A method for generating an electrode trajectory and a related device

Abstract

The present invention discloses a method for generating an electrode trajectory and a related device. The electrode is implanted in a user's target area; the method for generating the electrode trajectory includes: obtaining an image file of the user's target area, the image file including image data of different positions of the target area, extracting electrode data of each electrode in the image data, the electrode data including at least electrode coordinates; fitting the electrode data of the electrode to obtain an initial electrode trajectory of the electrode; and fusing the initial electrode trajectory according to the electrode configuration parameters of the electrode to obtain a target electrode trajectory of the electrode. The present invention not only improves the accuracy and applicability of the electrode trajectory, but also enables an intuitive understanding of the layout and morphology of the electrode.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a method for generating an electrode trajectory and a related device. Background Art

[0002] In modern medical technology, especially in neuroscience and neurostimulation therapy, accurate visualization tools are crucial for physicians to plan surgeries, adjust programming parameters, and evaluate treatment outcomes. Electrode implantation is an effective treatment for a variety of neurological disorders, such as Parkinson's disease and epilepsy. The accuracy of its placement and morphology is directly related to treatment efficacy and the patient's quality of life. However, current techniques for visualizing electrode trajectories face a number of challenges.

[0003] Existing image files, such as the widely used DICOM (Digital Imaging and Communications in Medicine) format, are typically stored in a slice-by-slice structure. While this structure can capture three-dimensional information within the human body, it often results in distortion in electrode data due to the limited angles at which data is collected. This distortion is particularly pronounced when electrodes are implanted at depth or at complex angles, compromising accurate determination of electrode position and morphology.

[0004] Rendering electrodes is a key task in the visualization programming process. The relative positional relationship between the electrodes and specific nuclei in the brain is crucial for doctors to adjust stimulation parameters, because even slight positional deviations can have a significant impact on the treatment effect. By analyzing DICOM images, doctors can extract the coordinate positions of the electrodes in the patient's brain and build an electrode model based on these coordinate information. However, traditional electrode rendering methods can simplify the rendering process when dealing with electrodes of the same model (because there is no need to distinguish between different models of electrodes), but they are unable to deal with electrode position deviations caused by data acquisition distortion.

[0005] In order to solve at least one of the above problems, the present invention proposes an electrode trajectory generation method and related devices. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for generating an electrode trajectory and a related device, which can not only improve the accuracy and applicability of the electrode trajectory, but also enable an intuitive understanding of the layout and morphology of the electrodes.

[0007] The purpose of the present invention is achieved by the following technical solutions:

[0008] In one aspect, the present invention provides a method for generating an electrode trajectory, wherein the electrode is implanted at a target site of a user;

[0009] The method for generating the electrode trajectory includes:

[0010] Acquire an image file of a target part of the user, the image file including image data of different positions of the target part, and extract electrode data of an electrode in each of the image data, the electrode data including at least electrode coordinates;

[0011] Fitting and processing the electrode data of the electrode to obtain an initial electrode trajectory of the electrode;

[0012] The initial electrode trajectory is fused according to the electrode configuration parameters of the electrode to obtain the target electrode trajectory of the electrode.

[0013] The beneficial effects of the above scheme are: by obtaining the image file of the target part and extracting the electrode data (such as electrode coordinates), the electrode position can be accurately captured; the target electrode trajectory obtained by fusing the initial electrode trajectory and the electrode configuration parameters can not only improve the accuracy and applicability of the electrode trajectory, but also enable an intuitive understanding of the layout and morphology of the electrode.

[0014] Furthermore, the fitting process of the electrode data of the electrode to obtain the initial electrode trajectory of the electrode includes:

[0015] preprocessing electrode data of the electrodes;

[0016] Based on a fitting algorithm, the preprocessed electrode data of the electrode is fitted to obtain an initial electrode trajectory of the electrode.

[0017] The beneficial effect of the above scheme is that by preprocessing the electrode data and using the fitting algorithm, a more accurate and smoother initial electrode trajectory can be obtained, which provides a basis for subsequent optimization and fusion processing.

[0018] Furthermore, the preprocessing includes at least one of the following: noise removal, smoothing and data cleaning;

[0019] The fitting algorithm includes at least one of the following: least squares method, nonlinear regression and genetic algorithm.

[0020] The beneficial effects of the above scheme are: the preprocessing steps (such as noise removal, smoothing and data cleaning) improve the accuracy of the data; and the selection of multiple fitting algorithms (such as least squares method, nonlinear regression and genetic algorithm) can obtain the best fitting effect according to the actual situation.

[0021] Furthermore, the extracting electrode data of each electrode in the image data includes:

[0022] Extracting electrode position data from each of the image data according to electrode identification features, wherein the electrode identification features include voxel values ​​of the electrodes;

[0023] Obtaining an electrode coordinate set in the image file based on the electrode position data and a positional relationship between all the image data;

[0024] Based on a preset conversion matrix, the electrode position coordinate set is converted into a preset reference coordinate system to obtain a target electrode coordinate set.

[0025] The beneficial effect of the above scheme is that by extracting electrode position data through electrode identification features and converting it into a reference coordinate system, a unified and accurate electrode coordinate set can be obtained, which not only provides a reliable data basis for subsequent processing, but also improves the applicability of this application.

[0026] Furthermore, the performing fusion processing on the initial electrode trajectory according to the electrode configuration parameters of the electrode to obtain the target electrode trajectory of the electrode includes:

[0027] determining an electrode model of the electrode according to electrode configuration parameters of the electrode, wherein the electrode configuration parameters include at least the size of the electrode sheet, the spacing between adjacent electrode sheets, and the number of electrode sheets;

[0028] The implantation end of the initial electrode track and the implantation end of the electrode model are aligned to obtain a fused target electrode track.

[0029] The beneficial effect of the above solution is that the electrode model is determined according to the electrode configuration parameters, and the initial electrode trajectory is fused with the electrode model to obtain a target electrode trajectory that conforms to the actual electrode trajectory.

