Nerve stimulation action field rendering method and device, storage medium and electronic equipment

Abstract

The invention discloses a nerve stimulation action field rendering method and device, a storage medium and electronic equipment. The method comprises the steps that a pre-configuration file of a target object is acquired, the pre-configuration file comprises multiple voxel grid data of the target object in a nerve stimulation scene, and grid precisions corresponding to the multiple voxel grid data are different; obtaining target nerve stimulation information, and determining target voxel grid data in the pre-configuration file based on the target nerve stimulation information; and rendering based on the target voxel grid data to obtain a nerve stimulation action field corresponding to the target nerve stimulation information. The voxel grid data with different grid precisions are provided for rendering of the nerve stimulation model by adopting the pre-configuration file, the problem that the grid precision of the voxel grid data is single is solved, and the effect of meeting the rendering requirements of a user on the nerve stimulation model on different grid precisions is achieved.

Description

Technical Field

[0001] The present application relates to the field of medical equipment technology, and in particular to a method, device, storage medium and electronic device for rendering a nerve stimulation action field. Background Art

[0002] The programming process is the process of adjusting the parameters of the stimulator to the values ​​that best suit the patient's condition. Before adjusting the parameters of the stimulator, in order to improve the accuracy of the parameter adjustment, the neural stimulation domain of the stimulator can be rendered to facilitate the doctor to understand the neural stimulation domain corresponding to the parameters of the stimulator, so as to better determine the parameters of the stimulator.

[0003] In the process of implementing this application, it is found that there are at least the following technical problems in the prior art: Currently, the rendering of the neural stimulation scope is generally based on NIFITI files. NIFITI files are a kind of uniform voxel grid data, which results in the rendering of the neural stimulation scope being limited by the grid accuracy and poor flexibility. Summary of the invention

[0004] The present application provides a method, device, storage medium and electronic device for rendering a neural stimulation action field, so as to dynamically determine voxel grid data of different precisions according to the model rendering requirements, thereby improving the flexibility of neural stimulation model rendering.

[0005] According to one aspect of the present application, a method for rendering a neural stimulation action field is provided, comprising:

[0006] Acquire a pre-configuration file of a target object, wherein the pre-configuration file includes a plurality of voxel grid data of the target object in a neural stimulation scenario, and the grid precisions corresponding to the plurality of voxel grid data are different;

[0007] acquiring target neural stimulation information, and determining target voxel grid data in the pre-configuration file based on the target neural stimulation information;

[0008] A neural stimulation action field corresponding to the target neural stimulation information is obtained by rendering based on the target voxel grid data.

[0009] According to another aspect of the present application, a neural stimulation action field rendering device is provided, comprising:

[0010] A file acquisition module, used to acquire a pre-configuration file of a target object, wherein the pre-configuration file includes a plurality of voxel grid data of the target object in a neural stimulation scenario, and the grid precisions corresponding to the plurality of voxel grid data are different;

[0011] a target voxel grid data determination module, configured to obtain target nerve stimulation information, and determine target voxel grid data in the pre-configuration file based on the target nerve stimulation information;

[0012] A rendering module is used to render the target voxel grid data to obtain a nerve stimulation field corresponding to the target nerve stimulation information.

[0013] According to another aspect of the present application, a program-controlled device is provided, the program-controlled device comprising:

[0014] A program control interface, configured to provide a doctor with an interface for inputting program control parameters;

[0015] A processor is configured to receive and store a pre-configuration file of a target object, and execute the neural stimulation action field rendering method provided by the embodiment of the present disclosure according to the program-controlled parameters input by the doctor, so as to obtain a neural stimulation action field model of the target object;

[0016] The display interface is configured to display the nerve stimulation field of the target object in at least three dimensions.

[0017] According to another aspect of the present application, an implantable stimulation system is provided, the system comprising:

[0018] a pulse generator implanted in the target subject and connected to stimulation electrodes implanted in the target subject's brain;

[0019] The program control device is configured to communicate with the pulse generator and receive and send program control instructions to the pulse generator.

[0020] According to another aspect of the present application, an electronic device is provided, the electronic device comprising:

[0021] at least one processor; and

[0022] a memory communicatively connected to the at least one processor; wherein,

[0023] 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 neural stimulation action field rendering method described in any embodiment of the present application.

[0024] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the neural stimulation action field rendering method described in any embodiment of the present application when executed.

[0025] The technical solution of the embodiment of the present application is to set a pre-configuration file for the target object, wherein the pre-configuration file includes multiple voxel grid data with different grid precisions of the target object. The target neural stimulation information is obtained, and the target voxel grid data is determined from the multiple voxel grid data stored in the pre-configuration file, and the neural stimulation field corresponding to the target neural stimulation information is obtained by rendering based on the target voxel grid data. By using the pre-configuration file to provide voxel grid data with different grid precisions for the rendering of the neural stimulation model, the problem of single grid precision of the voxel grid data is overcome, thereby achieving the effect of satisfying the user's rendering requirements for the neural stimulation model at different grid precisions.

[0026] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 is a flow chart of a method for rendering a neural stimulation action field provided in an embodiment of the present application;

[0029] Figure 2 is a flowchart of a method for creating a pre-configuration file provided in an embodiment of the present application;

[0030] Figure 3 It is a structural schematic diagram of a neural stimulation action field rendering device provided in an embodiment of the present application;

[0031] Figure 4 is a schematic diagram of a program-controlled device provided by an embodiment of the present disclosure;

[0032] Figure 5 is a schematic diagram of an implantable stimulation system provided by an embodiment of the present disclosure;

[0033] Figure 6 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] The following is a brief description of the technical field and related terms of the embodiments of the present application.

[0037] Implantable medical systems include implantable neural stimulation systems, implantable cardiac stimulation systems (also known as pacemakers), implantable drug delivery systems (IDDS), and lead switching systems. Examples of implantable neural stimulation systems include deep brain stimulation (DBS), implantable cortical nerve stimulation (CNS), implantable spinal cord stimulation (SCS), implantable sacral nerve stimulation (SNS), and implantable vagus nerve stimulation (VNS).

[0038] The implantable neural electrical stimulation system includes a stimulator implanted in the patient's body (i.e., an implantable neural stimulator) and a programmable device disposed outside the patient's body. In other words, the stimulator is a medical device, or in other words, the medical device includes the stimulator. The relevant neural regulation technology mainly implants electrodes (electrodes, for example, in the form of electrode wires) in specific parts of the tissues of the organism (i.e., target points) through stereotactic surgery, and emits discharge pulses to the target points through the electrodes to regulate the electrical activity and function of the corresponding neural structures and networks, thereby improving symptoms and relieving pain.

