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

Abstract

A nerve stimulation action field rendering method and apparatus, and a storage medium and an electronic device. The method comprises: acquiring a pre-configuration file of a target object, wherein the pre-configuration file comprises a plurality of pieces of voxel grid data of the target object in neural stimulation scenarios, and the plurality of pieces of voxel grid data correspond to different grid precision levels (S110); acquiring target nerve stimulation information, and on the basis of the target nerve stimulation information, determining target voxel grid data from the pre-configuration file (S120); and on the basis of the target voxel grid data, performing rendering to obtain a nerve stimulation action field corresponding to the target nerve stimulation information (S130).

Description

Methods, devices, storage media, and electronic equipment for rendering neural stimulation fields

[0001] This application claims priority to Chinese Patent Application No. 202411944414.X, filed with the Chinese Patent Office on December 26, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of medical device technology, such as a method, apparatus, storage medium, and electronic device for rendering a neural stimulation field. Background Technology

[0003] The programming process involves adjusting the stimulator's parameters to the values ​​most suitable for the patient's condition. Before adjusting the stimulator's parameters, to improve the accuracy of the adjustment, the stimulator's neural stimulation domain can be rendered, allowing doctors to understand the neural stimulation domain corresponding to the stimulator's parameters and thus better determine the appropriate parameters.

[0004] Currently, the rendering of neural stimulation domains is generally based on Neuroimaging Informatics Technology Initiative (NIfTI) files. NIfTI files are uniform voxel mesh data, which limits the rendering of neural stimulation domains due to mesh precision and reduces flexibility. Summary of the Invention

[0005] This application provides a method, apparatus, storage medium, and electronic device for rendering neural stimulation fields, which dynamically determines voxel mesh data of different precisions according to the model rendering requirements, thereby improving the flexibility of neural stimulation model rendering.

[0006] According to one aspect of this application, a method for rendering a neural stimulation field is provided, comprising:

[0007] Obtain a pre-configuration file of the target object, the pre-configuration file including multiple voxel grid data of the target object in a neural stimulation scenario, the multiple voxel grid data corresponding to different grid precipitates;

[0008] Acquire target neural stimulation information, and determine target voxel grid data in the pre-configuration file based on the target neural stimulation information;

[0009] The neural stimulation field corresponding to the target neural stimulation information is obtained by rendering based on the target voxel mesh data.

[0010] According to another aspect of this application, a neural stimulation field rendering device is provided, comprising:

[0011] The file acquisition module is configured to acquire a pre-configuration file of the target object, which includes multiple voxel grid data of the target object in a neural stimulation scenario, and the multiple voxel grid data correspond to different grid precipitates.

[0012] The target voxel grid data determination module is configured to acquire target neural stimulation information and determine target voxel grid data in the pre-configuration file based on the target neural stimulation information.

[0013] The rendering module is configured to render the neural stimulation field corresponding to the target neural stimulation information based on the target voxel mesh data.

[0014] According to another aspect of this application, a programmable device is provided, the programmable device comprising:

[0015] The programmable interface is configured to provide doctors with an interface for inputting programmable parameters;

[0016] The programmable processor is configured to receive and store a pre-configuration file of the target object, and execute the neural stimulation field rendering method provided in this application embodiment according to the programmable parameters input by the doctor, so as to obtain a neural stimulation field model of the target object.

[0017] The display interface is configured to display the neural stimulation field of the target object in at least three dimensions.

[0018] According to another aspect of this application, an implantable stimulation system is provided, the system comprising:

[0019] A pulse generator is implanted into the target body and connected to stimulation electrodes implanted in the target brain;

[0020] The programmable device is configured to communicate with the pulse generator and to receive programmable parameters and send programmable commands to the pulse generator.

[0021] According to another aspect of this application, an electronic device is provided, the electronic device comprising:

[0022] At least one processor; and

[0023] A memory communicatively connected to the at least one processor; wherein,

[0024] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the neural stimulation field rendering method according to any embodiment of this application.

[0025] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the neural stimulation field rendering method according to any embodiment of this application. Attached Figure Description

[0026] Figure 1 is a flowchart of a method for rendering a neural stimulation field according to an embodiment of this application;

[0027] Figure 2 is a flowchart of a method for creating a pre-configuration file provided in an embodiment of this application;

[0028] Figure 3 is a schematic diagram of a neural stimulation field rendering device provided in an embodiment of this application;

[0029] Figure 4 is a schematic diagram of a programmable device provided in an embodiment of this application;

[0030] Figure 5 is a schematic diagram of an implantable stimulation system provided in an embodiment of this application;

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

[0032] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Such data may be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] The technical field and related terms of the embodiments of this application are briefly described below.

[0034] Implantable medical systems include implantable neurostimulation systems, implantable cardiac stimulation systems (also known as pacemakers), implantable drug delivery systems (IDDS), and lead transfer systems. Examples of implantable neurostimulation systems include deep brain stimulation (DBS), cortical nerve stimulation (CNS), spinal cord stimulation (SCS), sacral nerve stimulation (SNS), and vagus nerve stimulation (VNS).

[0035] Implantable neurostimulation systems consist of a stimulator implanted in the patient's body (i.e., an implantable neurostimulator) and a programmed device placed outside the patient's body. In other words, the stimulator is a medical device, or medical devices include stimulators. Related neuromodulation techniques primarily involve stereotactic surgery to implant electrodes (e.g., electrode wires) at specific sites (target points) in the body's tissues. Discharge pulses are then delivered through these electrodes to the target points, modulating the electrical activity and function of corresponding neural structures and networks, thereby improving symptoms and alleviating pain.

[0036] As an example, DBS includes an implantable pulse generator (IPG), extension leads, and electrode leads, with the IPG connected to the electrode leads via the extension leads. The IPG is implanted in the patient's body, for example, in the chest or other internal location.

