Electrode dissection positioning system based on multi-modal image fusion

Abstract

The invention relates to an intelligent medical instrument, in particular to an electrode anatomy positioning system before intractable epilepsy lesion resection operation, which is used for solving the problems that before resection operation, intracranial electrode contact anatomy attribute judgment is lack of standards, classification depends on artificial experience and is difficult to repeat, and multi-modal images are lack of fusion and quantitative analysis. According to the scheme, brain segmentation is carried out based on a T1w image of a patient before electrode implantation, and each brain region segmentation image is made into a mask image; rigid body registration is carried out based on the brain CT with the implanted electrode and the T1w image, an electrode entry point and a target point are marked, an electrode contact is reconstructed, then the position of the contact is judged according to the coordinates of the electrode contact and the mask image, electrode classification and positioning are achieved, a doctor can conveniently distinguish the source of an intracranial electroencephalogram signal, and then the position of a focus is determined. In addition, the position relation and the metabolism condition of the electrode contacts and the cortex layers of the different brain partitions can be clearly checked by fusing multi-mode images.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to intelligent medical instruments, and in particular to an electrode anatomical positioning system for preoperative epileptogenic focus resection. BACKGROUND

[0002] Intracranial monitoring of epilepsy patients requires the implantation of multiple electrodes, and each electrode has 6-15 electrode contacts. In order to accurately carry out surgical planning and electrode contact reconstruction, it is necessary to mark the electrode points one by one against the CT image. However, the existing contact reconstruction is completed manually by electrode CT, which not only takes a long time, but also cannot quantify the brain region position of the electrode contact.

[0003] In addition, although the existing technology supports multi-modal image registration and foreground-background overlap for data browsing, and can also perform 3D visualization of the brain surface, it cannot clearly see the brain region where the contact is located, which is not conducive to the doctor to confirm the lesion. SUMMARY

[0004] In order to solve the problems of lack of consistent standard for anatomical attribute determination of intracranial electrode contacts, dependence on artificial experience for classification results, difficulty in repetition, lack of quantitative analysis of preoperative multi-modal images, and insufficient fusion interaction in preoperative planning of intractable epilepsy, the present disclosure proposes an electrode anatomical positioning system based on multi-modal image fusion to improve the standardization level and repeatability of preoperative planning. On this basis, PET metabolic data is mapped to the cortical surface to render the cortical surface structure and metabolism integrated browsing.

[0005] In a first aspect, the present disclosure proposes an electrode anatomical positioning system, comprising: a mask making module configured to perform brain segmentation based on a preoperative T1w image of a patient, and make a mask image based on each brain region segmentation image; an electrode reconstruction module configured to rigidly register a brain CT after implanting an electrode to the preoperative T1w image, mark the electrode entry point and target point after registration, and perform electrode contact reconstruction based on the marked electrode entry point and target point; a positioning module configured to classify the contacts according to the mask image and the reconstructed electrode contact coordinates, the classification being to determine whether the contacts are located in the gray matter, white matter or extracerebral structure; and determine the brain region to which each contact belongs according to the partition label of each segmented brain region, to realize the positioning of the contact brain region. By realizing the classification and positioning of the electrode, it is convenient for the doctor to identify the source of the intracranial electroencephalogram signal, and then determine the lesion position.

[0006] In an embodiment of the above technical solution, the electrode contact reconstruction based on the marked electrode entry point and target point comprises: based on the marked electrode entry point and target point, using the fixed ratio point division method and electrode spacing calculation to reconstruct the complete electrode contact coordinates, the formula being as follows:

[0007]

[0008]

[0009] wherein d is the spatial distance between the entry point and the target point, 、 、 is the three-dimensional coordinate of the electrode entry point, 、 、 is the three-dimensional coordinate of the electrode target point, and D is the electrode contact spacing, is the inter-channel distance ratio, x’ 、 y’ 、 z’ is the three-dimensional coordinate of the reconstructed electrode contact.

