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

Abstract

The embodiment of the invention discloses an electrode stimulation electric field simulation method and device, electronic equipment and a storage medium, and relates to the technical field of information, and the method comprises the steps: obtaining a brain image of a target user implanted with a stimulation electrode and an electrode implantation position of the stimulation electrode; performing brain tissue category division on the brain image to determine a first brain point cloud corresponding to the target user based on a brain tissue category division result; determining brain electrical characteristic distribution information corresponding to the target user based on a plurality of first data points in the first brain point cloud and the corresponding brain tissue electrical characteristic parameters; when the stimulation parameter is applied to the target contact of the stimulation electrode, the brain stimulation electric field distribution information corresponding to the stimulation parameter is determined based on the electrode implantation position, the brain electrical characteristic distribution information and the stimulation parameter, so that the reliability and the accuracy of the electrode stimulation electric field distribution information are improved; and the simulation efficiency and the real-time performance of the electric field distribution around the stimulating electrode are improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an electrode stimulation electric field simulation method, device, electronic equipment and storage medium. Background Art

[0002] Deep Brain Stimulation (DBS) is an advanced treatment method for various neurological diseases. In DBS treatment, accurate prediction of the electric field distribution around the stimulation electrode is crucial to optimize stimulation parameters and improve treatment efficacy.

[0003] At present, there are two commonly used electric field distribution simulation methods. The first is: when certain stimulation parameters are applied to the stimulation electrodes, the electric field distribution around the stimulation electrodes is predicted by using the pre-stored standard electric field data corresponding to various human tissues. The second is: when certain stimulation parameters are applied to the stimulation electrodes, the finite element method is used to perform electric field simulation based on the user's MRI or CT data to predict the electric field distribution around the stimulation electrodes.

[0004] However, the problem with the first method mentioned above is that the use of pre-stored standard electric field data for electric field prediction simulation cannot adapt to the brain tissue characteristics of different patients, and there are problems with poor reliability and low accuracy of the electric field distribution information; the problem with the second method mentioned above is that when performing electric field simulation operations, it is necessary to import a complete brain tissue image model of a user before simulation operations can be performed. The volume of these model data is large, which makes the prediction process of the electric field distribution information require a lot of time and computing resources, and there are problems with low efficiency and poor real-time performance of the electric field distribution simulation. Summary of the invention

[0005] The present invention provides an electrode stimulation electric field simulation method, device, electronic device and storage medium to improve the reliability and accuracy of electrode stimulation electric field distribution information and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulation electrode.

[0006] In a first aspect, an embodiment of the present invention provides an electrode stimulation electric field simulation method, the method comprising: Acquire a brain image of a target user having at least one stimulation electrode implanted in the brain, and an electrode implantation position of the at least one stimulation electrode in the brain of the target user; Classifying the brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result; wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; Determining brain electrical characteristic distribution information corresponding to the target user based on the plurality of first data points and the corresponding brain tissue electrical characteristic parameters in the first brain point cloud; When stimulation parameters are applied to the target contact of the stimulation electrode, brain stimulation electric field distribution information corresponding to the stimulation parameters is determined based on the electrode implantation position, the brain electrical characteristic distribution information and the stimulation parameters.

[0007] In a second aspect, an embodiment of the present invention further provides an electrode stimulation electric field simulation device, the device comprising: A data acquisition module, used to acquire a brain image of a target user having at least one stimulation electrode implanted in the brain, and an electrode implantation position of the at least one stimulation electrode in the brain of the target user; an initial point cloud determination module, configured to classify the brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result; wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; an electrical property distribution determination module, configured to determine brain electrical property distribution information corresponding to the target user based on the plurality of first data points in the first brain point cloud and the corresponding electrical property parameters of the brain tissue; The electric field distribution determination module is used to determine the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation position, the brain electrical characteristics distribution information and the stimulation parameters when the stimulation parameters are applied to the target contact of the stimulation electrode.

[0008] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising: one or more processors; A storage device is used to store one or more programs. When the one or more programs are executed by one or more processors, the one or more processors implement the electrode stimulation electric field simulation method as any one of the embodiments of the present invention.

[0009] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising 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 the present invention.

[0010] In a fifth aspect, an embodiment of the present invention further provides a medical system, the medical system comprising: An implantable medical device, the implantable medical device at least comprising a pulse generator implanted in a target user's body and an electrode wire implanted in the target user's brain, the implanted end of the electrode wire being provided with at least a plurality of electrode contacts, the pulse generator being connected to the electrode wire; A display, used to display the brain stimulation electric field distribution information; The processor is configured to obtain a brain image of a target user having at least one stimulation electrode implanted in the brain, execute any electrode stimulation electric field simulation method of the embodiments of the present invention to determine brain stimulation electric field distribution information of the target user's brain for stimulation parameters, and use the display to display the brain stimulation electric field distribution information.

[0011] The technical solution of the embodiment of the present invention is to obtain a brain image of a target user with stimulation electrodes implanted in the brain, and the electrode implantation position of the stimulation electrodes in the target user's brain, and then classify the preoperative brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result, wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; further, based on the plurality of 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, so that when the stimulation parameters are applied to the target contact of the stimulation electrode, based on the electrode implantation position, the brain electrical characteristic distribution information and the stimulation parameters, determine the brain stimulation electric field distribution information corresponding to the stimulation parameters. The technical solution of this embodiment converts the user's brain tissue information into point cloud data, and determines the brain electrical characteristic distribution information corresponding to the target user through the point cloud data, so as to perform electric field simulation calculations based on the brain electrical characteristic distribution information. It can not only adapt to the brain tissue characteristics of different users, provide personalized simulation results, and improve the reliability and accuracy of the electrode stimulation electric field distribution information; but also greatly reduce the amount of data, improve the processing speed, and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulation electrode. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required to describe the embodiments. Obviously, the drawings introduced are only drawings of a part of the embodiments to be described in the present invention, rather than all the drawings. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 is a schematic diagram of a stimulation electrode involved in an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of an electrode stimulation electric field simulation method provided by an embodiment of the present invention; Figure 3 A schematic flow chart of another electrode stimulation electric field simulation method provided by an embodiment of the present invention; Figure 4 A schematic diagram of the implementation process of the electric field distribution simulation method involved in an embodiment of the present invention; Figure 5 A schematic diagram of the structure of an electrode stimulation electric field simulation device provided by an embodiment of the present invention; Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention; Figure 7 A schematic diagram of the structure of a medical system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] Below, the present application is further described in conjunction with the accompanying drawings and specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0015] Below, one of the application fields (i.e., implantable devices) of the embodiments of the present application is first briefly described. An implantable neural stimulation system (an implantable medical system) mainly includes a stimulator implanted in the patient's body and a programmable device arranged outside the patient's body. Existing neural regulation technology mainly implants electrodes in specific structures (i.e., target points) in the body through stereotactic surgery, and the stimulator implanted in the patient's body emits discharge pulses to the target point through the electrodes, regulates the electrical activity and function of the corresponding neural structure and network, thereby improving symptoms and relieving pain. Among them, the stimulator can be any one of an implantable neural electrical stimulation device, an implantable cardiac electrical stimulation system (also known as a pacemaker), an implantable drug delivery system (Implantable Drug Delivery System, referred to as IDDS) and a lead switching device. Implantable neural stimulation devices include, for example, deep brain stimulation system (DBS), implantable cortical nerve stimulation system (CNS), implantable spinal cord stimulation system (SCS), implantable sacral nerve stimulation system (SNS), implantable vagus nerve stimulation system (VNS), etc.

