An electrode stimulation electric field simulation method, device, electronic device and storage medium

Abstract

An electrode stimulation electric field simulation method, device, electronic device, and storage medium are disclosed in an embodiment of the present invention, which relates to the field of information technology. The method includes: obtaining a brain image of a target user implanted with a stimulating electrode and the electrode implantation position of the stimulating electrode; performing brain tissue category division on the brain image to determine a first brain point cloud corresponding to the target user based on the brain tissue category division result; determining brain electrical property distribution information corresponding to the target user based on multiple first data points in the first brain point cloud and corresponding brain tissue electrical property parameters; when applying stimulation parameters to a target contact of the stimulating electrode, determining brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation position, brain electrical property distribution information, and stimulation parameters, improving the reliability and accuracy of the electrode stimulation electric field distribution information, and enhancing the simulation efficiency and real-time performance of the electric field distribution around the stimulating electrode.

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, apparatus, electronic device, and storage medium. Background Art

[0002] Deep Brain Stimulation (DBS) is an advanced treatment method for treating various neurological diseases. In DBS treatment, accurately predicting the electric field distribution around the stimulating electrode is crucial for optimizing the stimulation parameters and improving the treatment effect.

[0003] Currently, there are two commonly used methods for simulating the electric field distribution. The first method is: when applying certain stimulation parameters to the stimulating electrode, predicting the electric field distribution around the stimulating electrode through pre-stored standard electric field data corresponding to various human tissues. The second method is: when applying certain stimulation parameters to the stimulating electrode, performing electric field simulation using the finite element method based on the user's MRI or CT data to predict the electric field distribution around the stimulating electrode.

[0004] However, the problem with the first method above is that using pre-stored standard electric field data for electric field prediction simulation cannot adapt to the brain tissue characteristics of different patients, resulting in poor reliability and low accuracy of the electric field distribution information. The problem with the second method above is that when performing electric field simulation operations, it is necessary to import the complete brain tissue image model of a certain user to perform simulation operations. The volume of these model data is large, which makes the prediction process of the electric field distribution information consume a large amount of time and computing resources, resulting in 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, apparatus, electronic device, and storage medium to improve the reliability and accuracy of the electrode stimulation electric field distribution information and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulating electrode.

[0006] In a first aspect, an embodiment of the present invention provides an electrode stimulation electric field simulation method, which includes:

[0007] Obtaining the brain image of a target user with at least one stimulating electrode implanted in the brain, and the electrode implantation position of the at least one stimulating electrode in the brain of the target user;

[0008] Performing brain tissue category division on the brain image to determine a first brain point cloud corresponding to the target user based on the brain tissue category division result; wherein, the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical property parameters;

[0009] Determine the brain electrical property 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 property parameters;

[0010] When applying a stimulation parameter to the target contact of the stimulation electrode, determine the brain stimulation electric field distribution information corresponding to the stimulation parameter based on the electrode implantation position, the brain electrical property distribution information, and the stimulation parameter.

[0011] In a second aspect, an embodiment of the present invention further provides an electrode stimulation electric field simulation device, which includes:

[0012] A data acquisition module, configured to acquire a brain image of a target user with at least one stimulation electrode implanted in the brain, and the electrode implantation position of the at least one stimulation electrode in the brain of the target user;

[0013] An initial point cloud determination module, configured to perform brain tissue category division on the brain image, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category division result; wherein, the first brain point cloud includes multiple first data points and corresponding brain tissue electrical property parameters;

[0014] An electrical property distribution determination module, configured to determine the brain electrical property 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 property parameters;

[0015] An electric field distribution determination module, configured to determine the brain stimulation electric field distribution information corresponding to the stimulation parameter based on the electrode implantation position, the brain electrical property distribution information, and the stimulation parameter when applying the stimulation parameter to the target contact of the stimulation electrode.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, which includes:

[0017] One or more processors;

[0018] A storage device, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, enable the one or more processors to implement the electrode stimulation electric field simulation method according to any one of the embodiments of the present invention.

[0019] In a fourth aspect, an embodiment of the present invention further provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the electrode stimulation electric field simulation method according to any one of the embodiments of the present invention when executed by a computer processor.

[0020] In a fifth aspect, an embodiment of the present invention further provides a medical system, and the medical system includes:

[0021] An implantable medical device, the implantable medical device at least includes a pulse generator implanted into a target user's body and an electrode lead implanted into the target user's brain, at least a plurality of electrode contacts are arranged at the implanted end of the electrode lead, and the pulse generator is connected to the electrode lead;

[0022] A display for displaying the brain stimulation electric field distribution information;

[0023] A processor, configured to obtain a brain image of a target user with at least one stimulating electrode implanted in the brain, execute the electrode stimulation electric field simulation method of any one of the embodiments of the present invention to determine the brain stimulation electric field distribution information of the target user for the stimulation parameters, and use the display to display the brain stimulation electric field distribution information.

[0024] The technical solution of the embodiment of the present invention, by obtaining the brain image of a target user with a stimulating electrode implanted in the brain, and the electrode implantation position of the stimulating electrode in the target user's brain, further, classifying the brain tissue categories of the preoperative brain image, so as to determine the 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 property parameters; further, based on the plurality of first data points and corresponding brain tissue electrical property parameters in the first brain point cloud, determine the brain electrical property distribution information corresponding to the target user, so that when a stimulation parameter is applied to the target contact of the stimulating electrode, based on the electrode implantation position, the brain electrical property distribution information and the stimulation parameter, determine the brain stimulation electric field distribution information corresponding to the stimulation parameter. The technical solution of this embodiment converts the user's brain tissue information into point cloud data, and determines the brain electrical property distribution information corresponding to the target user through the point cloud data, so as to perform electric field simulation operations according to the brain electrical property distribution information. It can not only adapt to the brain tissue characteristics of different users, provide personalized simulation results, improve the reliability and accuracy of the electrode stimulation electric field distribution information; but also greatly reduce the data volume, improve the processing speed, and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulating electrode. Brief Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the introduced drawings are only the drawings of a part of the embodiments to be described in the present invention, rather than all the drawings. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0026] Figure 1 It is a schematic diagram of the stimulating electrode involved in the embodiment of the present invention;

[0027] Figure 2 Schematic flowchart of an electrode stimulation electric field simulation method provided by an embodiment of the present invention;

[0028] Figure 3 Schematic flowchart of another electrode stimulation electric field simulation method provided by an embodiment of the present invention;

[0029] Figure 4 Schematic diagram of the implementation process of the electric field distribution simulation method involved in an embodiment of the present invention;

[0030] Figure 5 Schematic diagram of the structure of an electrode stimulation electric field simulation device provided by an embodiment of the present invention;

[0031] Figure 6 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention;

[0032] Figure 7 Schematic diagram of the structure of a medical system provided by an embodiment of the present invention. Detailed implementation manners

[0033] Next, in combination with the accompanying drawings and specific implementation manners, the present application will be further described. It should be noted that, on the premise of no conflict, the following described embodiments or technical features can be combined arbitrarily to form new embodiments.