[0037] As another example, DBS includes an IPG and electrode leads, with the IPG directly connected to the electrode leads. The IPG is implanted in the patient's head, for example, by creating a groove in the patient's skull and then placing the IPG in the groove. In this case, the IPG may not protrude from the outer surface of the skull, or it may protrude partially from the outer surface of the skull.

[0038] In this system, the IPG responds to programmed commands sent by a programmable device, relying on sealed batteries and circuits to provide controllable electrical stimulation therapy (or electrical stimulation energy) to tissues within the body. The IPG delivers one or more controllable specific electrical stimuli to specific areas of tissues within the body via electrode leads.

[0039] In some embodiments, the extension wire is used in conjunction with the IPG as a medium for transmitting electrical stimulation, thereby transmitting the electrical stimulation generated by the IPG to the electrode wire.

[0040] In some embodiments, electrical stimulation can be delivered in the form of a pulsed signal or a non-pulsed signal. For example, electrical stimulation can be delivered as a signal with various waveform shapes, frequencies, and amplitudes. Therefore, non-pulsed signal electrical stimulation can be a continuous signal, which can have a sinusoidal waveform or other continuous waveforms.

[0041] After receiving electrical stimulation from the IPG or extension leads, the electrode leads deliver the stimulation to specific areas of the body's tissues via multiple electrode contacts. The stimulator can have one or more electrode leads on one or both sides, with multiple electrode contacts on each lead. These contacts can be evenly or non-uniformly arranged circumferentially on the electrode leads. As an example, the electrode contacts can be arranged in a 4x3 array (a total of 12 contacts) circumferentially on the electrode leads. The electrode contacts can include stimulating electrode contacts and / or collecting electrode contacts. The electrode contacts can be sheet-like, ring-like, or dot-like in shape.

[0042] In some embodiments, the stimulated tissue may be the patient's brain tissue, and the stimulated site may be a specific location within the brain tissue. Generally, the stimulated site differs depending on the patient's disease type, and the number of stimulation contacts (single-source or multi-source), the application of one or more specific electrical stimulation pathways (single-channel or multi-channel), and the stimulation parameters (values) also vary.

[0043] This application's embodiments are applicable to various disease types, including those suitable for DBS, SCS, sacral nerve stimulation, gastric stimulation, peripheral nerve stimulation, and functional electrical stimulation. Specifically, DBS can be used to treat or manage diseases such as: spastic disorders (e.g., epilepsy), pain, migraines, mental illnesses (e.g., major depressive disorder (MDD)), bipolar disorder, anxiety disorders, post-traumatic stress disorder, mild depression, obsessive-compulsive disorder (OCD), behavioral disorders, mood disorders, memory disorders, mental state disorders, mobility disorders (e.g., essential tremor or Parkinson's disease), Huntington's disease, Alzheimer's disease, drug addiction, autism, or other neurological or psychiatric disorders and impairments.

[0044] In this embodiment of the 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, with different stimulation parameters corresponding to different electrical stimuli). Alternatively, the stimulator can sense the patient's electrophysiological activity to collect electrophysiological signals, and the collected electrophysiological signals can be used to continue adjusting the stimulation parameters of the stimulator to achieve closed-loop control (or adaptive adjustment) of the stimulation parameters.

[0045] Stimulation parameters may include at least one of the following: electrode contact identification for delivering electrical stimulation (e.g., electrode contact #2 and electrode contact #3), frequency (e.g., the number of electrical stimulation pulse signals per second, in Hz), pulse width (duration of each pulse, in μs), amplitude (generally expressed as voltage, i.e., the intensity of each pulse, in V), timing (e.g., continuous or bursty, bursty 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), physician control upper and lower limits (the range that the physician can adjust), and patient control upper and lower limits (the range that the patient can adjust independently).

[0046] In some embodiments, the stimulation parameters of the stimulator can be adjusted in current mode or voltage mode.

[0047] Programmable devices can include physician-controlled devices (i.e., devices used by physicians) and / or patient-controlled devices (i.e., devices used by patients). Physician-controlled devices are, for example, smart terminal devices such as tablets, laptops, desktop computers, and mobile phones equipped with programming software. Patient-controlled devices are, for example, smart terminal devices such as tablets, laptops, desktop computers, and mobile phones equipped with programming software; patient-controlled devices can also be other electronic devices with programming functions (e.g., chargers with programming functions, electrophysiological acquisition devices, etc.).

[0048] Remote programming is a control process that uses internet technology to allow patients to remotely adjust the parameters of a stimulator to the most suitable values ​​for the patient's condition from home or from the hospital.

[0049] Visualized programming uses 3D modeling technology to render the brain nuclei, implanted electrodes, stimulation range, and nerve fibers into a three-dimensional model. Through precise 3D visualization, doctors can better understand the relationship between electrode locations and surrounding nerve fiber bundles, optimize electrical stimulation parameter settings, and improve treatment outcomes.

[0050] Neurostimulation domain rendering is a technique used to visualize and analyze the extent and effects of neural stimulation 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.

[0051] Point cloud 3D reconstruction refers to the process of measuring and sampling multiple points on the surface of an object to obtain a 3D dataset, and then processing and analyzing the dataset to reconstruct the 3D shape and structure of the object.

[0052] NIfTI files are uniform voxel meshes that can be used to perform 3D reconstruction of point clouds, thereby rendering neural stimulation domains. The level of detail in the domain model is affected by the mesh precision of the point cloud. When the mesh precision is sparse, the rendering result is coarse. When the mesh precision is dense, the rendering result is fine, but the storage space occupied by the point cloud data will also increase dramatically.

