CN115312181A - Brain network disease surgery prognosis judgment method, system and device - Google Patents
Brain network disease surgery prognosis judgment method, system and device Download PDFInfo
- Publication number
- CN115312181A CN115312181A CN202210859838.0A CN202210859838A CN115312181A CN 115312181 A CN115312181 A CN 115312181A CN 202210859838 A CN202210859838 A CN 202210859838A CN 115312181 A CN115312181 A CN 115312181A
- Authority
- CN
- China
- Prior art keywords
- brain
- space
- data
- pet
- clusters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 210000004556 brain Anatomy 0.000 title claims abstract description 94
- 201000010099 disease Diseases 0.000 title claims abstract description 53
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 53
- 238000004393 prognosis Methods 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000001356 surgical procedure Methods 0.000 title claims description 22
- 238000002600 positron emission tomography Methods 0.000 claims abstract description 64
- 230000002503 metabolic effect Effects 0.000 claims abstract description 57
- 238000004458 analytical method Methods 0.000 claims abstract description 21
- 210000004884 grey matter Anatomy 0.000 claims abstract description 21
- 238000007619 statistical method Methods 0.000 claims abstract description 14
- 230000000877 morphologic effect Effects 0.000 claims abstract description 13
- 238000004590 computer program Methods 0.000 claims description 6
- 230000011218 segmentation Effects 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 5
- 230000001360 synchronised effect Effects 0.000 claims description 4
- 238000000692 Student's t-test Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims description 3
- 238000012353 t test Methods 0.000 claims description 3
- 206010015037 epilepsy Diseases 0.000 description 22
- 206010010904 Convulsion Diseases 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000002397 epileptogenic effect Effects 0.000 description 3
- 210000004885 white matter Anatomy 0.000 description 3
- 206010021034 Hypometabolism Diseases 0.000 description 2
- 206010061334 Partial seizures Diseases 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000001054 cortical effect Effects 0.000 description 2
- 230000001037 epileptic effect Effects 0.000 description 2
- 201000007186 focal epilepsy Diseases 0.000 description 2
- 230000003902 lesion Effects 0.000 description 2
- 238000012805 post-processing Methods 0.000 description 2
- 230000002980 postoperative effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010176 18-FDG-positron emission tomography Methods 0.000 description 1
- 208000019901 Anxiety disease Diseases 0.000 description 1
- 206010003805 Autism Diseases 0.000 description 1
- 208000020706 Autistic disease Diseases 0.000 description 1
- 206010020674 Hypermetabolism Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000036506 anxiety Effects 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 210000003710 cerebral cortex Anatomy 0.000 description 1
- 230000002490 cerebral effect Effects 0.000 description 1
- 210000001175 cerebrospinal fluid Anatomy 0.000 description 1
- 238000007428 craniotomy Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 150000002303 glucose derivatives Chemical class 0.000 description 1
- 230000004153 glucose metabolism Effects 0.000 description 1
- 230000002806 hypometabolic effect Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003068 molecular probe Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000036285 pathological change Effects 0.000 description 1
- 231100000915 pathological change Toxicity 0.000 description 1
- 230000007170 pathology Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 239000000523 sample Substances 0.000 description 1
- 201000000980 schizophrenia Diseases 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000008733 trauma Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
- A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
- A61B6/037—Emission tomography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/501—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of the head, e.g. neuroimaging or craniography
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Biomedical Technology (AREA)
- Pathology (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Molecular Biology (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Biophysics (AREA)
- Heart & Thoracic Surgery (AREA)
- Veterinary Medicine (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Neurology (AREA)
- High Energy & Nuclear Physics (AREA)
- Radiology & Medical Imaging (AREA)
- Optics & Photonics (AREA)
- Neurosurgery (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Dentistry (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Physiology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Nuclear Medicine (AREA)
Abstract
The embodiment of the specification provides a method, a system and a device for judging brain network disease surgical prognosis, 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-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; and (3) keeping registration coordinates corresponding to PET metabolic statistical data of a preset percentage of the whole brain to form a plurality of clusters, and performing brain network disease surgical prognosis analysis according to the average size of the clusters and the number of the clusters.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, a system, and a device for determining a prognosis of brain network disease after surgery.
Background
In the prior art, magnetic resonance negative epilepsy, namely epilepsy with definite brain structure change which cannot be found by magnetic resonance and conventional means, is divided into two major categories of focal epilepsy and generalized/multifocal epilepsy. Among them, focal epilepsy has an opportunity of operative cure, and the seizure-free rate of epilepsy after 10 years of operation is 59% in the latest literature. The overall/multifocal effect has no chance of developing radical focus elimination, and the prognosis is poor. If the possibility of radical operation of a patient is determined through quantitative analysis before an operation, a large number of patients can be prevented from misjudging the trauma of craniotomy, unnecessary examination and medical cost are reduced, and the prior resources are used for a more reasonable conservative treatment scheme.
