabolic statistical data; determining regions of deviation from normal based on the PET metabolic statistics, and locating the regions of deviation from normal as foci.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, a system, and an apparatus for positioning an onset of brain network disease.
Background
In the prior art, the magnetic resonance negative epilepsy, namely the epilepsy with definite brain structure change can not be found by the magnetic resonance and the conventional means, and the seizure-free rate of the epilepsy after 10 years is only 59 percent in the latest literature. At present, the latest method for realizing positioning based on the comparison of PET and a healthy person template in the world mainly depends on a Statistical Parametric Mapping (SPM) technical system, and the positioning efficiency of the system is different from 40 to 69 percent according to the positioning difficulty of the grouped cases. Currently, magnetic resonance negative epilepsy cannot be effectively localized by a single examination method on a global scale, and therefore, the effect of surgical diagnosis and treatment is severely limited.
The non-temporal lobe derived magnetic resonance negative epilepsy technology can not find structural focus due to routine examination, can not distinguish from normal cortex under microscope in craniotomy, and does not consider surgical treatment in the past. However, as technology has developed, the proportion and number of magnetic resonance negative epilepsy procedures performed has increased over the last decade. Recent clinical studies have shown that 90% of negative epileptic patients of focal origin can benefit from surgical procedures. By 10 years post-surgery, however, only 59% of patients reach a seizure-free state.
The improvement of the operation curative effect mainly depends on the improvement of the positioning accuracy. However, the traditional method is difficult and serious: firstly, noninvasive electroencephalography (EEG) collected from scalp can only find that the surface of brain is more than 6cm 2 The cooperative discharge of the brain cannot collect the brain electricity of the deep part of the brain (temporal fundus, orbital forehead, deep side of the longitudinal columns of the brain, and the like), and because the origin of the epilepsy lacks specificity, the missed diagnosis risk is also large; secondly, because the encephalic brain electricity is invasive, the coverage range is limited, the earliest detected electrical activity is probably a conducted signal rather than an initial signal, and if the noninvasive means is inaccurate, the accuracy of the invasive means cannot be further ensured; and thirdly, as the name suggests, the magnetic resonance negative epilepsy is the most accurate structural scanning method, and the magnetic resonance can not find clear focus. The above aspects illustrate that magnetic resonance negative epilepsy is challenging to locate by means in the prior art literature. At present, no single checking method can realize the full accuracy of epilepsy locationismAnd covering, and the preoperative evaluation depends on the experience of a doctor, integrates multiple examinations, discusses authenticity and judges the position of the epilepsy empirically.
Research shows that epilepsy is mainly the pathological change of cortical gray matter, and when a technical system of the traditional SPM is compared with a healthy person, the epilepsy not only contains cortical gray matter, but also contains white matter, so that the statistical accuracy is reduced. Moreover, when the SPM is used for comparing the testees, the different tested sulci brains with different walks cannot be completely projected to a uniform space, so that the accuracy of comparison among the testees is reduced.
Currently, F-FDG-PET can realize the observation of the metabolic activity of the cerebral cortex through the uptake of a glucose analog labeled radionuclide. Clinically, early localization of epilepsy is currently discussed primarily by relying on the flesh eye to look for areas of decreased glucose metabolism during the interval between seizures. However, it has limitations in that: there are physiological hypometabolism areas in the brain, i.e., hypometabolism areas that exist in healthy people; ② in patients, hypo-metabolic regions may also be present in the brain areas to which epilepsy spreads; ③ hypermetabolism may also occur in the focus of epilepsy, because this part of the brain is higher in the healthy people, even if slightly reduced, it may be higher than other brain areas in the brain. Therefore, the accurate judgment of the single mode of the epileptogenic focus cannot be realized by reading the picture clinically by naked eyes.
In the image post-processing, based on the method of SPM, the first-line method is to transform the brain PET data of the patient and the healthy brain into a unified template for comparison through deformation in the original state of a 3D matrix.
As mentioned above, the latest first-line technology in the prior art is mainly the post-processing technology under the SPM system. Because different individuals have different sulci gyrus depths and bending degrees, the method system can only unify the widths, the lengths and the like of different brains, can not ensure the regular alignment of the same gyrus of different individuals on spatial positions, has clear registration errors generally, and obstructs the observation of tiny focuses.