[0039] As an example, DBS includes an IPG (Implantable Pulse Generator), an extension wire and an electrode wire, and the IPG is connected to the electrode wire via the extension wire. The IPG is implanted in the patient's body, for example, in the patient's chest or other body parts.

[0040] As another example, DBS includes an IPG and an electrode lead, and the IPG is directly connected to the electrode lead. The IPG is implanted in the patient's head, for example, a groove is made in the patient's skull, and then the IPG is installed in the groove of the skull. In this case, the IPG may not protrude from the outer surface of the skull, or may partially protrude from the outer surface of the skull.

[0041] The IPG responds to the program-controlled instructions sent by the program-controlled device, and relies on sealed batteries and circuits to provide controllable electrical stimulation therapy (or electrical stimulation energy) to the tissues in the body. The IPG delivers one or more controllable specific electrical stimulations to specific areas of the tissues in the body through electrode wires.

[0042] In some embodiments, the extension lead is used in conjunction with the IPG as a transmission medium for electrical stimulation, and transmits the electrical stimulation generated by the IPG to the electrode lead.

[0043] In some embodiments, the electrical stimulation may be delivered in the form of a pulse signal or in the form of a non-pulse signal. For example, the electrical stimulation may be delivered as a signal having various waveform shapes, frequencies, and amplitudes. Thus, the electrical stimulation in the form of a non-pulse signal may be a continuous signal, which may have a sinusoidal waveform or other continuous waveform.

[0044] After receiving the electrical stimulation transmitted by the IPG or the extension wire, the electrode wire delivers the electrical stimulation to a specific area of ​​the tissue in the body through a plurality of electrode contacts. The stimulator is provided with, for example, one or more electrode wires on one side or both sides, and a plurality of electrode contacts are provided on the electrode wire, and the electrode contacts can be arranged uniformly or non-uniformly in the circumferential direction of the electrode wire. As an example, the electrode contacts can be arranged in an array of 4 rows and 3 columns (a total of 12 electrode contacts) in the circumferential direction of the electrode wire. The electrode contacts can include stimulation electrode contacts and / or collection electrode contacts. The electrode contacts can be in the shape of sheets, rings, dots, etc., for example.

[0045] In some embodiments, the stimulated in vivo tissue may be the patient's brain tissue, and the stimulated site may be a specific site of the brain tissue. When the patient's disease type is different, the stimulated site is generally different, and the number of stimulation contacts (single source or multiple sources), the use of one or more (single channel or multiple channels) of specific electrical stimulation, and the stimulation parameters (values) are also different.

[0046] The embodiments of the present application do not limit the types of diseases that can be used, and they can be the types of diseases that deep brain stimulation (DBS), spinal cord stimulation (SCS), sacral nerve stimulation, gastric stimulation, peripheral nerve stimulation, and functional electrical stimulation can be used for. Among them, the types of diseases that DBS can be used to treat or manage include, but are not limited to: spastic diseases (e.g., epilepsy), pain, migraine, mental illness (e.g., major depressive disorder (MDD)), bipolar disorder, anxiety, post-traumatic stress disorder, mild depression, obsessive-compulsive disorder (OCD), behavioral disorders, mood disorders, memory disorders, mental state disorders, movement disorders (e.g., essential tremor or Parkinson's disease), Huntington's disease, Alzheimer's disease, drug addiction, autism or other neurological or psychiatric diseases and injuries.

[0047] In the embodiment of the present application, when the programmable device and the stimulator establish a programmable connection, the programmable device can be used to adjust one or more stimulation parameters of the stimulator (or one or more stimulation parameters of the pulse generator, different stimulation parameters correspond to different electrical stimulations), and the stimulator can also sense the patient's electrophysiological activities to collect electrophysiological signals, and the collected electrophysiological signals can be used to continue to adjust the stimulation parameters of the stimulator, thereby realizing closed-loop control (or adaptive adjustment) of the stimulation parameters.

[0048] The stimulation parameters may include at least one of the following: the electrode contact identification used to deliver electrical stimulation (for example, electrode contact #2 and electrode contact #3), frequency (for example, the number of electrical stimulation pulse signals within a unit time of 1s, in Hz), pulse width (the duration of each pulse, in μs), amplitude (generally expressed in voltage, that is, the intensity of each pulse, in V), timing (for example, it can be continuous or burst, and burst refers to discontinuous timing behavior composed of multiple processes), stimulation mode (including one or more of current mode, voltage mode, timed stimulation mode and cyclic stimulation mode), upper and lower limits controlled by the doctor (the range that can be adjusted by the doctor) and upper and lower limits controlled by the patient (the range that can be adjusted autonomously by the patient).

[0049] In some embodiments, various stimulation parameters of the stimulator can be adjusted in current mode or voltage mode.

[0050] Programmable devices may include doctor programmable devices (i.e. programmable devices used by doctors) and / or patient programmable devices (i.e. programmable devices used by patients). Doctor programmable devices are, for example, tablet computers, laptop computers, desktop computers, mobile phones and other intelligent terminal devices equipped with programmable software. Patient programmable devices are, for example, tablet computers, laptop computers, desktop computers, mobile phones and other intelligent terminal devices equipped with programmable software. Patient programmable devices may also be other electronic devices with programmable functions (e.g., chargers with programmable functions, electrophysiological acquisition devices, etc.).

[0051] Remote programming can be understood as a control process in which, through the Internet technology, the patient at home and the doctor in the hospital can remotely adjust the parameters of the stimulator to the values ​​most suitable for the patient's condition.

[0052] Visual programming uses 3D modeling technology to render the patient's brain nuclei, implanted electrodes, stimulation range, and nerve fibers into a three-dimensional model. Through precise three-dimensional visualization, doctors can better understand the relationship between the electrode position and the surrounding nerve fiber bundles, optimize the electrical stimulation parameter settings, and improve the treatment effect.

[0053] Neurostimulation scope rendering is a technique used to visualize and analyze the scope and effects of neurostimulation in the brain. This technique can help researchers understand the effects of electrical or chemical stimulation on different areas of the brain and optimize neuromodulation therapies.

[0054] Point cloud 3D reconstruction refers to measuring and sampling multiple points on the surface of an object to obtain a 3D data set, and then processing and analyzing the data set to reconstruct the 3D shape and structure of the object.

[0055] NIFITI files are uniform voxel grids that can be used to perform 3D reconstruction of point clouds to render neural stimulation domains. The level of detail of the domain model is affected by the mesh accuracy of the point cloud. When the mesh accuracy is sparse, the rendering result is rough. When the mesh accuracy is dense, the rendering result is fine, but the storage space occupied by the point cloud data will also increase dramatically.