At present, the method for realizing positioning based on the comparison between PET and a healthy person template in the prior art mainly relies on a Statistical Parametric Mapping (SPM) technical system, which is not accurate for analyzing the affected range of epilepsy, for the following reasons:
(1) the research shows that the epilepsy is mainly the pathological change of the cortical gray matter, and the technical system of the traditional SPM not only incorporates the cortical gray matter, but also incorporates the white matter when comparing healthy people, thereby reducing the statistical precision.
(2) When the SPM is used for comparing the testees, the different tested cerebral sulcus gyrus with different walking directions 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 mainly by looking for areas of reduced glucose metabolism during the interval between seizures in the flesh eye. However, it has limitations in that: (1) physiological hypometabolism areas exist 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 regions to which epilepsy spreads; (3) hypermetabolism may also occur in epileptogenic foci, since this part of the brain region is in the healthy population, i.e. the metabolic level is higher, even if slightly reduced, than other brain regions in the brain. Therefore, the clinical reading of naked eyes cannot realize the accurate judgment of the single mode of the epileptogenic focus and the judgment of the operation prognosis.
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 one-line technology is mainly the post-processing technology under the SPM system. Because different individuals have different sulci gyrus depths and bending degrees, the method system only can unify the widths, lengths and the like of different brains, but still cannot ensure the regular alignment of the same gyrus of different individuals on spatial positions, so that clear registration errors generally exist, and the observation of tiny lesions is hindered.
Epilepsy is a gray-colored pathology. In the traditional method, adjacent cerebrospinal fluid and white matter structures are simultaneously brought into the traditional method during statistics, so that when data are compared and analyzed, the number of mixed factors brought into the data is increased, and the range of focus extraction is not good enough.
Disclosure of Invention
The invention aims to provide a method, a system and a device for judging the prognosis of brain network disease operation, and aims to solve the problems in the prior art.
The invention provides a brain network disease surgery prognosis judgment method, which comprises the following steps:
projecting PET metabolic data of a subject to the same structural space, wherein the subject comprises in particular: patients with brain network diseases 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;
and (3) keeping registration coordinates corresponding to PET metabolic statistical data of a preset percentage of the whole brain to form a plurality of clusters, and performing brain network disease surgical prognosis analysis according to the average size of the clusters and the number of the clusters.
The invention provides a brain network disease surgery prognosis judgment system, 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-structure 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 analysis module is used for reserving registration coordinates corresponding to the PET metabolic statistical data of a preset percentage of the whole brain to form a plurality of clusters, and performing surgical prognosis analysis on the brain network diseases according to the average size of the clusters and the number of the clusters.
The embodiment of the invention also provides a device for judging the prognosis of brain network disease operation, which comprises: the brain network disease surgery prognosis judging method comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the computer program realizes the steps of the brain network disease surgery prognosis judging method when being executed by the processor.
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 when the program is executed by a processor, the steps of the brain network disease surgery prognosis judgment method are implemented.
By adopting the embodiment of the invention, the accurate alignment problem of homologous brain areas 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; accurate projection of brain metabolic data unified space of a single patient and healthy people is achieved, projection difference among testees is fully eliminated, meanwhile, a gray matter range is defined based on 3-dimensional reconstruction segmentation, metabolic signals in the middle of the gray matter are accurately extracted, comparison among samples is conducted, positioning accuracy is improved, and further, the size and the number of the accumulated range are quantitatively analyzed, the surgery prognosis of MR negative epilepsy is accurately judged, and scientific basis is provided for operation or not.
Drawings
In order to solve two key problems of an SPM technical system in the prior art, brain metabolic data of a single patient and healthy people are accurately projected in a unified space, so that the projection difference among subjects is fully eliminated, meanwhile, an grey matter range is defined based on 3-dimensional reconstruction segmentation, a metabolic signal in the middle of the grey matter is accurately extracted, sample-to-sample comparison is carried out, and the accuracy of judging the epilepsy accumulation range is improved. And further, the operation prognosis of the MR negative epilepsy is accurately judged by quantitatively analyzing the size and the number of the accumulated range.
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings used in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments described in the present specification, and that other drawings may be obtained by those skilled in the art without inventive labor.