Epilepsy is a gray-colored pathology. In the traditional method, adjacent cerebrospinal fluid and white matter structures are simultaneously brought into the statistics, so that the number of mixed factors brought into the data is increased when the data is compared and analyzed. For various reasons, the accuracy of the traditional first-line method for epileptogenic focus is only 40% -69%. Among them, the accuracy rate of the simple temporal lobe epilepsy is about 69%, and the accuracy rate of the magnetic resonance negative epilepsy (the epilepsy with the highest positioning difficulty in the report) in which the cortical function silent region originates and the PET original data can not be sufficiently positioned is only about 40%.
Disclosure of Invention
The invention aims to provide a method, a system and a device for positioning an attack initiation part of a brain network disease, and aims to solve the problems in the prior art.
The invention provides a method for positioning an onset part of brain network diseases, which comprises the following steps:
projecting PET metabolic data of a subject to the same structural space, wherein the subject comprises in particular: brain network disease patients and healthy people;
in the same structure space, eliminating the morphological difference among the subjects, and carrying out the same-original structure registration among the subjects;
specifically extracting PET (positron emission tomography) metabolic data of gray matter based on the registration coordinates between the subjects in the same structural space, and performing statistical analysis to obtain PET metabolic statistical data;
determining a region deviating from normal based on the PET metabolic statistics, and locating the region deviating from normal as a focus.
The invention provides a system for positioning the onset part of brain network diseases, which comprises:
a projection module for projecting the PET metabolic data of a subject to the same structural space, wherein the subject comprises in particular: brain network disease patients and healthy people;
the registration module is used for eliminating the morphological difference among the subjects in the same structural space and carrying out the same-original structural registration among the subjects;
the extraction module is used for specifically extracting the PET metabolic data of the gray matter based on the registration coordinates between the subjects in the same structural space, and performing statistical analysis to obtain the PET metabolic statistical data;
and the positioning module is used for determining a region deviating from normal based on the PET metabolic statistical data and positioning the region deviating from normal as a focus.
The embodiment of the present invention further provides a device for positioning an onset part of brain network diseases, including: the computer program is stored on the memory and can run on the processor, and when being executed by the processor, the computer program realizes the steps of the method for positioning the onset part of the brain network disease.
The embodiment of the invention also provides a computer readable storage medium, wherein an implementation program for information transmission is stored on the computer readable storage medium, and the program is executed by a processor to implement the steps of the method for positioning the onset part of the brain network disease.
By adopting the embodiment of the invention, the accurate projection of the brain metabolic data of a single patient and healthy people in a unified space is realized, the projection difference among the subjects is fully eliminated, meanwhile, the gray matter range is defined based on 3-dimensional reconstruction and segmentation, the PET metabolic signal in the middle of the gray matter is accurately extracted, the comparison among samples is carried out, and the positioning accuracy is improved.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and that other drawings can be obtained by those skilled in the art without inventive exercise.
FIG. 1 is a flowchart of a method for locating an onset of brain network disease according to an embodiment of the present invention;
FIG. 2 is a graphical representation of PET values at 3D brain surface vertices for an embodiment of the present invention;
FIG. 3 is a schematic diagram of the effect of locating actual problem cases according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a system for locating the onset of brain network disease according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a device for locating an onset site of brain network disease according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.
Method embodiment
According to an embodiment of the present invention, a method for positioning an onset start site of a brain network disease is provided, fig. 1 is a flowchart of the method for positioning the onset start site of the brain network disease according to the embodiment of the present invention, as shown in fig. 1, the method for positioning the onset start site of the brain network disease according to the embodiment of the present invention specifically includes:
step 101, projecting PET metabolic data of a subject to the same structural space, wherein the subject specifically comprises: brain network disease patients and healthy people; wherein, in the embodiment of the invention, the brain network diseases are: magnetic resonance negative epilepsy, and may also include other brain network disorders, such as parkinson, depression, schizophrenia, autism, anxiety, and the like.
Step 101 specifically includes:
acquiring PET metabolic data of a subject and 3D MR T1 weighted data, namely structural space data, of a magnetic resonance negative epileptic patient, registering the PET metabolic data into a T12D space, and performing brain structure reconstruction and grey-white substance segmentation of a T13D space through a T13D reconstruction algorithm of FreeSenfer technical standard based on the 3D MR T1 weighted data, wherein the same structural space comprises a T12D space and a T13D space.