[0056] Embodiment 1

[0057] Figure 1 This is a flowchart of a method for rendering a neural stimulation field provided by an embodiment of the present application. The method for rendering a neural stimulation field is applied to an implantable neural stimulation system. This embodiment can be applied to rendering a neural stimulation field before adjusting the parameters of the stimulator to simulate and display the neural stimulation field of the stimulator. The method can be executed by a neural stimulation field rendering device. The neural stimulation field rendering device can be implemented in the form of hardware and / or software. The neural stimulation field rendering device can be configured in a program-controlled device. Figure 1 As shown, the method includes:

[0058] S110, obtaining a pre-configuration file of the target object, wherein the pre-configuration file includes a plurality of voxel grid data of the target object in different neural stimulation scenarios, and the grid precisions corresponding to the plurality of voxel grid data are different.

[0059] S120 , obtaining target neural stimulation information, and determining target voxel grid data in a pre-configuration file based on the target neural stimulation information.

[0060] S130. Rendering based on the target voxel grid data to obtain a neural stimulation field corresponding to the target neural stimulation information.

[0061] In this embodiment, the target object can be understood as an object configured with a stimulator, such as a human body or an animal body, and the pre-configuration file of the target object can be understood as a file storing multiple voxel grid data of the target object.

[0062] It can be understood that the stimulator may include multiple electrode contacts, and the electrical stimulation applied by each electrode contact may be a positive voltage, a negative voltage, and no voltage. The electrical stimulation here may be, for example, an electric pulse. Accordingly, the multiple electrode contacts of the stimulator may form a variety of electrical stimulation combinations. A pre-configuration file may be set for each electrical stimulation combination. Any pre-configuration file includes multiple voxel grid data in a neural stimulation scenario corresponding to the electrical stimulation combination.

[0063] By dividing the three-dimensional space into a three-dimensional space grid, the three-dimensional space grid includes regular three-dimensional cube units, each unit is called a voxel. In this embodiment, the voxel grid data can be understood as the data formed by mapping the electric field point cloud data of the target object in the neural stimulation scene to each voxel of the three-dimensional space grid.

[0064] The grid accuracy corresponding to the voxel grid data can be understood as the degree of refinement of the grid division of the three-dimensional space. The grid accuracy can be characterized by the size of the three-dimensional cube unit. The larger the size of the three-dimensional cube unit, the lower the grid accuracy, and the smaller the size of the three-dimensional cube unit, the higher the grid accuracy. For the same three-dimensional space, the higher the grid accuracy corresponding to the voxel grid data, the larger the data volume of the voxel grid data, and the larger the storage space occupied. The lower the grid accuracy corresponding to the voxel grid data, the smaller the data volume of the voxel grid data, and the smaller the storage space occupied.

[0065] The pre-configuration file includes voxel grid data corresponding to different neural stimulation scenarios. Different neural stimulation scenarios correspond to different neural stimulation information. Accordingly, the pre-configuration file includes voxel grid data corresponding to at least one neural stimulation information; wherein the neural stimulation information may be one or more of the stimulation parameters. Optionally, the neural stimulation information is a parameter in the stimulation parameters that affects the scope of the neural stimulation domain. For example, the neural stimulation information may include the amplitude of the delivered electrical pulse, i.e., voltage, pulse width, and frequency.

[0066] In some embodiments, each neural stimulation information in the pre-configuration file corresponds to a voxel grid data, and the grid precision of the voxel grid data corresponding to different neural stimulation information is different, so as to meet the rendering requirements of the neural stimulation action field corresponding to different neural stimulation information. Exemplarily, the pre-configuration file includes voxel grid data corresponding to amplitude A, amplitude B and amplitude C, respectively, amplitude A corresponds to voxel grid data with grid precision 1, amplitude B corresponds to voxel grid data with grid precision 2, and amplitude C corresponds to voxel grid data with grid precision 3.

[0067] In some embodiments, the same neural stimulation information in the preconfiguration file corresponds to multiple voxel grid data, and the grid precisions of the multiple voxel grid data corresponding to the same neural stimulation information are different. Exemplarily, the preconfiguration file includes multiple voxel grid data corresponding to amplitude A, amplitude B and amplitude C, respectively, amplitude A corresponds to multiple voxel grid data with different grid precisions, amplitude B corresponds to multiple voxel grid data with different grid precisions, and so on. For example, n different grid precisions may be pre-set, and for each amplitude, voxel grid data corresponding to n different grid precisions are set to meet the rendering requirements of neural stimulation action fields with different grid precisions for the same neural stimulation information.

[0068] For multiple voxel grid data of different grid precisions corresponding to the same neural stimulation information, the lower the grid precision, the smaller the data volume of the voxel grid data, and the shorter the resources and duration consumed by the rendering process; the higher the grid precision, the larger the data volume of the voxel grid data, the better the rendering effect of the neural network model, and correspondingly, the longer the resources and duration consumed by the rendering process. The preconfiguration file can provide voxel grid data of different amplitudes and / or different grid precisions for the rendering of the neural stimulation model, which is conducive to dynamic switching of the amplitude and / or grid precision during the rendering process of the neural stimulation model.

[0069] Optionally, each voxel grid data in the pre-configuration file is respectively configured with corresponding neural stimulation information and grid precision, and the neural stimulation information and grid precision can be used as index information for querying the voxel grid data.

[0070] The target neural stimulation information can be understood as the neural stimulation information corresponding to the electric pulse to be delivered to the target object, which can represent the user's rendering requirements for the neural stimulation field. The target neural stimulation information can be collected through the interactive interface displayed by the program-controlled device. For example, the interactive interface of the program-controlled device includes a neural stimulation information control, and the neural stimulation information control can be an input control to collect the target neural stimulation information input by the user. The target neural stimulation information is matched in the pre-configuration file, and the voxel grid data matching the target neural stimulation information is determined as the target voxel grid data. For example, taking the neural stimulation information as the amplitude of the delivered electric pulse as an example, it can be based on the target amplitude in the target neural stimulation information. Matching is performed in multiple amplitudes in the pre-configuration file to determine the same amplitude or the closest amplitude of the target amplitude; the voxel grid data corresponding to the target amplitude or the closest amplitude is determined as the target voxel grid data.

[0071] Optionally, the target neural stimulation information may carry a target network accuracy. The interactive interface of the program-controlled device also includes a grid accuracy control, which may be a selection control that can display multiple grid accuracy. Exemplarily, the multiple grid accuracy displayed by the grid accuracy control may be grid accuracy values, or may be grid accuracy levels, such as level information such as "high", "medium" or "low". In response to the selection operation of the grid accuracy control, the target grid accuracy is determined. In the case of voxel grid data of multiple different grid accuracy corresponding to the target amplitude or the closest amplitude, the target network accuracy is obtained, and the voxel grid data corresponding to the target grid accuracy is determined as the target voxel grid data.