FIG. 1 is a flowchart of a method for determining a prognosis of brain network disease after surgery according to an embodiment of the present invention;
FIG. 2 is a graph showing PET values at 3D brain surface vertices according to an embodiment of the present invention;
FIG. 3a is a graph of the results of an analysis in a non-postoperative patient according to an embodiment of the present invention;
FIG. 3b is a graph of the results of an analysis in a patient with an episode after surgery according to an embodiment of the present invention;
FIG. 4a is a graph of the results of a traditional first line technique post-operative non-responder analysis in a patient according to an embodiment of the present invention;
FIG. 4b is a graph of the results of an analysis in a seizure patient after conventional first line techniques in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a system for determining the prognosis of brain network disease after surgery, according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a brain network disease surgery prognosis determination device 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, there is provided a method for determining surgical prognosis of brain network disease, fig. 1 is a flowchart of the method for determining surgical prognosis of brain network disease according to the embodiment of the present invention, as shown in fig. 1, the method for determining surgical prognosis of 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; step 101 specifically includes:
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 T1D space, and performing brain structure reconstruction and grey-white segmentation of the T1D space through a T1 3D reconstruction algorithm of FreeSharfer technical standard based on the 3D MR T1 weighted data, wherein the same structural space comprises the T1D space and the T1 3D 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 T1D space and the T1D space, the morphological difference of the brain structure between the testees is eliminated through a synchronous volume and surface CVSR algorithm, and the homostructure registration of the brain structure between the testees 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. Wherein the predetermined statistical method specifically comprises one of: z-transformation and t inspection;
and step 104, reserving registration coordinates corresponding to PET metabolic statistical data of a preset percentage of the whole brain to form a plurality of clusters, and performing brain network disease surgical prognosis analysis according to the average size of the clusters and the number of the clusters. The predetermined percentage is one-half percent.
Step 104 specifically includes: and if the average size of the clusters is greater than or equal to the threshold value of the number of the preset coordinate points and the number of the clusters is greater than or equal to the threshold value of the number of the preset clusters, determining that the brain network disease has poor surgical prognosis and no surgical indication.
In the embodiment of the invention, the accurate alignment problem of homologous brain areas 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 gray matter structure are extracted for statistics, and the method is more suitable for the attribute that the epilepsy is gray matter disease. The method provided by the research further obtains the accumulated range and the number of the epileptic diseases, and further judges the operation prognosis.
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. And the operation prognosis of the MR negative epilepsy is accurately judged by quantitatively analyzing the size and the number of the accumulated range. The method comprises the following specific steps:
step 1, pretreatment.
1. Space division:
the embodiment of the invention requires that the patient has 3D MR T1 weighted data at the same time, which 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 T1D space;
based on the 3D MR T1 data, brain structure reconstruction and grey-white substance segmentation are carried out through a T1D reconstruction algorithm of FreeScherfer technical standard.
2. Inter-subject registration, unifying structural space of patients and healthy people
Inter-subject Registration is further performed by the 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;
3. extracting the PET metabolic information of the grey matter median.
Fig. 2 is a diagram illustrating the PET values at the vertices of the 3D brain surface according to an embodiment of the present invention, and as shown in fig. 2, the PET signals are interpolated into corresponding structural coordinates based on the inter-subject registration coordinates of the structure, and vertex-to-vertex statistics are 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). The statistical methods include, but are not limited to: z-transform (standard deviation of statistical patients from normal human-averaged mean), t-test of single patients on healthy population, etc.;
and 2, carrying out prognosis prediction analysis.
In the present example, the coordinates corresponding to the 0.5% metabolic value of the whole brain were retained to form clusters, and the prognosis of epilepsy was analyzed based on the average size of clusters and the number of clusters. The average Cluster is more than 1000 coordinate points (voxels and fixed points), and the Cluster number is more than 5, thus prompting that the prognosis is poor and no surgical indication is available. As shown in fig. 3a, fig. 3b, fig. 4a and fig. 4b, the analysis result of the conventional first-line method in the post-operation patients with seizures is not significantly different from the analysis result of the non-operation patients, while the analysis result of the embodiment of the present invention in the post-operation patients with seizures is significantly different from the analysis result of the non-operation patients.
In addition, based on the embodiment of the present invention, a normative can be added according to different PET molecular probes for determining the prognosis of surgery for other brain network diseases such as parkinson, depression, schizophrenia, autism, and anxiety.
In conclusion, the embodiment of the invention realizes the position matching between the brain of the patient and the brain homomorphic structure of the healthy person by eliminating the brain morphological difference among the subjects, thereby providing a basis for further quantitative statistics; providing a metabolic value in accurate grey matter extraction to count the degree of deviation of a patient from a normal population; by the quantification method, the size and the number of the lesions are observed, and the judgment of the surgery prognosis is realized.
