Processing method of electric field action range model and related device

Abstract

The invention discloses a processing method of an electric field action range model and a related device. The processing system of the electric field action range model comprises a collector configured to collect medical image data of a patient, and the medical image data comprises electrode parameters and electric field parameters corresponding to stimulation parameters delivered to the patient; the first processor is configured to map the electric field parameters into the three-dimensional empty network to obtain first voxel grid data; performing compensation processing on the first voxel grid data by using the electrode parameters to obtain second voxel grid data; and according to the second voxel grid data, constructing and obtaining an electric field action range model corresponding to the stimulation parameters. According to the processing method of the electric field action range model and the related device, compensation processing can be performed on the first voxel grid data by using the electrode parameters, the problem of model collapse caused by data sparsity is effectively solved, and the constructed electric field action range model is more accurate and complete.

Description

Technical Field

[0001] The present invention relates to the technical field of data compensation, and in particular to a method for processing an electric field action domain model and a related device. Background Art

[0002] In the field of neuroscience and medical engineering, the processing of electric field action models is crucial for optimizing neurostimulation therapy. Especially in the application of brain pacemakers (such as deep brain stimulators, DBS), parameter adjustment and visualization of stimulation action are of great significance for improving treatment effects and patient quality of life. The traditional programming method, that is, face-to-face parameter adjustment, usually needs to be performed in a surgical unit or hospital, which not only increases the patient's medical cost and time cost, but may also be limited due to the patient's physical condition.

[0003] In order to solve this problem, remote programming technology came into being. It uses Internet technology to allow doctors to remotely adjust the parameters of the stimulator in the patient's home in the hospital, thereby achieving personalized and precise treatment. However, relying solely on remote programming is still not enough to meet the needs of in-depth understanding and optimization of the stimulation range. Therefore, visual programming technology was introduced, which uses 3D models to render the patient's brain nuclei, electrode implantation locations, and stimulation range, providing doctors with a more intuitive and accurate basis for judgment.

[0004] Furthermore, the neurostimulation scope rendering technology provides researchers with a powerful tool for visualizing and analyzing the scope and effect of neurostimulation in the brain. This technology not only helps to understand the effects of electrical or chemical stimulation on different brain regions, but also optimizes neuromodulation therapy and improves treatment effects.

[0005] However, in practical applications, especially when performing 3D reconstruction of point clouds, there is a challenge: when the output voltage of the electrode is small, the sampled 3D data becomes sparse. This causes the domain of the electrode that should have covered the output stimulation to collapse inside the electrode in the 3D model, thereby losing the characteristics of the original nerve stimulation domain. This data sparsity not only affects the accuracy of the model, but also limits doctors' in-depth understanding of the stimulation domain and the optimization of parameter adjustment.

[0006] To overcome this problem, existing technologies attempt to perform data compensation and model optimization through different methods. However, these methods are often complex and have limited effects, especially when the amount of small-amplitude data sets is scarce, making it difficult to establish a good three-dimensional model of the scope.

[0007] In summary, the present invention proposes a method for processing an electric field action model and a related device. Summary of the invention

[0008] The purpose of the present invention is to provide a processing method and related device for an electric field domain model, which can use electrode parameters to compensate the first voxel grid data, effectively solve the model collapse problem caused by data sparsity, and make the constructed electric field domain model more accurate and complete.

[0009] The purpose of the present invention is achieved by the following technical solutions:

[0010] In one aspect, the present invention provides a processing system for an electric field action model, which is applied to an implantable medical device;

[0011] The processing system of the electric field domain model includes:

[0012] A collector configured to collect medical imaging data of a patient, wherein the medical imaging data includes: electrode parameters and electric field parameters corresponding to stimulation parameters delivered to the patient;

[0013] The first processor is configured to map the electric field parameters into a three-dimensional empty network to obtain first voxel grid data; use electrode parameters to compensate the first voxel grid data to obtain second voxel grid data; and construct an electric field domain model corresponding to the stimulation parameters based on the second voxel grid data.

[0014] The beneficial effect of the above scheme is that the first processor of the present invention maps the electric field parameters into the three-dimensional empty network to generate the first voxel grid data. This process ensures the three-dimensional spatial expression of the electric field distribution data and provides a basic framework for subsequent processing. The first voxel grid data is compensated by using electrode parameters, which effectively solves the model collapse problem caused by data sparsity, making the constructed electric field domain model more accurate and complete. The accurate construction of the electric field domain model enables doctors to more intuitively understand the distribution and scope of the electric field in the brain, providing a reliable basis for program-controlled adjustment.

[0015] Furthermore, the first processor is further configured to:

[0016] The electric field domain model is rendered into a nucleus model and / or a fiber model to obtain a three-dimensional image.