[0072] The technical solution of this embodiment is to set a pre-configuration file of the target object, and the pre-configuration file includes multiple voxel grid data of the target object with different grid precisions. The target neural stimulation information is obtained, and the target voxel grid data is determined from the multiple voxel grid data stored in the pre-configuration file, and the neural stimulation field corresponding to the target neural stimulation information is rendered based on the target voxel grid data. By adopting a pre-configuration file including multiple voxel grid data with different grid precisions, the problem of single grid precision of voxel grid data is overcome, so as to meet the user's rendering requirements for neural stimulation models at different grid precisions.

[0073] Optionally, the multiple voxel grid data in the preconfiguration file are respectively configured with corresponding neural stimulation information. The multiple voxel grid data are spaced based on a preset spacing method. For example, the preset spacing method can be a blank line spacing method, that is, a blank line is set between any two voxel grid data. For example, the preset spacing method is a spacing method of a preset spacer, that is, a preset spacer is set between any two voxel grid data. The specific form of the preset spacing method is not limited here, and it can be achieved by spacing multiple voxel grid data in the same file. Optionally, the preconfiguration file can be a txt file.

[0074] It is understandable that the spatial range of voxel grid data corresponding to different neural stimulation information is different. Taking the neural stimulation information as the amplitude as an example, the spatial range of the voxel grid data is positively correlated with the amplitude, that is, the larger the amplitude of the electrical stimulation, the larger the spatial range of the voxel grid data, and the larger the amount of electric field point cloud data within the spatial range.

[0075] In this embodiment, in order to take into account both the rendering effect and the purpose of saving storage space, the grid accuracy of the voxel grid data in the pre-configuration file is negatively correlated with the spatial range of the voxel grid data, that is, the larger the spatial range of the voxel grid data, the lower the grid accuracy of the voxel grid data, thereby reducing the storage space occupied by the voxel grid data; the smaller the spatial range of the voxel grid data, the higher the grid accuracy of the voxel grid data, thereby improving the rendering effect of the neural stimulation model.

[0076] Exemplarily, when the amplitude A, the amplitude B, and the amplitude C increase in sequence, the grid accuracy 1, the grid accuracy 2, and the grid accuracy 3 gradually decrease.

[0077] Determining target voxel grid data in a preconfiguration file based on target neural stimulation information includes: matching the target neural stimulation information with multiple neural stimulation information in the preconfiguration file to determine the neural stimulation information that matches the target neural stimulation information; determining the target voxel grid data from the voxel grid data corresponding to the neural stimulation information that matches the target neural stimulation information.

[0078] The neural stimulation information that matches the target neural stimulation information may be the same neural stimulation information as the target neural stimulation information, or the neural stimulation information that is closest to the target neural stimulation information. Taking the target neural stimulation information as the target amplitude as an example, the difference between the target amplitude and multiple amplitudes in the pre-configuration file is determined respectively, and the amplitude with a difference of zero is determined as the amplitude that matches the target amplitude, or the amplitude with the minimum difference is determined as the amplitude that matches the target amplitude.

[0079] Exemplarily, when the target neural stimulation information is amplitude B, the target voxel grid data matched in the preconfiguration file is voxel grid data with a grid precision of 2. When the target neural stimulation information is amplitude A, the target voxel grid data matched in the preconfiguration file is voxel grid data with a grid precision of 1. When the target neural stimulation information changes, the grid precision of the matched target voxel grid data also changes accordingly, and there is no need to set the grid precision separately, thereby realizing the dynamic switching of the grid precision of the target voxel grid data with the target neural stimulation information.

[0080] On the basis of the above embodiment, the target voxel grid data is acquired, and the target voxel grid data is rendered to obtain a neural stimulation model.

[0081] It is understandable that the pre-configuration file can be updated as needed, such as adding voxel grid data corresponding to the new grid size, etc., to provide a comprehensive data basis for subsequent rendering of the neural stimulation model.

[0082] The technical solution provided in this embodiment stores voxel grid data corresponding to different neural stimulation information in a preconfiguration file, and the larger the spatial range corresponding to the voxel grid data, the lower the grid accuracy of the voxel grid data. When the spatial range of the neural stimulation model is large, voxel grid data with low grid accuracy is collected for rendering to save storage space and model construction time; when the spatial range of the neural stimulation model is small, voxel grid data with high grid accuracy is used for rendering to improve the model rendering effect. By setting the grid accuracy of the voxel grid data to be negatively correlated with the spatial range corresponding to the voxel grid data, the problem of excessive storage space occupied by the voxel grid data due to a single grid accuracy or poor rendering effect of the neural stimulation model rendered with the voxel grid data is overcome, and the effect of balancing the rendering effect and storage space occupancy is achieved.

[0083] Embodiment 2

[0084] Figure 2 This is a flow chart of a method for creating a pre-configuration file provided in an embodiment of the present application. By creating a pre-configuration file, voxel grid data of multiple grid precisions is stored to provide a data basis for flexible rendering of a neural stimulation model. Figure 2 As shown, the method includes:

[0085] S210, acquiring three-dimensional environmental information of the target object and implantation information of the stimulation electrode implanted into the target point.

[0086] S220. According to the three-dimensional environment information and the implantation information, a simulation is performed to determine the electric field point cloud data corresponding to different nerve stimulation information.

[0087] S230, determining the preset grid accuracy corresponding to different neural stimulation information.

[0088] S240, mapping the preset grid precision corresponding to different neural stimulation information to the spatial grid where the corresponding electric field point cloud data is located, so as to obtain voxel grid data corresponding to the different neural stimulation information.

[0089] In this embodiment, the implantable neural stimulation system at least includes a stimulation electrode implanted into a target point of a target object.

[0090] Create three-dimensional environment information of the target object, which may be the three-dimensional environment information of the implantation site of the stimulation electrode of the target target of the target object, for example, the implantation site may be the head, and correspondingly, the three-dimensional environment information may be a head model. The three-dimensional environment information may be constructed by medical imaging data of the target object, for example, the medical imaging data includes but is not limited to CT images and nuclear magnetic resonance images, etc. The construction process of the above three-dimensional environment information is not limited here.

[0091] The implantation information of the stimulation electrode implanted into the target target point may include the implantation position of the stimulation electrode implanted into the target target point.

[0092] The electric field point cloud data is obtained by performing neural stimulation simulation with different neural stimulation information for the three-dimensional environmental information and implantation information of the target object. According to the implantation information of the stimulating electrode implanted into the target target, neural stimulation simulation is performed on the three-dimensional environmental information of the target object to obtain the electric field point cloud data of the target object in the neural stimulation state, wherein the electric field point cloud data includes the point cloud data of multiple points in the three-dimensional environmental information, and the point cloud data of any point includes the three-dimensional coordinate data and electric field data of the point, and the electric field data may be electric field intensity data. The electric field intensity data of different points are different, and the size of the electric field intensity data of any point is negatively correlated with the distance from the point to the implantation position of the stimulator. The smaller the distance from the point to the implantation position of the stimulator, the larger the electric field intensity data of the point.