[0034] Next, a brief description will be given first to one of the application fields (i.e., implantable devices) of the embodiments of the present application. An implantable nerve stimulation system (a type of implantable medical system) mainly includes a stimulator implanted in a patient's body and a programming device arranged outside the patient's body. Existing nerve regulation technologies mainly implant electrodes at specific structures (i.e., target points) in the body through stereotactic surgery, and the stimulator implanted in the patient's body sends electrical pulses to the target points through the electrodes to regulate the electrical activities and functions of the corresponding nerve structures and networks, thereby improving symptoms and relieving pain. Among them, the stimulator can be any one of an implantable nerve electrical stimulation device, an implantable cardiac electrical stimulation system (also known as a cardiac pacemaker), an implantable drug delivery device (Implantable Drug Delivery System, abbreviated as IDDS), and a lead adapter device. The implantable nerve electrical stimulation device is, for example, a deep brain stimulation system (Deep Brain Stimulation, abbreviated as DBS), an implantable cortical nerve stimulation system (Cortical Nerve Stimulation, abbreviated as CNS), an implantable spinal cord stimulation system (Spinal Cord Stimulation, abbreviated as SCS), an implantable sacral nerve stimulation system (Sacral Nerve Stimulation, abbreviated as SNS), an implantable vagus nerve stimulation system (Vagus Nerve Stimulation, abbreviated as VNS), etc.

[0035] In some embodiments, the stimulator may include an implantable pulse generator (IPG), an electrode lead, and an extension lead arranged between the implantable pulse generator and the electrode lead. Data interaction between the implantable pulse generator and the electrode lead is achieved through the extension lead, and the implantable pulse generator is arranged in the patient's body. In response to a programming instruction sent by the programming device, it provides controllable electrical stimulation energy to the tissue in the body relying on a sealed battery and a circuit, and delivers one or two controllable specific electrical stimulations to a specific area of the tissue in the body through the implanted extension lead and electrode lead. The extension lead is used in cooperation with the implantable pulse generator as a transmission medium for electrical stimulation signals to transmit the electrical stimulation signals generated by the implantable pulse generator to the electrode lead. The electrode lead delivers electrical stimulation to a specific area of the tissue in the body through the electrode contacts thereon. The stimulator is provided with one or more electrode leads on one or both sides, and a plurality of electrode contacts are arranged on the electrode lead.

[0036] In some other embodiments, the stimulator may only include an implantable pulse generator and an electrode lead. Among them, the implantable pulse generator can be embedded in the patient's skull, and the electrode lead is implanted in the patient's intracranial cavity. At this time, the implantable pulse generator is directly connected to the electrode lead without an extension lead.

[0037] The electrode lead can be a nerve stimulation electrode. Through multiple electrode contacts, the electrode lead delivers electrical stimulation to a specific area of the body tissue. The stimulator is provided with one or more electrode leads on one or both sides. Multiple electrode contacts are provided on the electrode lead, and the electrode contacts can be arranged evenly or unevenly in the circumferential direction of the electrode lead. As an example, the electrode contacts can be arranged in an array of 4 rows and 3 columns (a total of 12 electrode contacts) in the circumferential direction of the electrode lead. The electrode contacts can include stimulation contacts and / or acquisition contacts. The electrode contacts can be in the shape of, for example, flakes, rings, dots, etc.

[0038] In some possible ways, the body tissue to be stimulated can be the patient's brain tissue, and the stimulated site can be a specific site of the brain tissue. When the patient's disease types are different, generally the stimulated sites are different, and the number of stimulation contacts (single-source or multi-source), the application of one or more (single-channel or multi-channel) 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 and multi-channel (multi-channel), a larger amount of data will be generated compared to single-source and single-channel.

[0039] The embodiments of the present application do not limit the applicable disease types, which can be the disease types applicable to deep brain stimulation (DBS), spinal cord stimulation (SCS), pelvic stimulation, gastric stimulation, peripheral nerve stimulation, and functional electrical stimulation. Among them, the disease types that DBS can be used to treat or manage include but are not limited to: spastic diseases (such as epilepsy), pain, migraine, mental diseases (such as major depressive disorder (MDD)), bipolar disorder, anxiety disorder, post-traumatic stress disorder, dysthymia, obsessive-compulsive disorder (OCD), behavioral disorders, mood disorders, memory disorders, mental state disorders, movement disorders (such as essential tremor or Parkinson's disease), Huntington's disease, Alzheimer's disease, drug addiction, autism, or other neurological or psychiatric diseases and impairments.

[0040] The stimulation parameters can include: frequency (for example, the number of electrical stimulation pulse signals within 1 s of unit time, unit: Hz), pulse width (the duration of each pulse, unit: μs), amplitude (generally expressed by voltage, that is, the intensity of each pulse, unit: V), timing sequence (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), doctor control upper and lower limits (the adjustable range by the doctor), and patient control upper and lower limits (the adjustable range by the patient).

[0041] The technical solution provided by the embodiments of the present invention is mainly applied to the field of implantable medical devices. Implantable medical devices mainly include a pulse generator, an electrode lead, and a programming device (or medical device). The pulse generator is implanted into the patient's body (such as the chest cavity, skull, etc.). One end of the electrode lead is connected to the pulse generator subcutaneously, and the other end is configured with a stimulating electrode.

[0042] For a schematic diagram of the stimulating electrode, see Figure 1 , as Figure 1 shown. The stimulating electrode includes at least one metal contact for outputting a stimulating source. These metal contacts can be circular rings or directional electrodes composed of multiple segmented electrode contacts. The stimulating electrode is partially implanted at a specified position in the patient's brain (such as a nucleus, nerve tissue, etc. associated with the disease). The doctor sends programming parameters to the pulse generator through the programming device. The pulse generator delivers an electrical stimulus to at least one metal contact in the stimulating electrode through the electrode lead, so that the at least one metal contact generates an electric field to treat the corresponding disease. The purpose of the embodiments of the present invention is: for different target users, construct the brain electrical property distribution information according to the corresponding brain image data, so that when applying simulated stimulation parameters to any metal contact of the stimulating electrode, accurately predict the electric field distribution around the metal contact through the brain electrical property distribution information, thereby accurately and efficiently simulating the stimulation range of the stimulating electrode.