[0017] The beneficial effect of the above scheme is that by combining the electric field action model with the patient's biological structure (such as nuclei and fibers), the doctor can intuitively see the relationship between the electric field and the biological structure, which helps to evaluate the safety and effectiveness of the treatment.

[0018] Furthermore, the electric field domain model processing system further includes:

[0019] A display is configured to display the electric field domain model and / or the three-dimensional image.

[0020] The beneficial effect of the above scheme is that the present invention enables doctors to understand and analyze the electric field action model more easily by providing intuitive visual feedback, thereby making more accurate diagnosis and treatment decisions.

[0021] Furthermore, the stimulation parameter is less than or equal to a preset stimulation parameter threshold.

[0022] The beneficial effect of the above scheme is that the present invention avoids unnecessary harm to the patient by ensuring that the stimulation parameters are within a safe range.

[0023] Furthermore, the stimulation parameters at least include the amplitude value of the stimulation pulse.

[0024] The beneficial effect of the above scheme is that the present invention helps doctors to more accurately control the intensity of the electric field by clarifying the composition of the stimulation parameters.

[0025] Furthermore, the preset stimulation parameter threshold is set based on the biological tissue environment where the stimulation electrode is located.

[0026] The beneficial effect of the above scheme is that the present invention can adapt to the biological tissue environment of different patients and improve the safety and effectiveness of treatment by making the setting of stimulation parameters more personalized.

[0027] Furthermore, the preset stimulation parameter threshold is less than or equal to 4V.

[0028] The beneficial effect of the above scheme is that the present invention ensures the safety of treatment by further limiting the range of stimulation parameters, while providing a certain flexibility to adapt to different treatment needs.

[0029] Furthermore, the medical imaging data also includes: stimulation parameters delivered to the patient and the biological tissue environment in which the stimulation electrodes are located;

[0030] The first processor is also configured to use a preset simulation algorithm to perform electric field simulation based on the stimulation parameters delivered to the patient and information about the biological tissue environment in which the stimulation electrodes are located, so as to obtain electric field point cloud data.

[0031] The beneficial effect of the above scheme is that the present invention can more accurately simulate the distribution and effect of the electric field in the patient's body by acquiring more comprehensive medical imaging data.

[0032] Furthermore, the first processor is further configured to:

[0033] Using the preset simulation algorithm, perform electrode simulation according to electrode parameters to obtain electrode point cloud data;

[0034] Determining a three-dimensional empty network according to the data size of the electric field point cloud data;

[0035] Mapping the electric field point cloud data to the three-dimensional empty grid to obtain first voxel grid data;

[0036] Mapping the electrode point cloud data to the three-dimensional empty grid to obtain electrode voxel grid data;

[0037] The first voxel grid data is compensated according to the electrode voxel grid data to obtain second voxel grid data.

[0038] The beneficial effect of the above scheme is that the present invention can predict the position of the electric field and the electrode in the patient's body through the simulation algorithm, and provide accurate data support for constructing the electric field scope model.

[0039] Furthermore, the first processor is further configured to perform electrode simulation through the following procedure:

[0040] determining an electrode implantation trajectory in the patient's brain according to electrode parameters, wherein the electrode parameters include electrode coordinates;

[0041] The electrode implantation trajectory is fused according to the electrode configuration parameters to obtain an electrode model to obtain electrode point cloud data of each electrode sheet. The electrode configuration parameters at least include the electrode sheet length, the electrode sheet radius and the distance between the electrode sheets.

[0042] The beneficial effect of the above scheme is that the present invention can accurately simulate the implantation trajectory and shape of the electrode in the patient's brain, and provide accurate electrode information for the construction of the electric field action model.

[0043] Furthermore, the first processor is further configured to perform compensation processing on the first voxel grid data through the following procedure:

[0044] Determining a boundary voxel range of the electric field according to the first voxel grid data;

[0045] Determining a boundary voxel range of the electrode according to the electrode voxel grid data;

[0046] The boundary voxel range of the electric field and the boundary voxel range of the electrode are fused to obtain second voxel grid data.

[0047] The beneficial effect of the above scheme is that the present invention ensures that the electric field domain model can completely include the electrode area by fusing the boundary voxel range of the electric field and the electrode, thereby compensating for the data collapse problem and improving the accuracy of the model.

[0048] In a second aspect, the present invention introduces a method for processing an electric field domain model, the processing method comprising:

[0049] Acquiring medical imaging data of the patient, the medical imaging data including: electrode parameters and electric field parameters corresponding to stimulation parameters delivered to the patient;

[0050] Mapping the electric field parameters into the three-dimensional empty network to obtain first voxel grid data;

[0051] Using the electrode parameters to perform compensation processing on the first voxel grid data to obtain second voxel grid data;

[0052] An electric field domain model corresponding to the stimulation parameters is constructed according to the second voxel grid data.