102, eliminating morphological differences among the subjects in the same structural space, and carrying out original structure registration among the subjects; step 102 specifically includes:
based on the brain structure information in the T12D space and the T13D space, morphological differences of the inter-subject brain structures are eliminated through a synchronous volume and surface CVSR algorithm, and the homomorphic structure registration of the inter-subject brain structures is carried out.
103, specifically extracting PET (positron emission tomography) metabolic data of gray matter based on the registration coordinates between the subjects in the same structural space, and performing statistical analysis to obtain PET metabolic statistical data; step 103 specifically comprises:
and interpolating the PET metabolic data into the registration coordinates of the corresponding brain structures based on the registration coordinates among the subjects of the brain structures in the same space, and performing peak-to-peak statistics on the PET metabolic data values of the same space structure at the level of the peak of the 3D model surface by adopting a preset statistical method to obtain PET metabolic statistical data. In an embodiment of the present invention, the predetermined statistical method specifically includes one of the following: z-transformation and t inspection;
and 104, determining a deviation area from normal based on the PET metabolic statistical data, and positioning the deviation area from normal as a focus. Step 104 specifically includes: based on the PET metabolism statistical data, determining a deviation area from normal according to a position corresponding to a lowest z value of the whole brain or a position corresponding to a value with statistical difference in a T distribution diagram of a T13D space, and positioning the deviation area from normal as a focus.
Preferably, in the embodiment of the present invention, the brain space after locating the focus can be used as a standard space of a population level for scientific research or observation of rules and attributes of population level focus distribution; in addition, the brain space after the focus is positioned can be projected to the individual space of the patient through the inverse algorithm of the synchronous volume and surface CVSR algorithm, and is guided into a surgical navigation and robot system for guiding surgical resection.
In conclusion, according to the technical scheme of the embodiment of the invention, the position matching between the brain of the patient and the brain homomorphism of the healthy person is realized by eliminating the brain morphological difference among the subjects, so that a basis is provided for further quantitative statistics; the embodiment of the invention provides a method for accurately extracting the metabolic value in grey matter to count the degree of deviation of a patient from a normal population; according to the embodiment of the invention, the most obvious change part is accurately found through the quantification method, and the surgical treatment is guided.
The following describes the above technical solution of the embodiment of the present invention in detail by taking magnetic resonance negative epilepsy as an example with reference to the accompanying drawings.
According to the embodiment of the invention, PET metabolic data of a patient and PET metabolic data of healthy people are projected to the same space, on the premise of fully eliminating morphological differences among subjects, gray matter signals are specifically extracted, statistical comparison is carried out on epilepsy which is a gray matter disease, and a region deviating from normal is found, namely an epileptogenic focus. The method comprises the following concrete steps:
step 1, pretreatment.
Step 101, space division:
the embodiment of the invention requires that the patient has 3D MR T1 weighted data at the same time, and the weighted data can be acquired by synchronous PET/MR data acquisition and can also be acquired by PET/CT and common magnetic resonance acquisition respectively; registering the PET into T12D space;
based on the 3D MR T1 data, brain structure reconstruction and grey-white substance segmentation were performed by the FreSurfer's Standard T13D reconstruction algorithm.
Step 102, registration among subjects, unifying structural spaces of patients and healthy people:
inter-subject Registration is further performed by a Combined Volumetric and Surface Registration algorithm. The algorithm simultaneously refers to grey white matter information in a 2D space and structure information reconstructed in a 3D space, so that the matching degree of the same structure alignment among the testees can reach more than 95 percent;
step 103, extracting gray matter median PET metabolic information:
as shown in fig. 2, the PET signal is interpolated into corresponding structure coordinates based on the inter-subject registration coordinates of the structure, and vertex-to-vertex statistics is performed on the PET signal values of the same spatial structure at the level of the vertices of the 3D model plane (corresponding to the pixels of the 2D plane). Statistical methods include, but are not limited to: z-transform (statistical standard deviation of patients from normal human-mean), t-test of single patient against healthy population, etc.;
and 2, performing positioning analysis.
Specifically, seizure-inducing property is defined by the lowest z-value of the whole brain, or the position corresponding to a statistically different value in the 3D t distribution map (mathematically meaning the most significant position from the normal population). The positioning effect is shown in fig. 3.
The brain space after focus positioning can continue to be a standard space of a crowd level and is used for scientific research and observation of the rule and attribute of the crowd level seizure focus distribution;
or projected to the individual space of the patient through the inverse algorithm in step 102, and introduced into the surgical navigation and robotic system to guide the surgical resection.