[0053] Example 1

[0054] Figure 1 is a flowchart of a neural stimulation field rendering method provided in an embodiment of this application. This neural stimulation field rendering method is applied to an implantable neural stimulation system. This embodiment is applicable to rendering the neural stimulation field before adjusting the parameters of the stimulator to simulate and display the neural stimulation field of the stimulator. This method can be executed by a neural stimulation field rendering device, which can be implemented in hardware and / or software and can be configured in a programmable device. As shown in Figure 1, the method includes:

[0055] S110. Obtain the pre-configuration file of the target object. The pre-configuration file includes multiple voxel grid data of the target object under different neural stimulation scenarios. The grid precision of the multiple voxel grid data is different.

[0056] S120. Obtain target neural stimulation information and determine target voxel grid data in the pre-configuration file based on the target neural stimulation information.

[0057] S130. Render the neural stimulation field corresponding to the target neural stimulation information based on the target voxel grid data.

[0058] 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 that stores multiple voxel grid data of the target object.

[0059] The stimulator may include multiple electrode contacts, and the electrical stimulation applied to each electrode contact can be positive voltage, negative voltage, or no voltage applied. The electrical stimulation here can be, for example, an electrical pulse. Accordingly, the multiple electrode contacts of the stimulator can form various combinations of electrical stimulation. For each combination of electrical stimulation, a pre-configuration file can be set, and any pre-configuration file includes multiple voxel grid data under the neural stimulation scenario of the corresponding combination of electrical stimulation.

[0060] By dividing the three-dimensional space into a three-dimensional spatial grid, which consists of regular three-dimensional cubic 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 under a neural stimulation scenario to each voxel of the three-dimensional spatial grid.

[0061] The mesh precision of voxel mesh data can be understood as the fineness of meshing a 3D space. Mesh precision can be characterized by the size of the cubic unit; the larger the cubic unit, the lower the mesh precision, and vice versa. For the same 3D space, higher mesh precision in voxel mesh data results in a larger data volume and greater storage space usage; conversely, lower mesh precision results in a smaller data volume and less storage space usage.

[0062] 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. The neural stimulation information can be one or more of the stimulation parameters. For example, the neural stimulation information is a parameter among the stimulation parameters that affects the range of neural stimulation action. For example, the neural stimulation information can include the amplitude (i.e., voltage), pulse width, and frequency of the delivered electrical pulse.

[0063] In some embodiments, each neural stimulus information in the pre-configuration file corresponds to a voxel grid data. The grid precision of the voxel grid data corresponding to different neural stimulus information is different to meet the rendering requirements of the neural stimulus field corresponding to different neural stimulus information. For example, 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.

[0064] In some embodiments, the same neural stimulation information in the pre-configuration file corresponds to multiple voxel grid data, and the grid precision of the multiple voxel grid data corresponding to the same neural stimulation information is different. For example, the pre-configuration 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 can be pre-set, and for each amplitude, n different voxel grid data corresponding to each grid precision can be set to meet the rendering requirements of neural stimulation fields with different grid precisions for the same neural stimulation information.

[0065] For multiple voxel mesh data with different mesh retrievals corresponding to the same neural stimulation information, the lower the mesh retrieval, the smaller the data volume of the voxel mesh data, and the less resources and time are consumed in the rendering process; the higher the mesh retrieval, the larger the data volume of the voxel mesh data, the better the rendering effect of the neural stimulation field, and correspondingly, the greater the resources and time consumed in the rendering process. Pre-configuration files can provide voxel mesh data with different amplitudes and / or different mesh retrievals for the rendering of neural stimulation models, which is beneficial for dynamically switching amplitudes and / or mesh retrievals during the rendering process of neural stimulation models.

[0066] In some embodiments, each voxel grid data in the pre-configuration file is configured with corresponding neural stimulation information and grid precision, which can be used as index information for querying voxel grid data.

[0067] Target neural stimulation information can be understood as the neural stimulation information corresponding to the electrical pulse to be delivered to the target object, which can characterize the user's rendering requirements for the neural stimulation field. This target neural stimulation information can be collected through an interactive interface displayed by a programmable device. For example, the interactive interface of the programmable device includes a neural stimulation information control, which can be an input control to collect the target neural stimulation information input by the user. The target neural stimulation information is matched against a pre-configured file to determine the voxel grid data that matches the target neural stimulation information as the target voxel grid data. For example, taking the amplitude of the delivered electrical pulse as the neural stimulation information, it can be determined by matching the target amplitude in the target neural stimulation information against multiple amplitudes in the pre-configured file to determine the same amplitude or the closest amplitude; then, the voxel grid data corresponding to the target amplitude or the closest amplitude is determined as the target voxel grid data.

[0068] In some embodiments, the target neural stimulation information may carry a target grid precision. The interactive interface of the programmable device also includes a grid precision control, which can be a selection control displaying multiple grid precisions. For example, the multiple grid precisions displayed by the grid precision control can be grid precision values, or they can be grid precision levels, such as "high," "medium," or "low." In response to a selection operation on the grid precision control, a target grid precision is determined. In the case of multiple voxel grid data with different grid precisions corresponding to the target amplitude or the closest amplitude, the target network precision is obtained, and the voxel grid data corresponding to the target grid precision is determined as the target voxel grid data.

[0069] This embodiment sets a pre-configuration file for the target object, which includes multiple voxel grid data of the target object with different grid precisions. By acquiring target neural stimulation information and determining the target voxel grid data from the multiple voxel grid data stored in the pre-configuration file, the neural stimulation field corresponding to the target neural stimulation information is rendered based on the target voxel grid data. By using a pre-configuration file including multiple voxel grid data with different grid precisions, voxel grid data with different grid precisions is provided for rendering the neural stimulation model, overcoming the problem of single grid precision in voxel grid data and meeting the user's rendering needs for neural stimulation models at different grid precisions.