[0010] In an embodiment of the above technical solution, the step of determining whether the contact is located in the white matter, the grey matter, the subcortical structure or outside the brain comprises: converting the anatomical coordinate system to the image coordinate system, constructing a first preset voxel size cube region centered on each electrode contact in the thalamus mask space, and determining that the electrode contact is located in the thalamus if the total number of voxels in the region is greater than zero in the thalamus mask; on this basis, constructing a second preset voxel size neighborhood in the white matter mask, the second preset voxel size being smaller than the first preset voxel size, and more finely calculating the proportion of local white matter voxels, and determining that the electrode contact is located in the white matter if the proportion is greater than a first preset value; for the electrode determined to be located in the thalamus and not in the white matter, creating a first preset voxel size cube space in the cortex mask space, and determining that the electrode contact is located in the grey matter if the number of voxels in the cube space exceeds a second preset value of the total number of voxels in the space, otherwise determining that the electrode contact is located in the subcortical structure.

[0011] In an embodiment of the above technical solution, after determining the brain region to which each contact belongs according to the partition label of the brain segmentation, the electrode coordinates are registered to the standard brain surface space.

[0012] In an embodiment of the above technical solution, the system comprises a fusion module configured to implement the following information processing: after the PET metabolic value of the patient is corrected for volume effect, the value is registered to the cortex space of the patient, and the normalized uptake value compared with the cerebellum is calculated; the normalized uptake value is transmitted to the reconstructed cortex surface to realize the fusion of metabolic data and brain structure.

[0013] In an embodiment of the above technical solution, the system comprises a visualization module configured to perform cortical visualization of brain metabolism values after fusion of the metabolic data and the brain structure, superimpose three-dimensional information of a cortical surface, partition labels and electrode position information to present the brain structure and construct a three-dimensional interactive visualization interface, the visualization interface realizes adjustable transparency of the cortical surface, adjustable display and hiding of the partition labels, shieldable high-contrast area around the electrodes and / or color labeling of the electrodes according to the brain regions or functional regions to which the electrodes belong. The visualization can solve the problem of insufficient fusion interactivity and clearly show the position relationship between the electrode contacts and the cortical surface of different brain partitions.

[0014] In an embodiment of the above technical solution, the brain is segmented based on a preoperative T1w image of the patient, and a mask image is made based on the segmented images of the brain regions, and the steps include: segmenting the T1w to obtain an endbrain image, a white matter image and a cortical image; removing non-endbrain regions from the endbrain segmented image, the non-endbrain regions including the brainstem, cerebellum, ventricle, cerebrospinal fluid and choroid plexus, to form an endbrain mask; setting a threshold for the white matter image, setting the image higher than the threshold to 0 to remove the ventricle signal and normalize the voxel intensity, to generate a binary white matter mask; setting voxels with a value greater than 0 in the cortical image to 1 and then converting to a binary mask.

[0015] In an embodiment of the above technical solution, the brain region to which each contact belongs is determined according to the partition label of each segmented brain region, and the steps include: obtaining a preset space centered on the contact according to the position coordinates of the gray matter and subcortical structure contacts and the cortical anatomical partition, judging the brain region where the space is located, and assigning the contact with the brain region label.

[0016] In a second aspect, the disclosure provides a computer-readable storage medium storing a computer program capable of being loaded and executed by a processor to perform the information processing of any of the above systems, for realizing the information processing of the present solution.

[0017] The present solution has the advantages of high standardization, good operation repeatability, convenient visualization interaction, etc., and is suitable for application scenarios such as preoperative planning of epilepsy epileptogenic focus resection surgery and research data analysis. The present solution classifies and positions the electrodes implanted in the brain of the patient, which facilitates the doctor to identify the source of the intracranial electroencephalogram signal and further determine the lesion position, and the further visualization design can solve the problem of insufficient fusion interactivity and clearly show the position relationship between the electrode contacts and the cortical surface of different brain partitions. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0019] Figure 1 Flowchart for an embodiment.

[0020] Figure 2 Visualization interface overview.

[0021] Figure 3 Electrode rendering detail. DETAILED DESCRIPTION

[0022] In the existing preoperative planning of intractable epilepsy, the prior art has the following problems: (1) the existing SEEG electrode contact reconstruction method is low in efficiency, needs to be marked manually point by point, and 80-120 contacts need to be marked on average for a single surgery, which takes 2-3 hours; (2) the existing method lacks a unified quantitative judgment standard for the neocortical / subcortical structure / white matter classification of SEEG electrode contacts, which is prone to subjective bias, resulting in poor consistency of electrode evaluation; (3) there is still a lack of a set of interactive and repeatable image fusion and electrode positioning analysis tool chain, which is difficult to meet the needs of clinical preoperative fine evaluation.