[0093] Based on different neural stimulation information, neural stimulation simulation is performed on the three-dimensional environmental information of the target object to obtain electric field point cloud data corresponding to the different neural stimulation information. It is understandable that in the process of neural stimulation simulation of the three-dimensional environmental information of the target object, other stimulation parameters need to be set, such as frequency, stimulation mode and pulse width, which can be set according to simulation requirements and are not limited here.

[0094] Optionally, the neural stimulation simulation of the three-dimensional environmental information can be realized by the preset electric field simulation software. Specifically, the three-dimensional environmental information and implantation information of the target object can be displayed in the preset electric field simulation software. For example, the three-dimensional environmental information of the target object can be a head model. The interactive page of the preset electric field simulation software also includes a stimulation parameter configuration area, in which stimulation parameters are set, such as neural stimulation information (such as amplitude), frequency, stimulation mode and pulse width.

[0095] Through the preset electric field simulation software, the three-dimensional environmental information is simulated for neural stimulation based on the above neural stimulation information, and the electric field point cloud data corresponding to the neural stimulation information is displayed. It can be understood that the smaller the electric field strength data, the worse the neural stimulation effect. Optionally, the electric field point cloud data corresponding to the three-dimensional environmental information is sampled based on a preset electric field strength threshold to obtain effective electric field point cloud data. Specifically, the point cloud data whose electric field strength data is greater than or equal to the preset electric field strength threshold is extracted from the electric field point cloud data corresponding to any neural stimulation information, and the point cloud data whose electric field strength data is less than the preset electric field strength threshold is deleted.

[0096] Optionally, based on the numerical range of the neural stimulation information, multiple neural stimulation information are determined, and neural stimulation simulation is performed based on the above multiple neural stimulation information respectively, so as to obtain electric field point cloud data corresponding to the multiple neural stimulation information respectively. For example, the numerical range of the neural stimulation information can be preset, or the numerical range of the neural stimulation information can also be determined according to one or more of the implantation position of the stimulator, the disease type of the target object, the disease severity of the target object, etc. For example, it can be based on a preset number n of neural stimulation information, and multiple neural stimulation information can be uniformly determined in the numerical range of the neural stimulation information. For example, it can be based on a preset data interval to uniformly determine multiple neural stimulation information in the numerical range of the neural stimulation information. For example, multiple neural stimulation information can be determined based on the frequency of use of each neural stimulation information; for example, it can also be to receive multiple neural stimulation information input by a user (for example, a doctor).

[0097] The electric field point cloud data obtained by simulating different nerve stimulation information can be sampled by different electric field intensity thresholds to obtain effective electric field point cloud data. Optionally, the electric field point cloud data is non-uniformly distributed point cloud data.

[0098] Determine the preset grid accuracy corresponding to different neural stimulation information, and for the electric field point cloud data corresponding to any neural stimulation information (the electric field point cloud data here and in the following text may be the effective electric field point cloud data obtained by sampling), map the preset grid accuracy corresponding to the neural stimulation information to the spatial grid where the electric field point cloud data corresponding to the neural stimulation information is located, to obtain the voxel grid data corresponding to the neural stimulation information.

[0099] In some embodiments, determining the preset grid precision corresponding to different neural stimulation information includes: determining the neural stimulation information range adapted to the target object, and dividing the neural stimulation information range into a plurality of stimulation segments based on a preset division rule, wherein different stimulation segments correspond to different preset grid precisions.

[0100] The range of the neural stimulation information adapted to the target object can be understood as the limit range of the electric pulse adapted to the target object. Taking the amplitude of the electric pulse as an example, the neural stimulation information range can be understood as the limit range of the amplitude of the electric pulse adapted to the target object, including the maximum value and the minimum value. Exemplarily, the range of the neural stimulation information adapted to the target object can be 0.4V-2V.

[0101] The neural stimulation information range adapted to the target object is divided into multiple stimulation segments, and different preset grid precisions are set for different stimulation segments. Multiple neural stimulation information located in the same stimulation segment corresponds to the same preset grid precision, that is, the preset grid precision corresponding to the stimulation segment.

[0102] Optionally, the neural stimulation information range adapted to the target object is divided into a plurality of stimulation sections by a preset division rule, and the preset division rule may include an equal division method or an incremental division method.

[0103] The equal division method can be understood as a method of evenly dividing the range of neural stimulation information, and the segment lengths of different stimulation segments are the same. Exemplarily, the neural stimulation information range 0.4V-2V is evenly divided into five equally divided stimulation segments of 0.4-0.8V, 0.8-1.2V, 1.2-1.6V, and 1.6-2V. Optionally, the stimulation segments are equally divided based on the number of stimulation segments or the length of the stimulation segments.

[0104] The incremental division method can be understood as a method of dividing the neural stimulation information range into unequal lengths, and the segment lengths of the multiple stimulation segments obtained by division are successively increased. For example, the neural stimulation information range 0.4V-2V is divided into five incremental segments of 0.4-0.5V, 0.5-0.7V, 0.7-1.0V, 1.0-1.4V, and 1.4-2.0V in an incremental division method.

[0105] The preset grid accuracy corresponding to each stimulation segment is negatively correlated with the intensity of the nerve stimulation information of the stimulation segment. Optionally, the preset grid accuracy corresponding to any stimulation segment may be negatively correlated with the intensity of any one of the intermediate nerve stimulation information, the minimum intermediate nerve stimulation information, and the maximum intermediate nerve stimulation information of the stimulation segment. For example, for the stimulation segment 0.4-0.8V, the preset grid accuracy corresponding to the stimulation segment may be negatively correlated with the intermediate nerve stimulation information 0.6V.

[0106] Optionally, a grid accuracy range is pre-set, and the preset grid accuracy corresponding to each stimulation segment is determined within the grid accuracy range. According to the number of stimulation segments, multiple grid accuracy are sampled within the grid accuracy range, for example, it can be implemented by uniform sampling, that is, the intervals of multiple grid accuracy are the same; for example, it can be implemented by incremental sampling, that is, the intervals of adjacent grid accuracy are increased in sequence.

[0107] Based on the correspondence between the stimulation area and the grid accuracy (i.e., negative correlation), the preset grid accuracy corresponding to the stimulation segment is determined among the multiple grid accuracies obtained by sampling. Taking the five equally divided stimulation segments of 0.4-0.8V, 0.8-1.2V, 1.2-1.6V, and 1.6-2V as an example, among the multiple grid accuracies obtained by sampling, the stimulation segment 0.4-0.8V corresponds to the maximum grid accuracy, the stimulation segment 1.6-2V corresponds to the minimum grid accuracy, and so on.