[0070] In some embodiments, multiple voxel grid data in the pre-configuration file are each configured with corresponding neural stimulation information. The multiple voxel grid data are spaced according to a preset interval method. For example, the preset interval method can be a blank line interval method, meaning a blank line is set between any two voxel grid data. Alternatively, the preset interval method can be a preset spacer interval method, meaning a preset spacer is set between any two voxel grid data. The specific form of the preset interval method is sufficient to achieve the separation of multiple voxel grid data within the same file. Optionally, the pre-configuration file can be a txt file.

[0071] Different neural stimulation information corresponds to different spatial ranges of voxel grid data. Taking the amplitude of neural stimulation information as an example, the spatial range of voxel grid data is positively correlated with the amplitude. That is, the larger the amplitude of electrical stimulation, the larger the spatial range of voxel grid data, and the larger the amount of electric field point cloud data within that spatial range.

[0072] In this embodiment, in order to balance rendering effect and saving storage space, the mesh precision of the voxel mesh data in the pre-configuration file is negatively correlated with the spatial range of the voxel mesh data. That is, the larger the spatial range of the voxel mesh data, the lower the mesh precision of the voxel mesh data, thus reducing the storage space occupied by the voxel mesh data; the smaller the spatial range of the voxel mesh data, the higher the mesh precision of the voxel mesh data, thus improving the rendering effect of the neural stimulation model.

[0073] For example, as amplitude A, amplitude B and amplitude C increase sequentially, grid precision 1, grid precision 2 and grid precision 3 gradually decrease.

[0074] Determining target voxel grid data in a pre-configuration file based on target neural stimulation information includes: matching 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 determining the voxel grid data corresponding to the neural stimulation information that matches the target neural stimulation information as the target voxel grid data.

[0075] In this embodiment, the neural stimulation information matching the target neural stimulation information can be the same 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 differences between the target amplitude and multiple amplitudes in the pre-configuration file are determined respectively. The amplitude with a difference of zero is determined as the amplitude matching the target amplitude, or the amplitude with the smallest difference is determined as the amplitude matching the target amplitude.

[0076] For example, when the target neural stimulus information is amplitude B, the pre-configured file matches target voxel grid data with a grid precision of 2. When the target neural stimulus information is amplitude A, the pre-configured file matches target voxel grid data with a grid precision of 1. As the target neural stimulus information changes, the grid precision of the matched target voxel grid data also changes accordingly. There is no need to set the grid precision separately; this allows for dynamic switching of the target voxel grid data's grid precision based on the target neural stimulus information.

[0077] Based on the above embodiments, target voxel grid data is obtained, and the target voxel grid data is rendered to obtain a neural stimulation model.

[0078] The pre-configured files can be updated as needed, such as adding voxel mesh data corresponding to the new mesh size, providing a comprehensive data foundation for subsequent rendering of the neural stimulation model.

[0079] In this embodiment, by storing voxel mesh data corresponding to different neural stimulation information in a pre-configuration file, and with the larger the spatial range of the voxel mesh data corresponding to the voxel mesh data, the lower the mesh precision of the voxel mesh data, it is possible to collect low-precision voxel mesh data for rendering when the spatial range of the neural stimulation model is large, thereby saving storage space and model construction time; and to use high-precision voxel mesh data for rendering when the spatial range of the neural stimulation model is small, thereby improving the model rendering effect. By setting the mesh precision of the voxel mesh data to be negatively correlated with the spatial range of the voxel mesh data, the problem of excessive storage space occupied by voxel mesh data due to a single mesh precision, or poor rendering effect of the neural stimulation model obtained by rendering voxel mesh data, is overcome, thus achieving a balance between rendering effect and storage space usage.

[0080] Example 2

[0081] Figure 2 is a flowchart of a method for creating a pre-configuration file according to an embodiment of this application. By creating a pre-configuration file, voxel mesh data with multiple mesh resolutions are stored, providing a data foundation for flexible rendering of neural stimulation models. As shown in Figure 2, the method includes:

[0082] S210. Obtain the three-dimensional environmental information of the target object and the implantation information of the stimulation electrode implanted into the target target.

[0083] S220. Based on the three-dimensional environmental information and the implantation information, simulate and determine the electric field point cloud data corresponding to different neural stimulation information.

[0084] S230. Determine the preset grid precision corresponding to different neural stimulation information.

[0085] S240. Map 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 the voxel grid data corresponding to different neural stimulation information.

[0086] In this embodiment, the implantable neurostimulation system includes at least a stimulation electrode implanted into the target site of the target object.

[0087] Create three-dimensional environmental information for the target object. This three-dimensional environmental information can be the three-dimensional environmental information of the implantation site of the stimulation electrode at the target point of the target object. For example, the implantation site can be the head, and correspondingly, the three-dimensional environmental information can be a head model. This three-dimensional environmental information can be constructed from the medical imaging data of the target object, such as computed tomography (CT) images and MRI images.

[0088] Implantation information of the stimulating electrode to the target site may include the implantation location of the stimulating electrode to the target site.

[0089] Electric field point cloud data is obtained through neural stimulation simulation using different neural stimulation information based on the 3D environmental information and implantation information of the target object. According to the implantation information of the stimulating electrode at the target point, neural stimulation simulation is performed on the 3D environmental information of the target object to obtain the electric field point cloud data of the target object under neural stimulation. The electric field point cloud data includes point cloud data of multiple points in the 3D environmental information. The point cloud data of any point includes the 3D coordinate data and electric field data of that point, which can be electric field intensity data. The electric field intensity data of different points are different. The magnitude of the electric field intensity data of any point is negatively correlated with the distance from that point to the implantation location of the stimulator; the smaller the distance from the point to the implantation location of the stimulator, the larger the electric field intensity data of that point.

[0090] 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 different neural stimulation information. During the neural stimulation simulation of the target object's three-dimensional environmental information, other stimulation parameters, such as frequency, stimulation mode, and pulse width, need to be set according to the simulation requirements.