In the embodiment of the invention, based on grouped 42 magnetic resonance negative epilepsy originated from the cortex of the functional silent zone (the case with the highest positioning difficulty all over the world), the SPM method system (first-line method) in the prior art is adopted, so that the epilepsy positioning rate can be only 47%, and the positioning rate can reach 87% by the technical scheme of the embodiment of the invention, so that the problem of magnetic resonance negative epilepsy operation evaluation can be fully solved.
In conclusion, by means of the technical scheme of the embodiment of the invention, the problem of accurate alignment of homologous brain regions of different subjects is fully solved, so that the unified structures of patients and healthy people can be perfectly aligned and overlapped, and the uniformity of space during statistics is ensured; meanwhile, only signals of the grey matter structure are extracted for statistics, and the attributes of the epileptic grey matter disease are better met. By adopting the technical scheme of the embodiment of the invention, even if the epilepsy is in a magnetic resonance negative area, a PET non-specific area and a functional silent area, the positioning accuracy rate reaches 87%. The positioning accuracy is obviously improved.
It should be noted that based on the technical scheme of the embodiment of the present invention, a norm can be added according to different PET molecular probes to find therapeutic targets for other brain network diseases such as parkinson, depression, schizophrenia, autism, anxiety and the like.
System embodiment
According to an embodiment of the present invention, a system for locating a brain network disease attack initiation site is provided, fig. 4 is a schematic view of the system for locating a brain network disease attack initiation site according to an embodiment of the present invention, as shown in fig. 4, the system for locating a brain network disease attack initiation site according to an embodiment of the present invention specifically includes:
a projection module 40 for projecting the PET metabolic data of the subject to the same structural space, wherein the subject comprises in particular: brain network disease patients and healthy people; the projection module 40 is specifically configured to:
acquiring PET metabolic data of a subject and 3D MR T1 weighted data, namely structural space data, of a brain network disease patient, registering the PET metabolic data into a T12D space, and performing brain structural reconstruction and grey-white substance segmentation of a T13D space through a T13D reconstruction algorithm of FreeSenfer technical standard based on the 3D MR T1 weighted data, wherein the same structural space comprises a T12D space and a T13D space.
A registration module 42, configured to eliminate morphological differences between subjects in the same structural space, and perform orthotopic structural registration between subjects; the registration module 42 is specifically configured to:
based on the brain structure information in the T12D space and the T13D space, morphological differences of the inter-subject brain structures are eliminated through a synchronous volume and surface CVSR algorithm, and the homomorphic structure registration of the inter-subject brain structures is carried out.
An extraction module 44, configured to specifically extract the gray PET metabolic data based on the registration coordinates between the subjects in the same structural space, and perform statistical analysis to obtain PET metabolic statistical data; the extraction module 44 is specifically configured to: and interpolating the PET metabolic data into the registration coordinates of the corresponding brain structures based on the registration coordinates among the subjects of the brain structures in the same space, and performing peak-to-peak statistics on the PET metabolic data values of the same space structure at the level of the peak of the 3D model surface by adopting a preset statistical method to obtain PET metabolic statistical data. The predetermined statistical method specifically comprises one of: z-transformation and t inspection;
a localization module 46 for determining regions deviating from normal based on the PET metabolic statistics, and localizing the regions deviating from normal as foci. The positioning module 46 is specifically configured to: based on the PET metabolic statistics, determining an off-normal region according to a position corresponding to a lowest z value of the whole brain or a position corresponding to a statistically different value in a T distribution diagram of a T13D space, and positioning the off-normal region as a lesion.
In the embodiment of the present invention, the method further includes:
the first processing module is used for taking the brain space after the focus is caused to be positioned as a standard space of a crowd level and is used for scientific research or observing the rule and the attribute of the distribution of the focus caused to be caused to the crowd level;
and the second processing module is used for projecting the brain space after the focus is positioned to the individual space of the patient through the inverse algorithm of the synchronous volume and surface CVSR algorithm, and guiding the brain space to an operation navigation and robot system for guiding the surgical resection.
The embodiment of the present invention is a system embodiment corresponding to the above method embodiment, and specific operations of each module may be understood with reference to the description of the method embodiment, which is not described herein again.