[0023] SEEG involved in the above is the abbreviation of stereoelectroencephalogram. Stereoelectroencephalogram is a minimally invasive surgery for identifying seizure brain areas. Through SEEG, the seizure site that cannot be detected by conventional scalp EEG can be identified, and the origin of the seizure can be accurately located. Radiofrequency thermocoagulation treatment of the epileptogenic focus is a conventional minimally invasive surgical treatment.

[0024] In view of the above problems, the present scheme proposes an automatic electrode contact anatomical classification method based on structural images and a dynamic three-dimensional visualization processing technology to improve the standardization level and repeatability of preoperative planning of epileptogenic focus. The present scheme only needs to mark the entry point and target point of each electrode contact, does not need to accurately mark each contact, and automatically generates intermediate contacts. At the same time, according to the electrode contact position and brain segmentation image, a voxel threshold method based on brain segmentation is developed to screen the electrode contacts located in the neocortex / subcortical structure / white matter, and each electrode contact located in the gray matter is assigned to the brain area, which assists surgical planning. Through the visualization rendering method proposed in the present scheme, the position relationship between the electrode contact and the different brain division cortex can be clearly viewed, and the PET metabolic data is mapped to the cortex surface rendering to form an integrated browsing of the cortex surface structure and metabolism.

[0025] The tool used in this scheme is introduced: 3D Slicer as a representative open source medical image analysis platform, which provides multi-modal image browsing, supports simple three-dimensional visualization display, and provides basic electrode contact manual labeling positioning tool.

[0026] The technical scheme of the present application will be described below in conjunction with the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the scope of the present application.

[0027] The present scheme is based on preoperative T1 weighted MRI image of the patient, and proposes an automatic processing flow for preoperative electrode anatomical determination and visualization analysis of epileptogenic focus. Referring to Figure 1 .

[0028] The present scheme first pre-processes the preoperative T1w MRI image, and performs brain segmentation. The generated gray matter, white matter segmentation images are used to make a mask, and the cortical surface is reconstructed, which provides anatomical reference for contact classification and brain area positioning. Then the electrode CT image (POCT) is rigidly registered to the preoperative T1w image, the electrode entry point and target point are marked, and the complete electrode contact coordinates are reconstructed by using the fixed ratio point division method and electrode spacing calculation. According to the reconstructed electrode contact coordinates and the mask file, a cube neighborhood of different sizes is constructed around the contact in the T1w image space, and the multi-threshold voxel analysis method is used to judge whether the contact is located in the gray matter, white matter, subcortical structure or outside the brain, so as to realize automatic classification. According to the cortical partition label, the brain area to which each contact belongs is determined to realize contact positioning. The linear conversion matrix of individual image to standard brain template is used to multiply the individual RAS coordinates of the patient by the conversion matrix, so as to realize the standardization of the contact coordinates, and the visualization of the electrode contact in the standard brain surface space is realized by using the standardized coordinates.

[0029] The PET data is pre-processed, the volume effect correction method is used to eliminate the signal aliasing caused by resolution, then the standardized uptake value (SUV) is calculated, and the metabolic results are mapped to the cortical space for subsequent multi-modal fusion analysis. Finally, the present scheme fuses the PET metabolic value, the T1w cortical model and the contact classification result to construct a three-dimensional interactive visualization interface. The visualization interface realizes the adjustment of cortical transparency, the switching of viewing angle, the control of electrode display and hidden, and the color display of functional area, realizes the preoperative epileptogenic focus positioning and visualization interaction.

[0030] Specifically, first, pre-process the preoperative T1w image, including image standardization and removing image inhomogeneity field, then perform brain segmentation to obtain brain segmentation images including whole brain automatic segmentation image, white matter image and cortex-specific segmentation image. The whole brain automatic segmentation image contains white matter, gray matter, subcortical nuclei (such as hippocampus, thalamus) and other structures. The white matter image includes white matter and ventricle regions and other structures, which is mainly used for subsequent contact classification step as a reference for white matter boundary. The cortex-specific segmentation image file does not contain subcortical nuclei, cerebellum and brainstem segmentation regions, and is mainly used for subsequent subcortical nuclei contact judgment.