[0091] In some embodiments, neural stimulation simulation of three-dimensional environmental information can be achieved through preset electric field simulation software. For example, the three-dimensional environmental information and implantation information of the target object can be displayed in the preset electric field simulation software. For instance, 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, such as neural stimulation information (e.g., amplitude), frequency, stimulation mode, and pulse width, are set.

[0092] Using pre-set electric field simulation software, neural stimulation simulation is performed on the three-dimensional environment information based on the aforementioned neural stimulation information, and the electric field point cloud data corresponding to the neural stimulation information is displayed. The smaller the electric field strength data, the worse the neural stimulation effect. In some embodiments, the electric field point cloud data corresponding to the three-dimensional environment information is sampled based on a preset electric field strength threshold to obtain effective electric field point cloud data. For example, point cloud data with electric field strength data 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 point cloud data with electric field strength data less than the preset electric field strength threshold is deleted.

[0093] In some embodiments, multiple neural stimulation information is determined based on the numerical range of the neural stimulation information, and neural stimulation simulation is performed based on each of the multiple neural stimulation information to obtain electric field point cloud data corresponding to each of the multiple neural stimulation information. 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 following: the implantation location of the stimulator, the disease type of the target object, the severity of the target object's disease, etc. For example, multiple neural stimulation information can be uniformly determined within the numerical range of the neural stimulation information based on a preset number n of neural stimulation information; for example, multiple neural stimulation information can be uniformly determined within the numerical range of the neural stimulation information based on a preset data interval. For example, multiple neural stimulation information can be determined based on the usage frequency of each neural stimulation information; for example, multiple neural stimulation information can also be received from user (e.g., doctor) input.

[0094] For electric field point cloud data obtained from simulations of different neural stimulation information, effective electric field point cloud data can be obtained by sampling using different electric field intensity thresholds. For example, the electric field point cloud data is non-uniformly distributed.

[0095] Determine the preset grid precision corresponding to different neural stimulation information. For any electric field point cloud data corresponding to any neural stimulation information (the electric field point cloud data here and in the following text can be the effective electric field point cloud data obtained by sampling), map the preset grid precision 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, and obtain the voxel grid data corresponding to the neural stimulation information.

[0096] In some embodiments, determining the preset grid precision corresponding to different neural stimulation information includes: determining the range of neural stimulation information adapted to the target object, and dividing the range of neural stimulation information into multiple stimulation segments based on preset division rules, wherein the preset grid precision corresponding to different stimulation segments is different.

[0097] In this embodiment, the range of neural stimulation information adapted to the target object can be understood as the limit range of the electrical pulse adapted to the target object. Taking the amplitude of the electrical pulse as an example, the range of neural stimulation information can be understood as the limit range of the amplitude of the electrical pulse adapted to the target object, including the maximum and minimum values. For example, the range of neural stimulation information adapted to the target object can be 0.4V-2V.

[0098] The range of neural stimulation information adapted to the target object is divided into multiple stimulation segments. Different preset grid precisions are set for different stimulation segments. Multiple neural stimulation information in the same stimulation segment corresponds to the same preset grid precision, that is, the preset grid precision corresponding to the stimulation segment.

[0099] In some embodiments, the range of neural stimulation information suitable for the target object is divided into multiple stimulation segments by a preset division rule. The preset division rule may include an equal division method or an incremental division method.

[0100] The equal division method can be understood as a way to uniformly divide the range of neural stimulation information, with different stimulation segments having the same segment length. For example, the neural stimulation information range of 0.4V-2V is uniformly divided into four equal stimulation segments: 0.4-0.8V, 0.8-1.2V, 1.2-1.6V, and 1.6-2V. In some embodiments, the equal division of stimulation segments can be based on the number of stimulation segments or the length of the stimulation segments.

[0101] The incremental division method can be understood as dividing the range of neural stimulation information into segments of unequal length, with the length of each segment increasing sequentially. For example, the range of neural stimulation information 0.4V-2V can be divided into five incremental segments: 0.4-0.5V, 0.5-0.7V, 0.7-1.0V, 1.0-1.4V, and 1.4-2.0V.

[0102] The preset grid precision corresponding to each stimulation segment is negatively correlated with the intensity of the neural stimulation information of the stimulation segment. For example, the preset grid precision corresponding to any stimulation segment may be negatively correlated with the intensity of any one of the intermediate, minimum, and maximum neural stimulation information of that stimulation segment. For instance, for a stimulation segment of 0.4-0.8V, the preset grid precision corresponding to that stimulation segment may be negatively correlated with the intermediate neural stimulation information of 0.6V.

[0103] In some embodiments, a grid precision range can be preset, and a preset grid precision corresponding to each stimulation segment can be determined within the grid precision range. Based on the number of stimulation segments, multiple grid precisions are sampled within the grid precision range. For example, it can be a uniform sampling method, i.e., the intervals between multiple grid precisions are the same; or it can be an incremental sampling method, i.e., the intervals between adjacent grid precisions increase sequentially.

[0104] Based on the correspondence (i.e., negative correlation) between stimulation regions and grid precision, a preset grid precision is determined among the multiple sampled grid precisions. Taking four equally divided stimulation regions of 0.4-0.8V, 0.8-1.2V, 1.2-1.6V, and 1.6-2V as examples, among the multiple sampled grid precisions, the stimulation region of 0.4-0.8V corresponds to the maximum grid precision, the stimulation region of 1.6-2V corresponds to the minimum grid precision, and so on.

[0105] For any neural stimulus, the stimulation segment in which the stimulus is located is determined, and the preset grid precision corresponding to that stimulation segment is set as the preset grid precision corresponding to the neural stimulus. Taking neural stimuli of 0.5V and 0.6V as examples, both are located in the stimulation segment 0.4-0.8V, and the preset grid precision corresponding to neural stimuli of 0.5V and 0.6V are both the preset grid precision corresponding to the stimulation segment 0.4-0.8V.