[0053] Furthermore, the processing method also includes:

[0054] The electric field domain model is rendered into a nucleus model and / or a fiber model to obtain a three-dimensional image.

[0055] Furthermore, the medical imaging data also includes: stimulation parameters delivered to the patient and the biological tissue environment in which the stimulation electrodes are located;

[0056] The method for determining the three-dimensional empty network includes:

[0057] Using a preset simulation algorithm, an electric field simulation is performed according to the stimulation parameters delivered to the patient and the information of the biological tissue environment where the stimulation electrode is located, so as to obtain electric field point cloud data;

[0058] A three-dimensional empty network is determined according to the data size of the electric field point cloud data.

[0059] Furthermore, the method for obtaining the second voxel grid data includes:

[0060] Using the preset simulation algorithm, perform electrode simulation according to electrode parameters to obtain electrode point cloud data;

[0061] Mapping the electric field point cloud data to the three-dimensional empty grid to obtain first voxel grid data;

[0062] Mapping the electrode point cloud data to the three-dimensional empty grid to obtain electrode voxel grid data;

[0063] The first voxel grid data is compensated according to the electrode voxel grid data to obtain second voxel grid data.

[0064] Further, the electrode simulation according to the electrode parameters includes:

[0065] determining an electrode implantation trajectory in the patient's brain according to electrode parameters, wherein the electrode parameters include electrode coordinates;

[0066] The electrode implantation trajectory is fused according to the electrode configuration parameters to obtain an electrode model to obtain electrode point cloud data of each electrode sheet. The electrode configuration parameters at least include the electrode sheet length, the electrode sheet radius and the distance between the electrode sheets.

[0067] Furthermore, the compensation process includes:

[0068] Determining a boundary voxel range of the electric field according to the first voxel grid data;

[0069] Determining a boundary voxel range of the electrode according to the electrode voxel grid data;

[0070] The boundary voxel range of the electric field and the boundary voxel range of the electrode are fused to obtain second voxel grid data.

[0071] The beneficial effects of the above scheme are: the processing method of the present invention introduces in detail the construction of the electric field domain model through processes such as data acquisition, mapping, compensation and imaging, which effectively solves the model collapse problem caused by data sparsity, makes the constructed electric field domain model more accurate and complete, and provides doctors with an accurate and reliable electric field domain model, which helps to optimize treatment parameters and improve treatment effects.

[0072] In a third aspect, the present invention provides a processing device for an electric field action model, the processing device comprising:

[0073] An acquisition module, used to acquire medical imaging data of a patient, wherein the medical imaging data includes: electrode parameters and electric field parameters corresponding to stimulation parameters delivered to the patient;

[0074] A mapping module, used for mapping the electric field parameters into the three-dimensional empty network to obtain first voxel grid data;

[0075] A compensation module, used for performing compensation processing on the first voxel grid data using the electrode parameters to obtain second voxel grid data;

[0076] An imaging module is used to construct an electric field domain model corresponding to the stimulation parameters according to the second voxel grid data.

[0077] The beneficial effect of the above scheme is that the processing device of the present invention integrates functional modules such as acquisition, mapping, compensation and imaging, and can build an accurate and complete electric field domain model, thereby improving work efficiency and accuracy.

[0078] In a fourth aspect, the present invention provides a medical device, comprising a second processor, a memory, and a computer program stored in the memory, wherein the second processor executes the computer program to implement the steps of the above-mentioned electric field domain model processing method.

[0079] The beneficial effect of the above scheme is that the medical device of the present invention implements the processing method of the electric field domain model by executing a computer program, providing doctors with a powerful tool to optimize treatment parameters and evaluate treatment effects, which helps to improve the safety and effectiveness of implantable medical devices.

[0080] Compared with the prior art, the beneficial effects of the present invention include at least:

[0081] The first processor of the present invention maps the electric field parameters to the three-dimensional empty network to generate the first voxel grid data. This process ensures the three-dimensional spatial expression of the electric field distribution data and provides a basic framework for subsequent processing. The first voxel grid data is compensated using electrode parameters, which effectively solves the model collapse problem caused by data sparsity, making the constructed electric field domain model more accurate and complete. The accurate construction of the electric field domain model enables doctors to more intuitively understand the distribution and scope of the electric field in the brain, providing a reliable basis for program-controlled adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a structural schematic diagram of a processing system according to an embodiment of the present invention.

[0083] Figure 2 It is a flowchart of a processing method according to an embodiment of the present invention.

[0084] Figure 3 It is a structural schematic diagram of a processing device according to an embodiment of the present invention.