[0043] Embodiment 1

[0044] Figure 2 FIG. is a schematic flowchart of an electrode stimulation electric field simulation method provided by an embodiment of the present invention. This embodiment is applicable to any situation that requires accurately predicting the electric field distribution around a stimulating electrode in deep brain stimulation applications. This method can be executed by an electrode stimulation electric field simulation device, which can be implemented in the form of software and / or hardware. The hardware can be an electronic device, such as a mobile terminal, a PC, or a server, etc.

[0045] As Figure 2 shown, the electrode stimulation electric field simulation method includes:

[0046] S110. Obtain the brain image of a target user with at least one stimulating electrode implanted in the brain, and the electrode implantation position of at least one stimulating electrode in the target user's brain.

[0047] Among them, the target user is the one who is about to predict the electric field distribution of the stimulating electrodes implanted in his / her brain. One or more stimulating electrodes have been implanted in the brain of the target user. The brain image can be a brain MRI image, a brain CT image, etc. Optionally, the brain image can be the brain image collected before the stimulating electrodes are implanted in the brain of the target user. A brain MRI image refers to the three-dimensional brain imaging data obtained by scanning the cranium of the target user using magnetic resonance imaging (MRI) technology; a brain CT image refers to the three-dimensional brain imaging data obtained by tomographically scanning the cranium of the target user using computed tomography (CT) technology.

[0048] Among them, the electrode implantation position refers to the position information of the stimulating electrode in the intracranial of the target user. Specifically, the electrode implantation position can be the electrode track coordinates corresponding to the stimulating electrode.

[0049] Specifically, before and after the target user undergoes deep brain stimulation surgery, preoperative and postoperative brain images can be collected respectively through brain imaging techniques, such as MRI technology or CT technology. Preoperative refers to the situation where the stimulating electrodes have not been implanted in the patient's brain, and postoperative refers to the situation where the stimulating electrodes have been implanted in the patient's brain. Further, the position information of the stimulating electrode in the brain of the target user can be extracted from the postoperative brain image, so as to obtain the electrode implantation position of at least one stimulating electrode in the brain of the target user. When it is necessary to perform stimulation electric field prediction simulation on the target user using the technical solution provided in this embodiment, the preoperative brain image and the electrode implantation position corresponding to the target user can be retrieved.

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

[0051] Among them, the postoperative brain image refers to the brain image collected after the stimulating electrodes are implanted in the brain of the target user. Optionally, the postoperative brain image can be a postoperative brain CT image because: since the stimulating electrodes have been implanted in the brain of the target user, if MRI technology is used to collect the brain image of the target user, it is easy to cause the metal contacts on the stimulating electrodes to heat up, resulting in damage to the brain of the target user. There is no such risk when using CT technology.

[0052] Among them, the electrode trajectory coordinates refer to the position coordinates of the stimulating 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) ……, (X Y Z)}. The meaning of each element in the vector is the three-dimensional coordinates corresponding to the voxels that make up the stimulating electrode in the postoperative brain image.

[0053] In this embodiment, one or more stimulating electrodes can be implanted in the brain of the target user, and the processing method for each stimulating electrode is the same. Next, an example will be given with one stimulating electrode implanted in the brain of the target user.

[0054] In this embodiment, the target user can acquire preoperative brain images through MRI technology or CT technology before the operation, and acquire postoperative brain images through CT technology after the operation. Thus, the preoperative brain image and the postoperative brain image corresponding to the target user can be obtained. Further, the postoperative brain image can be registered into the three-dimensional space corresponding to the preoperative brain image to obtain the registered postoperative brain image to be processed. Further, an 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.

[0055] S120. Perform brain tissue category division on the preoperative brain image to determine the first brain point cloud corresponding to the target user based on the brain tissue category division result.

[0056] Among them, the brain tissue category division result is a plurality of 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.

[0057] 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 brain tissue electrical property parameters, such as conductivity, relative permittivity, etc.

[0058] Specifically, perform brain tissue category segmentation processing on the brain image to obtain a plurality of three-dimensional coordinate points and the corresponding brain tissue category label values for each three-dimensional coordinate point. These results are the brain tissue category division results. Thus, these three-dimensional coordinate points can be used as the first data points, the corresponding brain tissue category label values can be used as the first attribute information, and the data point set composed of these discrete first data points can be used as the first brain point cloud.

[0059] Optionally, the specific implementation manner 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 the brain image based on the brain tissue segmentation model to obtain the 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 property parameter corresponding to the brain tissue category of the first grid unit, so as to obtain the first brain point cloud corresponding to the target user.

[0060] Among them, the brain tissue category division result includes multiple first grid units and the brain tissue category corresponding to each first grid unit. Among them, the brain tissue category can be concretely represented by the brain tissue category label value. Specifically, 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 brain gray matter tissue, the white matter category corresponding to the brain white matter tissue, and the cerebrospinal fluid category corresponding to the cerebrospinal fluid. Correspondingly, the brain tissue category label value may include: the gray matter category label value, the white matter category label value, and the cerebrospinal fluid category label value. Exemplarily, the gray matter category label value may be represented by the number "1"; the white matter category label value may be represented by the number "2"; the cerebrospinal fluid category label value may be represented by the number "3".

[0061] In this embodiment, a brain tissue segmentation model for performing brain tissue category division on the brain image may be pre-trained. In specific applications, the preoperative brain image may be input into the brain tissue segmentation model, and the brain tissue segmentation model may 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 property parameters, therefore, in the case of determining the brain tissue category corresponding to a certain first grid unit, the brain tissue electrical property parameter corresponding to the first grid unit can be retrieved. Based on the same method, the brain tissue electrical property parameters corresponding to each first grid unit can be obtained, so as to construct the first brain point cloud corresponding to the target user from each first data point and the corresponding brain tissue electrical property parameters.

[0062] S130. Determine the brain electrical property distribution information corresponding to the target user based on multiple first data points in the first brain point cloud and the corresponding brain tissue electrical property parameters.

[0063] Among them, the brain electrical property distribution information is used to characterize the distribution of electrical property parameters corresponding to different positions in the brain. Optionally, the concrete representation of the brain electrical property distribution information can be a brain electrical property piecewise function or a brain electrical property distribution point cloud. Among them, the domain of each segment function in the brain electrical property piecewise function is a partial brain space range, and the range is the electrical property parameter value corresponding to the partial brain space range. The brain electrical property distribution point cloud includes a large number of data points, and each data point has a corresponding brain tissue electrical property parameter value.