[0108] For any nerve stimulation information, the stimulation section where the nerve stimulation information is located is determined, and the preset grid accuracy corresponding to the stimulation section is determined as the preset grid accuracy corresponding to the nerve stimulation information. Taking the nerve stimulation information 0.5V and 0.6V as examples, both are located in the stimulation section 0.4-0.8V, and the preset grid accuracy corresponding to the nerve stimulation information 0.5V and 0.6V are both the preset grid accuracy corresponding to the stimulation section 0.4-0.8V.

[0109] In some embodiments, determining the preset grid precision corresponding to different neural stimulation information includes: determining the spatial range of the electric field point cloud data corresponding to the neural stimulation information, wherein different spatial ranges correspond to different preset grid precisions. The preset grid precision corresponding to the neural stimulation information is negatively correlated with the spatial range of the electric field point cloud data of the preset grid precision.

[0110] The spatial range of the electric field point cloud data can be determined based on the three-dimensional coordinate data of each point in the electric field point cloud data. For any coordinate direction, the minimum coordinate value and the maximum coordinate value of each point in the electric field point cloud data in the coordinate direction can be determined to determine the range of the space where the electric field point cloud data is located in the coordinate direction. Accordingly, the spatial range of the electric field point cloud data can be determined based on the range of the space where the electric field point cloud data is located in the three directions of XYZ. The space where the electric field point cloud data corresponding to different nerve stimulation information is located can be marked, such as Si.

[0111] The spatial range of the electric field point cloud data corresponding to different neural stimulation information is obtained, and the grid accuracy range is obtained. Based on the corresponding relationship between the spatial range and the grid accuracy (i.e., a negative correlation), the preset grid accuracy corresponding to each spatial range is determined within the grid accuracy range, i.e., the preset grid accuracy corresponding to different neural stimulation information.

[0112] By meshing the space where the electric field point cloud data is located, a spatial grid is obtained, and the spatial grid includes regular cubic units. The space is meshed based on different grid accuracies, and the grid size of the obtained spatial grid is different. The higher the grid accuracy, the smaller the grid size of the spatial grid. Exemplarily, the grid size corresponding to different grid accuracies may include but is not limited to 1mm, 1.5mm and 2mm, etc., which can be set according to the requirements of the grid accuracy.

[0113] The electric field point cloud data corresponding to any neural stimulation information is obtained, and based on the preset grid accuracy corresponding to the neural stimulation information, it is mapped to the spatial grid where the electric field point cloud data corresponding to the neural stimulation information is located, so as to obtain the voxel grid data corresponding to the neural stimulation information.

[0114] Exemplarily, through the three-dimensional environmental information and implantation information of the target object, the electric field point cloud data corresponding to each of the n neural stimulation information are determined by simulation. The preset grid accuracy of each neural stimulation information is mapped to the corresponding electric field point cloud data to obtain the voxel grid data of each neural stimulation information. The voxel grid data corresponding to the different neural stimulation information are stored in a pre-configuration file, which includes n voxel grid data to meet the user's different rendering requirements for the neural stimulation field. Optionally, multiple voxel grid data in the pre-configuration file are stored based on a preset interval method. For example, the preset interval method can be a blank line interval method or a separator interval method.

[0115] The process of determining the voxel grid data corresponding to any neural stimulation information may be to map the preset grid precision corresponding to the neural stimulation information with the corresponding electric field point cloud data to form voxel grid data. Specifically, each regular cubic unit in the spatial grid is used as a grid, and each grid includes multiple grid vertices. Different preset grid precisions can be mapped to different spatial grids. Determine the spatial grid mapped by the preset grid precision corresponding to the neural stimulation information, obtain the point cloud data corresponding to multiple grid vertices in the spatial grid, and form voxel grid data. For each point cloud data in the electric field point cloud data, the grid vertex closest to the point cloud data is determined based on the three-dimensional coordinate data in the point cloud data. For example, the grid vertex closest to the point cloud data may be determined by the distance between the three-dimensional coordinate data in the point cloud data and the three-dimensional coordinate data of each grid vertex, or the grid vertex corresponding to the minimum distance may be determined as the grid vertex closest to the point cloud data. The point cloud data is mapped to the nearest mesh vertex of the point cloud data. For example, the three-dimensional coordinate data of the point cloud data can be updated to the three-dimensional coordinate data of the nearest mesh vertex. Accordingly, based on the electric field strength data of the point cloud data and the updated three-dimensional coordinate data, the point cloud data mapped to the mesh vertex is obtained.

[0116] It is understandable that the point cloud data mapped to the same mesh vertex may be one or more. After one or more point cloud data are mapped to any mesh vertex, the average of the one or more point cloud data may be determined as the target point cloud data of the mesh vertex, or a random point cloud data of the one or more point cloud data may be determined as the target point cloud data of the mesh vertex; or the target point cloud data of the mesh vertex may be determined according to the mapping sequence of the one or more point cloud data. The method for determining the target point cloud data of the mesh vertex is not limited here.

[0117] The larger the neural stimulation information, the larger the spatial range of the electric field point cloud data corresponding to the neural stimulation information, the larger the data volume of the electric field point cloud data, and accordingly, the lower the grid precision corresponding to the space where the electric field point cloud data is located, which can reduce the data volume of the formed voxel grid data and reduce the storage space occupied by the voxel grid data. The smaller the neural stimulation information, the smaller the spatial range of the electric field point cloud data corresponding to the neural stimulation information, the smaller the data volume of the electric field point cloud data, and accordingly, the higher the grid precision corresponding to the space where the electric field point cloud data is located, which can improve the data precision of the formed voxel grid data and have a good rendering effect in the rendering process of the neural stimulation model.

[0118] On the basis of the above embodiment, optionally, target voxel grid data is determined in the pre-configuration file based on the target neural stimulation information, including: determining a preset grid precision corresponding to the target neural stimulation information according to a target stimulation segment corresponding to the target neural stimulation information; determining a target spatial range according to electric field point cloud data corresponding to the target stimulation information; and loading the preset grid precision corresponding to the target neural stimulation information into the target spatial range to obtain the target voxel grid data.

[0119] The target nerve stimulation information is matched with multiple stimulation segments respectively, and the stimulation area where the target nerve stimulation information is located is determined as the target stimulation segment. The preset grid accuracy corresponding to the target stimulation segment is used as the preset grid accuracy corresponding to the target nerve stimulation information. The electric field point cloud data corresponding to the target nerve stimulation information is obtained, and the target spatial range of the corresponding electric field point cloud data is determined. For example, the electric field point cloud data corresponding to the target nerve stimulation information can be read from a pre-configured file. The target spatial range of the electric field point cloud data can be determined based on the three-dimensional coordinate data of each point in the electric field point cloud data corresponding to the target nerve stimulation information.