[0031] Secondly, the brain segmentation image is retained by intensity normalization and threshold method to obtain the brain mask, endbrain mask and white matter mask. The whole brain automatic segmentation image is pre-processed to remove non-endbrain regions including brainstem, cerebellum, ventricle, cerebrospinal fluid and choroid plexus to form an endbrain mask. According to a preset threshold (for example, the threshold is 200), the ventricle signal greater than the set threshold in the white matter image is removed and the voxel intensity is normalized to generate a binary white matter mask; for the voxels with a voxel value greater than 0 in the cortex-specific segmentation image, the voxel value of the voxel is set to 1, and then converted into a binary mask.

[0032] Then, the electrode CT (POCT) is rigidly registered to the preoperative T1w, and the different electrodes are sequentially labeled and distinguished into entry points and target points. To achieve rigid registration, the electrode CT (POCT) is pre-processed by size cropping and the like.

[0033] The electrode channel position is reconstructed, and according to the entry point and target point positions, the RAS (Right, Anterior, Superior) coordinates of other contacts of the electrode in the preoperative T1w image space are calculated. The distance between two points is calculated using formula (1), and the fixed ratio point division formula 2 is used to calculate the coordinates of other electrode contacts according to the electrode contact spacing and the number of electrode contacts of each electrode, and all electrode channel reconstruction is completed, as shown in formula 3. The reconstructed coordinate space is the RAS coordinate system.

[0034]

[0035] Wherein d is the spatial distance between the entry point and the target point, , , is the three-dimensional coordinate of the electrode entry point, , , is the three-dimensional coordinate of the electrode target point.

[0036]

[0037] Wherein Dfor electrode contact spacing, u for inter-channel distance ratio.

[0038]

[0039] wherein, x’ , y’ , z’ for reconstructing three-dimensional coordinates of electrode contacts.

[0040] Then, using the generated mask and the coordinates of each electrode contact, the SEEG electrode contacts are classified into gray matter, white matter, subcortical structures and outside the brain. Since the electrode contact reconstruction uses an anatomical coordinate system, which is a continuous three-dimensional space sampled from the image, RAS is often used as the 3D reference. For each image data, an image coordinate system is used to create a rectangular point array starting from the top left corner, saving the intensity value of each voxel, the origin and the anatomical coordinate spacing. Therefore, an affine transformation is used to convert the anatomical coordinate system to the image coordinate system, and the transformation formula is shown in equation 2.5.

[0041]

[0042] wherein, is the coordinate of the anatomical coordinate system, is the coordinate of the image coordinate system, A is a transformation matrix carrying spatial direction and axial scaling information, is a vector recording the geometric position of the first voxel.

[0043] A multi-threshold voxel analysis method is used to determine whether the contact is located in the gray matter, white matter, subcortical structure or outside the brain, including: the image space after coordinate system conversion is constructed into a cubic region with a first preset voxel size, such as 5x5x5, with each electrode contact as the center. If the total number of voxels in this region is greater than zero in the endbrain mask, it is determined that the electrode is located in the endbrain. It is tested that using a larger voxel region can better compensate for the problem of not fine segmentation of the frontal region, and the electrode channel located in the superficial layer of the cerebral cortex or the intercerebral gap is retained. On this basis, a second preset voxel size neighborhood is constructed, which is generally smaller than the first preset voxel size, such as 5x5x5. Here, the second preset voxel size is set to 3x3x3, and the local white matter voxel ratio is calculated. If the proportion is greater than the first preset value, such as 40%, it is determined to be a white matter contact. For the electrode channel that has been determined to be located in the endbrain and not in the white matter, a cubic space with a first preset voxel size (5x5x5) is created in the mask space, and it is calculated whether the number of voxels in the cubic space exceeds the second preset value of the entire space, such as 30%, i.e. the number of voxels in the cubic space exceeds 37. If it exceeds the threshold, the channel is located in the gray matter; otherwise, the channel is located in the subcortical structure.