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

[0065] Exemplarily, the first brain point cloud includes 300 first data points. According to the three-dimensional space coordinates and brain tissue electrical property parameters of these 300 first data points, N target brain space ranges can be obtained. Among them, the brain tissue electrical property parameter corresponding to the first target brain space range is the electrical property parameter k1 corresponding to the brain gray matter tissue, the brain tissue electrical property parameter corresponding to the second target brain space range is the electrical property parameter k2 corresponding to the brain white matter tissue, the brain tissue electrical property parameter corresponding to the third target brain space range is the electrical property parameter k3 corresponding to the cerebrospinal fluid, the cerebrospinal fluid corresponding to the fourth target brain space range has the electrical property parameter k3, the brain tissue electrical property parameter corresponding to the fifth target brain space range is the electrical property parameter k2 corresponding to the brain white matter tissue, …, the brain tissue electrical property parameter corresponding to the Nth target brain space range is the electrical property parameter k1 corresponding to the brain gray matter tissue. Then, the brain electrical property piecewise function corresponding to the target user can be expressed as:

[0066] (1);

[0067] Specifically, in the case where the brain electrical property distribution information is a brain electrical property distribution point cloud, the specific implementation method for determining the brain electrical property distribution point cloud corresponding to the target user based on multiple first data points in the first brain point cloud and the corresponding brain tissue electrical property parameters may include: inserting at least one second data point between the multiple first data points in the first brain point cloud, and determining the brain tissue electrical property 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 property determination function, so as to obtain the brain electrical property distribution point cloud.

[0068] Among them, 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 the brain tissue electrical property parameters. The brain tissue electrical property parameters corresponding to the second data point are parameters describing the basic electrical performance of a certain second data point. For example, they may include, but are not limited to, conductivity, relative permittivity, etc.

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

[0070] In this embodiment, the data points in the first brain point cloud are relatively sparsely distributed. If the stimulation electrode electric field distribution simulation processing 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 property distribution point cloud with a higher data point density, and this brain electrical property distribution point cloud is the brain electrical property distribution point cloud. Based on this, the electrode stimulation electric field distribution information is determined according to the high-density distribution point cloud to improve the accuracy.

[0071] Specifically, a plurality of second data points can be inserted between a plurality of first data points of the first brain point cloud. Based on the insertion of the plurality of second data points, it is necessary to determine the second attribute information corresponding to each second data point. The determination method of the second attribute information of each second data point is the same. Here, any one of the second data points is taken as the current second data point for illustration. For the current second data point, at least one adjacent first data point can be determined from the first brain point cloud. The tissue category calculation value corresponding to it can be determined according to the tissue category label value of each first data point. Furthermore, based on the tissue category calculation value and the preset electrical property determination function, the brain tissue electrical property parameter corresponding to the current second data point can be determined. In the same way, the brain tissue electrical property parameters corresponding to each second data point can be determined. Thus, the data set including all the first data points, the brain tissue electrical property parameters of each first data point, all the second data points, and the brain tissue electrical property parameters of each second data point can be determined as the brain electrical property distribution point cloud.

[0072] S140. When applying a stimulation parameter to the target contact of the stimulation electrode, based on the electrode implantation position, the brain electrical property distribution information, and the stimulation parameter, determine the brain stimulation electric field distribution information corresponding to the stimulation parameter.

[0073] Among them, the target contact refers to the metal contact to which the stimulation parameter is applied, and the target contact can be one or several metal contacts on the stimulation electrode. The stimulation parameter is a series of parameters set by simulating real stimulation conditions, and these parameters usually include but are not limited to waveform, pulse width, frequency, intensity, phase, period, intermission time, duty cycle, current direction, etc. The brain stimulation electric field distribution information is the spatial distribution of the electric field in the brain of the target user when simulating the stimulation parameter for brain stimulation. Specifically, the brain stimulation electric field distribution information can be the three-dimensional coordinates and corresponding electrical property values at the stimulated position in the brain electrical property distribution information. The electrical property value can be, for example, a current value, a voltage value, etc.

[0074] Specifically, when applying a stimulation parameter to a certain target contact of the stimulation electrode, according to the relative position of the target contact on the stimulation electrode and the electrode implantation position, the position information of the target contact in the brain electrical property distribution point cloud can be determined; thus, the position information of the target contact, the brain electrical property distribution information, and the stimulation parameter can be used as the input parameters of the electric field simulation solving module. The electric field simulation solving module can obtain the brain stimulation electric field distribution information corresponding to the stimulation parameter through processing and calculation of these parameters. Further, the brain stimulation electric field distribution information can be displayed on the target display page.

[0075] In the technical solution of the embodiment of the present invention, by obtaining the brain image of the target user with a stimulating electrode implanted in the brain and the electrode implantation position of the stimulating electrode in the brain of the target user, and then classifying the brain tissue categories of the preoperative brain image to determine the first brain point cloud corresponding to the target user based on the brain tissue category classification result, where the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical property parameters; further, based on the plurality of first data points and the corresponding brain tissue electrical property parameters in the first brain point cloud, the brain electrical property distribution information corresponding to the target user is determined. Thus, when a stimulation parameter is applied to the target contact of the stimulating electrode, based on the electrode implantation position, the brain electrical property distribution information, and the stimulation parameter, the brain stimulation electric field distribution information corresponding to the stimulation parameter is determined. The technical solution of this embodiment converts the brain tissue information of the user into point cloud data, and determines the brain electrical property distribution information corresponding to the target user through the point cloud data, so as to perform electric field simulation operations according to the brain electrical property 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 data volume, improve the processing speed, and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulating electrode.

[0076] Embodiment 2

[0077] Figure 3 It is a schematic flowchart of another method for simulating the electrode stimulation electric field provided by the embodiment of the present invention. On the basis of the foregoing embodiment, S130 and S140 are further refined, and the specific implementation manner can refer to the technical solution of this embodiment. Among them, the same or corresponding technical terms as those in the above embodiment will not be elaborated here.

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

[0079] S210. Obtain the brain image of the target user with at least one stimulating electrode implanted in the brain, and the electrode implantation position of the at least one stimulating electrode in the brain of the target user.

[0080] S220. Classify the brain tissue categories of the brain image to determine the first brain point cloud corresponding to the target user based on the brain tissue category classification result; where the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical property parameters.

[0081] S230. Insert at least one second data point between the plurality of first data points in the first brain point cloud.

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

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

[0084] The second method is to determine key brain regions based on electrode implantation positions; based on a first grid density, insert at least one second data point between each pair of first data points within the key brain regions; based on a second grid density, insert at least one second data point between each pair of first data points outside the key brain regions.

[0085] Among them, the density value of the first grid density is higher than that of the second grid density.

[0086] In this embodiment, the specific implementation of determining key brain regions based on electrode implantation positions may include: determining electrode stimulation targets according to electrode implantation positions and the disease type of the target user; determining key brain regions according to the positions of the electrode stimulation targets.

[0087] Among them, the electrode stimulation target refers to the specific brain position where the stimulation electrode needs to apply electrical stimulation.

[0088] Generally speaking, different disease types correspond to different electrode stimulation regions. Since the disease type corresponding to the target user is determined, based on this, on the basis of obtaining the electrode implantation position, the final electrode stimulation region can be determined according to the electrode implantation position and the disease type of the target user, and this region is the electrode stimulation target. Thus, the brain region adjacent to this position can be determined as the key brain region according to the position of the electrode stimulation target. In this embodiment, by inserting a larger number of and denser second data points in the key brain regions and a relatively smaller number of second data points outside the key brain regions, the prediction accuracy of the electric field distribution around the stimulation electrode can be improved without wasting data computing resources.

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

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

[0091] In this embodiment, a spatial index structure "{(x, y, z); (value1, value2, value3......)}" can be constructed according to the point cloud file corresponding to the first brain point cloud. Among them, (x, y, z) represents the three-dimensional coordinates corresponding to a certain first data point in the first brain point cloud, and (value1, value2, value3......) represents the first attribute information corresponding to the first data point. The first attribute information at least includes the tissue category label value and the brain tissue electrical property parameters. When inserting at least one second data point, the three-dimensional coordinate information of the query point corresponding to the current second data point is known. Therefore, the three-dimensional coordinate information of the query point can be used to find each first data point within the preset neighborhood range of the current second data point in the spatial index structure, and these first data points are the first target data points.

[0092] S250. Determine the target brain tissue type reference value corresponding to the current second data point based on the brain tissue category corresponding to the first target data point.

[0093] Among them, the target brain tissue type reference value is a quantization value that plays an auxiliary calculation role when determining the brain tissue electrical property 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.

[0094] 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. Therefore, data operations can be performed on the tissue category label values of at least one first target data point to obtain the target brain tissue type reference value corresponding to the current second data point.

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

[0096] The first one is: perform an averaging process on the tissue category label values of the brain tissue categories corresponding to each first target data point to obtain the target brain tissue type reference value corresponding to the current second data point.

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

[0098] The second one is: construct a target interpolation function based on the position coordinate information and the corresponding tissue category label values of each first target data point; use the target position coordinate of the current second data point 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.

[0099] 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:

[0100] (2);

[0101] In the formula, represents the tissue type reference value of a certain second data point, represents the three-dimensional coordinate information corresponding to this second data point.

[0102] Based on this, taking the target position coordinate of the current second data point as the input variable of the target interpolation function, and calculating the target position coordinate through the target interpolation function, the target brain tissue type reference value corresponding to the current second data point is obtained.

[0103] S260. Determine the brain tissue electrical property parameters corresponding to the current second data point based on the target brain tissue type reference value and the preset electrical property determination function.

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

[0105] (3);

[0106] In the formula, represents the brain tissue electrical property parameters corresponding to the second data point, represents the gray matter electrical property parameters corresponding to the brain gray matter tissue, represents the white matter electrical property parameters corresponding to the brain white matter tissue, represents the cerebrospinal fluid electrical property parameters, , , and are preset tissue type reference thresholds. It should be particularly noted that , , and The specific values of are not limited.

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

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

[0109] (4);

[0110] On this basis, assuming that the reference value of the target brain tissue type of the current second data point is 1.2, then the conductivity of the current second data point , and the relative permittivity is ; assuming that the reference value of the target brain tissue type of the current second data point is 2, then the conductivity of the current second data point , and the relative permittivity is ; assuming that the reference value of the target brain tissue type of the current second data point is 2.6, then the conductivity of the current second data point , and the relative permittivity is . Based on the same method, the electrical property parameters corresponding to each second data point can be obtained.

[0111] S270. Determine the brain electrical property distribution point cloud based on the data set composed of all the first data points, the brain electrical property parameters of each first data point, all the second data points, and the brain electrical property parameters of each second data point.

[0112] On the basis of the above embodiments, when the brain electrical property distribution information is the brain electrical property distribution point cloud, the steps of determining the brain stimulation electric field distribution information include S280 - S290.

[0113] S280. Based on the electrode implantation position, determine the target contact position of the stimulation parameter in the brain electrical property distribution point cloud.

[0114] Among them, the target contact position refers to the three - dimensional position coordinates of the target contact in the space of the brain electrical property distribution point cloud. The target contact position can be a set of multiple three - dimensional position coordinates.

[0115] In this embodiment, when applying the stimulation parameter to a certain target contact of the stimulation electrode, the position information of the stimulation electrode in the brain electrical property distribution point cloud can be determined according to the electrode implantation position, and the relative position of the target contact on the stimulation electrode is determined, so that the target contact position of the target contact in the brain electrical property distribution point cloud can be determined.

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

[0117] Among them, the electric field simulation and solution module is a data processing unit for predicting the stimulation electric field according to the contact position, the stimulation parameters corresponding to the contact, and the electrical characteristics of the tissues around the contact.

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

[0119] Exemplarily, for the schematic diagram of the implementation process of the electric field distribution simulation method provided in this embodiment, see Figure 4 . As Figure 4 shown, for the target user with a stimulation electrode implanted in the brain, the preoperative brain image and the postoperative brain image of the target user can be obtained. According to the postoperative brain image, the electrode implantation position of the stimulation electrode in the brain of the target user can be determined. The brain tissue category can be divided for the preoperative brain image to determine the first brain point cloud according to the brain tissue category division result; furthermore, a plurality of second data points can be inserted into the first brain point cloud, and the electrical characteristic parameters corresponding to the second data points can be determined according to the first brain point cloud and the preset electrical characteristic determination function, so as to obtain the point cloud of the electrical characteristic distribution of the brain; when the simulated stimulation parameters are applied to the target contact of the stimulation electrode, the target contact position of the simulated stimulation parameters in the point cloud of the electrical characteristic distribution of the brain can be determined according to the electrode implantation position, so as to determine the brain stimulation electric field distribution information corresponding to the target user according to the electrode implantation position, the point cloud of the electrical characteristic distribution of the brain, and the simulated stimulation parameters.

[0120] In the technical solution of the embodiment of the present invention, when determining the brain electrical property distribution point cloud, at least one second data point is inserted between multiple first data points of the first brain point cloud. Then, for each second data point, at least one first target data point within the preset neighborhood range of the current second data point is determined. Based on the tissue category label value of the first target data point, the target brain tissue type reference value corresponding to the current second data point is determined. Thus, based on the target brain tissue type reference value and the preset electrical property determination function, the electrical property parameter corresponding to the current second data point is determined. Thus, the brain electrical property distribution point cloud is composed of all the first data points, second data points, and the brain tissue electrical property parameters of each data point. The technical solution of this embodiment upsamples the first brain point cloud, quickly and accurately determines the electrical properties of the newly added data points through the electrical property piecewise function, and obtains the brain electrical property distribution point cloud. Thus, the electric field simulation operation is performed according to the brain electrical property distribution point cloud, improving the reliability and accuracy of the electrode stimulation electric field distribution information; using the target brain tissue type reference value corresponding to the second data point as an auxiliary variable, and adopting the preset electrical property determination function to determine the electrical property parameter corresponding to the second data point, the brain electrical property distribution point cloud can be quickly and accurately determined without a large amount of complex calculations, 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 stimulating electrode.