[0120] The preset grid accuracy corresponding to the target nerve stimulation information is loaded into the target space range of the corresponding electric field point cloud data to realize grid division of the target space range, and the obtained spatial grid is mapped with the corresponding electric field point cloud data to obtain the target voxel grid data.

[0121] The technical solution of this embodiment performs neural stimulation simulation on the three-dimensional environment information and implantation information of the target object to obtain the electric field point cloud data corresponding to different neural stimulation information of the target object, and divides the space where the electric field point cloud data is located into grids by preset grid precision to obtain spatial grids with preset grid precision, and maps the electric field point cloud data to the spatial grid to obtain voxel grid data with preset grid precision. Voxel grid data with different preset grid precisions are stored in a pre-configured file, which can provide a diverse data basis for the rendering process of the neural stimulation action field.

[0122] Embodiment 3

[0123] Figure 3 Schematic diagram of a neural stimulation field rendering device provided in Example 3 of the present application. Figure 3 As shown, the device comprises:

[0124] A file acquisition module 310 is used to acquire a pre-configuration file of a target object, wherein the pre-configuration file includes a plurality of voxel grid data of the target object in a nerve stimulation scenario, and the grid precisions corresponding to the plurality of voxel grid data are different;

[0125] a target voxel grid data determination module 320, configured to obtain target nerve stimulation information, and determine target voxel grid data in the pre-configuration file based on the target nerve stimulation information;

[0126] The rendering module 330 is used to render the target neural stimulation field corresponding to the target neural stimulation information based on the target voxel grid data.

[0127] The technical solution of this embodiment is to set a pre-configuration file of the target object, which includes multiple voxel grid data of the target object with different grid precisions. The target neural stimulation information is obtained, and the target voxel grid data is determined from the multiple voxel grid data stored in the pre-configuration file. The neural stimulation action field corresponding to the target neural stimulation information is rendered based on the target voxel grid data to meet the user's rendering requirements for neural stimulation models at different grid precisions.

[0128] Based on the above embodiment, optionally, the pre-configuration file includes voxel grid data corresponding to multiple neural stimulation information; the spatial ranges of the voxel grid data corresponding to different neural stimulation information are different; and the grid accuracy of the voxel grid data is negatively correlated with the spatial range of the voxel grid data.

[0129] Optionally, the target voxel grid data determination module 320 is used to: match the target neural stimulation information with multiple neural stimulation information in the pre-configuration file to determine the neural stimulation information that matches the target neural stimulation information; and determine the target voxel grid data using the voxel grid data corresponding to the neural stimulation information that matches the target neural stimulation information.

[0130] Based on the above embodiment, optionally, the implantable neural stimulation system at least includes a stimulation electrode implanted into a target point of the target object;

[0131] The device also includes a file construction module for:

[0132] Acquire the three-dimensional environmental information of the target object and the implantation information of the stimulation electrode implanted into the target point; determine the electric field point cloud data corresponding to different neural stimulation information by simulation based on the three-dimensional environmental information and the implantation information; determine the preset grid accuracy corresponding to the different neural stimulation information; map the preset grid accuracy corresponding to the different neural stimulation information to the spatial grid where the corresponding electric field point cloud data is located, so as to obtain the voxel grid data corresponding to the different neural stimulation information.

[0133] Optionally, the file construction module is further used to: determine the neural stimulation information range adapted to the target object; divide the neural stimulation information range into a plurality of stimulation sections based on a preset division rule, wherein different stimulation sections correspond to different preset grid accuracies;

[0134] Optionally, the preset division rule includes one or more of an equal division method and an incremental division method.

[0135] Optionally, the target voxel grid data determination module 320 is used to: determine the preset grid accuracy corresponding to the target neural stimulation information according to the target stimulation segment corresponding to the target neural stimulation information; determine the target spatial range according to the electric field point cloud data corresponding to the target stimulation information; load the preset grid accuracy corresponding to the target neural stimulation information into the target spatial range to obtain the target voxel grid data.

[0136] Optionally, the neural stimulation information comprises the amplitude of the delivered electrical pulses.

[0137] Optionally, the preset grid accuracy corresponding to each stimulation segment is negatively correlated with the intensity of the neural stimulation information of the stimulation segment.

[0138] Optionally, the plurality of voxel grid data in the pre-configured file are stored in intervals based on a preset interval manner.

[0139] The neural stimulation field rendering device provided in the embodiment of the present application can execute the neural stimulation field rendering method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method.

[0140] Embodiment 4

[0141] Figure 4 is a schematic diagram of a program-controlled device provided in an embodiment of the present disclosure, the program-controlled device comprising:

[0142] The programming interface 410 is configured to provide an interface for doctors to input programming parameters.

[0143] The processor 420 is configured to receive and store a pre-configured file of the target object, and execute a neural stimulation field rendering method according to program parameters input by a doctor to obtain a neural stimulation field model of the target object.

[0144] The display interface 430 is configured to display the nerve stimulation field of the target object in at least three dimensions.

[0145] Optionally, the program-controlled device may include a processor and two display components, the first display component displays a program-controlled interface, and the second display component displays a display interface. The doctor inputs program-controlled parameters through the program-controlled interface 410, and the program-controlled parameters may include target nerve stimulation information. The processor processes the program-controlled parameters to obtain a nerve stimulation field model of the target object, and displays the nerve stimulation field of the target object in at least three dimensions through the display interface 430.

[0146] Optionally, the program-controlled device may include a display component and a processor, and the display component may be a display or a display screen. The display component is configured as a program-controlled interface or a display interface at different stages. For example, in the pre-processing stage, the display component displays a program-controlled interface 410, and obtains program-controlled parameters set by the doctor for the target object, that is, target nerve stimulation information, through the program-controlled interface. The program-controlled parameters are transmitted to the processor, and the nerve stimulation field rendering method provided in the above embodiment is executed through the processor and the pre-stored pre-configuration file of the target object to obtain the nerve stimulation field model of the target object.

[0147] In the post-processing stage, the display component displays a display interface, through which the nerve stimulation field of the target object is displayed in at least three dimensions.

[0148] The technical solution provided in this embodiment realizes a visualized programming parameter setting process by providing a programming interface for doctors, processes the programming parameters through a processor to obtain a neural stimulation field model of the target object, and displays the neural stimulation field of the target object in at least three dimensions through a display interface, thereby realizing a visualized display of the neural stimulation field corresponding to the programming parameters. Before delivering the electric pulses corresponding to the programming parameters to the target object, the neural stimulation field is simulated and rendered, which facilitates doctors to confirm the neural stimulation field corresponding to the programming parameters, further confirm or adjust the programming parameters, and improve the safety and adaptability of neural stimulation to the target object.