[0016] In some embodiments, the stimulator may include an implantable pulse generator (IPG), an electrode wire, and an extension wire disposed between the pulse generator and the electrode wire, and data interaction between the pulse generator and the electrode wire is realized through the extension wire, and the pulse generator is disposed in the patient's body. In response to the program-controlled instructions sent by the program-controlled device, the sealed battery and the circuit are used to provide controllable electrical stimulation energy to the tissue in the body, and one or two controllable specific electrical stimulations are delivered to specific areas of the tissue in the body through the implanted extension wire and the electrode wire. The extension wire is used in conjunction with the pulse generator as a transmission medium for the electrical stimulation signal, and transmits the electrical stimulation signal generated by the pulse generator to the electrode wire. The electrode wire delivers electrical stimulation to specific areas of the tissue in the body through the electrode contacts thereon. The stimulator is provided with one or more electrode wires on one side or both sides, and a plurality of electrode contacts are provided on the electrode wire.

[0017] In other embodiments, the stimulator may only include a pulse generator and an electrode wire, wherein the pulse generator may be embedded in the patient's skull and the electrode wire may be implanted in the patient's skull, in which case the pulse generator is directly connected to the electrode wire without extending the wire.

[0018] The electrode wire can be a nerve stimulation electrode, and the electrode wire delivers electrical stimulation to a specific area of ​​the tissue in the body through a plurality of electrode contacts. The stimulator is provided with one or more electrode wires on one side or both sides, and a plurality of electrode contacts are provided on the electrode wire, and the electrode contacts can be arranged uniformly or non-uniformly on the circumference of the electrode wire. As an example, the electrode contacts can be arranged in an array of 4 rows and 3 columns (a total of 12 electrode contacts) on the circumference of the electrode wire. The electrode contacts may include stimulation contacts and / or collection contacts. The electrode contacts may be in the shape of sheets, rings, dots, etc., for example.

[0019] In some possible ways, the stimulated in vivo tissue may be the patient's brain tissue, and the stimulated site may be a specific site of the brain tissue. When the patient's disease type is different, the stimulated site is generally different, and the number of stimulation contacts (single source or multiple sources), the use of one or more (single channel or multiple channels) of specific electrical stimulation signals, and the stimulation parameter data are also different. It can be considered that when the stimulation contacts used are multi-source, multi-channel (multi-channel), a larger amount of data will be generated compared to a single source, single channel.

[0020] The embodiments of the present application do not limit the types of diseases that can be used, and they can be the types of diseases that deep brain stimulation (DBS), spinal cord stimulation (SCS), pelvic stimulation, gastric stimulation, peripheral nerve stimulation, and functional electrical stimulation can be used for. Among them, the types of diseases that DBS can be used to treat or manage include, but are not limited to: spastic diseases (e.g., epilepsy), pain, migraine, mental illness (e.g., major depressive disorder (MDD)), bipolar disorder, anxiety, post-traumatic stress disorder, mild depression, obsessive-compulsive disorder (OCD), behavioral disorders, mood disorders, memory disorders, mental state disorders, movement 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 injuries.

[0021] Stimulation parameters may include: frequency (for example, the number of electrical stimulation pulse signals within a unit time of 1s, in Hz), pulse width (duration of each pulse, in μs), amplitude (generally expressed in voltage, that is, the intensity of each pulse, in V), timing (for example, it can be continuous or triggered), stimulation mode (including one or more of current mode, voltage mode, timed stimulation mode and cyclic stimulation mode), one or more of the upper and lower limits controlled by the doctor (the range adjustable by the doctor) and the upper and lower limits controlled by the patient (the range adjustable by the patient).

[0022] The technical solution provided by the embodiment of the present invention is mainly applied to the field of implantable medical devices. The implantable medical devices mainly include a pulse generator, an electrode wire and a programmable device (or medical device). The pulse generator is implanted in the patient's body (such as the chest cavity, skull, etc.), and the electrode wire is connected to the pulse generator at one end under the skin, and a stimulation electrode is provided at the other end.

[0023] Schematic diagram of the stimulation electrodes can be found in Figure 1 ,like Figure 1 As shown, the stimulation electrode includes at least one metal contact for outputting a stimulation source, and these metal contacts may be in the shape of a ring, or may be a directional electrode composed of a plurality of segmented electrode contacts. The stimulation electrode is partially implanted in a designated location of the patient's brain (such as a nucleus, neural tissue, etc. associated with the disease), and the doctor generates program parameters to the pulse generator through a program-controlled device, and the pulse generator delivers electrical stimulation to at least one metal contact in the stimulation electrode through an electrode wire, so that the at least one metal contact generates an electric field to treat the corresponding disease. The purpose of the embodiment of the present invention is to construct brain electrical characteristic distribution information according to the corresponding brain image data for different target users, so that when applying simulated stimulation parameters to any metal contact of the stimulation electrode, the electric field distribution around the metal contact can be accurately predicted by the brain electrical characteristic distribution information, thereby accurately and efficiently simulating the stimulation range of the stimulation electrode.

[0024] Embodiment 1 Figure 2 A flow chart of an electrode stimulation electric field simulation method provided in an embodiment of the present invention. This embodiment can be applied to any situation where it is necessary to accurately predict the electric field distribution around the stimulation electrode in deep brain stimulation applications. The method can be performed 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 PC or a server.

[0025] like Figure 2 As shown, the electrode stimulation electric field simulation method includes: S110, obtaining a brain image of a target user having at least one stimulation electrode implanted in the brain, and an electrode implantation position of the at least one stimulation electrode in the brain of the target user.