[0106] 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 with the preset grid precision.

[0107] The spatial extent of electric field point cloud data can be determined based on the three-dimensional coordinates of each point within the data. For any coordinate direction, determining the minimum and maximum coordinate values ​​of each point in that direction defines the spatial extent of the electric field point cloud data within that direction. Similarly, the spatial extent of the electric field point cloud data can be determined based on its extent in the X, Y, and Z directions. The spatial extent of the electric field point cloud data corresponding to different neural stimulation information can be labeled, for example, as Si.

[0108] The spatial range of electric field point cloud data corresponding to different neural stimulation information is obtained, and the grid precision range is obtained. Based on the correspondence between spatial range and grid precision (i.e., negative correlation), the preset grid precision corresponding to each spatial range is determined within the grid precision range, that is, the preset grid precision corresponding to different neural stimulation information.

[0109] By dividing the space containing the electric field point cloud data into a grid, a spatial grid is obtained, which includes regular cubic units. The grid size of the resulting spatial grid varies depending on the grid precision. Higher grid precision results in a smaller grid size. For example, grid sizes corresponding to different precisions may include 1mm, 1.5mm, and 2mm, which can be set according to the required grid precision.

[0110] Obtain the electric field point cloud data corresponding to any neural stimulation information, and map it to the spatial grid where the electric field point cloud data corresponding to the neural stimulation information is located based on the preset grid precision, so as to obtain the voxel grid data corresponding to the neural stimulation information.

[0111] For example, using the 3D environmental information and implantation information of the target object, simulation is used to determine the electric field point cloud data corresponding to each of the n neural stimulation information. The preset mesh precision corresponding to each neural stimulation information is mapped to the corresponding electric field point cloud data to obtain the voxel mesh data corresponding to each neural stimulation information. The voxel mesh data corresponding to different neural stimulation information are stored in a pre-configuration file, which includes n voxel mesh data to meet different rendering requirements of the user for the neural stimulation field. In some embodiments, the multiple voxel mesh data in the pre-configuration file are stored at intervals based on a preset interval method, such as a blank line interval method or a spacer interval method.

[0112] The process of determining the voxel grid data corresponding to any neural stimulus information can be as follows: Voxel grid data is formed by mapping the preset grid precision corresponding to the neural stimulus information to the corresponding electric field point cloud data. For example, each regular cubic unit in the spatial grid is considered a grid, and each grid includes multiple grid vertices. Different preset grid precisions can map to different spatial grids. The spatial grid mapped by the preset grid precision corresponding to the neural stimulus information is determined, and the point cloud data corresponding to multiple grid vertices in the spatial grid are obtained to form voxel grid data. For each point cloud data in the electric field point cloud data, the nearest grid vertex is determined based on the three-dimensional coordinate data in the point cloud data. For example, the nearest grid vertex can 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 smallest distance can be determined as the nearest grid vertex. Point cloud data is mapped to the nearest grid vertex. For example, the 3D coordinate data of the point cloud data can be updated to the 3D coordinate data of the nearest grid vertex. Correspondingly, based on the electric field intensity data of the point cloud data and the updated 3D coordinate data, point cloud data mapped to the grid vertex is obtained.

[0113] Point cloud data mapped to the same grid vertex can be one or more. After mapping one or more point cloud data to any grid vertex, the average of the one or more point cloud data obtained by mapping can be determined as the target point cloud data of the grid vertex; or, a random point cloud data from the one or more point cloud data obtained by mapping can be determined as the target point cloud data of the grid vertex; or, the target point cloud data of the grid vertex can be determined according to the mapping time sequence of the one or more point cloud data obtained by mapping.

[0114] The larger the neural stimulation information, the larger the spatial range of the corresponding electric field point cloud data, and the larger the data volume of the electric field point cloud data. Correspondingly, the lower the mesh precision of the space corresponding to the electric field point cloud data, the smaller the data volume of the resulting voxel mesh data, and the smaller the storage space occupied by the voxel mesh data. Conversely, the smaller the neural stimulation information, the smaller the spatial range of the corresponding electric field point cloud data, and the smaller the data volume of the electric field point cloud data. Correspondingly, the higher the mesh precision of the space corresponding to the electric field point cloud data, the higher the data precision of the resulting voxel mesh data, resulting in better rendering effects during the rendering of neural stimulation models.

[0115] Based on the above embodiments, determining the target voxel grid data in the pre-configuration file based on the target neural stimulation information may include: determining the preset grid precision corresponding to the target neural stimulation information according to the target stimulation segment corresponding to the target neural stimulation information; determining the target spatial range according to the electric field point cloud data corresponding to the target neural 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.

[0116] The target neural stimulation information is matched with multiple stimulation segments to determine the stimulation segment containing the target neural stimulation information, which is then designated as the target stimulation segment. The preset grid precision corresponding to the target stimulation segment is used as the preset grid precision corresponding to the target neural stimulation information. Electric field point cloud data corresponding to the target neural stimulation information is acquired, 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 neural 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 neural stimulation information.

[0117] The preset grid precision corresponding to the target neural stimulation information is loaded into the target spatial range of the corresponding electric field point cloud data to realize the grid division of the target spatial range. The obtained spatial grid is mapped with the corresponding electric field point cloud data to obtain the target voxel grid data.

[0118] In this embodiment, neural stimulation simulation is performed on the three-dimensional environmental information and implantation information of the target object to obtain electric field point cloud data corresponding to different neural stimulation information. The space containing the electric field point cloud data is divided into grids with a preset grid precision to obtain a spatial grid with the preset grid precision. Based on the electric field point cloud data, the spatial grid is mapped to obtain voxel grid data with the preset grid precision. Voxel grid data with different preset grid precisions are stored in a pre-configuration file, which can provide a diverse data foundation for the rendering process of the neural stimulation field.