[0030] Furthermore, after obtaining the target electrode trajectory of the electrode, the method for generating the electrode trajectory further includes:

[0031] determining a target biological tissue through which the electrode passes based on a preset electrode implantation path of the electrode;

[0032] determining the carrying capacity of different target biological tissues for the electrode;

[0033] determining, based on the load-bearing capacity, bending coefficients of electrodes at different positions in the electrode implantation path;

[0034] The target electrode trajectory is optimized according to the bending coefficient to obtain an optimized target electrode trajectory.

[0035] The beneficial effect of the above scheme is that by considering the load-bearing capacity of different biological tissues on electrodes and determining the bending coefficient of electrodes at each location, the target electrode trajectory can be optimized to make it more consistent with the principles of biomechanics, so as to obtain a target electrode trajectory that matches the actual electrode trajectory.

[0036] Furthermore, the determining of the target biological tissue through which the electrode passes based on the preset electrode implantation path of the electrode includes:

[0037] Extracting biological tissue data of a target part from the image data, and constructing a three-dimensional biological tissue model of the target part based on the biological tissue data;

[0038] The electrode implantation path is fused with the biological tissue three-dimensional model to determine the target biological tissue through which the electrode passes.

[0039] The beneficial effect of the above scheme is that by constructing a three-dimensional model of biological tissue and fusing it with the electrode implantation path, the target biological tissue through which the electrode passes can be clearly understood, providing a basis for optimized processing.

[0040] Furthermore, determining the carrying capacity of different target biological tissues for the electrode includes:

[0041] acquiring target biological tissue parameters of the target biological tissue and electrode configuration parameters of the electrode;

[0042] Based on the principles of biomechanics, a biomechanical model is established according to the target biological tissue parameters and the electrode configuration parameters, and the interaction between the target biological tissue and the electrode is analyzed according to the biomechanical model to obtain the bearing capacity of the target biological tissue on the electrode.

[0043] The beneficial effects of the above scheme are: by establishing a biomechanical model based on biomechanical principles and analyzing the interaction between the target biological tissue and the electrode, the bearing capacity of the target biological tissue on the electrode can be obtained, providing key parameters for optimizing the target electrode trajectory.

[0044] Furthermore, the target biological tissue parameter includes at least one of the following: texture of the target biological tissue, structure of the target biological tissue, and blood supply of the target biological tissue; and / or,

[0045] The electrode configuration parameters include at least one of the following: the spacing between adjacent electrode sheets, the number of electrode sheets, the size of the electrode sheets, the shape of the electrode sheets, the material properties of the electrode sheets, and the implantation method of the electrode sheets.

[0046] The beneficial effect of the above scheme is that it lists in detail the factors that affect the load-bearing capacity of the target biological tissue, including the texture, structure, blood supply and electrode configuration parameters of the biological tissue, providing comprehensive data support for the establishment of the biomechanical model.

[0047] Furthermore, analyzing the interaction between the target biological tissue and the electrode according to a biomechanical model includes:

[0048] Convert the biomechanical model into a finite element analysis model;

[0049] Based on a preset biomechanical database, the finite element analysis model is solved using finite element analysis software to obtain the interaction between the target biological tissue and the electrode sheet of the electrode;

[0050] Based on a preset relationship curve, the bearing capacity of each region of the target biological tissue on the electrode sheet is determined according to the interaction between the target biological tissue and the electrode sheet.

[0051] The beneficial effect of the above scheme is that by converting the biomechanical model into a finite element analysis model and solving it using finite element analysis software, the interaction between the target biological tissue and the electrode can be obtained, providing a scientific method for determining the bearing capacity.

[0052] Furthermore, the interaction between the target biological tissue and the electrode sheet includes: stress distribution of the target biological tissue or deformation distribution of the target biological tissue;

[0053] The biomechanical database includes: a preset biomechanical database of biological tissue-electrode interaction;

[0054] The predetermined relationship curve includes: a predetermined relationship curve between biological tissue stress distribution and bearing capacity or a predetermined relationship curve between biological tissue deformation distribution and bearing capacity.

[0055] The beneficial effects of the above scheme are: clarifying the specific content of the interaction between the target biological tissue and the electrode (such as stress distribution, deformation distribution) as well as the biomechanical database and relationship curves used, providing a clear direction and basis for analysis.

[0056] Furthermore, the target electrode trajectory is optimized according to the bending coefficient to obtain an optimized target electrode trajectory, including:

[0057] According to the bearing capacity of each area of ​​the target biological tissue on the electrode sheet, the coordinates of each electrode sheet in the target electrode trajectory are adjusted to obtain an optimized target electrode trajectory.

[0058] The beneficial effect of the above solution is that the coordinates of the electrode piece in the electrode trajectory are adjusted according to the bearing capacity of the target biological tissue on the electrode piece, so that the target electrode trajectory can be optimized to better match the actual trajectory of the electrode.

[0059] Furthermore, after obtaining the optimized target electrode trajectory, the method for generating the electrode trajectory further includes:

[0060] Obtaining a parameter to be verified in the optimized target electrode trajectory, and comparing the parameter to be verified with the electrode configuration parameter of the electrode: when the difference between the parameter to be verified and the corresponding electrode configuration parameter is less than a preset threshold, the verification passes; otherwise, the verification fails;

[0061] The parameters to be tested include: the size of the electrode sheet, the length of the electrode wire between adjacent electrode sheets, the number of electrode sheets, and the radius of the electrode wire;

[0062] The electrode configuration parameters include: the size of the electrode sheet, the spacing between adjacent electrode sheets, the number of electrode sheets, and the radius of the electrode wire.

[0063] The beneficial effect of the above scheme is that by comparing the difference between the parameters to be tested and the electrode configuration parameters, it is possible to verify whether the optimized target electrode trajectory is distorted, thereby improving the accuracy and reliability of the electrode trajectory.

[0064] Furthermore, the target biological tissue includes nerve nuclei, cerebrospinal fluid, and nerve fibers.

[0065] The beneficial effect of the above scheme is that it clarifies the specific types of biological tissues (such as nerve nuclei, cerebrospinal fluid, and nerve fibers), providing a more specific biological background for the generation and optimization of electrode trajectories.

[0066] Furthermore, the image file is a computed tomography (CT) image.