[0026] Among them, the target user is a user whose brain is about to be implanted with a stimulating electrode for electric field distribution prediction. The target user's brain has one or more stimulating electrodes implanted in it. The brain image may be a brain MRI image, a brain CT image, etc. Optionally, the brain image may be a brain image collected before the stimulating electrode is implanted in the target user's brain. Brain MRI image refers to the three-dimensional brain imaging data obtained by scanning the target user's brain using magnetic resonance imaging (MRI) technology; brain CT image refers to the three-dimensional brain imaging data obtained by tomography scanning the target user's brain using computed tomography (CT) technology.

[0027] The electrode implantation position refers to the position information of the stimulation electrode in the target user's skull. Specifically, the electrode implantation position may be the electrode track coordinates corresponding to the stimulation electrode.

[0028] Specifically, before and after the target user undergoes deep brain stimulation surgery, brain imaging technology, such as MRI technology or CT technology, can be used to collect preoperative brain images and postoperative brain images. Preoperative refers to the situation where the stimulation electrodes have not been implanted in the patient's brain, and postoperative refers to the situation where the stimulation electrodes have been implanted in the patient's brain. Furthermore, the position information of the stimulation electrodes in the target user's brain can be extracted through the postoperative brain images, thereby obtaining the electrode implantation position of at least one stimulation electrode in the target user's brain. When it is necessary to use the technical solution provided in this embodiment to perform stimulation electric field prediction simulation on the target user, the preoperative brain image and electrode implantation position corresponding to the target user can be retrieved.

[0029] Optionally, the specific implementation steps of determining the electrode implantation position of at least one stimulation electrode in the target user's brain may include: obtaining preoperative brain images and postoperative brain images of the target user in which at least one stimulation electrode is implanted in the brain; performing registration processing on the preoperative brain images and the postoperative brain images to determine the electrode trajectory coordinates of at least one stimulation electrode in the three-dimensional space corresponding to the preoperative brain image; and for at least one stimulation electrode, determining the electrode trajectory coordinates as the electrode implantation position of the stimulation electrode in the target user's brain.

[0030] The postoperative brain image refers to the brain image collected after the stimulation electrode is implanted in the target user's brain. Optionally, the postoperative brain image can be a postoperative brain CT image. This is because: the stimulation electrode has been implanted in the target user's brain. If the MRI technology is used to collect the target user's brain image, it is easy to heat up the metal contacts on the stimulation electrode, causing brain damage to the target user. The use of CT technology will not have such a risk.

[0031] Among them, the electrode trajectory coordinates refer to the position coordinates of the stimulation electrode in a certain space. For example, the electrode trajectory coordinate data can be expressed as {(103 131 142), (103 131 143), (104 131 144), (105 132 145)..., (XYZ)}, and the meaning of each element in the vector is the three-dimensional coordinate corresponding to the voxel constituting the stimulation electrode in the postoperative brain image.

[0032] In this embodiment, one or more stimulation electrodes may be implanted in the target user's skull, and the processing method for each stimulation electrode is consistent. The following is an example of implanting one stimulation electrode in the target user's skull.

[0033] In this embodiment, the target user can collect preoperative brain images through MRI technology or CT technology before the operation, and collect postoperative brain images through CT technology after the operation. Thus, the preoperative brain image and postoperative brain image corresponding to the target user can be obtained. Further, the postoperative brain image can be registered to the three-dimensional space corresponding to the preoperative brain image to obtain the registered postoperative brain image to be processed. Further, the electrode trajectory tracking algorithm can be used to reconstruct the trajectory information of the stimulating electrode in the three-dimensional space of the preoperative brain image according to the postoperative brain image to be processed, so as to obtain the electrode trajectory coordinates. Thus, the electrode trajectory coordinates can be determined as the electrode implantation position of the stimulating electrode in the brain of the target user.

[0034] S120 , classifying the preoperative brain image into brain tissue categories, and determining a first brain point cloud corresponding to the target user based on the brain tissue category classification result.

[0035] The brain tissue classification results are multiple segmentation units corresponding to the preoperative brain image 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 the point.

[0036] In this embodiment, the first brain point cloud includes a plurality of first data points and corresponding first attribute information. The first attribute information is a parameter characterizing the attribute characteristics of the first data point. The first attribute information may include electrical characteristic parameters of brain tissue, such as conductivity, relative dielectric constant, etc.

[0037] Specifically, the brain image is processed for brain tissue classification to obtain a plurality of three-dimensional coordinate points and a brain tissue classification label value corresponding to each three-dimensional coordinate point. These results are the brain tissue classification results. Therefore, these three-dimensional coordinate points can be used as first data points, the corresponding brain tissue classification label value can be used as first attribute information, and the data point set composed of these discrete first data points can be used as the first brain point cloud.

[0038] Optionally, a specific implementation method of determining the first brain point cloud corresponding to the target user based on the brain tissue category division result may include: performing brain tissue category division on brain images based on a brain tissue segmentation model to obtain a brain tissue category division result; for multiple first grid units, determining each first grid unit as a first data point, and determining the brain tissue electrical characteristic parameters of the brain tissue category corresponding to the first grid unit, to obtain the first brain point cloud corresponding to the target user.

[0039] Among them, the brain tissue category division result includes multiple first grid units and the brain tissue category corresponding to each first grid unit, wherein the brain tissue category can be concretely represented by the brain tissue category label value. In particular, the first grid unit is composed of at least one voxel in the brain image. Specifically, the brain tissue category includes: at least one of the gray matter category corresponding to the gray matter tissue of the brain, the white matter category corresponding to the white matter tissue of the brain, and the cerebrospinal fluid category corresponding to the cerebrospinal fluid. Correspondingly, the brain tissue category label value may include: gray matter category label value, white matter category label value and cerebrospinal fluid category label value. Exemplarily, 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".

[0040] In this embodiment, a brain tissue segmentation model for classifying brain tissues in brain images can be pre-trained. In specific applications, the preoperative brain image can be input into the brain tissue segmentation model, and the brain tissue segmentation model can output the three-dimensional coordinates of multiple first grid units and the tissue category label value corresponding to each first grid unit. Thus, each first grid unit can be determined as a first data point. Since different brain tissue categories correspond to different electrical characteristic parameters, when determining the brain tissue category corresponding to a certain first grid unit, the brain tissue electrical characteristic parameters corresponding to the first grid unit can be retrieved. Based on the same method, the brain tissue electrical characteristic parameters corresponding to each first grid unit can be obtained, so that the first brain point cloud corresponding to the target user is constructed by each first data point and the corresponding brain tissue electrical characteristic parameters.

[0041] S130. Determine brain electrical characteristic distribution information corresponding to the target user based on the multiple first data points in the first brain point cloud and the corresponding brain tissue electrical characteristic parameters.