[0119] Example 3

[0120] Figure 3 is a schematic diagram of a neural stimulation field rendering device provided in Embodiment 3 of this application. As shown in Figure 3, the device includes:

[0121] The file acquisition module 310 is configured to acquire a pre-configuration file of a target object, the pre-configuration file including multiple voxel grid data of the target object in a neural stimulation scenario, the multiple voxel grid data corresponding to different grid precision;

[0122] The target voxel grid data determination module 320 is configured to acquire target neural stimulation information and determine target voxel grid data in the pre-configuration file based on the target neural stimulation information.

[0123] The rendering module 330 is configured to render the neural stimulation field corresponding to the target neural stimulation information based on the target voxel mesh data.

[0124] In this embodiment, a pre-configuration file for the target object is set, which includes multiple voxel grid data of the target object with different grid precisions. By acquiring the target neural stimulation information and determining the target voxel grid data from the multiple voxel grid data stored in the pre-configuration file, the neural stimulation field corresponding to the target neural stimulation information is rendered based on the target voxel grid data, thereby meeting the user's rendering needs for the neural stimulation model at different grid precisions.

[0125] In some embodiments, the pre-configuration file includes voxel grid data corresponding to multiple 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 with the spatial range of the voxel grid data.

[0126] In some embodiments, the target voxel grid data determination module 320 is configured 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 voxel grid data corresponding to the neural stimulation information that matches the target neural stimulation information as the target voxel grid data.

[0127] In some embodiments, the implantable neurostimulation system includes at least a stimulation electrode implanted into a target site of the target object;

[0128] The device also includes a file building module, configured as follows:

[0129] The three-dimensional environmental information of the target object and the implantation information of the stimulation electrode implanted into the target target point are obtained; based on the three-dimensional environmental information and the implantation information, the electric field point cloud data corresponding to different neural stimulation information are determined by simulation; the preset grid precision corresponding to different neural stimulation information is determined; 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 to obtain the voxel grid data corresponding to different neural stimulation information.

[0130] In some embodiments, the file construction module is further configured to: determine the range of neural stimulation information suitable for the target object; and divide the range of neural stimulation information into multiple stimulation segments based on preset division rules, wherein different stimulation segments correspond to different preset grid precisions;

[0131] In some embodiments, the preset division rules include one or more of equal division and incremental division methods.

[0132] In some embodiments, the target voxel grid data determination module 320 is configured to: determine a preset grid precision corresponding to the target neural stimulation information based on the target stimulation segment corresponding to the target neural stimulation information; determine a target spatial range based on the electric field point cloud data corresponding to the target neural stimulation information; and load the preset grid precision corresponding to the target neural stimulation information into the target spatial range to obtain the target voxel grid data.

[0133] In some embodiments, the neural stimulation information includes the amplitude of the delivered electrical pulse.

[0134] In some embodiments, the preset grid precision corresponding to each stimulation segment is negatively correlated with the intensity of the neural stimulation information of the stimulation segment.

[0135] In some embodiments, the plurality of voxel grid data in the pre-configuration file are stored at intervals based on a preset interval method.

[0136] The neural stimulation field rendering apparatus provided in this application embodiment can execute the neural stimulation field rendering method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the method execution.

[0137] Example 4

[0138] Figure 4 is a schematic diagram of a programmable device provided in an embodiment of this application. The programmable device includes:

[0139] The programmable interface 410 is configured to provide doctors with an interface for inputting programmable parameters.

[0140] The programmable processor 420 is configured to receive and store a pre-configuration file of the target object, and execute a neural stimulation field rendering method according to the programmable parameters input by the doctor to obtain a neural stimulation field model of the target object.

[0141] Display interface 430 is configured to display the neural stimulation field of the target object in at least three dimensions.

[0142] In some embodiments, the programmable device may include a programmable processor and two display components. The first display component displays the programmable interface, and the second display component displays the display interface. The doctor inputs programmable parameters through the programmable interface 410, which may include target neural stimulation information. The programmable processor processes the programmable parameters 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 the display interface 430.

[0143] In some embodiments, the programmable device may include a display component and a programmable processor. The display component may be a monitor or a display screen. The display component is configured as a programmable interface or a display interface at different stages. For example, in the pre-processing stage, the display component displays a programmable interface 410, through which the programmable parameters set by the doctor for the target object, i.e., the target neural stimulation information, are obtained. The programmable parameters are transmitted to the programmable processor, and through the programmable processor and the pre-stored pre-configuration file of the target object, the neural stimulation field rendering method provided in the above embodiments is executed to obtain a neural stimulation field model of the target object.

[0144] In the post-processing stage, the display component presents a display interface, which displays the neural stimulation field of the target object in at least three dimensions.

[0145] In this embodiment, by providing doctors with a programming interface, a visualized programming parameter setting process is achieved. The programming parameters are processed by a programming processor to obtain a neural stimulation field model of the target object. The neural stimulation field of the target object is then displayed in at least three dimensions through a display interface, realizing a visualized display of the neural stimulation field corresponding to the programming parameters. Before delivering the electrical pulses corresponding to the programming parameters to the target object, the neural stimulation field is simulated and rendered, making it easier for doctors to confirm the neural stimulation field corresponding to the programming parameters, confirm or adjust the programming parameters, and improve the safety and adaptability of neural stimulation to the target object.

[0146] Example 5

[0147] Figure 5 is a schematic diagram of an implantable stimulation system provided in an embodiment of this application. The system includes a pulse generator 510 and a programmable device 520.

[0148] The pulse generator 510 is implanted into the target body and connected to the stimulation electrode implanted in the target brain.