Apparatus embodiment one
The embodiment of the present invention provides a device for positioning an onset part of brain network diseases, as shown in fig. 5, including: a memory 50, a processor 52 and a computer program stored on the memory 50 and executable on the processor 52, which computer program, when executed by the processor 52, performs the steps as described in the method embodiments.
Device embodiment II
An embodiment of the present invention provides a computer-readable storage medium, on which an implementation program for information transmission is stored, and when the program is executed by the processor 52, the steps described in the method embodiment are implemented.
The computer-readable storage medium of this embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, and the like.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for locating the onset of brain network disease, comprising:
projecting PET metabolic data of a subject to the same structural space, wherein the subject comprises in particular: brain network disease patients and healthy people;
in the same structure space, eliminating the morphological difference among the subjects, and carrying out the same-original structure registration among the subjects;
specifically extracting PET (positron emission tomography) metabolic data of gray matter based on the registration coordinates between the subjects in the same structural space, and performing statistical analysis to obtain PET metabolic statistical data;
determining regions of deviation from normal based on the PET metabolic statistics, and locating the regions of deviation from normal as foci.
2. The method of claim 1, wherein projecting the subject's PET metabolic data to the same structural space specifically comprises:
acquiring PET metabolic data of a subject and 3D MR T1 weighted data, namely structural space data, of a brain network disease patient, registering the PET metabolic data into a T12D space, and performing brain structure reconstruction and grey white segmentation of a T13D space through a T13D reconstruction algorithm of FreeKufer technical standard based on the 3D MR T1 weighted data, wherein the same structural space comprises a T12D space and a T13D space.
3. The method according to claim 2, wherein inter-subject morphological differences are eliminated in the same structural space, and performing inter-subject orthotopic structural registration specifically comprises:
based on the brain structure information in the T12D space and the T13D space, morphological differences of the inter-subject brain structures are eliminated through a synchronous volume and surface CVSR algorithm, and the homomorphic structure registration of the inter-subject brain structures is carried out.
4. The method according to claim 1, wherein PET metabolic data of gray matter are specifically extracted and statistically analyzed based on the inter-subject registration coordinates of the same structural space, and the obtaining of PET metabolic statistical data specifically comprises:
and interpolating the PET metabolic data into the registration coordinates of the corresponding brain structures based on the registration coordinates among the subjects of the brain structures in the same space, and performing peak-to-peak statistics on the PET metabolic data values of the same space structure at the level of the peak of the 3D model surface by adopting a preset statistical method to obtain PET metabolic statistical data.
5. The method according to claim 4, wherein the predetermined statistical method comprises in particular one of: z-transformation and t-test.
6. The method of claim 1, wherein determining regions of departure from normality based on the PET metabolic statistics, the locating regions of departure from normality as a pathogenic cooktop body comprises:
based on the PET metabolic statistics, determining an off-normal region according to a position corresponding to a lowest z value of the whole brain or a position corresponding to a statistically different value in a T distribution diagram of a T13D space, and positioning the off-normal region as a lesion.
7. The method of claim 1, further comprising:
taking the brain space after the focus is positioned as a standard space of the crowd level, and using the brain space for scientific research or observing the rule and the attribute of the distribution of the focus of the crowd level;
the brain space after the focus is positioned is projected to the individual space of the patient through the inverse algorithm of the synchronous volume and surface CVSR algorithm, and is guided into a surgical navigation and robot system for guiding surgical resection.
8. A system for locating the onset of brain network disease, comprising:
a projection module for projecting the PET metabolic data of a subject to the same structural space, wherein the subject comprises in particular: brain network disease patients and healthy people;
the registration module is used for eliminating the morphological difference among the subjects in the same structural space and carrying out the same-original structural registration among the subjects;
the extraction module is used for specifically extracting the PET metabolic data of the gray matter based on the registration coordinates between the subjects in the same structural space, and performing statistical analysis to obtain the PET metabolic statistical data;
and the positioning module is used for determining a region deviating from the normal based on the PET metabolic statistical data and positioning the region deviating from the normal as a focus.
9. A device for locating the onset of brain network disease, comprising: memory, processor and computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out the steps of the method of brain network seizure onset localization according to any one of claims 1 to 7.
10. A computer-readable storage medium, wherein a program for implementing information transmission is stored on the computer-readable storage medium, and when the program is executed by a processor, the program implements the steps of the method for locating the onset of brain network disease onset according to any one of claims 1 to 7.
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