[0042] Among them, the brain electrical characteristic distribution information is used to characterize the distribution of electrical characteristic parameters corresponding to different positions of the brain. Optionally, the concrete representation of the brain electrical characteristic distribution information can be a piecewise function of the brain electrical characteristics, or it can be a point cloud of the brain electrical characteristics distribution. Among them, the domain of each segment of the brain electrical characteristic piecewise function is a part of the brain space range, and the range is the electrical characteristic parameter value corresponding to the part of the brain space range. The brain electrical characteristic distribution point cloud includes a large number of data points, and each data point has a corresponding value of the brain tissue electrical characteristic parameter.

[0043] Specifically, in the case where the brain electrical characteristic distribution information is a piecewise function of the brain electrical characteristic, based on multiple first data points in the first brain point cloud and corresponding brain tissue electrical characteristic parameters, the specific implementation method of determining the piecewise function of the brain electrical characteristic corresponding to the target user may 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 certain sets of 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 according to the first brain point cloud, each target brain spatial range corresponds to a brain tissue electrical characteristic parameter, based on which, according to each target brain spatial range and the corresponding brain tissue electrical characteristic parameter, a piecewise function of brain electrical characteristics can be constructed.

[0044] Exemplarily, the first brain point cloud includes 300 first data points. According to the three-dimensional spatial coordinates of the 300 first data points and the electrical characteristic parameters of the brain tissue, N target brain space ranges can be obtained, wherein the electrical characteristic parameters of the brain tissue corresponding to the first target brain space range are the electrical characteristic parameters k1 corresponding to the gray matter tissue of the brain, the electrical characteristic parameters of the brain tissue corresponding to the second target brain space range are the electrical characteristic parameters k2 corresponding to the white matter tissue of the brain, the electrical characteristic parameters of the brain tissue corresponding to the third target brain space range are the electrical characteristic parameters k3 corresponding to the cerebrospinal fluid, the electrical characteristic parameters k3 corresponding to the cerebrospinal fluid of the fourth target brain space range, the electrical characteristic parameters of the brain tissue corresponding to the fifth target brain space range are the electrical characteristic parameters k2 corresponding to the white matter tissue of the brain, ..., the electrical characteristic parameters of the brain tissue corresponding to the Nth target brain space range are the electrical characteristic parameters k1 corresponding to the gray matter tissue of the brain, then the brain electrical characteristic piecewise function corresponding to the target user can be expressed as: (1); Specifically, in the case where the brain electrical characteristic distribution information is a brain electrical characteristic distribution point cloud, based on multiple first data points in the first brain point cloud and corresponding brain tissue electrical characteristic parameters, the specific implementation method of determining the brain electrical characteristic distribution point cloud corresponding to the target user may include: inserting at least one second data point between multiple first data points of the first brain point cloud, and determining 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, to obtain a brain electrical characteristic distribution point cloud.

[0045] The second data point is a newly added data point different from the first data point. Each second data point has corresponding second attribute information, and the second attribute information at least includes 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 a second data point, for example, may include but are not limited to conductivity, relative dielectric constant, etc.

[0046] The preset electrical characteristic determination function is used to determine the electrical characteristic parameters of the brain tissue corresponding to a certain second data point according to the calculated value of the tissue category. The calculated value of the tissue category is determined according to the tissue category label value of the tissue category corresponding to at least one first data point within the neighborhood of the second data point. The brain electrical characteristic distribution point cloud is a brain point cloud composed of multiple first data points, second data points, and attribute information of each data point.

[0047] In this embodiment, the data points in the first brain point cloud are relatively sparsely distributed. If the stimulation electrode electric field distribution simulation is performed based on the first brain point cloud, there is a problem of inaccurate prediction results. Based on this, the data points of the first brain point cloud can be expanded to obtain a brain electrical characteristic distribution point cloud with a higher data point density. The 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 based on the high-density distribution point cloud to improve accuracy.

[0048] Specifically, multiple second data points can be inserted between multiple first data points of the first brain point cloud. On the basis of inserting 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, and any one of the second data points is used as an example for explanation. For the current second data point, at least one first data point adjacent to it can be determined from the first brain point cloud, and the corresponding tissue category calculation value can be determined according to the tissue category label value of each first data point, and then, according to the tissue category calculation value and the preset electrical characteristic determination function, the brain tissue electrical characteristic parameter corresponding to the current second data point is determined. Based on the same method, the brain tissue electrical characteristic parameter corresponding to each second data point can be determined. Thus, 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 can be determined as a brain electrical characteristic distribution point cloud.

[0049] S140. 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 position, the brain electrical characteristic distribution information and the stimulation parameters.

[0050] Among them, the target contact refers to the metal contact to which the stimulation parameters are applied, and the target contact may be one or several metal contacts on the stimulation electrode. The stimulation parameters are a series of parameters set by simulating real stimulation conditions. These parameters usually include but are not limited to waveform, pulse width, frequency, intensity, phase, period, interval time, on-off ratio, current direction, etc. Brain stimulation electric field distribution information refers to the spatial distribution of the electric field in the brain of the target user when the stimulation parameters are simulated for brain stimulation. Specifically, the brain stimulation electric field distribution information may be the three-dimensional coordinates and corresponding electrical characteristic values ​​corresponding to the stimulated position in the brain electrical characteristic distribution information. The electrical characteristic values ​​may be, for example, current values, voltage values, etc.

[0051] Specifically, when stimulation parameters are applied to a target contact of the stimulation electrode, the position information of the target contact in the brain electrical characteristic distribution point cloud can be determined according to the relative position of the target contact on the stimulation electrode and the electrode implantation position; thus, the position information of the target contact, the brain electrical characteristic distribution information and the stimulation parameters can be used as input parameters of the electric field simulation solution module, and the electric field simulation solution module can obtain the brain stimulation electric field distribution information corresponding to the stimulation parameters by processing and calculating these parameters. Furthermore, the brain stimulation electric field distribution information can be displayed in the target display page.

[0052] The technical solution of the embodiment of the present invention is to obtain a brain image of a target user with stimulation electrodes implanted in the brain, and the electrode implantation position of the stimulation electrodes in the target user's brain, and then classify the preoperative brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result, wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; further, based on the plurality of 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, so that when the stimulation parameters are applied to the target contact of the stimulation electrode, based on the electrode implantation position, the brain electrical characteristic distribution information and the stimulation parameters, determine the brain stimulation electric field distribution information corresponding to the stimulation parameters. The technical solution of this embodiment converts the user's brain tissue information into point cloud data, and determines the brain electrical characteristic distribution information corresponding to the target user through the point cloud data, so as to perform electric field simulation calculations based on the brain electrical characteristic distribution information. It can not only adapt to the brain tissue characteristics of different users, provide personalized simulation results, and improve the reliability and accuracy of the electrode stimulation electric field distribution information; but also greatly reduce the amount of data, improve the processing speed, and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulation electrode.