[0149] The programmable device 520 is configured to communicate with the pulse generator and to receive programmable parameters and send programmable commands to the pulse generator.

[0150] In this embodiment, the programmable device 520 determines the neural stimulation field model corresponding to at least one programmed parameter suitable for the target object and displays the corresponding neural stimulation field, providing a reference for doctors to determine the programmed parameters suitable for the target object.

[0151] The programmable device 520 acquires programmable parameters and sends programmable instructions to the pulse generator. These instructions may include target programmable parameters adapted to the target object, i.e., target neural stimulation information. The programmable device 520 transmits the programmable instructions to the pulse generator 510. In response to the programmable instructions, the pulse generator 510 delivers electrical pulses to the target object through stimulation electrodes implanted in the target object's brain, thereby stimulating the target object's nerves.

[0152] Example 6

[0153] Figure 6 is a schematic diagram of an electronic device provided in Embodiment Six of this application. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, 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.

[0154] As shown in Figure 6, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from the storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0155] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0156] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Processor 11 may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various special-purpose Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the neural stimulation field rendering method.

[0157] In some embodiments, the neural stimulation field rendering method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the neural stimulation field rendering method described above may be performed. In other embodiments, processor 11 may be configured to execute the neural stimulation field rendering method by any other suitable means (e.g., by means of firmware).

[0158] Various embodiments 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-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0159] Computer programs used to implement the neural stimulation field rendering method of this application can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0160] Example 7

[0161] Embodiment 7 of this application also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a neural stimulation field rendering method, the method comprising:

[0162] Obtain a pre-configuration file for the target object, which includes multiple voxel grid data of the target object under a neural stimulation scenario, wherein the multiple voxel grid data correspond to different grid precipitates; obtain target neural stimulation information, and determine target voxel grid data in the pre-configuration file based on the target neural stimulation information; render the neural stimulation field corresponding to the target neural stimulation information based on the target voxel grid data.

[0163] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), flash memory, optical fiber, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0164] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0165] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0166] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

Claims

1. A method for rendering a neural stimulation field, the method being applied to an implantable neural stimulation system, comprising: Obtain a pre-configuration file of the target object, which includes multiple voxel grid data of the target object under different neural stimulation scenarios, and the grid precision of the multiple voxel grid data is different; Acquire target neural stimulation information, and determine target voxel grid data in the pre-configuration file based on the target neural stimulation information; The neural stimulation field corresponding to the target neural stimulation information is obtained by rendering based on the target voxel mesh data.

2. The method according to claim 1, wherein, The pre-configuration file includes voxel grid data corresponding to multiple 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 with the spatial range of the voxel grid data.

3. The method according to claim 2, wherein, The step of determining the target voxel grid data in the pre-configuration file based on the target neural stimulation information includes: The target neural stimulation information is matched with multiple neural stimulation information in the pre-configuration file to determine the neural stimulation information that matches the target neural stimulation information. The voxel grid data corresponding to the neural stimulation information that matches the target neural stimulation information is determined as the target voxel grid data.

4. The method according to claim 1, wherein, The implantable neurostimulation system includes at least one stimulating electrode implanted at a target site on the target subject; the pre-configuration file is constructed in the following manner: Acquire the three-dimensional environmental information of the target object, and the implantation information of the stimulation electrode to the target point; Based on the three-dimensional environmental information and the implantation information, the electric field point cloud data corresponding to different neural stimulation information are determined by simulation. Determine the preset grid precision 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 different neural stimulation information.

5. The method according to claim 4, wherein, Determine the preset grid precision corresponding to different neural stimulation information, including: Determine the range of neural stimulation information that the target object can adapt to; Based on preset division rules, the range of neural stimulation information is divided into multiple stimulation segments, where different stimulation segments correspond to different preset grid precisions.

6. The method according to claim 5, wherein, The preset division rules include equal division or incremental division.

7. The method according to claim 5, wherein, The preset grid precision corresponding to each stimulation segment is negatively correlated with the intensity of the neural stimulation information of the stimulation segment.

8. The method according to claim 5, wherein, Based on the target neural stimulation information, target voxel grid data is determined in the pre-configuration file, including: Based on the target stimulation segment corresponding to the target neural stimulation information, the preset grid precision corresponding to the target neural stimulation information is determined; The target spatial range is determined based on the electric field point cloud data corresponding to the target neural stimulation information; The preset grid precision corresponding to the target neural stimulation information is loaded into the target spatial range to obtain the target voxel grid data.

9. The method according to any one of claims 2-5, wherein, The neural stimulation information includes the amplitude of the delivered electrical pulse.

10. The method according to claim 1 or 4, wherein, The multiple voxel grid data in the pre-configuration file are stored at intervals based on a preset interval method.

11. A neural stimulation field rendering device, integrated into an implantable neural stimulation system, comprising: The file acquisition module is configured to acquire a pre-configuration file of the target object. The pre-configuration file includes multiple voxel grid data of the target object under different neural stimulation scenarios. The grid precision of the multiple voxel grid data is different. The target voxel grid data determination module is configured to acquire target neural stimulation information and determine target voxel grid data in the pre-configuration file based on the target neural stimulation information. The rendering module is configured to render the neural stimulation field corresponding to the target neural stimulation information based on the target voxel mesh data.

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

13. An implantable stimulation system, comprising: A pulse generator is implanted into the target body and connected to stimulation electrodes implanted in the target brain; The programmable device is configured to communicate with the pulse generator and to receive programmable parameters and send programmable commands to the pulse generator.

14. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which is executed by the at least one processor to enable the at least one processor to perform the neural stimulation field rendering method according to any one of claims 1-10.

15. A computer-readable storage medium storing computer instructions for causing a processor to execute the neural stimulation field rendering method according to any one of claims 1-10.

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