[0149] Embodiment 5

[0150] Figure 5 is a schematic diagram of an implantable stimulation system provided in an embodiment of the present disclosure, the system comprising: a pulse generator 510 and a program-controlled device 520 .

[0151] The pulse generator 510 is implanted into the body of the target subject and connected to the stimulation electrodes implanted into the brain of the target subject.

[0152] The program control device 520 is configured to communicate with the pulse generator and receive and send program control instructions to the pulse generator.

[0153] In this embodiment, the neural stimulation field model corresponding to at least one programming parameter adapted for the target object is determined by the programming device 520, and the corresponding neural stimulation field is displayed, providing a reference for doctors to determine the programming parameters suitable for the target object.

[0154] The program control device 520 obtains and sends a program control instruction to the pulse generator, and the program control instruction may include a target program control parameter adapted to the target object, that is, target neural stimulation information. The program control device 520 transmits the program control instruction to the pulse generator 510, and the pulse generator 510 responds to the program control instruction and delivers an electrical pulse to the target object through a stimulation electrode implanted in the brain of the target object, thereby achieving neural stimulation of the target object.

[0155] Embodiment 6

[0156] Figure 6 1 is a schematic diagram of the structure of an electronic device provided in Example 6 of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0157] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0158] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0159] The processor 11 may be a variety of general and / or dedicated processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a neural stimulation action field rendering method.

[0160] In some embodiments, the neural stimulation action field rendering method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the neural stimulation action field rendering method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the neural stimulation action field rendering method in any other appropriate manner (e.g., by means of firmware).

[0161] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0162] The computer program for implementing the neural stimulation action field rendering method of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0163] Embodiment 7

[0164] Embodiment 7 of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute a method for rendering a neural stimulation action field, the method comprising:

[0165] Obtain a pre-configuration file of the target object, wherein the pre-configuration file includes multiple voxel grid data of the target object in a neural stimulation scenario, and the grid precisions corresponding to the multiple voxel grid data are different; obtain target neural stimulation information, and determine the target voxel grid data in the pre-configuration file based on the target neural stimulation information; and render based on the target voxel grid data to obtain a neural stimulation field corresponding to the target neural stimulation information.

[0166] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0167] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0168] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0169] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0170] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0171] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for rendering a neural stimulation field, characterized in that: The method is applied to an implantable neural stimulation system, comprising: Acquire a pre-configuration file of a target object, wherein the pre-configuration file includes a plurality of voxel grid data of the target object in different neural stimulation scenarios, and the grid precisions corresponding to the plurality of voxel grid data are different; acquiring target neural stimulation information, and determining target voxel grid data in the pre-configuration file based on the target neural stimulation information; A neural stimulation action field corresponding to the target neural stimulation information is obtained by rendering based on the target voxel grid data.

2. The method according to claim 1, characterized in that: The preconfiguration file includes voxel grid data corresponding to a plurality of neural stimulation information; the spatial range of the voxel grid data corresponding to different neural stimulation information is different; The grid accuracy of the voxel grid data is negatively correlated to the spatial extent of the voxel grid data.

3. The method according to claim 2, characterized in that The determining target voxel grid data in the pre-configuration file based on the target neural stimulation information comprises: Matching the target neural stimulation information with the plurality of neural stimulation information in the preconfiguration file to determine the neural stimulation information matching the target neural stimulation information; The target voxel grid data is determined by using the voxel grid data corresponding to the nerve stimulation information matching the target nerve stimulation information.

4. The method according to claim 1, characterized in that: The implantable neural stimulation system at least includes a stimulation electrode implanted into a target point of a target object; the pre-configuration file is constructed in the following manner: Acquiring three-dimensional environmental information of the target object and implantation information of the stimulation electrode implanted into the target point; Determine, by simulation, electric field point cloud data corresponding to different nerve stimulation information according to the three-dimensional environment information and the implantation information; Determine the preset grid accuracy corresponding to different neural stimulation information; The preset grid precision corresponding to different neural stimulation information is mapped to the spatial grid where the corresponding electric field point cloud data is located, so as to obtain the voxel grid data corresponding to the different neural stimulation information.

5. The method according to claim 4, characterized in that Determine the preset grid accuracy corresponding to different neural stimulation information, including: determining a range of neural stimulation information adapted to the target object; Based on a preset division rule, the neural stimulation information range is divided into a plurality of stimulation sections, wherein different stimulation sections correspond to different preset grid accuracies.

6. The method according to claim 5, characterized in that The preset division rule includes an equal division method or an incremental division method.

7. The method according to claim 5, characterized in that The preset grid accuracy corresponding to each stimulation section is negatively correlated with the intensity of the neural stimulation information of the stimulation section.

8. The method according to claim 5, characterized in that Determining target voxel grid data in the pre-configuration file based on the target neural stimulation information includes: Determining a preset grid accuracy corresponding to the target nerve stimulation information according to the target stimulation section corresponding to the target nerve stimulation information; Determine the target space range according to the electric field point cloud data corresponding to the target stimulation information; The preset grid precision corresponding to the target nerve stimulation information is loaded into the target space range to obtain the target voxel grid data.

9. The method according to any one of claims 2 to 5, characterized in that: The neural stimulation information includes the amplitude of the delivered electrical pulses.

10. The method according to claim 1 or 4, characterized in that: The plurality of voxel grid data in the preconfigured configuration file are stored in intervals based on a preset interval manner.

11. A neural stimulation field rendering device, characterized in that: Integrated into implantable neurostimulation systems, including: A file acquisition module, used to acquire a pre-configuration file of a target object, wherein the pre-configuration file includes a plurality of voxel grid data of the target object in different neural stimulation scenarios, and the grid precisions corresponding to the plurality of voxel grid data are different; a target voxel grid data determination module, configured to obtain target nerve stimulation information, and determine target voxel grid data in the pre-configuration file based on the target nerve stimulation information; A rendering module is used to render the target voxel grid data to obtain a nerve stimulation field corresponding to the target nerve stimulation information.

12. A program-controlled device, characterized in that: The program-controlled device comprises: A program control interface, configured to provide a doctor with an interface for inputting program control parameters; A processor configured to receive and store a pre-configuration file of a target object, and execute the method according to any one of claims 1 to 10 according to the program control parameters input by a doctor to obtain a neural stimulation field model of the target object; The display interface is configured to display the nerve stimulation field of the target object in at least three dimensions.

13. An implantable stimulation system, characterized in that: The system comprises: a pulse generator implanted in the target subject and connected to stimulation electrodes implanted in the target subject's brain; The program control device is configured to communicate with the pulse generator and receive and send program control instructions to the pulse generator.

14. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the nerve stimulation action field rendering method according to any one of claims 1 to 10.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for rendering a nerve stimulation action field according to any one of claims 1 to 10 when executed.

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