[0044] In order to preserve the relative position between electrode contacts, the SEEG electrode RAS coordinates are mapped from the individual original RAS space to the standard brain surface space using linear registration method. In the registered standard space, according to the position coordinates of the gray matter and subcortical structure contacts and the cortical anatomical division of the Desikan-Killiany Atlas atlas, the brain region position where the preset space (such as 5x5x5 size) centered on the contact is located is judged, and the brain region label of the contact is assigned for contact brain region positioning. If the space belongs to two brain regions, in one embodiment, both brain regions are identified at the same time for further judgment by the doctor. The brain region position includes white matter, gray matter and subcortex. The present scheme uses the Desikan-Killiany Atlas atlas for contact brain region display, which is widely used in neuroscience, brain imaging and clinical diagnosis. The atlas contains 34 cortical regions / hemisphere (a total of 68 regions, left and right symmetry), each region corresponds to a specific gyrus or sulcus (such as superior frontal gyrus, middle temporal gyrus, etc.). Figure 2 For a visualization example, four brain regions located at ctx-lh-fusiform (left fusiform gyrus), ctx-lh-middletemporal (left middle temporal gyrus), ctx-lh-insula (left insular lobe), ctx-rh-middletemporal (right middle temporal gyrus) are displayed. Figure 3 For an enlarged view.

[0045] After the PET metabolic value of the patient is corrected by volume effect, it is registered to the patient's cortical space, and the standardized uptake value (SUV) compared with the cerebellum is calculated. The data values (such as cortical thickness, curvature, etc.) of the patient's individual cortical data are mapped to the cortical space (MRI surface RAS), and the cortical feature values are combined with the brain cortex geometric model to assign values to each vertex of the reconstructed brain cortex surface, and the feature activation of each brain surface vertex is intuitively displayed on the brain surface through color mapping. The SUV is transmitted to the brain surface model as source data to realize data fusion, complete cortical visualization of brain metabolism, solve the problem of insufficient fusion interactivity, clearly view the relationship between electrode contacts and different brain partition cortical positions, and map PET metabolic data to the cortical surface rendering to form an integrated browsing of cortical surface structure and metabolism.

[0046] In order to realize the interactive visualization display of preoperative data, the present scheme constructs a three-dimensional fusion interface to three-dimensionally superimpose the cortical surface, partition label and electrode position information to present the brain structure. In the three-dimensional fusion interface, the cortical model transparency (0%-100%) can be adjusted in real time through the sliding bar, the display and hiding of the electrode label can be controlled by one key, the standard observation view angle such as frontal view can be reset, and the high-contrast area around the electrode can be shielded to optimize the rendering effect, such as Figure 3The electrodes are colored and labeled according to the brain area or functional area, facilitating preoperative fine positioning and decision support. The scheme has the advantages of high standardization, good operation repeatability, and convenient visual interaction, and is suitable for application scenarios such as clinical epilepsy focus surgery planning and research data analysis.

[0047] The accurate electrode classification and positioning of the patient implanted with the electrode in the application can help doctors identify the source of intracranial brain electrical signals, and thus is not limited to the determination of the position of the refractory epilepsy focus, but can also be used for the determination of the position of other brain disease foci, and can also be used for the transmission of brain electrical signals to the patient by external application.

[0048] The system of the application can be a computer program product, and the system of the application is informationized. The computer program product can include a computer readable storage medium, which is loaded with computer readable program instructions for enabling a processor to implement various aspects of the application.

[0049] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or a concave-convex structure in a slot, and any suitable combination of the above. The computer readable storage medium used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (for example, an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0050] The computer readable program instructions described herein can be downloaded from the computer readable storage medium to each computing / processing device, or to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer readable program instructions from the network and forwards the computer readable program instructions to the computer readable storage medium for storage in the computing / processing device.

[0051] Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0052] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0053] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0054] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0055] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logic functions. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0056] Embodiments of the application have been described above. The description is illustrative of the embodiments of the application and is not meant to be limiting. Numerous modifications and variations are possible in light of the above teachings without departing from the scope and spirit of the described embodiments of the application. No limitation is intended to the details of construction or design except as described in the claims which follow.