[0067] The beneficial effect of the above solution is that it specifies the type of image file (such as computed tomography (CT) image), thereby providing a clear data source for the extraction and processing of electrode data.

[0068] In a second aspect, the present invention provides a device for generating an electrode curve, comprising:

[0069] A coordinate acquisition module is used to acquire an image file of the user's target part, the image file including image data of different positions of the target part, and extract electrode data of each electrode in the image data, the electrode data including at least electrode coordinates;

[0070] An initial fitting module, used for fitting and processing the electrode data of the electrode to obtain the initial electrode trajectory of the electrode;

[0071] A fusion module is used to perform fusion processing on the initial electrode trajectory according to the electrode configuration parameters of the electrode to obtain the target electrode trajectory of the electrode.

[0072] In a third aspect, the present invention provides an implantable medical system, comprising:

[0073] An implantable medical device for implantation in a patient, the implantable medical device comprising a stimulation electrode implanted in the patient's brain;

[0074] A first processor is used to receive an image file of a user and is configured to execute the above-mentioned method for generating electrode trajectories to process the image file;

[0075] a display device configured to display the target electrode trajectory obtained by processing by the first processor;

[0076] The image file includes a three-dimensional image of brain tissue in the patient's brain.

[0077] In a fourth aspect, the present invention provides a medical device comprising a second processor, a memory, and a computer program stored in the memory, wherein the second processor executes the computer program to implement the steps of the above-mentioned method for generating electrode trajectories.

[0078] In a fifth aspect, the present invention provides a computer medical program product, comprising a computer medical program / instruction, which, when executed by a third processor, implements the steps of the above-mentioned electrode trajectory generation method.

[0079] Compared with the prior art, the beneficial effects of the present invention include at least:

[0080] The present invention can accurately capture the electrode position by acquiring the image file of the target part and extracting the electrode data (such as electrode coordinates); the target electrode trajectory obtained by fusing the initial electrode trajectory and the electrode configuration parameters can not only improve the accuracy and applicability of the electrode trajectory, but also enable an intuitive understanding of the electrode layout and morphology. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a flowchart of a method for generating an electrode trajectory according to an embodiment of the present invention.

[0082] Figure 2 Schematic diagram of the optimized target electrode trajectory according to an embodiment of the present invention.

[0083] Figure 3 It is a structural schematic diagram of a device for generating an electrode curve according to an embodiment of the present invention.

[0084] Figure 4Schematic diagram of the structure of an implantable medical system according to an embodiment of the present invention.

[0085] Figure 5 It is a structural diagram of a medical device according to an embodiment of the present invention.

[0086] Figure 6 It is a structural diagram of a computer medical program product according to an embodiment of the present invention. DETAILED DESCRIPTION

[0087] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concepts of the example embodiments to those skilled in the art. Identical reference numerals in the drawings represent identical or similar structures, and thus repeated descriptions thereof will be omitted.

[0088] The words expressing positions and directions described in the present invention are all explained with reference to the accompanying drawings as examples, but can be modified as needed, and all such modifications are within the scope of protection of the present invention.

[0089] Below, we first briefly describe one of the application fields of the embodiment of the present invention (i.e., implantable devices). The implantable neural stimulation system (an implantable medical system) mainly includes a stimulator implanted in the patient's body and a programmable device arranged outside the patient's body. The existing neural regulation technology mainly implants electrodes in specific structures (i.e., targets) in the body through stereotactic surgery, and the stimulator implanted in the patient's body emits discharge pulse width to the target through the electrodes, regulates the electrical activity and function of the corresponding neural structure and network, thereby improving symptoms and alleviating pain. Among them, the stimulator can be any one of an implantable neural electrical stimulation device, an implantable cardiac electrical stimulation system (also known as a pacemaker), an implantable drug delivery system (IDDS) and a lead adapter. Examples of implantable neurostimulation devices include deep brain stimulation (DBS), cortical nerve stimulation (CNS), spinal cord stimulation (SCS), sacral nerve stimulation (SNS), and vagus nerve stimulation (VNS).

[0090] In some embodiments, the stimulator may include an implantable pulse generator (IPG), an electrode wire, and an extension wire arranged between the implantable pulse generator and the electrode wire, and data interaction between the implantable pulse generator and the electrode wire is achieved through the extension wire, and the implantable pulse generator is arranged in the patient's body. In response to the program-controlled instructions sent by the program-controlled device, the sealed battery and circuit are used to provide controllable electrical stimulation energy to the tissue in the body, and one or two controllable specific electrical stimulations are delivered to specific areas of the tissue in the body through the implanted extension wire and the electrode wire. The extension wire is used in conjunction with the implantable pulse generator as a transmission medium for the electrical stimulation signal, and transmits the electrical stimulation signal generated by the implantable pulse generator to the electrode wire. The electrode wire delivers electrical stimulation to specific areas of the tissue in the body through the electrode contacts thereon. The stimulator is provided with one or more electrode wires on one side or both sides, and a plurality of electrode contacts are provided on the electrode wire.

[0091] In other embodiments, the stimulator may include only an implantable pulse generator and electrode leads. The implantable pulse generator may be embedded in the patient's skull, and the electrode leads may be implanted in the patient's skull. In this case, the implantable pulse generator and the electrode leads are directly connected, without the need for extension leads.

[0092] The electrode wire can be a nerve stimulation electrode, and the electrode wire delivers electrical stimulation to a specific area of ​​tissue in the body through a plurality of electrode contacts. The stimulator is provided with one or more electrode wires on one side or both sides, and a plurality of electrode contacts are provided on the electrode wire, and the electrode contacts can be arranged uniformly or non-uniformly in the circumference of the electrode wire. As an example, the electrode contacts can be arranged in an array of 4 rows and 3 columns (a total of 12 electrode contacts) in the circumference of the electrode wire. The electrode contacts may include stimulation contacts and / or collection contacts. The electrode contacts can, for example, be in the shape of sheets, rings, dots, etc.

[0093] In some possible embodiments, the stimulated tissue in the body can be the patient's brain tissue, and the stimulated site can be a specific site of the brain tissue. When the patient's disease type is different, the stimulated site is generally different, and the number of stimulation contacts used (single source or multiple sources), the use of one or more (single channel or multiple channels) specific electrical stimulation signals, and the stimulation parameter data are also different. It can be considered that when the stimulation contacts used are multi-source, multi-channel (multi-channel), a larger amount of data will be generated compared to a single source, single channel.

[0094] The embodiments of the present invention do not limit the types of diseases that can be treated with DBS, which can be any of the diseases for which deep brain stimulation (DBS), spinal cord stimulation (SCS), pelvic stimulation, gastric stimulation, peripheral nerve stimulation, and functional electrical stimulation are applicable. The types of diseases that DBS can be used to treat or manage include, but are not limited to, spastic disorders (e.g., epilepsy), pain, migraine, mental illness (e.g., major depressive disorder (MDD)), bipolar disorder, anxiety, post-traumatic stress disorder, depression, obsessive-compulsive disorder (OCD), behavioral disorders, mood disorders, memory disorders, mental state disorders, mobility disorders (e.g., essential tremor or Parkinson's disease), Huntington's disease, Alzheimer's disease, drug addiction, autism, or other neurological or psychiatric diseases and impairments.

[0095] Stimulation parameters may include: stimulation frequency (for example, the number of electrical stimulation pulse width signals within a unit time of 1s, in Hz), pulse width (the duration of each pulse width, in μs), current amplitude (generally expressed in voltage, that is, the intensity of each pulse width, in V), timing (for example, it can be continuous or triggered), stimulation mode (including one or more of current mode, voltage mode, timed stimulation mode and cyclic stimulation mode), one or more of the upper and lower limits controlled by the doctor (the range that the doctor can adjust) and the upper and lower limits controlled by the patient (the range that the patient can adjust independently).

[0096] During the process of doctors programming patients, visual programming can be used to help doctors better understand the situation of electrodes in the patient's body. That is, through three-dimensional imaging of the patient's brain tissue and electrodes, doctors can more quickly determine the electrode positions and electrode combinations that need to deliver pulses. Therefore, in the three-dimensional imaging process, whether it is the imaging of the patient's own brain tissue, the imaging of electrodes implanted in the patient's brain, or the display of the electrode stimulation field range, there are very high requirements. Therefore, image processing in the above imaging process is an issue that needs to be urgently addressed.

[0097] Based on the above problems, the present invention proposes a method for generating an electrode trajectory and a related device, wherein the electrodes are implanted in the target part of the user, such as a designated neural nucleus in the patient's brain. The implanted nucleus location is different for different diseases.

[0098] refer to Figure 1 The method for generating an electrode trajectory of the present invention includes: Step S1 to Step S3. Furthermore, the method for generating an electrode trajectory of the present invention may also include: Step S4. Furthermore, the method for generating an electrode trajectory of the present invention may also include: Step S5.

[0099] Step S1: obtaining an image file of a target part of a user, wherein the image file includes image data of different positions of the target part, and extracting electrode data of electrodes in each of the image data, wherein the electrode data includes at least electrode coordinates.

[0100] When applied, the image file is a computed tomography (CT) image, and multiple CT image slices are collected in sequence at a preset distance based on a certain image acquisition direction. By acquiring the image file of the target area and extracting the electrode data (such as electrode coordinates), the electrode position can be accurately captured, which can ensure the subsequent accurate positioning of the electrode within the target area. Specifically, the medical imaging data can be processed and analyzed using the Digital Imaging and Communications in Medicine (DICOM) standard to extract the electrode data.

[0101] In actual application, the step S1 of extracting the electrode data of each electrode in the image data includes steps S11 to S13.

[0102] Step S11: extracting electrode position data from each of the image data according to electrode identification features, wherein the electrode identification features include voxel values ​​of the electrodes.

[0103] To accurately identify electrodes from complex imaging data, the electrode's voxel value is used as an identification feature. A voxel value is the brightness or density of each small cube (i.e., voxel) in the image data. For metal electrodes, their voxel values ​​are often significantly different from those of surrounding tissue. During application, each image data point is processed using image processing algorithms (such as threshold segmentation and edge detection). Based on a preset range of electrode voxel values, the electrode's outline or position information is extracted from the image data.

[0104] Step S12: Obtaining the electrode coordinate set in the image file based on the electrode position data and the positional relationship between all the image data.

[0105] Image data is composed of multiple layers or slices, so the positional relationship between these layers needs to be considered to construct the complete position information of the electrodes in three-dimensional space. In application, after determining the position of the electrodes in each image data, the coordinates of the electrodes on each layer are calculated based on the arrangement order of the image data and the spacing between the layers. These coordinates are then combined into an electrode coordinate set. Furthermore, the electrode coordinate set reflects the three-dimensional position information of the electrodes in the image file.

[0106] Step S13: Based on a preset conversion matrix, the electrode position coordinate set is converted into a preset reference coordinate system to obtain a target electrode coordinate set.

[0107] Since different imaging devices or imaging methods may produce different coordinate systems, in order to ensure the consistency and comparability of the electrode coordinates, it is necessary to preset a reference coordinate system (such as a DICOM coordinate system). At the same time, it is necessary to calculate a conversion matrix based on the metadata of the imaging data and the characteristics of the imaging device to convert the electrode coordinates from the original coordinate system to the reference coordinate system. When applied, the conversion matrix is ​​used to transform each coordinate in the electrode coordinate set obtained in step S12 to obtain the corresponding coordinates in the reference coordinate system. The converted coordinates constitute the target electrode coordinate set. Furthermore, the target electrode coordinate set represents the three-dimensional position information of the electrode in the reference coordinate system.

[0108] In practical applications, the electrode coordinate set in the image file originally adopts the IJK coordinate system, but for further processing, it needs to be converted into the XYZ coordinate system.

[0109] Step S2: fitting and processing the electrode data of the electrode to obtain the initial electrode trajectory of the electrode.

[0110] When applied, step S2 includes: step S21 - step S22.

[0111] Step S21: pre-processing the electrode data of the electrodes.

[0112] Preprocessing includes at least one of the following: noise removal, smoothing and data cleaning. When applied, when acquiring electrode data, due to the influence of various factors (such as equipment noise, data acquisition error, etc.), the electrode data may contain some noise, and the noise will interfere with the accurate judgment of the electrode trajectory. Therefore, a filtering algorithm (such as Gaussian filtering, mean filtering, etc.) is used to denoise the electrode data to reduce the impact of noise on the subsequent fitting process. Furthermore, in order to obtain a smoother electrode trajectory, it is also necessary to smooth the electrode data (for example, by moving average method, exponential smoothing method, etc.) so that the electrode data becomes smoother and more continuous while maintaining the original characteristics. Furthermore, in addition to noise removal and smoothing, the data also needs to be cleaned (for example, by removing duplicate data, filling missing data, correcting erroneous data, etc.) to ensure the integrity and accuracy of the electrode data.

[0113] Step S22: Based on a fitting algorithm, fitting the preprocessed electrode data of the electrode to obtain the initial electrode trajectory of the electrode.

[0114] When fitting electrode data, an appropriate fitting algorithm must be selected based on the characteristics of the electrode data and the fitting requirements. These algorithms can include least squares, nonlinear regression, genetic algorithms, polynomial fitting, and curve fitting. After selecting an appropriate fitting algorithm, the preprocessed electrode data is input into the algorithm for fitting. Specifically, an optimal fitting curve, known as the initial electrode trajectory, is derived based on the distribution and characteristics of the electrode data. This initial electrode trajectory reflects the approximate orientation and position of the electrodes in three-dimensional space, representing the true shape of the electrodes within the patient's brain.

[0115] In practical applications, after obtaining the initial electrode trajectory, the results need to be verified. Specifically, this can be done by comparing it with the original image data and calculating the fitting error. If the fitting result meets the preset accuracy requirements, the initial electrode trajectory is considered accurate and reliable. If the fitting result does not meet the preset accuracy requirements, the fitting algorithm or preprocessing steps need to be readjusted until the fitting result meets the preset accuracy requirements.

[0116] Step S3: performing fusion processing on the initial electrode trajectory according to the electrode configuration parameters of the electrode to obtain the target electrode trajectory of the electrode.

[0117] The electrode configuration parameters include at least one of the following: electrode model, electrode sheet size (such as the length, thickness and width of the electrode sheet), the spacing between adjacent electrode sheets, the number of electrode sheets, and the radius of the electrode wire. When applied, the initial electrode trajectory is fused according to the electrode configuration parameters, and the target electrode trajectory of the electrode is obtained, so that the doctor can understand the layout and morphology of the electrode more intuitively, and provide clearer guidance for the doctor's further diagnosis and treatment. Specifically, based on the electrode configuration parameters, rendering starts from the starting end of the initial electrode trajectory, and the electrode sheets are drawn one by one to obtain the target electrode trajectory.

[0118] In actual application, step S3 includes: step S31 - step S32.

[0119] Step S31: determining an electrode model of the electrode according to electrode configuration parameters of the electrode, wherein the electrode configuration parameters at least include the size of the electrode sheet, the spacing between adjacent electrode sheets, and the number of electrode sheets.

[0120] According to the configuration parameters of the electrode, a 3D modeling software or programming tool is used to construct an electrode model of the electrode. The obtained electrode model can accurately reflect the size, spacing and number of electrode pieces, as well as their arrangement on the wire.

[0121] Step S32: aligning the implantation end of the initial electrode trajectory and the implantation end of the electrode model to obtain a fused target electrode trajectory.

[0122] The initial electrode trajectory is usually a line segment that represents the approximate direction of the electrode wire in three-dimensional space. This line segment does not contain the actual shape and size information of the electrode. During application, since the electrode sheet is only arranged at the implantation end (i.e., the front end) of the electrode wire, in order to fuse the electrode model with the initial electrode trajectory, it is necessary to align the implantation end of the electrode model with the implantation end of the initial electrode trajectory. The alignment method can be based on the transformation of the coordinate system or on the matching of feature points. During the alignment process, it is necessary to ensure that the position and direction of the implantation end of the electrode model and the implantation end of the initial electrode trajectory in three-dimensional space are consistent. After the alignment process is completed, the electrode model is extended and arranged along the initial electrode trajectory to obtain the fused target electrode trajectory. The target electrode trajectory not only contains the direction information of the electrode wire in three-dimensional space, but also accurately reflects the actual morphological information such as the size, spacing and number of the electrode sheets.

[0123] Step S4: Optimize the target electrode trajectory to obtain the optimized target electrode trajectory, refer to Figure 2 .

[0124] When applied, step S4 of the present invention includes: step S41 to step S44.

[0125] Step S41: Based on the preset electrode implantation path of the electrode, determining the target biological tissue through which the electrode passes.

[0126] When applied, taking the DBS system as an example, the target biological tissues may include nerve nuclei, cerebrospinal fluid, and nerve fibers.

[0127] In actual application, step S41 includes: step S411 - step S412.

[0128] Step S411: extracting biological tissue data of a target part from the image data, and constructing a three-dimensional biological tissue model of the target part according to the biological tissue data.

[0129] Image data refers to medical imaging data, typically including CT and MRI imaging data, which provides detailed information about the biological tissues in a patient's body. During application, biological tissue data of the target biological tissue is extracted from the patient's medical imaging data. Biological tissue includes nerve nuclei, cerebrospinal fluid, and nerve fibers. Furthermore, a three-dimensional model of the biological tissue at the target site is constructed based on the extracted biological tissue data using three-dimensional modeling technology or image processing software. The three-dimensional biological tissue model can accurately reflect the structure, morphology, and positional information of the biological tissue at the target site.

[0130] Step S412: fusing the electrode implantation path with the biological tissue three-dimensional model to determine the target biological tissue through which the electrode passes.

[0131] Fusion of the pre-set electrode implantation path with the 3D model of biological tissue typically involves aligning and matching the coordinate information of the electrode implantation path with the coordinate information of the 3D model of biological tissue to ensure that both are in the same coordinate system. In application, this fusion process clearly visualizes the position and direction of the electrode implantation path within the 3D model of biological tissue, thereby determining the target biological tissue that the electrode passes through.

[0132] Step S42: determining the bearing capacity of different target biological tissues on the electrode.

[0133] In deep brain stimulation, electrodes are typically inserted into the brain at an angle to precisely stimulate specific neural nuclei. Because electrodes generate stress in different directions when in contact with different biological tissues, and because each tissue has different textures, structures, and blood supply, the electrode's load-bearing capacity also varies. Consequently, the degree of bending of the electrodes varies across different tissues.

[0134] When applied, step S42 includes: step S421-step S422.

[0135] Step S421: acquiring target biological tissue parameters of the target biological tissue and electrode configuration parameters of the electrode.

[0136] Electrode configuration parameters include: the spacing between adjacent electrode sheets, the number of electrode sheets, the size of the electrode sheets, the shape of the electrode sheets, the material properties of the electrode sheets and / or the implantation method of the electrode sheets.

[0137] Target tissue parameters include: texture, structure, and / or blood supply. In practice, medical imaging and pathological analysis can be used to obtain information about the texture of nerve nuclei, cerebrospinal fluid, and nerve fibers, such as hardness and elasticity. The microscopic and macroscopic structure of the target tissue includes information about the arrangement of nerve fibers and the distribution of cerebrospinal fluid. By assessing the blood supply of the target tissue, it is possible to understand the response and recovery capacity of the target tissue when stimulated by electrodes.

[0138] Step S422: Based on the principles of biomechanics, a biomechanical model is established according to the target biological tissue parameters and the electrode configuration parameters, and the interaction between the target biological tissue and the electrode is analyzed according to the biomechanical model to obtain the bearing capacity of the target biological tissue on the electrode.

[0139] During application, finite element analysis (FEA) or other biomechanical simulation software is used to build a biomechanical model of neural nuclei, cerebrospinal fluid, and nerve fibers based on the parameters of the target biological tissue and electrode configuration. This biomechanical model can simulate the tilted insertion process of the electrode into the skull and the interaction between the electrode and the target biological tissue.

[0140] Furthermore, biomechanical models are used to simulate the insertion of electrodes into different biological tissues and analyze the stress distribution, deformation, and damage risk of the electrodes on the tissues. Furthermore, the elasticity and recovery capacity of the tissues are considered to assess their response to electrode stimulation. Furthermore, based on the analysis results of the biomechanical model, the bearing capacity of the target tissue on the electrodes is calculated. This can include evaluating parameters such as the maximum stress, deformation, and damage threshold of the tissue under electrode stimulation.

[0141] When applied, the step S422 of analyzing the interaction between the target biological tissue and the electrode according to the biomechanical model includes: steps S4221 to S4223.

[0142] Step S4221: Convert the biomechanical model into a finite element analysis model.

[0143] During application, a biomechanical model is constructed using professional 3D modeling software (such as SolidWorks or CATIA) based on the image file of the target biological tissue (such as MRI or CT scan images), combined with the electrode geometry and electrode configuration parameters. The biomechanical model can accurately reflect the geometry, texture and structural characteristics of the target biological tissue. Next, the biomechanical model is imported into finite element analysis software (such as ANSYS or Abaqus) and meshed. Those skilled in the art can adjust the size and density of the mesh as needed to ensure the accuracy and efficiency of the calculation results. Then, based on the information in the biomechanical database, the target biological tissue and electrode are assigned corresponding material properties, including elastic modulus, Poisson's ratio, density, etc.

[0144] Step S4222: Based on a preset biomechanical database, the finite element analysis model is solved using finite element analysis software to obtain the interaction between the target biological tissue and the electrode sheet of the electrode.

[0145] The biomechanical database includes a preset biomechanical database of biological tissue-electrode interaction, and the interaction between the target biological tissue and the electrode sheet includes a stress distribution of the target biological tissue or a deformation distribution of the target biological tissue.

[0146] During application, appropriate boundary conditions are applied to the finite element analysis model based on the actual situation. These boundary conditions include the electrode insertion angle, depth, and speed, as well as the constraints of the target biological tissue. The model is then solved using the finite element analysis software's solver. This process simulates the electrode insertion process in the target biological tissue and calculates the stress and deformation distribution of the target tissue. The stress and deformation distribution data of the target biological tissue are then extracted from the solution results.

[0147] Step S4223: Based on a preset relationship curve, the bearing capacity of each region of the target biological tissue on the electrode sheet is determined according to the interaction between the target biological tissue and the electrode sheet.

[0148] The pre-set relationship curve includes: a preset relationship curve between biological tissue stress distribution and bearing capacity or a preset relationship curve between biological tissue deformation distribution and bearing capacity. When applied, a relationship curve between biological tissue stress distribution and bearing capacity or a relationship curve between biological tissue deformation distribution and bearing capacity that matches the target biological tissue type is selected from the preset biomechanical database. Then, based on the extracted stress distribution or deformation distribution data and combined with the selected relationship curve, the bearing capacity of each area of ​​the target biological tissue on the electrode is calculated. The magnitude of the bearing capacity will reflect the stability and durability of the target biological tissue under electrode extrusion and electrode stimulation. Then, the calculation results are analyzed to evaluate the bearing capacity of different target biological tissues on the electrode.

[0149] Step S43: Based on the load-bearing capacity, determining the bending coefficients of the electrodes at different positions in the electrode implantation path.

[0150] When applied, first, obtain the load-bearing capacity data evaluated in step S42, and the load-bearing capacity data includes the load-bearing capacity of the biological tissue at different positions (such as different depths and different directions) on the electrode implantation path. Obtain the geometric parameters of the electrode, which include the length, diameter, bending stiffness, etc. of the electrode sheet. Then, based on the load-bearing capacity data and the geometric parameters of the electrode, use the principles of biomechanics and material mechanics to calculate the bending coefficient of the electrode at different positions on the implantation path. The bending coefficient is an indicator that can reflect the degree of bending of the electrode at a specific position due to the influence of the load-bearing capacity of the biological tissue. Specifically, the calculation of the bending coefficient can take into account a variety of factors, such as the elastic modulus of the biological tissue, Poisson's ratio, the bending stiffness of the electrode, and the geometric shape of the implantation path.

[0151] Step S44: Optimizing the target electrode trajectory according to the bending coefficient to obtain an optimized target electrode trajectory.

[0152] When applied, step S44 includes: step S441.

[0153] Step S441: adjusting the coordinates of each electrode sheet in the target electrode trajectory according to the bearing capacity of each area of ​​the target biological tissue on the electrode sheet, to obtain an optimized target electrode trajectory.

[0154] During application, the coordinates of each electrode piece in the target electrode trajectory are adjusted based on the bending coefficient calculated in step S43 so that the obtained trajectory curve is closer to the actual trajectory of the electrode. In actual application, factors such as the length and size of the electrode piece and the length of the electrode wire between adjacent electrode pieces can also be considered during the adjustment process to ensure that the adjustment process complies with biomechanical principles and that the optimized target electrode trajectory is consistent with the actual trajectory of the electrode.

[0155] Step S5: Verify the optimized target electrode trajectory.

[0156] When applied, step S5 includes: step S51.

[0157] Step S51: Obtain the parameters to be verified in the optimized target electrode trajectory, and compare the parameters to be verified with the electrode configuration parameters of the electrode: when the difference between the parameters to be verified and the corresponding electrode configuration parameters is less than a preset threshold, the verification passes, otherwise the verification fails.

[0158] During application, the parameters to be verified are first extracted from the optimized target electrode trajectory, including the size (width and height) of the electrode sheet, the length of the electrode wire between adjacent electrode sheets, the number of electrode sheets, and the radius of the electrode wire. Next, the parameters to be verified are compared with the electrode configuration parameters. The electrode configuration parameters are provided by the electrode manufacturer and include the length and size of the electrode sheet, the spacing between adjacent electrode sheets, the number of electrode sheets, and the radius of the electrode wire.

[0159] The purpose of the comparison is to ensure that the electrode parameters in the optimized target electrode trajectory match the electrode configuration parameters to avoid distortion of details on the trajectory curve due to over-optimization, thereby losing its medical reference function.

[0160] When the difference between the parameter to be tested and the corresponding electrode configuration parameter is less than a preset threshold, the previous optimization step is considered reasonable and the verification is passed. Specifically, the preset threshold can be set based on factors such as the manufacturing accuracy of the electrode and the characteristics of the biological tissue.

[0161] When the difference between the parameter to be tested and the corresponding electrode configuration parameter is greater than or equal to the preset threshold, the previous optimization step is considered unreasonable and the verification fails. It is necessary to readjust the target electrode trajectory and re-optimize and verify until the difference between the parameter to be tested and the corresponding electrode configuration parameter is less than the preset threshold.

[0162] refer to Figure 3The electrode curve generation device of the present invention includes: a coordinate acquisition module, an initial fitting module and a fusion module. Furthermore, the generation device of the present invention may also include: an optimization module and a verification module.

[0163] The coordinate acquisition module is used to obtain an image file of the user's target part, wherein the image file includes image data of different positions of the target part, and extracts the electrode data of the electrode in each image data, wherein the electrode data at least includes electrode coordinates. The initial fitting module is used to fit the electrode data of the electrode to obtain the initial electrode trajectory of the electrode. The fusion module is used to fuse the initial electrode trajectory according to the electrode configuration parameters of the electrode to obtain the target electrode trajectory of the electrode. The optimization module is used to optimize the target electrode trajectory to obtain the optimized target electrode trajectory. The verification module is used to verify the optimized target electrode trajectory.

[0164] refer to Figure 4 The implantable medical system of the present invention includes: an implantable medical device, a first processor and a display device.

[0165] An implantable medical device is implanted in a patient, the implantable medical device comprising a stimulating electrode implanted in the patient's brain. A first processor is configured to receive an image file from a user and execute the electrode trajectory generation method to process the image file. A display device is configured to display the target electrode trajectory generated by the first processor. The image file comprises a three-dimensional image of brain tissue within the patient's brain.

[0166] refer to Figure 5 The medical device of the present invention includes a second processor, a memory, and a computer program stored in the memory. The second processor executes the computer program to implement the steps of the electrode trajectory generation method.

[0167] refer to Figure 6 The computer medical program product of the present invention includes a computer medical program / instruction. When the computer medical program / instruction is executed by the third processor, the steps of the method for generating the electrode trajectory are implemented.

[0168] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limiting the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the invention without departing from the principles and purpose of the present invention. All such changes shall fall within the scope of protection of the claims of the present invention.

Claims

1. A method for generating an electrode trajectory, characterized in that: The electrodes are implanted into a target area of ​​the user; The method for generating the electrode trajectory includes: Acquire an image file of a target part of the user, the image file including image data of different positions of the target part, and extract electrode data of an electrode in each of the image data, the electrode data including at least electrode coordinates; Fitting and processing the electrode data of the electrode to obtain an initial electrode trajectory of the electrode; performing fusion processing on the initial electrode trajectory according to the electrode configuration parameters of the electrode to obtain the target electrode trajectory of the electrode; After obtaining the target electrode trajectory of the electrode, the method for generating the electrode trajectory further includes: determining a target biological tissue through which the electrode passes based on a preset electrode implantation path of the electrode; determining the carrying capacity of different target biological tissues for the electrode; determining, based on the load-bearing capacity, bending coefficients of electrodes at different positions in the electrode implantation path; The target electrode trajectory is optimized according to the bending coefficient to obtain an optimized target electrode trajectory.

2. The method for generating an electrode trajectory according to claim 1, wherein: The fitting process of the electrode data of the electrode to obtain the initial electrode trajectory of the electrode includes: preprocessing electrode data of the electrodes; Based on a fitting algorithm, the preprocessed electrode data of the electrode is fitted to obtain an initial electrode trajectory of the electrode.

3. The method for generating an electrode trajectory according to claim 2, wherein: The preprocessing includes at least one of the following: noise removal, smoothing and data cleaning; The fitting algorithm includes at least one of the following: least squares method, nonlinear regression and genetic algorithm.

4. The method for generating an electrode trajectory according to claim 1, wherein: The extracting electrode data of each electrode in the image data includes: Extracting electrode position data from each of the image data according to electrode identification features, wherein the electrode identification features include voxel values ​​of the electrodes; Obtaining an electrode coordinate set in the image file based on the electrode position data and a positional relationship between all the image data; Based on a preset conversion matrix, the electrode position coordinate set is converted into a preset reference coordinate system to obtain a target electrode coordinate set.

5. The method for generating an electrode trajectory according to claim 1, wherein: The step of performing fusion processing on the initial electrode trajectory according to the electrode configuration parameters of the electrode to obtain the target electrode trajectory of the electrode includes: determining an electrode model of the electrode according to electrode configuration parameters of the electrode, wherein the electrode configuration parameters include at least the size of the electrode sheet, the spacing between adjacent electrode sheets, and the number of electrode sheets; The implantation end of the initial electrode track and the implantation end of the electrode model are aligned to obtain a fused target electrode track.

6. The method for generating an electrode trajectory according to claim 1, wherein: The step of determining the target biological tissue passed by the electrode based on the preset electrode implantation path of the electrode comprises: Extracting biological tissue data of a target part from the image data, and constructing a three-dimensional biological tissue model of the target part based on the biological tissue data; The electrode implantation path is fused with the biological tissue three-dimensional model to determine the target biological tissue through which the electrode passes.

7. The method for generating an electrode trajectory according to claim 1, wherein: Determining the carrying capacity of different target biological tissues on the electrode includes: acquiring target biological tissue parameters of the target biological tissue and electrode configuration parameters of the electrode; Based on the principles of biomechanics, a biomechanical model is established according to the target biological tissue parameters and the electrode configuration parameters, and the interaction between the target biological tissue and the electrode is analyzed according to the biomechanical model to obtain the bearing capacity of the target biological tissue on the electrode.

8. The method for generating an electrode trajectory according to claim 7, characterized in that: The target biological tissue parameter includes at least one of the following: texture of the target biological tissue, structure of the target biological tissue, and blood supply of the target biological tissue; and / or, The electrode configuration parameters include at least one of the following: the spacing between adjacent electrode sheets, the number of electrode sheets, the size of the electrode sheets, the shape of the electrode sheets, the material properties of the electrode sheets, and the implantation method of the electrode sheets.

9. The method for generating an electrode trajectory according to claim 7, wherein: Analyzing the interaction between the target biological tissue and the electrode according to a biomechanical model includes: Convert the biomechanical model into a finite element analysis model; Based on a preset biomechanical database, the finite element analysis model is solved using finite element analysis software to obtain the interaction between the target biological tissue and the electrode sheet of the electrode; Based on a preset relationship curve, the bearing capacity of each region of the target biological tissue on the electrode sheet is determined according to the interaction between the target biological tissue and the electrode sheet.

10. The method for generating an electrode trajectory according to claim 9, characterized in that: The interaction between the target biological tissue and the electrode sheet includes: stress distribution of the target biological tissue or deformation distribution of the target biological tissue; The biomechanical database includes: a preset biomechanical database of biological tissue-electrode interaction; The preset relationship curve includes: a preset relationship curve between biological tissue stress distribution and bearing capacity or a preset relationship curve between biological tissue deformation distribution and bearing capacity.

11. The method for generating an electrode trajectory according to claim 9, wherein: The step of optimizing the target electrode trajectory according to the bending coefficient to obtain an optimized target electrode trajectory includes: According to the bearing capacity of each area of ​​the target biological tissue on the electrode sheet, the coordinates of each electrode sheet in the target electrode trajectory are adjusted to obtain an optimized target electrode trajectory.

12. The method for generating an electrode trajectory according to claim 1, wherein: After obtaining the optimized target electrode trajectory, the method for generating the electrode trajectory further includes: Obtaining a parameter to be verified in the optimized target electrode trajectory, and comparing the parameter to be verified with the electrode configuration parameter of the electrode: when the difference between the parameter to be verified and the corresponding electrode configuration parameter is less than a preset threshold, the verification passes; otherwise, the verification fails; The parameters to be tested include: the size of the electrode sheet, the length of the electrode wire between adjacent electrode sheets, the number of electrode sheets, and the radius of the electrode wire; The electrode configuration parameters include: the size of the electrode sheet, the spacing between adjacent electrode sheets, the number of electrode sheets, and the radius of the electrode wire.

13. The method for generating an electrode trajectory according to claim 1, wherein: The target biological tissues include nerve nuclei, cerebrospinal fluid, and nerve fibers.

14. The method for generating an electrode trajectory according to claim 1, wherein: The image file is a computed tomography (CT) image.

15. A device for generating an electrode curve, characterized in that: include: A coordinate acquisition module is used to acquire an image file of the user's target part, the image file including image data of different positions of the target part, and extract electrode data of each electrode in the image data, the electrode data including at least electrode coordinates; An initial fitting module, used for fitting and processing the electrode data of the electrode to obtain the initial electrode trajectory of the electrode; a fusion module, configured to perform fusion processing on the initial electrode trajectory according to the electrode configuration parameters of the electrode to obtain a target electrode trajectory of the electrode; After obtaining the target electrode trajectory of the electrode, determining the target biological tissue that the electrode passes through based on the preset electrode implantation path of the electrode; determining the carrying capacity of different target biological tissues for the electrode; determining, based on the load-bearing capacity, bending coefficients of electrodes at different positions in the electrode implantation path; The target electrode trajectory is optimized according to the bending coefficient to obtain an optimized target electrode trajectory.

16. An implantable medical system, characterized in that: include: An implantable medical device for implantation in a patient, the implantable medical device comprising a stimulation electrode implanted in the patient's brain; a first processor, configured to receive an image file of a user and configured to execute the method for generating an electrode trajectory according to any one of claims 1 to 14 to process the image file; a display device configured to display the target electrode trajectory obtained by processing by the first processor; The image file includes a three-dimensional image of brain tissue in the patient's brain.

17. A medical device comprising a second processor, a memory, and a computer program stored in the memory, wherein: The second processor executes the computer program to implement the steps of the method for generating an electrode trajectory according to any one of claims 1 to 14.

18. A computer medical program product comprising a computer medical program / instructions, characterized in that When the computer medical program / instruction is executed by the third processor, the steps of the method for generating the electrode trajectory according to any one of claims 1 to 14 are implemented.

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