[0053] Embodiment 2 Figure 3 This is a flow chart of another electrode stimulation electric field simulation method provided by an embodiment of the present invention. On the basis of the above embodiment, S130 and S140 are further refined. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here.

[0054] like Figure 3 As shown, the method specifically comprises the following steps: S210, obtaining a brain image of a target user having at least one stimulation electrode implanted in the brain, and an electrode implantation position of the at least one stimulation electrode in the brain of the target user.

[0055] S220, classifying the brain image into brain tissue categories, and determining a first brain point cloud corresponding to the target user based on the brain tissue category classification result; wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters.

[0056] S230, inserting at least one second data point between multiple first data points of the first brain point cloud.

[0057] In this embodiment, the manner of inserting multiple second data points may include the following two methods: The first one is: uniformly inserting a preset number of second data points into a plurality of first data points based on a preset grid density.

[0058] The second is: determining the key brain area based on the electrode implantation position; inserting at least one second data point between each first data point in the key brain area based on the first grid density; inserting at least one second data point between each first data point outside the key brain area based on the second grid density.

[0059] The density value of the first grid density is higher than the density value of the second grid density.

[0060] In this embodiment, the specific implementation method of determining the key brain area based on the electrode implantation position may include: determining the electrode stimulation target according to the electrode implantation position and the disease type of the target user; determining the key brain area according to the position of the electrode stimulation target.

[0061] Among them, the electrode stimulation target refers to the specific location in the brain where the stimulation electrode needs to apply electrical stimulation.

[0062] Generally speaking, different types of symptoms correspond to different electrode stimulation areas. The type of disease corresponding to the target user is determined. Based on this, on the basis of obtaining the electrode implantation position, the final electrode stimulation area can be determined according to the electrode implantation position and the type of disease of the target user. This area is the electrode stimulation target. Thus, according to the position of the electrode stimulation target, the brain area adjacent to the position can be determined as the key brain area. In this embodiment, by inserting a larger number of more dense second data points in the key brain area and inserting a relatively small number of second data points outside the key brain area, the prediction accuracy of the electric field distribution around the stimulation electrode can be improved without wasting data computing resources.

[0063] S240: For each second data point, determine at least one first target data point within a preset neighborhood range of the current second data point.

[0064] The preset neighborhood range refers to a preset three-dimensional space range. For example, the preset neighborhood range may be a spherical three-dimensional space with a radius R and the current second data point as the center.

[0065] 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, where (x, y, z) represents the three-dimensional coordinates corresponding to a first data point in the first brain point cloud, and (value1, value2, value3...) represents the first attribute information corresponding to the first data point, and the first attribute information includes at least a tissue category label value and a brain tissue electrical characteristic parameter. 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, so that the first data points in the preset neighborhood range of the current second data point can be found in the spatial index structure through the three-dimensional coordinate information of the query point, and these first data points are the first target data points.

[0066] S250: Based on the brain tissue category corresponding to the first target data point, determine a target brain tissue type reference value corresponding to the current second data point.

[0067] Among them, the target brain tissue type reference value is a quantitative value that plays an auxiliary calculation role in determining the brain tissue electrical characteristic parameters of the current second data point, and does not represent the actual tissue category of the current second data point. The target brain tissue type reference value can be a decimal or an integer.

[0068] In this embodiment, for any first target data point, when its brain tissue category is known, the corresponding tissue category label value can be determined, so that data calculation 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.

[0069] Optionally, based on the brain tissue category corresponding to the first target data point, specific implementation methods for determining the reference value of the target brain tissue type corresponding to the current second data point may include at least the following two methods: The first method is: averaging the tissue category label values ​​of the brain tissue categories corresponding to the first target data points to obtain a target brain tissue type reference value corresponding to the current second data point.

[0070] In this embodiment, the tissue category label values ​​of all the first target data points may be averaged, and the calculated tissue type average value may be determined as the target brain tissue type reference value.

[0071] The second method is: based on the position coordinate information of each first target data point and the corresponding tissue category label value, a target interpolation function is constructed; the target position coordinate of the current second data point is used as the input parameter of the target interpolation function to obtain the target brain tissue type reference value corresponding to the current second data point.

[0072] In this embodiment, a target interpolation function can be constructed based on the position coordinate information of all first target data points and the corresponding tissue category label values. For example, the target interpolation function can be expressed as: (2); In the formula, represents a tissue type reference value for a second data point, Indicates the three-dimensional coordinate information corresponding to the second data point.

[0073] Based on this, the target position coordinates of the current second data point are used as input variables of the target interpolation function, and the target position coordinates are calculated through the target interpolation function to obtain the target brain tissue type reference value corresponding to the current second data point.

[0074] S260: Determine the brain tissue electrical characteristic parameter corresponding to the current second data point based on the target brain tissue type reference value and a preset electrical characteristic determination function.

[0075] In this embodiment, the preset electrical characteristic determination function may include: (3); In the formula, represents the electrical characteristic parameter of brain tissue corresponding to the second data point, Represents the gray matter electrical characteristic parameters corresponding to the gray matter tissue of the brain, Indicates the white matter electrical characteristic parameters corresponding to the brain white matter tissue, Represents the electrical characteristic parameters of cerebrospinal fluid, , , as well as is the preset tissue type reference threshold. It should be noted that , , as well as There is no restriction on the specific value of .

[0076] 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 brought into a preset electrical characteristic determination function for calculation to obtain the brain tissue electrical characteristic parameters corresponding to the current second data point.

[0077] Based on the above exemplary embodiment, the tissue type reference threshold is preset Can be set to 0, Can be set to 1.5, It can be set to 2.5. It can be set to 3.5, and the electrical characteristic parameters corresponding to the second data point include conductivity σ and relative dielectric constant µ, then the preset electrical characteristic determination function can be expressed as: (4); On this basis, it is assumed that the target brain tissue type reference value of the current second data point is 1.2, then the conductivity of the current second data point is , the relative dielectric constant is ; Assume that the target brain tissue type reference value of the current second data point is 2, then the conductivity of the current second data point is , the relative dielectric constant is ; Assume that the target brain tissue type reference value of the current second data point is 2.6, then the conductivity of the current second data point is , the relative dielectric constant is Based on the same method, the electrical characteristic parameters corresponding to each second data point can be obtained.