Claims

1. An electrode anatomical positioning system, comprising: The system comprises a mask making module configured to perform brain segmentation based on a preoperative T1w image of a patient, and to make a mask image based on the segmented image of each brain region; An electrode reconstruction module configured to rigidly register a brain CT after implanting an electrode to the preoperative T1w image, mark the registered electrode entry point and target point, and reconstruct the electrode contact based on the marked electrode entry point and target point; A positioning module configured to classify the contacts according to the mask image and the reconstructed electrode contact coordinates, and determine the brain region to which each contact belongs according to the partition label of each segmented brain region, so as to realize the positioning of the contacts in the brain region. The electrode contact reconstruction based on the marked electrode entry point and target point comprises: reconstructing the complete electrode contact coordinates based on the marked electrode entry point and target point by using the proportional point method and the electrode spacing calculation, and the formula is as follows: The judgment of whether the contact is located in the white matter, gray matter, subcortical structure or outside the brain is based on the mask image and the reconstructed electrode contact coordinates, and the electrode contact is divided into white matter, gray matter, subcortical structure or outside the brain, and the steps comprise:

2. The system of claim 1, wherein, Convert the anatomical coordinate system to the image coordinate system, construct a first preset voxel size cube region centered on each electrode contact in the endbrain mask space, and if the total number of voxels in the region is greater than zero in the endbrain mask, it is considered that the electrode contact is located in the endbrain; wherein d is the spatial distance between the entry point and the target point, , , is the three-dimensional coordinate of the electrode entry point, , , is the three-dimensional coordinate of the electrode target point, D is the electrode contact distance, is the inter-channel distance ratio, x’ , y’ , z’ is the three-dimensional coordinate of the reconstructed electrode contact.

3. The system of claim 1, wherein, On this basis, a second preset voxel size neighborhood is constructed in the white matter mask, the second preset voxel size is smaller than the first preset voxel size, the local white matter voxel proportion is calculated, and if the proportion is greater than a first preset value, it is considered that the electrode contact is located in the white matter; For the electrodes that have been judged to be located in the endbrain and not in the white matter, a first preset voxel size cube space is created in the cortical mask space, and if the number of voxels in the cube space exceeds a second preset value of the total space voxels, the electrode contact is located in the gray matter, otherwise the electrode contact is located in the subcortical structure. After determining the brain region to which each contact belongs according to the partition label of the brain segmentation, the electrode coordinates are registered to the standard brain surface space. The system comprises a fusion module configured to implement the following information processing:

4. The system of claim 1, wherein, After the PET metabolic value of the patient is corrected by the volume effect, it is registered to the cortical space of the patient, and the standardized uptake value compared with the cerebellum is calculated; the standardized uptake value is transmitted to the reconstructed cortical surface to realize the fusion of metabolic data and brain structure.

5. The system of claim 1, wherein, The system comprises a visualization module configured to perform cortical visualization of metabolic data after the fusion of metabolic data and brain structure, superimpose the cortical surface, partition label and electrode position information to present the brain structure in three dimensions, and construct a three-dimensional interactive visualization interface, which realizes adjustable transparency of the cortical surface, adjustable display and hiding of the partition label, shieldable high-contrast area around the electrode, and / or color labeling of the electrode according to the brain region or functional region to which it belongs. The system comprises a mask making module configured to perform brain segmentation based on a preoperative T1w image of a patient, and to make a mask image based on the segmented image of each brain region; 6. The system of claim 5, wherein, The T1w is segmented to obtain endbrain images, white matter images and cortical images; 7. The system of claim 1, wherein, ​ ​ The whole brain is automatically segmented to remove non-end brain regions, including the brain stem, cerebellum, brain ventricles, cerebrospinal fluid and choroid plexus, to form an end brain mask; A white matter image threshold is set, and images higher than the threshold are set to 0 to remove ventricle signals and normalize voxel intensity, generating a binary white matter mask; The voxels in the cortex image with a voxel value greater than 0 are set to 1 and then converted into a binary mask.

8. The system of claim 1, wherein, According to the partition labels of each segmented brain region, the brain region to which each contact belongs is determined, and the steps include: According to the position coordinates of the gray matter and subcortical structure contacts and the cortical anatomical partition, a preset space centered on the contact is obtained, the brain region position in which the space is located is determined, and the contact is assigned a brain region label.

9. A computer-readable storage medium, characterized in that: The computer program capable of being loaded and executed by the processor to perform information processing of the system according to any one of claims 1 to 8 is stored.

Images