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 only be 47%, and the positioning rate can reach 87% by the technical scheme of the embodiment of the invention, so that the problem of the 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 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 that epilepsy is a 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. And further acquiring the accumulated range and number of the epileptic diseases, and further judging the operation prognosis.
System embodiment
According to an embodiment of the present invention, there is provided a brain network disease surgery prognosis determination system, fig. 5 is a schematic diagram of the brain network disease surgery prognosis determination system according to the embodiment of the present invention, as shown in fig. 5, the brain network disease surgery prognosis determination system according to the embodiment of the present invention specifically includes:
a projection module 50 for 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; the projection module 50 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 T1D space, and performing brain structural reconstruction and grey-white substance segmentation of the T1D space through a T1D reconstruction algorithm of FreeSurfer technical standard based on the 3D MR T1 weighted data, wherein the same structural space comprises the T1D space and the T1 3D space.
A registration module 52, configured to eliminate morphological differences between subjects in the same structural space, and perform orthotopic structural registration between the subjects; the registration module 52 is specifically configured to:
based on the brain structure information in the T1D space and the T1D space, the morphological difference of the brain structures among the testees is eliminated through a synchronous volume and surface CVSR algorithm, and the homomorphic structure registration of the brain structures among the testees is carried out.
An extracting module 54, configured to specifically extract 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 54 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-test.
And the analysis module 56 is used for reserving the registration coordinates corresponding to the PET metabolic statistical data of the predetermined percentage of the whole brain to form a plurality of clusters, and performing the brain network disease surgery prognosis analysis according to the average size of the clusters and the number of the clusters. Wherein the predetermined percentage is one-half percent. The analysis module 56 is specifically configured to: and if the average size of the clusters is larger than or equal to the threshold value of the number of the preset coordinate points and the number of the clusters is larger than or equal to the threshold value of the number of the preset clusters, determining that the brain network disease has poor surgical prognosis and no surgical indication.
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
An embodiment of the present invention provides a device for determining surgical prognosis of brain network diseases, as shown in fig. 6, including: a memory 60, a processor 62 and a computer program stored on the memory 60 and executable on the processor 62, which computer program, when executed by the processor 62, performs the steps as described in the method embodiments.
Example II of the device
An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored, and when the program is executed by a processor 62, the program implements the steps described in the method embodiment.
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 judging the prognosis of brain network disease operation is characterized by comprising 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;
based on the registration coordinates among the subjects in the same structural space, specifically extracting PET (positron emission tomography) metabolic data of gray matter, and performing statistical analysis to obtain PET metabolic statistical data;
and (3) reserving registration coordinates corresponding to PET metabolic statistical data of a preset percentage of the whole brain to form a plurality of clusters, and performing brain network disease surgical prognosis analysis according to the average size of the clusters and the number of the clusters.
2. The method of claim 1, wherein projecting the PET metabolic data of the subject 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 T1D space, and performing brain structural reconstruction and grey-white substance segmentation of the T1D space through a T1D reconstruction algorithm of FreeSurfer technical standard based on the 3D MR T1 weighted data, wherein the same structural space comprises the T1D space and the T1 3D 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 T1D space and the T1D space, the morphological difference of the brain structure between the testees is eliminated through a synchronous volume and surface CVSR algorithm, and the homostructure registration of the brain structure between the testees 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 the predetermined percentage is one-half percent.
7. The method of claim 1, wherein performing a surgical prognostic analysis of brain network disease based on the average size of clusters and the number of clusters specifically comprises:
and if the average size of the clusters is larger than or equal to the threshold value of the number of the preset coordinate points and the number of the clusters is larger than or equal to the threshold value of the number of the preset clusters, determining that the brain network disease has poor surgical prognosis and no surgical indication.
8. A brain network disease surgery prognosis determination system, comprising:
a projection module for projecting the PET metabolic data of a subject to the same structural space, wherein the subject comprises in particular: patients with brain network diseases 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 analysis module is used for reserving the registration coordinates corresponding to the PET metabolic statistical data of the whole brain in a preset percentage to form a plurality of clusters, and performing surgical prognosis analysis on the brain network diseases according to the average size of the clusters and the number of the clusters.