[0078] S270, determining a 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 as a brain electrical characteristic distribution point cloud.

[0079] On the basis of the above-mentioned embodiment, 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.

[0080] S280. Based on the electrode implantation position, determine the target contact point position corresponding to the stimulation parameter in the brain electrical characteristic distribution point cloud.

[0081] The target contact point position refers to the three-dimensional position coordinates of the target contact point in the brain electrical characteristic distribution point cloud space. The target contact point position can be a collection of multiple three-dimensional position coordinates.

[0082] 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 characteristics distribution point cloud can be determined based on the electrode implantation position, and the relative position of the target contact on the stimulation electrode is determined, thereby determining the target contact position of the target contact in the brain electrical characteristics distribution point cloud.

[0083] S290, input the contact electrical characteristic parameters, the brain electrical characteristic distribution point cloud, the target contact position information and the stimulation parameters into the electric field simulation solution module for simulation solution to obtain the brain stimulation electric field distribution information corresponding to the stimulation parameters.

[0084] Among them, the electric field simulation solution module is a data processing unit used to predict the stimulation electric field based on the contact position, the stimulation parameters corresponding to the contact, and the electrical characteristics of the tissue around the contact.

[0085] 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 of the electric field simulation solution module. The electric field simulation solution module can output the brain stimulation electric field distribution information corresponding to the stimulation parameters by simulating and solving these input parameters.

[0086] For example, the implementation process diagram of the electric field distribution simulation method provided in this embodiment is shown in Figure 4 .like Figure 4 As shown, for a target user with a stimulation electrode implanted in the brain, the preoperative brain image and postoperative brain image 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 image. The preoperative brain image can be divided into brain tissue categories to determine the first brain point cloud based on the brain tissue category division result; further, a plurality of second data points can be inserted into the first brain point cloud, and a function can be determined based on the first brain point cloud and the preset electrical characteristics to determine the electrical characteristic parameters corresponding to the second data points, thereby obtaining a brain electrical characteristic distribution point cloud; when the simulated stimulation parameters are applied to the target contact of the stimulation electrode, the target contact position 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.

[0087] The technical solution of the embodiment of the present invention is to insert at least one second data point between multiple first data points of the first brain point cloud when determining the brain electrical characteristic distribution point cloud, and then for each second data point, determine at least one first target data point within a preset neighborhood range of the current second data point, and determine the target brain tissue type reference value corresponding to the current second data point based on the tissue category label value of the first target data point, thereby, based on the target brain tissue type reference value and the preset electrical characteristic determination function, determine the electrical characteristic parameters corresponding to the current second data point, thereby, 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, quickly and accurately determines the electrical characteristics of the newly added data points through the electrical characteristics piecewise function, and obtains the brain electrical characteristics distribution point cloud, so as to perform electric field simulation calculations based on the brain electrical characteristics distribution point cloud, thereby improving the reliability and accuracy of the electrode stimulation electric field distribution information; the target brain tissue type reference value corresponding to the second data point is used as an auxiliary variable, and the electrical characteristic parameters corresponding to the second data point are determined by using a preset electrical characteristic determination function, so that the brain electrical characteristics distribution point cloud can be quickly and accurately determined without a large number of complex calculations, thereby further improving 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 electrode.

[0088] Embodiment 3 Figure 5 This is a structural schematic diagram of an electrode stimulation electric field simulation device provided in an embodiment of the present invention. 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.

[0089] The data acquisition module 310 is used to acquire a brain image of a target user having at least one stimulation electrode implanted in the brain, and an electrode implantation position of the at least one stimulation electrode in the brain of the target user; The initial point cloud determination module 320 is used to classify the brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result; wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; An electrical property distribution determination module 330, configured to determine brain electrical property distribution information corresponding to the target user based on the plurality of first data points in the first brain point cloud and the corresponding electrical property parameters of the brain tissue; The electric field distribution determination module 340 is used to determine the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation position, the brain electrical characteristics distribution information and the stimulation parameters when the stimulation parameters are applied to the target contact of the stimulation electrode.

[0090] On the basis of the above device, optionally, the electrode stimulation electric field simulation device further includes: an electrode position determination module; the electrode position determination module includes: An image data acquisition unit, used to acquire pre-operative brain images and post-operative brain images of a target user having at least one stimulation electrode implanted in the brain; An electrode coordinate determination unit, used for performing registration processing on the preoperative brain image and the postoperative brain image to determine the electrode trajectory coordinates of the at least one stimulation electrode in the three-dimensional space corresponding to the preoperative brain image; The electrode position determination unit is used to determine the electrode trajectory coordinates of the at least one stimulation electrode as the electrode implantation position of the stimulation electrode in the brain of the target user.

[0091] Based on the above device, optionally, the initial point cloud determination module includes: A brain tissue classification unit, configured to classify the brain image into brain tissue categories based on a brain tissue segmentation model to obtain a brain tissue category classification result; wherein the brain tissue category classification result includes a plurality of first grid units and a brain tissue category corresponding to each of the first grid units; The first point cloud determination unit is used to determine each of the multiple first grid units as a first data point, and determine the brain tissue electrical characteristic parameters of the brain tissue category corresponding to the first grid unit, so as to obtain a first brain point cloud corresponding to the target user.

[0092] Based on the above device, optionally, the brain tissue category includes at least one of: a gray matter category corresponding to brain gray matter tissue, a white matter category corresponding to brain white matter tissue, and a cerebrospinal fluid category corresponding to cerebrospinal fluid.

[0093] On the basis of the above device, optionally, the electrical distribution determination module 330 includes: a piecewise function determination submodule and a distribution point cloud determination submodule; On the basis of the above-mentioned device, optionally, the brain electrical characteristic distribution information is a piecewise function of the brain electrical characteristics, and the piecewise function determination submodule is used to determine the piecewise function of the brain electrical characteristics based on the multiple first data points in the first brain point cloud and the electrical characteristic parameters of the brain tissue corresponding to each of the first data points; wherein the domain of each segment of the brain electrical characteristic piecewise function is a partial brain space range, and the range is the electrical characteristic parameter value corresponding to the partial brain space range.

[0094] Based on the above-mentioned device, optionally, the brain electrical characteristic distribution information is a brain electrical characteristic distribution point cloud, and the distribution point cloud determination submodule is used to insert at least one second data point between 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 to obtain a brain electrical characteristic distribution point cloud.

[0095] Based on the above device, optionally, the distribution point cloud determination submodule includes: a second data point determination unit; The second data point determination unit is specifically used to determine the key brain area based on the electrode implantation position; based on the first grid density, insert at least one second data point between each of the first data points in the key brain area; based on the second grid density, insert at least one second data point between each of the first data points outside the key brain area; wherein the density value of the first grid density is higher than the density value of the second grid density.