[0121] Embodiment III

[0122] Figure 5 FIG. 7 is a schematic structural diagram of an electrode stimulation electric field simulation device provided by an embodiment of the present invention. The device includes: a data acquisition module 310, an initial point cloud determination module 320, an electrical property distribution determination module 330, and an electric field distribution determination module 340.

[0123] Among them, the data acquisition module 310 is configured to acquire a brain image of a target user with at least one stimulating electrode implanted in the brain, and the electrode implantation positions of the at least one stimulating electrode in the brain of the target user;

[0124] The initial point cloud determination module 320 is configured to perform brain tissue category division on the brain image to determine a first brain point cloud corresponding to the target user based on the brain tissue category division result; wherein, the first brain point cloud includes multiple first data points and corresponding brain tissue electrical property parameters;

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

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

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

[0128] An image data acquisition unit, configured to acquire preoperative brain images and postoperative brain images of a target user with at least one stimulation electrode implanted in the brain;

[0129] An electrode coordinate determination unit, configured to perform registration processing on the preoperative brain images and the postoperative brain images to determine the electrode trajectory coordinates of the at least one stimulation electrode in the three-dimensional space corresponding to the preoperative brain images;

[0130] An electrode position determination unit, configured to determine the electrode trajectory coordinates as the electrode implantation position of the stimulation electrode in the brain of the target user for the at least one stimulation electrode.

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

[0132] A brain tissue division unit, configured to perform brain tissue category division on the brain images based on a brain tissue segmentation model to obtain a brain tissue category division result; wherein, the brain tissue category division result includes a plurality of first grid units and the brain tissue category corresponding to each first grid unit;

[0133] A first point cloud determination unit, configured to determine each first grid unit as a first data point based on the plurality of first grid units, and determine the brain tissue electrical property parameters of the brain tissue category corresponding to the first grid unit, to obtain a first brain point cloud corresponding to the target user.

[0134] 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.

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

[0136] Based on the above device, optionally, the brain electrical property distribution information is a piecewise function of brain electrical properties. The piecewise function determination sub-module is configured to determine the piecewise function of brain electrical properties based on the multiple first data points in the first brain point cloud and the brain tissue electrical property parameters corresponding to each of the first data points; wherein, the domain of each segment of the piecewise function of brain electrical properties is a partial brain space range, and the range is the electrical property parameter value corresponding to the partial brain space range.

[0137] Based on the above device, optionally, the brain electrical property distribution information is a point cloud of brain electrical property distribution. The distribution point cloud determination sub-module is configured to insert at least one second data point between the multiple first data points in the first brain point cloud, and determine the brain tissue electrical property parameters corresponding to each of the second data points based on the multiple first data points, the brain tissue category corresponding to each of the first data points, and a preset electrical property determination function, so as to obtain a point cloud of brain electrical property distribution.

[0138] Based on the above device, optionally, the distribution point cloud determination sub-module includes: a second data point determination unit;

[0139] The second data point determination unit is specifically configured to determine the key brain regions based on the electrode implantation positions; insert at least one second data point between each of the first data points in the key brain regions based on a first grid density; insert at least one second data point between each of the first data points outside the key brain regions based on a second grid density; wherein, the density value of the first grid density is higher than the density value of the second grid density.

[0140] Based on the above device, optionally, the second data point determination unit further includes a key region determination sub-unit; the key region determination sub-unit is specifically configured to determine the electrode stimulation target according to the electrode implantation position and the disease type of the target user; determine the key brain regions according to the position of the electrode stimulation target.

[0141] Based on the above device, optionally, the distribution point cloud determination sub-module further includes: an electrical parameter determination unit;

[0142] The target data point determination sub-unit is configured to determine at least one first target data point within a preset neighborhood range of the current second data point for each of the second data points;

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

[0144] An electrical parameter determination subunit, configured to determine the brain tissue electrical property parameter corresponding to the current second data point based on the target brain tissue type reference value and the preset electrical property determination function.

[0145] Based on the above device, optionally, the tissue type value determination subunit specifically performs an averaging process on the tissue category label values of the brain tissue categories corresponding to each of the first target data points to obtain a target brain tissue type reference value corresponding to the current second data point; or;

[0146] Based on the position coordinate information of each of the first target data points and the corresponding tissue category label values, construct a target interpolation function; use the target position coordinate of the current second data point as the input parameter of the target interpolation function to obtain a target brain tissue type reference value corresponding to the current second data point.

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

[0148] ;

[0149] In the formula, represents the brain tissue electrical property parameter corresponding to the second data point, represents the gray matter electrical property parameter corresponding to the brain gray matter tissue, represents the white matter electrical property parameter corresponding to the brain white matter tissue, represents the cerebrospinal fluid electrical property parameter, , , and are preset tissue type reference thresholds.

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

[0151] A contact position determination unit, configured to determine the target contact position information of the target contact corresponding to the stimulation parameter in the brain electrical property distribution information based on the electrode implantation position;

[0152] An electric field distribution information determination unit, configured to input the contact electrical property parameter, the brain electrical property distribution information, the target contact position information, and the stimulation parameter into an electric field simulation and solution module for simulation and solution to obtain the brain stimulation electric field distribution information corresponding to the stimulation parameter.

[0153] The technical solution of the embodiment of the present invention obtains the brain image of a target user with a stimulating electrode implanted in the brain and the electrode implantation position of the stimulating electrode in the brain of the target user. Furthermore, the brain tissue categories of the preoperative brain image are divided to determine the first brain point cloud corresponding to the target user based on the brain tissue category division result, where the first brain point cloud includes multiple first data points and corresponding brain tissue electrical property parameters. Further, based on the multiple first data points and corresponding brain tissue electrical property parameters in the first brain point cloud, the brain electrical property distribution information corresponding to the target user is determined. Thus, when a stimulation parameter is applied to the target contact of the stimulating electrode, based on the electrode implantation position, the brain electrical property distribution information, and the stimulation parameter, the brain stimulation electric field distribution information corresponding to the stimulation parameter is determined. The technical solution of this embodiment converts the brain tissue information of the user into point cloud data, and determines the brain electrical property distribution information corresponding to the target user through the point cloud data, so as to perform electric field simulation operations according to the brain electrical property 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 data volume, improve the processing speed, and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulating electrode.

[0154] The electrode stimulation electric field simulation device provided by the embodiment of the present invention can execute the electrode stimulation electric field simulation method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0155] It should be noted 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 realized. In addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the embodiments of the present invention.

[0156] Embodiment 4

[0157] Figure 6 It is a schematic structural diagram of an electronic device provided by the embodiment of the present invention. Figure 6 A block diagram of an exemplary electronic device 40 suitable for implementing the embodiment mode of the embodiment of the present invention is shown. Figure 6 The shown electronic device 40 is only an example and should not bring any limitation to the functions and usage scope of the embodiments of the present invention.

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

[0159] The bus 403 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, an Accelerated Graphics Port, a processor, or a local bus using any of the several bus architectures. By way of example, and not limitation, these architectures include the 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.

[0160] 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 both volatile and nonvolatile media, removable and non-removable media.

[0161] The system memory 402 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 404 and / or cache memory 405. The electronic device 40 may further include other removable / non-removable, volatile / nonvolatile computer system storage media. By way of example only, a storage system 406 can be used for reading from and writing to non-removable, nonvolatile magnetic media ( Figure 6 not shown, and typically called a "hard disk drive"). Although Figure 6 not shown in the figures, a disk drive for reading from and writing to a removable nonvolatile disk (e.g., a "floppy disk"), and an optical disk drive for reading from and writing to a removable nonvolatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) can be provided. In these instances, each drive can be connected to the bus 403 by one or more data media interfaces. The memory 402 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of various embodiments of the present invention.

[0162] A program / utility 408 having a set (at least one) of program modules 407 can be stored, for example, in the memory 402. Such program modules 407 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, and each of these examples or some combination thereof may include an implementation of a network environment. The program modules 407 generally carry out the functions and / or methods described in the embodiments of the present invention.

[0163] The electronic device 40 can also communicate with one or more external devices 409 (such as a keyboard, a pointing device, a display, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 40, and / or communicate with any device that enables the electronic device 40 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface 411. Moreover, the electronic device 40 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 412. As shown in the figure, the network adapter 412 communicates with other modules of the electronic device 40 through a bus 403. It should be understood that although Figure 6 not shown in the figure, other hardware and / or software modules can be used in combination 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, etc.

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

[0165] Embodiment Five

[0166] The embodiments of the present invention also provide a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute an electrode stimulation electric field simulation method when executed by a computer processor. The method includes:

[0167] Obtaining a brain image of a target user with at least one stimulating electrode implanted in the brain, and the electrode implantation positions of the at least one stimulating electrode in the brain of the target user;

[0168] Performing brain tissue category division on the brain image to determine a first brain point cloud corresponding to the target user based on the brain tissue category division result; wherein, the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical property parameters;

[0169] Determining brain electrical property distribution information corresponding to the target user based on the plurality of first data points and the corresponding brain tissue electrical property parameters in the first brain point cloud;

[0170] When applying stimulation parameters to the target contact of the stimulating electrode, determining brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation position, the brain electrical property distribution information, and the stimulation parameters.

[0171] The computer storage medium of the embodiments of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage media include: an electrical connection having 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 of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0172] The computer-readable signal media may include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0173] The program code contained on the computer-readable media may be transmitted by any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0174] The computer program code for performing the operations of the embodiments of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as an independent 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 (for example, by using an Internet service provider to connect through the Internet).

[0175] Embodiment Six

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

[0177] Among them, the implantable medical device 510 at least includes a pulse generator implanted into the body of a target user and an electrode lead implanted into the brain of the target user. At least a plurality of electrode contacts are provided at the implanted end of the electrode lead, and the pulse generator is connected to the electrode lead.

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

[0179] The processor 530 is configured to obtain a brain image of a target user with at least one stimulating electrode implanted in the brain, execute an electrode stimulation electric field simulation method to determine the brain stimulation electric field distribution information of the target user for the stimulation parameters, and use the display to display the brain stimulation electric field distribution information.

[0180] According to the technical solution of the embodiment of the present application, the medical system includes an implantable medical device, a display, and a processor. When the medical system is specifically applied, the processor obtains a brain image of a target user with a stimulating electrode implanted in the brain and the electrode implantation position of the stimulating electrode in the brain of the target user. Furthermore, the processor performs a brain tissue category division on the preoperative brain image to determine a first brain point cloud corresponding to the target user based on the brain tissue category division result, where the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical property parameters; further, based on the plurality of first data points and the corresponding brain tissue electrical property parameters in the first brain point cloud, the brain electrical property distribution information corresponding to the target user is determined. Thus, when a stimulation parameter is applied to the target contact of the stimulating electrode, based on the electrode implantation position, the brain electrical property distribution information, and the stimulation parameter, the brain stimulation electric field distribution information corresponding to the stimulation parameter is determined. Therefore, the brain stimulation electric field distribution information can be displayed on the display. According to the technical solution of this embodiment, the brain tissue information of the user is converted into point cloud data, and the brain electrical property distribution information corresponding to the target user is determined through the point cloud data. Thus, electric field simulation operations are performed according to the brain electrical property distribution information, which 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 data volume, improve the processing speed, and improve the simulation efficiency and real-time performance of the electric field distribution around the stimulating electrode.

[0181] Note that the above are only the preferred embodiments of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments here, and various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope 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 only. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An electrode stimulation electric field simulation method, characterized in that Including: Obtaining a brain image of a target user with at least one stimulating electrode implanted in the brain, and the electrode implantation positions of the at least one stimulating electrode in the brain of the target user; Performing brain tissue category division on the brain image to determine a first brain point cloud corresponding to the target user based on the brain tissue category division result; wherein, the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical property parameters; the brain tissue category division result includes a plurality of first grid units and the brain tissue category corresponding to each first grid unit; determining each first grid unit as a first data point, and determining the brain tissue electrical property parameters of the brain tissue category corresponding to the first grid unit, to obtain a first brain point cloud corresponding to the target user; Based on the plurality of first data points and the corresponding brain tissue electrical property parameters in the first brain point cloud, determining brain electrical property distribution information corresponding to the target user; wherein, the brain electrical property distribution information includes a brain electrical property piecewise function, and the domain of each function segment in the brain electrical property piecewise function is a partial brain space range, and the range is the electrical property parameter value corresponding to the partial brain space range; or, the brain electrical property distribution information includes a brain electrical property distribution point cloud, and the brain electrical property distribution point cloud is a brain point cloud composed of a plurality of first data points, second data points, and the brain tissue electrical property parameters of each data point, and the second data points are new data points different from the first data points; When applying a stimulation parameter to a target contact of the stimulating electrode, based on the electrode implantation position, the brain electrical property distribution information, and the stimulation parameter, determining brain stimulation electric field distribution information corresponding to the stimulation parameter.

2. The electrode stimulation electric field simulation method according to claim 1, wherein The method further includes: determining the electrode implantation positions of at least one stimulating electrode in the brain of the target user, including: Obtaining a preoperative brain image and a postoperative brain image of a target user with 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 stimulating electrode in the three-dimensional space corresponding to the preoperative brain image; Determining the electrode trajectory coordinates as the electrode implantation positions of the stimulating electrode in the brain of the target user.

3. The electrode stimulation electric field simulation method according to claim 1, characterized in that The performing brain tissue category division on the brain image to determine a first brain point cloud corresponding to the target user based on the brain tissue category division result includes: Performing brain tissue category division on the brain image based on a brain tissue segmentation model to obtain a brain tissue category division result.

4. The electrode stimulation electric field simulation method according to claim 3, characterized in that, The brain tissue categories include 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.

5. The electrode stimulation electric field simulation method according to claim 1, wherein The brain electrical property distribution information is a brain electrical property distribution point cloud, and the determining brain electrical property distribution information corresponding to the target user based on the plurality of first data points and the corresponding brain tissue electrical property parameters in the first brain point cloud includes: Insert at least one second data point between multiple first data points of the first brain point cloud, and determine the brain tissue electrical property 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 property determination function, so as to obtain a brain electrical property distribution point cloud.

6. The electrode stimulation electric field simulation method according to claim 5, characterized in that, The step of inserting at least one second data point between multiple first data points of the first brain point cloud includes: Determine key brain regions based on the electrode implantation positions; Insert at least one second data point between each of the first data points in the key brain regions based on a first grid density; Insert at least one second data point between each of the first data points outside the key brain regions based on a second grid density; wherein, the density value of the first grid density is higher than the density value of the second grid density.

7. The electrode stimulation electric field simulation method according to claim 6, characterized in that The step of determining key brain regions based on the electrode implantation positions includes: Determine electrode stimulation targets according to the electrode implantation positions and the disease types of the target users; Determine the key brain regions according to the positions of the electrode stimulation targets.

8. The electrode stimulation electric field simulation method according to claim 5, wherein The step of determining the brain tissue electrical property 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 property determination function includes: For each second data point, determine at least one first target data point within the preset neighborhood range of the current second data point; 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; Determine the brain tissue electrical property parameters corresponding to the current second data point based on the target brain tissue type reference value and the preset electrical property determination function.

9. The electrode stimulation electric field simulation method according to claim 8, wherein The step of determining 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 includes: Perform an averaging process on the tissue category label values of the brain tissue categories corresponding to each of the first target data points to obtain a target brain tissue type reference value corresponding to the current second data point; or, Construct a target interpolation function based on the position coordinate information of each of the first target data points and the corresponding tissue category label values; Use the target position coordinate of the current second data point as the input parameter of the target interpolation function to obtain a target brain tissue type reference value corresponding to the current second data point.

10. The electrode stimulation electric field simulation method according to claim 5, wherein The preset electrical property determination function includes: ； In the formula, represents the electrophysiological characteristic parameter of the brain tissue corresponding to the second data point, represents the electrophysiological characteristic parameter of the gray matter corresponding to the gray matter tissue of the brain, represents the electrophysiological characteristic parameter of the white matter corresponding to the white matter tissue of the brain, represents the electrophysiological characteristic parameter of the cerebrospinal fluid, , , and are preset reference thresholds for tissue types.

11. The electrode stimulation electric field simulation method according to claim 1, wherein The step of determining the brain stimulation electric field distribution information corresponding to the stimulation parameters based on the electrode implantation positions, the brain electrical property distribution information, and the stimulation parameters includes: Based on the electrode implantation positions, determine the target contact position information of the target contact corresponding to the stimulation parameters in the brain electrical property distribution information; Input the contact electrical property parameters, the brain electrical property distribution information, the target contact position information, and the stimulation parameters into an electric field simulation solution module for simulation solution to obtain the brain stimulation electric field distribution information corresponding to the stimulation parameters.

12. An electrode stimulation electric field simulation device, characterized in that The device includes: A data acquisition module, configured to acquire a brain image of a target user with at least one stimulating electrode implanted in the brain, and the electrode implantation positions of the at least one stimulating electrode in the brain of the target user; An initial point cloud determination module, configured to perform brain tissue category division on the brain image, so as to determine a first brain point cloud corresponding to the target user based on the brain tissue category division result; wherein, the first brain point cloud includes a plurality of first data points and corresponding brain tissue electrical property parameters; the brain tissue category division result includes a plurality of first grid units and the brain tissue category corresponding to each first grid unit; each first grid unit is determined as a first data point, and the brain tissue electrical property parameters of the brain tissue category corresponding to the first grid unit are determined, so as to obtain a first brain point cloud corresponding to the target user; 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 and the corresponding brain tissue electrical property parameters in the first brain point cloud; wherein, the brain electrical property distribution information includes a brain electrical property piecewise function, and the domain of each function segment in the brain electrical property piecewise function is a partial brain space range, and the range is the electrical property parameter value corresponding to the partial brain space range; alternatively, the brain electrical property distribution information includes a brain electrical property distribution point cloud, and the brain electrical property distribution point cloud is a brain point cloud composed of a plurality of first data points, second data points, and the brain tissue electrical property parameters of each data point, and the second data points are new data points different from the first data points; An electric field distribution determination module, configured to determine brain stimulation electric field distribution information corresponding to the stimulation parameter based on the electrode implantation position, the brain electrical property distribution information, and the stimulation parameter when a stimulation parameter is applied to a target contact of the stimulating electrode.

13. A medical system, characterized in that, The medical system includes: An implantable medical device, the implantable medical device at least includes a pulse generator implanted in the body of the target user and an electrode lead implanted in the brain of the target user, the implanted end of the electrode lead is provided with at least a plurality of electrode contacts, and the pulse generator is connected to the electrode lead; A display, configured to display the brain stimulation electric field distribution information; A processor, configured to acquire a brain image of a target user with at least one stimulating electrode implanted in the brain, execute the electrode stimulation electric field simulation method according to any one of claims 1-11 to determine the brain stimulation electric field distribution information of the target user's brain for the stimulation parameter, and use the display to display the brain stimulation electric field distribution information.

14. An electronic device, characterized in that, The electronic device includes: 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 execute the electrode stimulation electric field simulation method according to any one of claims 1-11.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to implement the electrode stimulation electric field simulation method according to any one of claims 1-11 when executed.

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