9. A brain network disease surgery prognosis determination device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the brain network disease surgical prognosis method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores thereon an information transfer implementation program, which when executed by a processor implements the steps of the brain network disease surgery prognosis method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210859838.0A CN115312181B (en) | 2022-07-22 | 2022-07-22 | Prognosis judging method, system and device for brain network disease operation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210859838.0A CN115312181B (en) | 2022-07-22 | 2022-07-22 | Prognosis judging method, system and device for brain network disease operation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115312181A true CN115312181A (en) | 2022-11-08 |
| CN115312181B CN115312181B (en) | 2023-05-09 |
Family
ID=83857630
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210859838.0A Active CN115312181B (en) | 2022-07-22 | 2022-07-22 | Prognosis judging method, system and device for brain network disease operation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115312181B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105816192A (en) * | 2016-03-03 | 2016-08-03 | 王雪原 | Method for three-dimensional registration and brain tissue extraction of individual human brain multimodality medical images |
| CN112967797A (en) * | 2021-02-02 | 2021-06-15 | 上海全景医学影像诊断中心有限公司 | Method for evaluating efficacy of smog surgery |
| CN113571195A (en) * | 2021-07-20 | 2021-10-29 | 南京脑科医院 | Early Alzheimer disease prediction model based on cerebellar function connection characteristics |
| CN114334130A (en) * | 2021-12-25 | 2022-04-12 | 浙江大学 | PET molecular image computer-aided diagnosis system based on brain symmetry |
-
2022
- 2022-07-22 CN CN202210859838.0A patent/CN115312181B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105816192A (en) * | 2016-03-03 | 2016-08-03 | 王雪原 | Method for three-dimensional registration and brain tissue extraction of individual human brain multimodality medical images |
| CN112967797A (en) * | 2021-02-02 | 2021-06-15 | 上海全景医学影像诊断中心有限公司 | Method for evaluating efficacy of smog surgery |
| CN113571195A (en) * | 2021-07-20 | 2021-10-29 | 南京脑科医院 | Early Alzheimer disease prediction model based on cerebellar function connection characteristics |
| CN114334130A (en) * | 2021-12-25 | 2022-04-12 | 浙江大学 | PET molecular image computer-aided diagnosis system based on brain symmetry |
Non-Patent Citations (1)
| Title |
|---|
| KUN GUO等: ""Assessment of localization accuracy and postsurgical prediction of simultaneous 18F-FDG PET/MRI in refractory epilepsy patients"" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115312181B (en) | 2023-05-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113571195B (en) | Early Alzheimer disease prediction model based on cerebellar function connection characteristics | |
| Eskildsen et al. | BEaST: brain extraction based on nonlocal segmentation technique | |
| US8280482B2 (en) | Method and apparatus for evaluating regional changes in three-dimensional tomographic images | |
| Somasundaram et al. | Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations | |
| JP6034299B2 (en) | System for estimating penumbra size and method of operation thereof | |
| Leung et al. | Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease | |
| CN110689536B (en) | Brain grey matter and white matter tracking method and device based on multi-mode magnetic resonance image | |
| Duchesne et al. | Amnestic MCI future clinical status prediction using baseline MRI features | |
| WO2011137370A2 (en) | Intelligent atlas for automatic image analysis of magnetic resonance imaging | |
| Klein et al. | Early diagnosis of dementia based on intersubject whole-brain dissimilarities | |
| CA2752370A1 (en) | Segmentation of structures for state determination | |
| Li et al. | Mapping fetal brain development based on automated segmentation and 4D brain atlasing | |
| Mega et al. | Automated brain tissue assessment in the elderly and demented population: construction and validation of a sub-volume probabilistic brain atlas | |
| Niaz et al. | Development and evaluation of a high resolution 0.5 mm isotropic T1-weighted template of the older adult brain | |
| Beheshti et al. | Accurate lateralization and classification of MRI-negative 18F-FDG-PET-positive temporal lobe epilepsy using double inversion recovery and machine-learning | |
| EP0832468B1 (en) | Apparatus for image enhancement, and related method | |
| CN115312181B (en) | Prognosis judging method, system and device for brain network disease operation | |
| KR102349360B1 (en) | Diagnosis Method and System of Idiopathic Normal Pressure Hydrocephalus Using Imaging Diagnostic Equipment | |
| Ranjbar et al. | Robust automatic whole brain extraction on magnetic resonance imaging of brain tumor patients using dense-Vnet | |
| Bae et al. | A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding | |
| CN115005802B (en) | Method, system and device for positioning initial part of brain network disease onset | |
| Kumari et al. | Analysis of Biomedical Image for Alzheimer's Disease Detection | |
| Leiberg et al. | Effects of anterior temporal lobe resection on cortical morphology | |
| 이수빈 | Texture of magnetic resonance images as an early biomarker of Alzheimers disease | |
| Eskildsen et al. | Improving prediction of Alzheimer's disease using patterns of cortical thinning and homogenizing images according to disease stage |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |