Electrode stimulation electric field simulation method and apparatus, electronic device, and storage medium

Abstract

Provided are an electrode stimulation electric field simulation method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring a brain image of a target user in whom a stimulation electrode is implanted and the electrode implantation position of the stimulation electrode (S110); segmenting the brain image according to brain tissue type, and determining, on the basis of the brain tissue type segmentation results, a first brain point cloud corresponding to the target user (S120); determining, on the basis of a plurality of first data points in the first brain point cloud and brain tissue electrical characteristic parameters corresponding to the plurality of first data points, brain electrical characteristic distribution information corresponding to the target user (S130); and when a stimulation parameter is applied to a target contact point of the stimulation electrode, determining, on the basis of the electrode implantation position, the brain electrical characteristic distribution information, and the stimulation parameter, brain stimulation electric field distribution information corresponding to the stimulation parameter (S140).

Description

Methods, devices, electronic equipment and storage media for simulating electric fields of electrode stimulation

[0001] This application claims priority to Chinese Patent Application No. 202510130985.8, filed with the Chinese Patent Office on February 6, 2025, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of information technology, such as a method, apparatus, electronic device, and storage medium for simulating electric fields of electrode stimulation. Background Technology

[0003] Deep brain stimulation (DBS) is an advanced treatment method used to treat a variety of neurological disorders. In DBS treatment, accurately predicting the electric field distribution around the stimulating electrodes is crucial for optimizing stimulation parameters and improving treatment efficacy.

[0004] There are generally two methods for simulating electric field distribution. The first method predicts the electric field distribution around the stimulation electrode by using pre-stored standard electric field data corresponding to various human tissues when certain stimulation parameters are applied to the stimulation electrode. The second method uses the finite element method to simulate the electric field based on the user's MRI or CT data when certain stimulation parameters are applied to the stimulation electrode, in order to predict the electric field distribution around the stimulation electrode.

[0005] However, the problem with the first method is that using pre-stored standard electric field data for electric field prediction simulation cannot adapt to the characteristics of different patients' brain tissues, resulting in poor reliability and low accuracy of electric field distribution information. The problem with the second method is that when performing electric field simulation calculations, it is necessary to import a complete brain tissue image model of a user before the simulation calculation can be performed. These model data are large in size, which makes the prediction process of electric field distribution information consume a lot of time and computing resources, resulting in low efficiency and poor real-time performance of electric field distribution simulation. Summary of the Invention

[0006] This application provides a method, apparatus, electronic device, and storage medium for simulating electric fields of electrode stimulation, so as to improve the reliability and accuracy of electric field distribution information of electrode stimulation, and improve the simulation efficiency and real-time performance of electric field distribution around the stimulation electrode.

[0007] This application provides a method for simulating an electric field for electrode stimulation, the method comprising:

[0008] Obtain brain images of a target user with at least one stimulating electrode implanted in the brain, and the electrode implantation location of the at least one stimulating electrode in the target user's brain;

[0009] The brain images are classified into brain tissue categories to determine a first brain point cloud corresponding to the target user based on the brain tissue classification results; wherein, the first brain point cloud includes multiple first data points and brain tissue electrical characteristic parameters corresponding to the multiple first data points;

[0010] Based on the plurality of first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the plurality of first data points, the brain electrical characteristic distribution information corresponding to the target user is determined;

[0011] In response to the application of stimulation parameters to the target contact of the stimulation electrode, the brain stimulation electric field distribution information corresponding to the stimulation parameters is determined based on the electrode implantation location, the brain electrical characteristic distribution information, and the stimulation parameters.

[0012] This application embodiment also provides an electrode stimulation electric field simulation device, the device comprising:

[0013] The data acquisition module is configured to acquire brain images of a target user whose brain has been implanted with at least one stimulating electrode, and the electrode implantation location of the at least one stimulating electrode in the target user's brain.

[0014] The initial point cloud determination module is configured to classify the brain tissue into categories based on the brain images, and to determine a first brain point cloud corresponding to the target user based on the brain tissue classification results; wherein, the first brain point cloud includes multiple first data points and brain tissue electrical characteristic parameters corresponding to the multiple first data points;

[0015] The electrical distribution determination module is configured to determine the brain electrical characteristic distribution information corresponding to the target user based on the plurality of first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the plurality of first data points;

[0016] The electric field distribution determination module is configured to, in response to applying stimulation parameters to the target contact of the stimulation electrode, determine the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation location, the brain electrical characteristic distribution information, and the stimulation parameters.

[0017] This application also provides an electronic device, which includes:

[0018] At least one processor;

[0019] A memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to implement an electrode stimulation electric field simulation method as described in any of the embodiments of this application.

[0021] Fourthly, embodiments of this application also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform an electrode stimulation electric field simulation method as described in any of the embodiments of this application.

[0022] Fifthly, embodiments of this application also provide a medical system, the medical system comprising:

[0023] An implantable medical device, comprising at least a pulse generator implanted in the body of a target user and an electrode wire implanted in the brain of the target user, wherein the implanted end of the electrode wire is provided with at least a plurality of electrode contacts, and the pulse generator is connected to the electrode wire;

[0024] The display is configured to show information about the distribution of electric fields stimulated in the brain;

[0025] The processor is configured to acquire brain images of a target user whose brain has at least one stimulating electrode implanted, execute an electrode stimulation electric field simulation method according to any embodiment of this application to determine the brain stimulation electric field distribution information of the target user's brain in response to stimulation parameters, and display the brain stimulation electric field distribution information using the display. Attached Figure Description

[0026] Figure 1 is a schematic diagram of the stimulation electrode involved in the embodiment of this application;

[0027] Figure 2 is a flowchart illustrating an electrode stimulation electric field simulation method provided in an embodiment of this application;

[0028] Figure 3 is a flowchart illustrating another electrode stimulation electric field simulation method provided in the embodiments of this application;

[0029] Figure 4 is a schematic diagram of the implementation process of the electric field distribution simulation method involved in the embodiments of this application;

[0030] Figure 5 is a schematic diagram of the structure of an electrode stimulation electric field simulation device provided in an embodiment of this application;

[0031] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0032] Figure 7 is a schematic diagram of the structure of a medical system provided in an embodiment of this application. Detailed Implementation

[0033] The present application will now be described in conjunction with the accompanying drawings and specific embodiments. Without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0034] Below, we will first briefly describe one application area (i.e., implantable devices) of the embodiments of this application. An implantable neurostimulation system (an implantable medical system) mainly includes a stimulator implanted in the patient's body and a programmed device placed outside the patient's body. Existing neuromodulation technology mainly involves implanting electrodes into specific structures (i.e., target points) in the body through stereotactic surgery, and then having the stimulator implanted in the patient's body send discharge pulses to the target points via the electrodes, thereby modulating the electrical activity and function of the corresponding neural structures and networks, thereby improving symptoms and relieving pain. The stimulator can be any one of an implantable neurostimulation device, an implantable cardiac stimulation system (also known as a pacemaker), an implantable drug delivery system (IDDS), and a lead adapter. 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).

[0035] In some embodiments, the stimulator may include an implantable pulse generator (IPG), electrode leads, and an extension lead disposed between the pulse generator and the electrode leads, through which data interaction between the pulse generator and the electrode leads is achieved. The pulse generator is disposed within the patient's body. Responding to programmed commands sent by a programmable device, controllable electrical stimulation energy is provided to the body's tissues via a sealed battery and circuitry. One or two controllable electrical stimuli are delivered to specific areas of the body's tissues via the implanted extension lead and electrode leads. The extension lead, used in conjunction with the pulse generator, serves as a medium for transmitting electrical stimulation signals, conveying the electrical stimulation signals generated by the pulse generator to the electrode leads. The electrode leads deliver electrical stimulation to specific areas of the body's tissues through their electrode contacts. The stimulator has one or more electrode leads on one or both sides, and each electrode lead has multiple electrode contacts.

[0036] In other embodiments, the stimulator may consist only of a pulse generator and electrode leads. The pulse generator may be embedded in the patient's skull, and the electrode leads may be implanted intracranially, in which case the pulse generator and electrode leads are directly connected without the need for extension leads.

[0037] The electrode leads can be neurostimulation electrodes, delivering electrical stimulation to specific areas of tissue within the body via multiple electrode contacts. The stimulator has one or more electrode leads on one or both sides, with multiple electrode contacts on each lead. These contacts can be evenly or non-uniformly arranged circumferentially on the electrode leads. As an example, the electrode contacts can be arranged in a 4x3 array (a total of 12 contacts) circumferentially on the electrode leads. The electrode contacts can include stimulation contacts and / or acquisition contacts. The electrode contacts can be, for example, sheet-like, ring-like, or dot-like shapes.

[0038] In some possible approaches, the stimulated tissue can be the patient's brain tissue, and the stimulated site can be a specific area of ​​the brain tissue. Generally, the stimulated site differs depending on the patient's disease type, as well as the number of stimulation contacts (single-source or multi-source), the application of one or more specific electrical stimulation signals (single-channel or multi-channel), and the stimulation parameter data. It can be assumed that using multiple stimulation contacts (multi-source, multi-channel) will generate a larger amount of data compared to using a single-source, single-channel approach.

[0039] This application does not limit the applicable disease types, but can be any disease type applicable to deep brain stimulation (DBS), spinal cord stimulation (SCS), pelvic stimulation, gastric stimulation, peripheral nerve stimulation, or functional electrical stimulation. DBS can be used to treat or manage diseases including, but not limited to: spastic disorders (e.g., epilepsy), pain, migraines, mental illnesses (e.g., major depressive disorder (MDD)), bipolar disorder, anxiety disorders, post-traumatic stress disorder, mild 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.

[0040] Stimulation parameters may include: frequency (e.g., the number of electrical stimulation pulses per second, in Hz), pulse width (the duration of each pulse, in μs), amplitude (generally expressed as voltage, i.e., the intensity of each pulse, in V), timing (e.g., continuous or triggered), stimulation mode (including one or more of current mode, voltage mode, timed stimulation mode, and cyclic stimulation mode), physician control upper and lower limits (the range that physicians can adjust), and patient control upper and lower limits (the range that patients can adjust independently).

[0041] The technical solutions provided in this application are mainly applied to the field of implantable medical devices. Implantable medical devices mainly include a pulse generator, electrode leads, and a programmable device (or medical device). The pulse generator is implanted into the patient's body (such as the chest cavity, skull, etc.), and the electrode leads are connected to the pulse generator at one end under the skin, with a stimulation electrode configured at the other end.

[0042] A schematic diagram of the stimulation electrode is shown in Figure 1. As shown in Figure 1, the stimulation electrode includes at least one metal contact that outputs a stimulation source. These metal contacts can be annular or directional electrodes composed of multiple segmented electrode contacts. The stimulation electrode is partially implanted into a designated location in the patient's brain (such as nuclei or neural tissue associated with the condition). The doctor sends programmed parameters to a pulse generator via a programming device. The pulse generator delivers electrical stimulation to at least one metal contact in the stimulation electrode through electrode wires, causing the at least one metal contact to generate an electric field to treat the corresponding condition. In this embodiment: for different target users, brain electrical characteristic distribution information is constructed based on corresponding brain imaging data. When simulated stimulation parameters are applied to any metal contact of the stimulation electrode, the electric field distribution around the metal contact is accurately predicted using the brain electrical characteristic distribution information, thereby accurately and efficiently simulating the stimulation range of the stimulation electrode.

[0043] Example 1

[0044] Figure 2 is a flowchart illustrating an electrode stimulation electric field simulation method provided in an embodiment of this application. This embodiment is applicable to any situation where it is necessary to accurately predict the electric field distribution around the stimulation electrode in deep brain stimulation applications. This method can be executed by an electrode stimulation electric field simulation device, which can be implemented in the form of software and / or hardware. The hardware can be an electronic device, such as a mobile terminal, a personal computer (PC) or a server.

[0045] As shown in Figure 2, the method for simulating the electric field of electrode stimulation includes:

[0046] S110. Obtain brain images of a target user whose brain has been implanted with at least one stimulating electrode, and the electrode implantation location of the at least one stimulating electrode in the target user's brain.

[0047] The target users are those whose brains will soon have stimulation electrodes implanted for electric field distribution prediction. One or more stimulation electrodes have already been implanted in the target user's brain. Brain images can be brain MRI images, brain CT images, etc., optionally, brain images can be images acquired before the stimulation electrodes are implanted in the target user's brain. Brain MRI images refer to three-dimensional brain imaging data obtained by scanning the target user's skull using magnetic resonance imaging (MRI) technology; brain CT images refer to three-dimensional brain imaging data obtained by performing tomographic scanning of the target user's skull using computed tomography (CT) technology.

[0048] Electrode implantation location refers to the positional information of the stimulating electrode within the target user's skull. The electrode implantation location can be the coordinates of the electrode trajectory corresponding to the stimulating electrode.

[0049] Before and after deep brain stimulation (DBS) surgery on the target user, brain imaging techniques such as MRI or CT can be used to acquire preoperative and postoperative brain images. Preoperative images refer to the situation before the stimulation electrodes are implanted in the patient's brain, while postoperative images refer to the situation after the stimulation electrodes have been implanted. The location information of the stimulation electrodes in the target user's brain can be extracted from the postoperative brain images, thus determining the electrode implantation location of at least one stimulation electrode in the target user's brain. When it is necessary to perform stimulation electric field prediction simulation on the target user using the technical solution provided in this embodiment, the preoperative brain images and electrode implantation locations corresponding to the target user can be retrieved.

[0050] Optionally, the steps for determining the electrode implantation location of at least one stimulating electrode in the brain of a target user may include: acquiring preoperative and postoperative brain images of the target user with at least one stimulating electrode implanted in the brain; performing registration processing on the preoperative and postoperative brain images to determine the electrode trajectory coordinates of at least one stimulating electrode in the three-dimensional space corresponding to the preoperative brain image; and determining the electrode trajectory coordinates as the electrode implantation location of the stimulating electrode in the brain of the target user for at least one stimulating electrode.

[0051] Postoperative brain imaging refers to brain images acquired after the implantation of stimulating electrodes in the target user's brain. Optionally, postoperative brain imaging can be postoperative brain CT images because: since stimulating electrodes have already been implanted in the target user's brain, if MRI technology is used to acquire brain images of the target user, it is easy to cause the metal contacts on the stimulating electrodes to heat up, causing brain damage to the target user. CT technology does not pose such a risk.

[0052] Electrode trajectory coordinates refer to the position coordinates of the stimulating electrode in a certain space. For example, electrode trajectory coordinate data can be represented as {(103 131 142), (103 131 143), (104 131 144), (105 132 145) ..., (XYZ)}, where each element in the vector represents the three-dimensional coordinates of the voxel that constitutes the stimulating electrode in the postoperative brain image.

[0053] In this embodiment, one or more stimulation electrodes can be implanted in the brain of the target user. The processing method for each stimulation electrode is the same. The following description takes the implantation of one stimulation electrode in the brain of the target user as an example.

[0054] In this embodiment, the target user can acquire preoperative brain images using MRI or CT technology, and postoperative brain images using CT technology. This allows for the acquisition of both preoperative and postoperative brain images corresponding to the target user. The postoperative brain images can be registered to the corresponding three-dimensional space of the preoperative brain images to obtain the registered postoperative brain images to be processed. An electrode trajectory tracking algorithm can be used to reconstruct the trajectory information of the stimulating electrodes in the three-dimensional space of the preoperative brain images based on the postoperative brain images to be processed, thereby obtaining the electrode trajectory coordinates. These electrode trajectory coordinates can then be used to determine the electrode implantation location of the stimulating electrodes in the target user's brain.

[0055] S120. Classify the brain tissues of the preoperative brain images to determine the first brain point cloud corresponding to the target user based on the brain tissue classification results.

[0056] The brain tissue classification results are multiple segmentation units corresponding to the preoperative brain images, and the brain tissue category corresponding to each segmentation unit. The brain point cloud is a set of discrete points used to represent the three-dimensional structure of the target user's brain. Each point contains three-dimensional coordinate information and can carry other information about the attributes of that point.

[0057] In this embodiment, the first brain point cloud includes multiple first data points and first attribute information corresponding to the multiple first data points. The first attribute information is a parameter characterizing the attribute features of the first data points. The first attribute information may include electrical property parameters of brain tissue, such as conductivity, relative permittivity, etc.

[0058] Brain images are segmented into brain tissue categories to obtain multiple three-dimensional coordinate points and the corresponding brain tissue category label value for each three-dimensional coordinate point. These results constitute the brain tissue category classification. Therefore, these three-dimensional coordinate points can be used as the first data points, and the corresponding brain tissue category label values ​​can be used as the first attribute information. The set of data points composed of these discrete first data points is then used as the first brain point cloud.

[0059] Optionally, the method for determining the first brain point cloud corresponding to the target user based on the brain tissue classification result may include: classifying brain images into brain tissue categories based on a brain tissue segmentation model to obtain brain tissue classification results; for multiple first grid cells, determining each first grid cell as a first data point, and determining the brain tissue electrical characteristic parameters of the brain tissue categories corresponding to the multiple first grid cells; and obtaining the first brain point cloud corresponding to the target user based on the multiple first grid cells and the brain tissue electrical characteristic parameters of the brain tissue categories corresponding to the multiple first grid cells.

[0060] The brain tissue classification result includes multiple first grid units and a corresponding brain tissue category for each first grid unit. The brain tissue category can be visualized using a brain tissue category label value. Specifically, each first grid unit consists of at least one voxel from a brain image. The brain tissue categories include at least one of the following: gray matter category corresponding to gray matter, white matter category corresponding to white matter, and cerebrospinal fluid category corresponding to cerebrospinal fluid. Correspondingly, the brain tissue category label value can include: gray matter category label value, white matter category label value, and cerebrospinal fluid category label value. For example, the gray matter category label value can be represented by the number "1"; the white matter category label value can be represented by the number "2"; and the cerebrospinal fluid category label value can be represented by the number "3".

[0061] In this embodiment, a brain tissue segmentation model for classifying brain tissue types in brain images can be pre-trained. During application, preoperative brain images can be input into the brain tissue segmentation model, which can output the three-dimensional coordinates of multiple first grid cells and the tissue category label value corresponding to each first grid cell. Thus, each first grid cell can be identified as a first data point. Since different brain tissue categories correspond to different electrical characteristic parameters, when the brain tissue category corresponding to a first grid cell is determined, the corresponding brain tissue electrical characteristic parameters can be retrieved. Using the same method, the brain tissue electrical characteristic parameters corresponding to each first grid cell can be obtained, thereby constructing a first brain point cloud corresponding to the target user from multiple first data points and the corresponding brain tissue electrical characteristic parameters.

[0062] S130. Based on multiple first data points in the first brain point cloud and the corresponding brain tissue electrical characteristic parameters, determine the brain electrical characteristic distribution information corresponding to the target user.

[0063] Information on the distribution of brain electrical properties is used to characterize the distribution of electrical property parameters at different locations in the brain. Optionally, the representation of the distribution of brain electrical properties can be a piecewise function of brain electrical properties or a point cloud of brain electrical property distribution. In the piecewise function of brain electrical properties, the domain of each segment is a portion of the brain's spatial range, and the range is the electrical property parameter value corresponding to that portion of the brain's spatial range. The point cloud of brain electrical property distribution includes a large number of data points, each with a corresponding brain tissue electrical property parameter value.

[0064] When the distribution information of brain electrical characteristics is a piecewise function of brain electrical characteristics, the implementation method for determining the piecewise function of brain electrical characteristics corresponding to the target user based on multiple first data points in the first brain point cloud and the corresponding brain tissue electrical characteristic parameters can include: for multiple first data points in the first brain point cloud, each first data point has a corresponding three-dimensional spatial coordinate and a corresponding brain tissue electrical characteristic parameter; if the three-dimensional spatial coordinates corresponding to some first data points are adjacent and the brain tissue electrical characteristic parameters are consistent, then the spatial range formed by these first data points can be determined as a target brain spatial range. Based on the same processing method, multiple target brain spatial ranges can be obtained from the first brain point cloud, and each target brain spatial range corresponds to a brain tissue electrical characteristic parameter. Based on this, a piecewise function of brain electrical characteristics can be constructed according to multiple target brain spatial ranges and corresponding brain tissue electrical characteristic parameters.

[0065] For example, the first brain point cloud includes 300 first data points. Based on the three-dimensional spatial coordinates of these 300 first data points and the electrical characteristic parameters of brain tissue, N target brain spatial ranges can be obtained. The electrical characteristic parameter of brain tissue corresponding to the first target brain spatial range is k1, corresponding to gray matter; the electrical characteristic parameter of brain tissue corresponding to the second target brain spatial range is k2, corresponding to white matter; the electrical characteristic parameter of brain tissue corresponding to the third target brain spatial range is k3, corresponding to cerebrospinal fluid; the electrical characteristic parameter of brain tissue corresponding to the fourth target brain spatial range is k3, corresponding to white matter; ..., the electrical characteristic parameter of brain tissue corresponding to the Nth target brain spatial range is k1, corresponding to gray matter. Then, the piecewise function of the brain electrical characteristics corresponding to the target user can be expressed as:

[0066] (1);

[0067] When the brain electrical characteristic distribution information is a brain electrical characteristic distribution point cloud, the implementation method of determining the brain electrical characteristic distribution point cloud corresponding to the target user based on multiple first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the multiple first data points may include: inserting at least one second data point among multiple first data points in the first brain point cloud, and determining the brain tissue electrical characteristic parameters corresponding to each second data point based on multiple first data points, the brain tissue category of each first data point, and a preset electrical characteristic determination function; and obtaining the brain electrical characteristic distribution point cloud according to multiple first data points, the brain tissue electrical characteristic parameters corresponding to the multiple first data points, at least one second data point, and the brain tissue electrical characteristic parameters corresponding to at least one second data point.

[0068] The second data point is a newly added data point that differs from the first data point. Each second data point has corresponding second attribute information, which includes at least brain tissue electrical characteristic parameters. The brain tissue electrical characteristic parameters corresponding to the second data point are parameters that describe the basic electrical properties of the second data point, and may include, but are not limited to, conductivity and relative permittivity.

[0069] A preset electrical property determination function is used to determine the corresponding brain tissue electrical property parameters based on the tissue category value calculated for a second data point. The tissue category value is determined based on the tissue category label value of at least one first data point within the neighborhood of the second data point. The brain electrical property distribution point cloud is a brain point cloud composed of multiple first data points, second data points, and attribute information of the first and second data points.

[0070] In this embodiment, the data points in the first brain point cloud are relatively sparsely distributed. If the electric field distribution of the stimulation electrodes is simulated based on the first brain point cloud, the prediction results may be inaccurate. Therefore, the data points in the first brain point cloud can be expanded to obtain a brain electrical characteristic distribution point cloud with a higher data point density. This brain electrical characteristic distribution point cloud is the brain electrical characteristic distribution point cloud. Based on this, the electrode stimulation electric field distribution information is determined according to the high-density distribution point cloud to improve accuracy.

[0071] Multiple second data points can be inserted among multiple first data points in the first brain point cloud. Based on the insertion of multiple second data points, it is necessary to determine the second attribute information corresponding to each second data point. The method for determining the second attribute information of each second data point is consistent; here, we will use any one of the second data points as an example. For the current second data point, at least one adjacent first data point can be determined from the first brain point cloud. Based on the tissue category label value of each first data point, the corresponding tissue category calculated value can be determined. Then, based on the tissue category calculated value and a preset electrical characteristic determination function, the brain tissue electrical characteristic parameters corresponding to the current second data point can be determined. The brain tissue electrical characteristic parameters corresponding to each second data point can be determined in the same way. Therefore, the data set including all first data points, the brain tissue electrical characteristic parameters of each first data point, all second data points, and the brain tissue electrical characteristic parameters of each second data point can be defined as the brain electrical characteristic distribution point cloud.

[0072] S140. When stimulation parameters are applied to the target contact point of the stimulation electrode, the brain stimulation electric field distribution information corresponding to the stimulation parameters is determined based on the electrode implantation location, brain electrical characteristic distribution information, and stimulation parameters.

[0073] The target contact refers to the metal contact on which stimulation parameters are applied. The target contact can be one or more metal contacts on the stimulation electrode. Stimulation parameters are a series of parameters set to simulate real stimulation conditions. These parameters typically include, but are not limited to, waveform, pulse width, frequency, intensity, phase, period, interval, on / off ratio, and current direction. Brain stimulation electric field distribution information refers to the spatial distribution of the electric field within the target user's brain when brain stimulation is performed using simulated stimulation parameters. This information can be the three-dimensional coordinates of the stimulated location and the corresponding electrical characteristic values, such as current and voltage values, from the brain's electrical characteristic distribution information.

[0074] When stimulation parameters are applied to a target contact point on the stimulation electrode, the position information of the target contact point in the brain's electrical characteristic distribution point cloud can be determined based on its relative position on the stimulation electrode and the electrode implantation location. Therefore, the position information of the target contact point, the brain's electrical characteristic distribution information, and the stimulation parameters can be used as input parameters for the electric field simulation solution module. By processing and calculating these parameters, the electric field simulation solution module can obtain the brain stimulation electric field distribution information corresponding to the stimulation parameters. This brain stimulation electric field distribution information can then be displayed on the target display page.

[0075] The technical solution of this application embodiment acquires brain images of a target user with implanted stimulation electrodes and the electrode implantation location in the target user's brain. Then, it classifies the preoperative brain images into brain tissue categories to determine a first brain point cloud corresponding to the target user based on the brain tissue classification results. The first brain point cloud includes multiple first data points and brain tissue electrical characteristic parameters corresponding to the multiple first data points. Based on the multiple first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the multiple first data points, it determines the brain electrical characteristic distribution information corresponding to the target user. Thus, when stimulation parameters are applied to the target contact point of the stimulation electrode, it determines the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation location, the brain electrical characteristic distribution information, and the stimulation parameters. The technical solution of this embodiment converts the user's brain tissue information into point cloud data, and determines the distribution information of the brain electrical characteristics of the target user through the point cloud data. Then, it performs electric field simulation calculation based on the distribution information of the brain electrical characteristics. This not only adapts to the brain tissue characteristics of different users and provides personalized simulation results, improving the reliability and accuracy of the electric field distribution information of electrode stimulation, but also greatly reduces the amount of data, improves the processing speed, and improves the simulation efficiency and real-time performance of the electric field distribution around the stimulation electrode.

[0076] Example 2

[0077] Figure 3 is a flowchart illustrating another electrode stimulation electric field simulation method provided in this application embodiment. Based on the foregoing embodiment, steps S130 and S140 are described, and their specific implementation can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0078] As shown in Figure 3, the method includes the following steps:

[0079] S210. Obtain brain images of a target user whose brain has been implanted with at least one stimulating electrode, and the electrode implantation location of the at least one stimulating electrode in the target user's brain.

[0080] S220. The brain images are classified into brain tissue categories to determine the first brain point cloud corresponding to the target user based on the brain tissue classification results; wherein, the first brain point cloud includes multiple first data points and brain tissue electrical characteristic parameters corresponding to the multiple first data points.

[0081] S230, insert at least one second data point among the plurality of first data points of the first brain point cloud.

[0082] In this embodiment, the method of inserting multiple second data points may include the following two:

[0083] The first method is to uniformly insert a preset number of second data points among multiple first data points based on a preset grid density.

[0084] The second method is: determining the key brain region based on the electrode implantation location; inserting at least one second data point between multiple first data points in the key brain region based on the first grid density; and inserting at least one second data point between multiple first data points outside the key brain region based on the second grid density.

[0085] The density value of the first grid density is higher than that of the second grid density.

[0086] In this embodiment, the method of determining key brain regions based on electrode implantation location may include: determining electrode stimulation target points according to the electrode implantation location and the target user's disease type; and determining key brain regions according to the location of the electrode stimulation target points.

[0087] Electrode stimulation target refers to the brain location where electrical stimulation electrodes are applied.

[0088] Generally, different disease types correspond to different electrode stimulation areas. The disease type of the target user is determined. Based on this, and given the electrode implantation location, the final electrode stimulation area can be determined according to the electrode implantation location and the target user's disease type; this area is the electrode stimulation target. Therefore, based on the location of the electrode stimulation target, the brain region adjacent to that location can be identified as a key brain region. In this embodiment, by inserting a larger number and denser number of second data points in the key brain regions and a relatively smaller number of second data points outside the key brain regions, the prediction accuracy of the electric field distribution around the stimulation electrode can be improved without wasting data processing resources.

[0089] S240. For each second data point, determine at least one first target data point within the preset neighborhood range of the current second data point.

[0090] The preset neighborhood range refers to a pre-defined three-dimensional spatial range. For example, the preset neighborhood range can be a spherical three-dimensional space with the current second data point as the center and a radius of R.

[0091] In this implementation, a spatial index structure "{(x, y, z); (value1, value2, value3, ...)}" can be constructed based on the point cloud file corresponding to the first brain point cloud. Here, (x, y, z) represents the three-dimensional coordinates of a first data point in the first brain point cloud, and (value1, value2, value3, ...) represents the first attribute information corresponding to that first data point. The first attribute information includes at least the tissue category label value and brain tissue electrical characteristic parameters. When inserting at least one second data point, the three-dimensional coordinate information of the query point corresponding to the current second data point is known. Therefore, by using the query point's three-dimensional coordinate information, at least one first data point within a preset neighborhood range of the current second data point can be found in the spatial index structure. These first data points are the first target data points.

[0092] S250. Based on the brain tissue category corresponding to the first target data point, determine the reference value of the target brain tissue type corresponding to the current second data point.

[0093] The target brain tissue type reference value is a quantitative value that plays an auxiliary role in determining the electrical characteristic parameters of the brain tissue at the current second data point. It does not represent the actual tissue type of the current second data point. The target brain tissue type reference value can be a decimal or an integer.

[0094] In this embodiment, for any first target data point, given its brain tissue category, the corresponding tissue category label value can be determined. Thus, data calculations can be performed on the tissue category label value of at least one first target data point to obtain the target brain tissue type reference value corresponding to the current second data point.

[0095] Optionally, the implementation methods for determining the target brain tissue type reference value corresponding to the current second data point based on the brain tissue category corresponding to the first target data point can include at least the following two:

[0096] The first method is to average the tissue category label values ​​of at least one first target data point corresponding to the brain tissue category to obtain a reference value for the target brain tissue type corresponding to the current second data point.

[0097] In this embodiment, the tissue category label values ​​of all the first target data points can be averaged, and the calculated average value of the tissue type can be determined as the reference value of the target brain tissue type.

[0098] The second method is to construct a target interpolation function based on the location coordinates of at least one first target data point and the corresponding tissue category label value; and to obtain a target brain tissue type reference value corresponding to the current second data point by using the target location coordinates of the current second data point as the input parameter of the target interpolation function.

[0099] In this embodiment, a target interpolation function can be constructed based on the location coordinates of all first target data points and their corresponding tissue category label values. For example, the target interpolation function can be expressed as:

[0100] (2);

[0101] In the formula, This represents a reference value for the organization type of a second data point. This indicates the three-dimensional coordinate information corresponding to the second data point.

[0102] Based on this, by using the target location coordinates of the current second data point as the input variable of the target interpolation function, the target location coordinates are calculated through the target interpolation function to obtain the target brain tissue type reference value corresponding to the current second data point.

[0103] S260. Based on the target brain tissue type reference value and the preset electrical characteristic determination function, determine the brain tissue electrical characteristic parameters corresponding to the current second data point.

[0104] In this embodiment, the preset electrical characteristic determination function may include:

[0105] (3);

[0106] In the formula, This represents the electrical properties of the brain tissue corresponding to the second data point. These represent the electrical properties of gray matter in brain tissue. These parameters represent the electrical properties of white matter in the brain's white matter tissue. Indicates the electrical properties of cerebrospinal fluid. , , as well as This is a preset reference threshold for tissue types. It should be noted that... , , as well as The value of is not limited.

[0107] In this embodiment, after obtaining the target brain tissue type reference value of the current second data point, the target brain tissue type reference value can be substituted into a preset electrical characteristic determination function for calculation to obtain the brain tissue electrical characteristic parameters corresponding to the current second data point.

[0108] Based on the above examples, a preset organization type reference threshold is established. It can be set to 0. It can be set to 1.5. It can be set to 2.5. It can be set to 3.5. The electrical characteristic parameters corresponding to the second data point include conductivity σ and relative permittivity µ. Then, the preset electrical characteristic determination function can be expressed as:

[0109] (4);

[0110] Based on this, assuming the current second data point's target brain tissue type reference value If the value is 1.2, then the conductivity of the current second data point is... The relative permittivity is Assuming the current second data point has a target brain tissue type reference value... If the value is 2, then the conductivity of the current second data point is... The relative permittivity is Assuming the current second data point has a target brain tissue type reference value... If the value is 2.6, then the conductivity of the current second data point is... The relative permittivity is The electrical characteristic parameters corresponding to each second data point can be obtained using the same method.

[0111] S270. The data set consisting of all first data points, the brain tissue electrical characteristic parameters of each first data point, all second data points, and the brain tissue electrical characteristic parameters of each second data point is determined as the brain electrical characteristic distribution point cloud.

[0112] Based on the above embodiments, when the brain electrical characteristic distribution information is a brain electrical characteristic distribution point cloud, the step of determining the brain stimulation electric field distribution information includes S280-S290.

[0113] S280. Based on the electrode implantation location, determine the target contact point location in the brain electrical characteristic distribution point cloud corresponding to the stimulation parameters.

[0114] The target contact point location refers to the three-dimensional coordinates of the target contact point in the point cloud space of the brain's electrical characteristics distribution. The target contact point location can be a set of multiple three-dimensional coordinates.

[0115] In this embodiment, when stimulation parameters are applied to a target contact of the stimulation electrode, the position information of the stimulation electrode in the brain electrical characteristic distribution point cloud can be determined according to the electrode implantation location, and the relative position of the target contact on the stimulation electrode is determined, thereby determining the target contact position in the brain electrical characteristic distribution point cloud.

[0116] S290. Input the contact electrical characteristic parameters, the brain electrical characteristic distribution point cloud, the target contact location information, and the stimulation parameters into the electric field simulation solution module for simulation and solution, and obtain the brain stimulation electric field distribution information corresponding to the stimulation parameters.

[0117] The electric field simulation solution module is a data processing unit used to predict the stimulation electric field based on the contact location, the stimulation parameters corresponding to the contact, and the electrical properties of the tissues surrounding the contact.

[0118] In this embodiment, the contact electrical characteristic parameters, the brain electrical characteristic distribution point cloud, the target contact position, and the simulated stimulation parameters can be used as input parameters for the electric field simulation solution module. By simulating and solving these input parameters, the electric field simulation solution module can output brain stimulation electric field distribution information corresponding to the stimulation parameters.

[0119] For example, a schematic diagram of the implementation process of the electric field distribution simulation method provided in this embodiment is shown in Figure 4. As shown in Figure 4, for a target user with implanted stimulation electrodes in the brain, preoperative and postoperative brain images of the target user can be obtained. The electrode implantation position of the stimulation electrode in the target user's brain can be determined based on the postoperative brain images. The brain tissue can be classified into categories in the preoperative brain images to determine the first brain point cloud based on the brain tissue classification results; then, multiple second data points can be inserted into the first brain point cloud, and the electrical characteristic parameters corresponding to the second data points can be determined based on the first brain point cloud and a preset electrical characteristic determination function, thereby obtaining the brain electrical characteristic distribution point cloud; when simulated stimulation parameters are applied to the target contact point of the stimulation electrode, the target contact point position of the target contact point corresponding to the simulated stimulation parameters in the brain electrical characteristic distribution point cloud can be determined based on the electrode implantation position, thereby determining the brain stimulation electric field distribution information corresponding to the target user based on the electrode implantation position, the brain electrical characteristic distribution point cloud, and the simulated stimulation parameters.

[0120] The technical solution of this application embodiment, when determining the brain electrical characteristic distribution point cloud, inserts at least one second data point among multiple first data points of the first brain point cloud, and then, for each second data point, determines at least one first target data point within a preset neighborhood range of the current second data point. Based on the tissue category label value of the first target data point, a target brain tissue type reference value corresponding to the current second data point is determined. Thus, based on the target brain tissue type reference value and a preset electrical characteristic determination function, the electrical characteristic parameter corresponding to the current second data point is determined. Therefore, the brain electrical characteristic distribution point cloud is composed of all the first data points, the second data points, and the brain tissue electrical characteristic parameters of each data point. The technical solution of this embodiment upsamples the first brain point cloud and quickly and accurately determines the electrical characteristics of the newly added data points using a piecewise electrical characteristic function, thus obtaining a brain electrical characteristic distribution point cloud. Electric field simulation calculations are then performed based on this distribution point cloud, improving the reliability and accuracy of the electrode stimulation electric field distribution information. Furthermore, the reference value of the target brain tissue type corresponding to the second data point is used as an auxiliary variable, and a preset electrical characteristic determination function is employed to determine the electrical characteristic parameters corresponding to the second data point. This allows for the rapid and accurate determination of the brain electrical characteristic distribution point cloud without requiring extensive complex calculations, further enhancing the reliability and accuracy of the electrode stimulation electric field distribution information and improving the simulation efficiency and real-time performance of the electric field distribution around the stimulation electrodes.

[0121] Example 3

[0122] Figure 5 is a schematic diagram of an electrode stimulation electric field simulation device provided in an embodiment of this application. The device includes: a data acquisition module 310, an initial point cloud determination module 320, an electrical distribution determination module 330, and an electric field distribution determination module 340.

[0123] The data acquisition module 310 is configured to acquire brain images of a target user whose brain has been implanted with at least one stimulating electrode, and the electrode implantation location of the at least one stimulating electrode in the brain of the target user.

[0124] The initial point cloud determination module 320 is configured to classify the brain images into brain tissue categories, and determine a first brain point cloud corresponding to the target user based on the brain tissue classification results; wherein, the first brain point cloud includes multiple first data points and brain tissue electrical characteristic parameters corresponding to the multiple first data points;

[0125] The electrical distribution determination module 330 is configured to determine the brain electrical characteristic distribution information corresponding to the target user based on the plurality of first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the plurality of first data points;

[0126] The electric field distribution determination module 340 is configured to determine the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation location, the brain electrical characteristic distribution information, and the stimulation parameters when stimulation parameters are applied to the target contact point of the stimulation electrode.

[0127] Based on the above-mentioned device, optionally, the electrode stimulation electric field simulation device further includes: an electrode position determination module; the electrode position determination module includes:

[0128] The image data acquisition unit is configured to acquire preoperative and postoperative brain images of target users who have had at least one stimulating electrode implanted in their brains.

[0129] The electrode coordinate determination unit is configured to perform registration processing on the preoperative brain image and the postoperative brain image to determine the electrode trajectory coordinates of the at least one stimulating electrode in the three-dimensional space corresponding to the preoperative brain image.

[0130] An electrode location determination unit is configured to determine the electrode trajectory coordinates of the at least one stimulating electrode as the electrode implantation location of the stimulating electrode in the brain of the target user.

[0131] Based on the above-mentioned device, the optional initial point cloud determination module includes:

[0132] The brain tissue segmentation unit is configured to perform brain tissue category segmentation on the brain image based on a brain tissue segmentation model to obtain brain tissue category segmentation results; wherein, the brain tissue category segmentation results include multiple first grid units and the brain tissue category corresponding to each first grid unit;

[0133] The first point cloud determination unit is configured to determine each of the plurality of first grid cells as a first data point and determine the brain tissue electrical characteristic parameters of the brain tissue category corresponding to the first grid cell, thereby obtaining a first brain point cloud corresponding to the target user.

[0134] Based on the above-mentioned device, optionally, the brain tissue category includes at least one of the following: gray matter category corresponding to gray matter tissue, white matter category corresponding to white matter tissue, and cerebrospinal fluid category corresponding to cerebrospinal fluid.

[0135] Based on the above-mentioned device, optionally, the electrical distribution determination module 330 includes: a piecewise function determination submodule and a distribution point cloud determination submodule;

[0136] Based on the above-mentioned device, optionally, the brain electrical characteristic distribution information is a piecewise function of brain electrical characteristics, and the piecewise function determination submodule is configured to determine the piecewise function of brain electrical characteristics based on the plurality of first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to each first data point; wherein, the domain of each segment function in the piecewise function of brain electrical characteristics is a part of the brain spatial range, and the value range is the electrical characteristic parameter value corresponding to the part of the brain spatial range.

[0137] Optionally, based on the above-mentioned device, the brain electrical characteristic distribution information is a brain electrical characteristic distribution point cloud. The distribution point cloud determination submodule is configured to insert at least one second data point among multiple first data points of the first brain point cloud, and determine the brain tissue electrical characteristic parameters corresponding to each second data point based on the multiple first data points, the brain tissue category of each first data point, and a preset electrical characteristic determination function. The brain electrical characteristic distribution point cloud is obtained according to the multiple first data points, the brain tissue electrical characteristic parameters corresponding to the multiple first data points, the at least one second data point, and the brain tissue electrical characteristic parameters corresponding to the at least one second data point.

[0138] Based on the above-mentioned device, optionally, the distributed point cloud determination submodule includes: a second data point determination unit;

[0139] The second data point determination unit is configured to determine a key brain region based on the electrode implantation location; insert at least one second data point among a plurality of first data points in the key brain region based on a first grid density; and insert at least one second data point among a plurality of first data points outside the key brain region based on a second grid density; wherein the density value of the first grid density is higher than the density value of the second grid density.

[0140] Optionally, based on the above-mentioned device, the second data point determination unit further includes a key region determination subunit; the key region determination subunit is configured to determine the electrode stimulation target point according to the electrode implantation location and the target user's disease type; and determine the key brain region according to the location of the electrode stimulation target point.

[0141] Based on the above device, the optional distributed point cloud determination submodule may also include: an electrical parameter determination unit;

[0142] The target data point determination subunit is configured to determine at least one first target data point within a preset neighborhood range of each second data point;

[0143] The tissue type value determination subunit is configured to determine a target brain tissue type reference value corresponding to the current second data point based on the brain tissue category corresponding to the first target data point.

[0144] The electrical parameter determination subunit is configured to determine the electrical characteristic parameters of the brain tissue corresponding to the current second data point based on the target brain tissue type reference value and the preset electrical characteristic determination function.

[0145] Based on the above-described apparatus, optionally, the tissue type value determination subunit is configured to average the tissue category label values ​​of at least one of the first target data points corresponding to the brain tissue category to obtain a target brain tissue type reference value corresponding to the current second data point; or;

[0146] Based on the location coordinates of at least one first target data point and the corresponding tissue category label value, a target interpolation function is constructed; the target location coordinates of the current second data point are used as the input parameters of the target interpolation function to obtain a target brain tissue type reference value corresponding to the current second data point.

[0147] Based on the above device, optionally, the preset electrical characteristic determination function includes:

[0148] ;

[0149] In the formula, This represents the electrical characteristic parameters of the brain tissue corresponding to the second data point. These represent the electrical properties of gray matter in brain tissue. These parameters represent the electrical properties of white matter in the brain's white matter tissue. Indicates the electrical properties of cerebrospinal fluid. , , as well as This is a preset reference threshold for organization types.

[0150] Based on the above-mentioned device, optionally, the electric field distribution determination module 340 includes:

[0151] The contact point location determination unit is configured to determine the target contact point location information of the target contact point corresponding to the stimulation parameter in the brain electrical characteristic distribution information based on the electrode implantation location;

[0152] The electric field distribution information determination unit is configured to input the contact electrical characteristic parameters, the brain electrical characteristic distribution information, the target contact position information, and the stimulation parameters into the electric field simulation solution module for simulation and solution, so as to obtain the brain stimulation electric field distribution information corresponding to the stimulation parameters.

[0153] The technical solution of this application embodiment acquires brain images of a target user with implanted stimulation electrodes and the electrode implantation location in the target user's brain. Then, it classifies the preoperative brain images into brain tissue categories to determine a first brain point cloud corresponding to the target user based on the brain tissue classification results. The first brain point cloud includes multiple first data points and brain tissue electrical characteristic parameters corresponding to the multiple first data points. Based on the multiple first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the multiple first data points, it determines the brain electrical characteristic distribution information corresponding to the target user. Thus, when stimulation parameters are applied to the target contact point of the stimulation electrode, it determines the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation location, the brain electrical characteristic distribution information, and the stimulation parameters. The technical solution of this embodiment converts the user's brain tissue information into point cloud data, and determines the distribution information of the brain electrical characteristics of the target user through the point cloud data. Then, it performs electric field simulation calculation based on the distribution information of the brain electrical characteristics. This not only adapts to the brain tissue characteristics of different users and provides personalized simulation results, improving the reliability and accuracy of the electric field distribution information of electrode stimulation, but also greatly reduces the amount of data, improves the processing speed, and improves the simulation efficiency and real-time performance of the electric field distribution around the stimulation electrode.

[0154] The electrode stimulation electric field simulation device provided in this application embodiment can execute the electrode stimulation electric field simulation method provided in any embodiment of this application, and has the corresponding functional modules for executing the method.

[0155] The multiple units and modules included in the above system are divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the names of the multiple functional units are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0156] Example 4

[0157] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 shows a block diagram of an exemplary electronic device 40 suitable for implementing the embodiments of this application. The electronic device 40 shown in Figure 6 is merely an example and should not impose any limitation on the function and scope of use of the embodiments of this application.

[0158] As shown in Figure 6, the electronic device 40 is presented in the form of a general-purpose computing device. The components of the electronic device 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0159] Bus 403 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MCA) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0160] Electronic device 40 includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 40, including volatile and non-volatile media, removable and non-removable media.

[0161] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 404 and / or cache memory 405. Electronic device 40 may also include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 406 may be configured to read and write non-removable, non-volatile magnetic media (not shown in Figure 6, commonly referred to as a "hard disk drive"). Although not shown in Figure 6, disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., portable compact disc read-only memory (CD-ROM), digital versatile disc-read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 403 via one or more data media interfaces. The memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present application.

[0162] A program / utility 408 having a set (at least one) of program modules 407 may be stored, for example, in memory 402. Such program modules 407 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or a combination of these examples may include an implementation of a network environment. Program modules 407 typically perform the functions and / or methods described in the embodiments of this application.

[0163] Electronic device 40 can also communicate with one or more external devices 409 (e.g., keyboard, pointing device, display, etc.), and with one or more devices that enable a user to interact with electronic device 40, and / or with any device that enables electronic device 40 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 411. Electronic device 40 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 412. As shown, network adapter 412 communicates with other modules of electronic device 40 via bus 403. It should be understood that, although not shown in Figure 6, other hardware and / or software modules can be used in conjunction with electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, Redundant Array of Independent Disks (RAID) systems, tape drives, and data backup storage systems.

[0164] The processing unit 401 executes various functional applications and page processing by running programs stored in the system memory 402, such as implementing the electrode stimulation electric field simulation method provided in the embodiments of this application.

[0165] Example 5

[0166] This application embodiment also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform an electrode stimulation electric field simulation method, the method comprising:

[0167] Obtain brain images of a target user with at least one stimulating electrode implanted in the brain, and the electrode implantation location of the at least one stimulating electrode in the target user's brain;

[0168] The brain images are classified into brain tissue categories to determine a first brain point cloud corresponding to the target user based on the brain tissue classification results; wherein, the first brain point cloud includes multiple first data points and brain tissue electrical characteristic parameters corresponding to the multiple first data points;

[0169] Based on the plurality of first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the plurality of first data points, the brain electrical characteristic distribution information corresponding to the target user is determined;

[0170] When stimulation parameters are applied to the target contact of the stimulation electrode, the brain stimulation electric field distribution information corresponding to the stimulation parameters is determined based on the electrode implantation location, the brain electrical characteristic distribution information, and the stimulation parameters.

[0171] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM, read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, CD-ROM, optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0172] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0173] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, radio frequency (RF), or any suitable combination thereof.

[0174] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LANs or WANs—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0175] Example 6

[0176] Figure 7 is a schematic diagram of a medical system provided in an embodiment of this application. The system includes: an implantable medical device 510, a display 520, and a processor 530. The implantable medical device 510, the display 520, and the processor 530 can communicate interactively. The processor is provided with a memory that stores a computer program. The processor is configured to execute the computer program and, when the computer program is executed, implement an electrode stimulation electric field simulation method.

[0177] The implantable medical device 510 includes a pulse generator implanted in the body of a target user and an electrode wire implanted in the brain of the target user. The implanted end of the electrode wire is provided with at least a plurality of electrode contacts, and the pulse generator is connected to the electrode wire.

[0178] Display 520 is configured to display the brain stimulation electric field distribution information.

[0179] The processor 530 is configured to acquire brain images of a target user whose brain has at least one stimulating electrode implanted, execute an electrode stimulation electric field simulation method to determine the brain stimulation electric field distribution information of the target user's brain in response to stimulation parameters, and display the brain stimulation electric field distribution information using the display.

[0180] The technical solution of this application embodiment includes a medical system comprising an implantable medical device, a display, and a processor. When applied, the processor acquires brain images of a target user with implanted stimulation electrodes and the electrode implantation location in the target user's brain. Then, it performs brain tissue classification on the preoperative brain images to determine a first brain point cloud corresponding to the target user based on the brain tissue classification results. The first brain point cloud includes multiple first data points and corresponding brain tissue electrical characteristic parameters. Based on the multiple first data points and corresponding brain tissue electrical characteristic parameters in the first brain point cloud, it determines brain electrical characteristic distribution information corresponding to the target user. Therefore, when stimulation parameters are applied to the target contact point of the stimulation electrode, based on the electrode implantation location, brain electrical characteristic distribution information, and stimulation parameters, it determines the brain stimulation electric field distribution information corresponding to the stimulation parameters. This brain stimulation electric field distribution information can then be displayed on the display. The technical solution of this embodiment converts the user's brain tissue information into point cloud data, and determines the distribution information of the brain electrical characteristics of the target user through the point cloud data. Then, it performs electric field simulation calculation based on the distribution information of the brain electrical characteristics. This not only adapts to the brain tissue characteristics of different users and provides personalized simulation results, improving the reliability and accuracy of the electric field distribution information of electrode stimulation, but also greatly reduces the amount of data, improves the processing speed, and improves the simulation efficiency and real-time performance of the electric field distribution around the stimulation electrode.

Claims

1. A method for simulating the electric field of electrode stimulation, comprising: Obtain brain images of a target user with at least one stimulating electrode implanted in the brain, and the electrode implantation location of the at least one stimulating electrode in the target user's brain; The brain images are classified into brain tissue categories to determine a first brain point cloud corresponding to the target user based on the brain tissue classification results; wherein, the first brain point cloud includes multiple first data points and brain tissue electrical characteristic parameters corresponding to the multiple first data points; Based on the plurality of first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the plurality of first data points, the brain electrical characteristic distribution information corresponding to the target user is determined; In response to the application of stimulation parameters to the target contact of the stimulation electrode, the brain stimulation electric field distribution information corresponding to the stimulation parameters is determined based on the electrode implantation location, the brain electrical characteristic distribution information, and the stimulation parameters.

2. The electrode stimulation electric field simulation method according to claim 1 further includes: Determining the implantation site of at least one stimulating electrode in the brain of the target user, including: Obtain preoperative and postoperative brain images of target users who have had at least one stimulating electrode implanted in their brains. The preoperative brain images and the postoperative brain images are registered to determine the electrode trajectory coordinates of the at least one stimulating electrode in the three-dimensional space corresponding to the preoperative brain image. The electrode trajectory coordinates are used to determine the electrode implantation location of the stimulation electrode in the target user's brain.

3. The electrode stimulation electric field simulation method according to claim 1, wherein, The step of classifying the brain images into brain tissue categories, and determining the first brain point cloud corresponding to the target user based on the brain tissue classification results, includes: The brain images are classified into brain tissue categories based on a brain tissue segmentation model to obtain brain tissue category classification results; wherein, the brain tissue category classification results include multiple first grid cells and the brain tissue category corresponding to each first grid cell; Each of the first grid cells is defined as a first data point, and the electrical characteristic parameters of the brain tissue corresponding to the brain tissue category of the plurality of first grid cells are determined to obtain the first brain point cloud corresponding to the target user.

4. The electrode stimulation electric field simulation method according to claim 3, wherein, The brain tissue categories include at least one of the following: gray matter category corresponding to gray matter tissue, white matter category corresponding to white matter tissue, and cerebrospinal fluid category corresponding to cerebrospinal fluid.

5. The electrode stimulation electric field simulation method according to claim 1, wherein, The brain electrical characteristic distribution information is a piecewise function of brain electrical characteristics. The step of determining the brain electrical characteristic distribution information corresponding to the target user based on the plurality of first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the plurality of first data points includes: Based on the multiple first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to each first data point, a piecewise function of brain electrical characteristics is determined; wherein, the domain of each piecewise function of brain electrical characteristics is a portion of the brain spatial range, and the range of each piecewise function of brain electrical characteristics is the electrical characteristic parameter value corresponding to the portion of the brain spatial range.

6. The electrode stimulation electric field simulation method according to claim 1, wherein, The brain electrical characteristic distribution information is a brain electrical characteristic distribution point cloud. The step of determining the brain electrical characteristic distribution information corresponding to the target user based on the plurality of first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the plurality of first data points includes: At least one second data point is inserted between multiple first data points in the first brain point cloud. Based on the multiple first data points, the brain tissue category corresponding to each first data point, and a preset electrical characteristic determination function, the brain tissue electrical characteristic parameters corresponding to each second data point are determined. Based on the multiple first data points, the brain tissue electrical characteristic parameters corresponding to the multiple first data points, the at least one second data point, and the brain tissue electrical characteristic parameters corresponding to the at least one second data point, a brain electrical characteristic distribution point cloud is obtained.

7. The electrode stimulation electric field simulation method according to claim 6, wherein, The insertion of at least one second data point among multiple first data points of the first brain point cloud includes: Key brain regions are determined based on the electrode implantation locations; Based on a first grid density, at least one second data point is inserted between multiple first data points in the key brain region; Based on a second grid density, at least one second data point is inserted between multiple first data points outside the key brain region; wherein the density value of the first grid density is higher than the density value of the second grid density.

8. The electrode stimulation electric field simulation method according to claim 7, wherein, The determination of key brain regions based on the electrode implantation location includes: The electrode stimulation target points are determined based on the electrode implantation location and the target user's disease type; The key brain region is determined based on the location of the electrode stimulation target.

9. The electrode stimulation electric field simulation method according to claim 6, wherein, The step of determining the brain tissue electrical characteristic parameters corresponding to each second data point based on the plurality of first data points, the brain tissue category corresponding to each first data point, and a preset electrical characteristic determination function includes: For each second data point, determine at least one first target data point within a preset neighborhood of the current second data point; Based on the brain tissue category corresponding to the at least one first target data point, determine the target brain tissue type reference value corresponding to the current second data point; Based on the target brain tissue type reference value and the preset electrical property determination function, the brain tissue electrical property parameters corresponding to the current second data point are determined.

10. The electrode stimulation electric field simulation method according to claim 9, wherein, The step of determining a reference value for the target brain tissue type corresponding to the current second data point based on the brain tissue category corresponding to the at least one first target data point includes: The tissue category label values ​​corresponding to the brain tissue categories of the at least one first target data point are averaged to obtain a target brain tissue type reference value corresponding to the current second data point; or, Based on the location coordinates of the at least one first target data point and the corresponding organization category label value, a target interpolation function is constructed. Using the target location coordinates of the current second data point as the input parameter of the target interpolation function, a reference value for the target brain tissue type corresponding to the current second data point is obtained.

11. The electrode stimulation electric field simulation method according to claim 6, wherein, The preset electrical characteristic determination function includes: ； In the formula, This represents the electrical characteristic parameters of the brain tissue corresponding to the second data point. These represent the electrical properties of gray matter in brain tissue. These parameters represent the electrical properties of white matter in the brain's white matter tissue. Indicates the electrical properties of cerebrospinal fluid. 、 、 as well as This is a preset reference threshold for organization types.

12. The electrode stimulation electric field simulation method according to claim 1, wherein, The step of determining the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation location, the brain electrical characteristic distribution information, and the stimulation parameters includes: Based on the electrode implantation location, the target contact point location information of the target contact point corresponding to the stimulation parameter in the brain electrical characteristic distribution information is determined; The contact electrical characteristic parameters, the brain electrical characteristic distribution information, the target contact location information, and the stimulation parameters are input into the electric field simulation solution module for simulation and solution to obtain the brain stimulation electric field distribution information corresponding to the stimulation parameters.

13. An electrode stimulation electric field simulation device, comprising: The data acquisition module is configured to acquire brain images of a target user whose brain has been implanted with at least one stimulating electrode, and the electrode implantation location of the at least one stimulating electrode in the target user's brain. The initial point cloud determination module is configured to classify the brain tissue into categories based on the brain images, and to determine a first brain point cloud corresponding to the target user based on the brain tissue classification results; wherein, the first brain point cloud includes multiple first data points and brain tissue electrical characteristic parameters corresponding to the multiple first data points; The electrical distribution determination module is configured to determine the brain electrical characteristic distribution information corresponding to the target user based on the plurality of first data points in the first brain point cloud and the brain tissue electrical characteristic parameters corresponding to the plurality of first data points; The electric field distribution determination module is configured to, in response to applying stimulation parameters to the target contact of the stimulation electrode, determine the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation location, the brain electrical characteristic distribution information, and the stimulation parameters.

14. A medical system comprising: An implantable medical device, comprising at least a pulse generator implanted in the body of a target user and an electrode wire implanted in the brain of the target user, wherein the implanted end of the electrode wire is provided with at least a plurality of electrode contacts, and the pulse generator is connected to the electrode wire; The display is configured to show information about the distribution of electric fields stimulated in the brain; The processor is configured to acquire brain images of a target user whose brain has at least one stimulating electrode implanted, execute the electrode stimulation electric field simulation method according to any one of claims 1-12 to determine the brain stimulation electric field distribution information of the target user's brain in response to stimulation parameters, and display the brain stimulation electric field distribution information using the display.

15. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the electrode stimulation electric field simulation method according to any one of claims 1-12.

16. A computer-readable storage medium storing computer instructions, said computer instructions being configured to cause a processor to execute the electrode stimulation electric field simulation method according to any one of claims 1-12.

17. A computer program product comprising a computer program that, when executed by a processor, implements the electrode stimulation electric field simulation method according to any one of claims 1-12.

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