[0096] Based on the above device, optionally, the second data point determination unit also includes a key area determination subunit; the key area determination subunit is specifically used to determine the electrode stimulation target according to the electrode implantation position and the disease type of the target user; and determine the key brain area according to the position of the electrode stimulation target.

[0097] On the basis of the above device, optionally, the distributed point cloud determination submodule further includes: an electrical parameter determination unit; a target data point determination subunit, configured to determine, for each of the second data points, at least one first target data point within a preset neighborhood of the current second data point; a tissue type value determination subunit, 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; The electrical parameter determination subunit is used to determine the brain tissue electrical characteristic parameter corresponding to the current second data point based on the target brain tissue type reference value and the preset electrical characteristic determination function.

[0098] Based on the above device, optionally, the tissue type value determination subunit specifically averages the tissue category label values ​​of the brain tissue categories corresponding to the first target data points to obtain a target brain tissue type reference value corresponding to the current second data point; or; Based on the position coordinate information of each of the first target data points and the corresponding tissue category label value, a target interpolation function is constructed; the target position coordinate of the current second data point is used as an input parameter of the target interpolation function to obtain a target brain tissue type reference value corresponding to the current second data point.

[0099] Based on the above device, optionally, the preset electrical characteristic determination function includes: ; In the formula, represents the electrical characteristic parameter of the brain tissue corresponding to the second data point, Represents the gray matter electrical characteristic parameters corresponding to the gray matter tissue of the brain, Indicates the white matter electrical characteristic parameters corresponding to the brain white matter tissue, Represents the electrical characteristic parameters of cerebrospinal fluid, , , as well as The reference threshold for the preset tissue type.

[0100] Based on the above device, optionally, the electric field distribution determination module 340 includes: A contact point position determination unit, configured to determine target contact point position information of a target contact point corresponding to the stimulation parameter in the brain electrical characteristic distribution information based on the electrode implantation position; The electric field distribution information determination unit is used 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 solution to obtain the brain stimulation electric field distribution information corresponding to the stimulation parameters.

[0101] The technical solution of the embodiment of the present invention is to obtain a brain image of a target user with stimulation electrodes implanted in the brain, and the electrode implantation position of the stimulation electrodes in the target user's brain, and then classify the preoperative brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result, wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; further, based on the plurality of 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, so that when the stimulation parameters are applied to the target contact of the stimulation electrode, based on the electrode implantation position, the brain electrical characteristic distribution information and the stimulation parameters, determine the brain stimulation electric field distribution information corresponding to the stimulation parameters. The technical solution of this embodiment converts the user's brain tissue information into point cloud data, and determines the brain electrical characteristic distribution information corresponding to the target user through the point cloud data, so as to perform electric field simulation calculations based on the brain electrical characteristic distribution information. It can not only adapt to the brain tissue characteristics of different users, provide personalized simulation results, and improve the reliability and accuracy of the electrode stimulation electric field distribution information; but also greatly reduce the amount of data, improve the processing speed, and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulation electrode.

[0102] The electrode stimulation electric field simulation device provided in the embodiment of the present invention can execute the electrode stimulation electric field simulation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0103] It is worth noting that the various units and modules included in the above system are only 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 specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present invention.

[0104] Embodiment 4 Figure 6 The present invention is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 6 A block diagram of an exemplary electronic device 40 suitable for implementing exemplary embodiments of the present invention is shown. Figure 6 The electronic device 40 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0105] like Figure 6 As shown, the electronic device 40 is 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, a system memory 402, and a bus 403 connecting different system components (including the system memory 402 and the processing unit 401).

[0106] Bus 403 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0107] The electronic device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 40, including volatile and non-volatile media, removable and non-removable media.

[0108] 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 further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 406 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 403 via one or more data medium interfaces. The memory 402 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0109] A program / utility 408 having a set (at least one) of program modules 407 may be stored, for example, in the memory 402, such program modules 407 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 407 generally perform the functions and / or methods of the embodiments described herein.

[0110] The electronic device 40 may also communicate with one or more external devices 409 (e.g., keyboards, pointing devices, displays, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 40, and / or may communicate with any device that enables the electronic device 40 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 411. Furthermore, the electronic device 40 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 412. As shown, the network adapter 412 communicates with other modules of the electronic device 40 via the bus 403. It should be understood that although Figure 6 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 40, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0111] The processing unit 401 executes various functional applications and page processing by running the programs stored in the system memory 402, such as implementing the electrode stimulation electric field simulation method provided in the embodiment of the present invention.

[0112] Embodiment 5 An embodiment of the present invention further provides a storage medium containing computer executable instructions, which are used to execute an electrode stimulation electric field simulation method when executed by a computer processor. The method includes: Acquire a brain image of a target user having at least one stimulation electrode implanted in the brain, and an electrode implantation position of the at least one stimulation electrode in the brain of the target user; Classifying the brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result; wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; Determining brain electrical characteristic distribution information corresponding to the target user based on the plurality of first data points and the corresponding brain tissue electrical characteristic parameters in the first brain point cloud; When stimulation parameters are applied to the target contact of the stimulation electrode, brain stimulation electric field distribution information corresponding to the stimulation parameters is determined based on the electrode implantation position, the brain electrical characteristic distribution information and the stimulation parameters.

[0113] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device.

[0114] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0115] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0116] Computer program code for performing the operation of the embodiments of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0117] Embodiment 6 Figure 7 A structural schematic diagram of a medical system provided in an embodiment of the present application, the system comprising: 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 with each other, a memory is provided in the processor, the memory stores a computer program, the processor is used to execute the computer program and implement an electrode stimulation electric field simulation method when the computer program is executed.

[0118] Among them, the implantable medical device 510 at least includes a pulse generator implanted in the body of the target user and an electrode wire implanted in the brain of the target user, the implantation 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.

[0119] The display 520 is used to display the brain stimulation electric field distribution information.

[0120] Processor 530 is configured to obtain a brain image of a target user having at least one stimulation electrode implanted in the brain, execute an electrode stimulation electric field simulation method to determine brain stimulation electric field distribution information of the target user's brain for stimulation parameters, and display the brain stimulation electric field distribution information using the display.

[0121] The technical solution of the embodiment of the present application is that the medical system includes an implantable medical device, a display, and a processor. When the medical system is used in a specific application, the processor obtains a brain image of a target user with stimulation electrodes implanted in the brain, and the electrode implantation position of the stimulation electrodes in the target user's brain, and then classifies the preoperative brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result, wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; further, based on the plurality of first data points in the first brain point cloud and the corresponding brain tissue electrical characteristic parameters, the brain electrical characteristic distribution information corresponding to the target user is determined, so that when the 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 position, the brain electrical characteristic distribution information and the stimulation parameters, so that the brain stimulation electric field distribution information can 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 brain electrical characteristic distribution information corresponding to the target user through the point cloud data, so as to perform electric field simulation calculations based on the brain electrical characteristic distribution information. It can not only adapt to the brain tissue characteristics of different users, provide personalized simulation results, and improve the reliability and accuracy of the electrode stimulation electric field distribution information; but also greatly reduce the amount of data, improve the processing speed, and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulation electrode.

[0122] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for simulating an electrode stimulation electric field, characterized in that: include: Acquire a brain image of a target user having at least one stimulation electrode implanted in the brain, and an electrode implantation position of the at least one stimulation electrode in the brain of the target user; Classifying the brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result; wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; Determining 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 corresponding brain tissue electrical characteristic parameters; When stimulation parameters are applied to the target contact of the stimulation electrode, brain stimulation electric field distribution information corresponding to the stimulation parameters is determined based on the electrode implantation position, the brain electrical characteristic distribution information and the stimulation parameters.

2. The electrode stimulation electric field simulation method according to claim 1, characterized in that: The method further includes: determining an electrode implantation position of at least one stimulation electrode in the target user's brain, including: obtaining pre-operative and post-operative brain images of a target user who has at least one stimulating electrode implanted in the brain; Performing registration processing on the preoperative brain image and the postoperative brain image to determine the electrode trajectory coordinates of the at least one stimulation electrode in the three-dimensional space corresponding to the preoperative brain image; The electrode trajectory coordinates are determined as the electrode implantation position of the stimulation electrode in the target user's brain.

3. The electrode stimulation electric field simulation method according to claim 1, characterized in that: The performing brain tissue classification on the brain image to determine a first brain point cloud corresponding to the target user based on the brain tissue classification result includes: Based on the brain tissue segmentation model, the brain image is classified into brain tissue categories to obtain a brain tissue category classification result; wherein the brain tissue category classification result includes a plurality of first grid units and a brain tissue category corresponding to each of the first grid units; Each of the first grid units is determined as a first data point, and brain tissue electrical characteristic parameters of the brain tissue category corresponding to the first grid unit are determined to obtain a first brain point cloud corresponding to the target user.

4. The electrode stimulation electric field simulation method according to claim 3, characterized in that: The brain tissue category includes at least one of a gray matter category corresponding to gray matter tissue of the brain, a white matter category corresponding to white matter tissue of the brain, and a cerebrospinal fluid category corresponding to cerebrospinal fluid.

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

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

7. The electrode stimulation electric field simulation method according to claim 6, characterized in that: The inserting at least one second data point between the plurality of first data points of the first brain point cloud comprises: determining critical brain regions based on the electrode implantation locations; interpolating at least one second data point between each of the first data points in the critical brain region based on a first grid density; Based on a second grid density, at least one second data point is inserted between each of the first data points outside the critical brain region; wherein a density value of the first grid density is higher than a density value of the second grid density.

8. The electrode stimulation electric field simulation method according to claim 7, characterized in that: The determining of the key brain area based on the electrode implantation position comprises: Determining electrode stimulation targets according to the electrode implantation position and the disease type of the target user; The key brain area is determined based on the location of the electrode stimulation target.

9. The electrode stimulation electric field simulation method according to claim 6, characterized in that: The determining, based on the plurality of first data points, the brain tissue category of each of the first data points, and a preset electrical characteristic determination function, a brain tissue electrical characteristic parameter corresponding to each of the second data points comprises: For each of the second data points, determining at least one first target data point within a preset neighborhood of the current second data point; Determining a target brain tissue type reference value corresponding to the current second data point based on the brain tissue type corresponding to the first target data point; Based on the target brain tissue type reference value and the preset electrical characteristic determination function, the brain tissue electrical characteristic parameter corresponding to the current second data point is determined.

10. The electrode stimulation electric field simulation method according to claim 9, characterized in that: The determining, based on the brain tissue category corresponding to the first target data point, a target brain tissue type reference value corresponding to the current second data point includes: Averaging the tissue category label values ​​of the brain tissue categories corresponding to the first target data points to obtain a target brain tissue type reference value corresponding to the current second data point; or, constructing a target interpolation function based on the position coordinate information of each of the first target data points and the corresponding tissue category label value; The target position coordinates of the current second data point are used as input parameters of the target interpolation function to obtain a target brain tissue type reference value corresponding to the current second data point.

11. The electrode stimulation electric field simulation method according to claim 6, characterized in that: The preset electrical characteristic determination function includes: ； In the formula, represents the electrical characteristic parameter of the brain tissue corresponding to the second data point, Represents the gray matter electrical characteristic parameters corresponding to the gray matter tissue of the brain, Indicates the white matter electrical characteristic parameters corresponding to the brain white matter tissue, Represents the electrical characteristic parameters of cerebrospinal fluid, , , as well as The reference threshold for the preset tissue type.

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

13. An electrode stimulation electric field simulation device, characterized in that: The device includes: A data acquisition module, used to acquire a brain image of a target user having at least one stimulation electrode implanted in the brain, and an electrode implantation position of the at least one stimulation electrode in the brain of the target user; an initial point cloud determination module, configured to classify the brain image into brain tissue categories, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category classification result; wherein the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical characteristic parameters; an electrical property distribution determination module, configured to determine brain electrical property distribution information corresponding to the target user based on the plurality of first data points in the first brain point cloud and the corresponding electrical property parameters of the brain tissue; The electric field distribution determination module is used to determine the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation position, the brain electrical characteristics distribution information and the stimulation parameters when the stimulation parameters are applied to the target contact of the stimulation electrode.

14. A medical system, characterized in that: The medical system includes: An implantable medical device, the implantable medical device at least comprising a pulse generator implanted in a target user's body and an electrode wire implanted in the target user's brain, the implanted end of the electrode wire being provided with at least a plurality of electrode contacts, the pulse generator being connected to the electrode wire; A display, used to display the brain stimulation electric field distribution information; The processor is configured to obtain a brain image of a target user having at least one stimulation electrode implanted in the brain, execute the electrode stimulation electric field simulation method described in any one of claims 1 to 12 to determine the brain stimulation electric field distribution information of the target user's brain for stimulation parameters, and use the display to display the brain stimulation electric field distribution information.

15. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the electrode stimulation electric field simulation method according to any one of claims 1-12.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the electrode stimulation electric field simulation method according to any one of claims 1 to 12 when executed.

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

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