[0085] Figure 4 It is a structural schematic diagram of a medical device according to an embodiment of the present invention.

[0086] Figure 5 It is a comparative schematic diagram of the electric field action model before and after the compensation process of the embodiment of the present invention.

[0087] Figure 6 It is a schematic diagram of a three-dimensional image according to an embodiment of the present invention.

[0088] In the figure: 11, electrode wire; 21, electrode contact; 31, electric field action domain. DETAILED DESCRIPTION

[0089] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided to make the present invention more comprehensive and complete and to fully convey the concepts of example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their repeated description will be omitted.

[0090] The words expressing positions and directions described in the present invention are all explained with reference to the accompanying drawings as examples, but they can be changed as needed, and all such changes are included in the protection scope of the present invention.

[0091] Below, one of the application fields of the embodiment of the present invention (i.e., implantable devices) is first briefly described. The implantable neural stimulation system (an implantable medical system) mainly includes a stimulator implanted in the patient's body and a programmable device arranged outside the patient's body. The existing neural regulation technology mainly implants electrodes in specific structures (i.e., target points) in the body through stereotactic surgery, and the stimulator implanted in the patient's body emits discharge pulse width to the target point through the electrodes, regulates the electrical activity and function of the corresponding neural structure and network, thereby improving symptoms and relieving pain. Among them, the stimulator can be any one of an implantable neural electrical stimulation device, an implantable cardiac electrical stimulation system (also known as a pacemaker), an implantable drug delivery system (Implantable Drug Delivery System, referred to as IDDS) and a lead switching device. Examples of implantable neural stimulation devices include deep brain stimulation system (DBS), implantable cortical nerve stimulation system (CNS), implantable spinal cord stimulation system (SCS), implantable sacral nerve stimulation system (SNS), implantable vagus nerve stimulation system (VNS), etc.

[0092] In some embodiments, the stimulator may include an implantable pulse generator (IPG), an electrode wire, and an extension wire arranged between the implantable pulse generator and the electrode wire, and the data interaction between the implantable pulse generator and the electrode wire is realized through the extension wire, and the implantable pulse generator is arranged in the patient's body. In response to the program-controlled instructions sent by the program-controlled device, the sealed battery and the circuit are used to provide controllable electrical stimulation energy to the body tissue, and one or two controllable specific electrical stimulations are delivered to specific areas of the body tissue through the implanted extension wire and the electrode wire. The extension wire is used in conjunction with the implantable pulse generator as a transmission medium for the electrical stimulation signal, and the electrical stimulation signal generated by the implantable pulse generator is transmitted to the electrode wire. The electrode wire delivers electrical stimulation to specific areas of the body tissue through the electrode contacts thereon. The stimulator is provided with one or more electrode wires on one side or both sides, and a plurality of electrode contacts are provided on the electrode wire.

[0093] In other embodiments, the stimulator may only include an implantable pulse generator and an electrode wire, wherein the implantable pulse generator may be embedded in the patient's skull, and the electrode wire may be implanted in the patient's skull, in which case the implantable pulse generator is directly connected to the electrode wire without the need for extending the wire.

[0094] The electrode wire can be a nerve stimulation electrode, and the electrode wire delivers electrical stimulation to a specific area of ​​tissue in the body through a plurality of electrode contacts. The stimulator is provided with one or more electrode wires on one side or both sides, and a plurality of electrode contacts are provided on the electrode wire, and the electrode contacts can be arranged uniformly or non-uniformly 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 contacts and / or collection contacts. The electrode contacts can be, for example: in the shape of sheets, rings, dots, etc.

[0095] In some possible ways, the stimulated in vivo tissue may be the patient's brain tissue, and the stimulated site may be a specific site of the brain tissue. When the patient's disease type is different, the stimulated site is generally different, and the number of stimulation contacts (single source or multiple sources), the use of one or more (single channel or multiple channels) of specific electrical stimulation signals, and the stimulation parameter data are also different. It can be considered that when the stimulation contacts used are multi-source, multi-channel (multi-channel), a larger amount of data will be generated compared to a single source, single channel.

[0096] The embodiments of the present invention do not limit the types of diseases that can be used, and they can be diseases that can be used for deep brain stimulation (DBS), spinal cord stimulation (SCS), pelvic stimulation, gastric stimulation, peripheral nerve stimulation, and functional electrical stimulation. 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.

[0097] Stimulation parameters may include: stimulation frequency (for example, the number of electrical stimulation pulse width signals within a unit time of 1s, in Hz), pulse width (the duration of each pulse width, in μs), current amplitude (generally expressed in voltage, that is, the intensity of each pulse width, in V), timing (for example, it can be continuous or triggered), stimulation mode (including one or more of current mode, voltage mode, timed stimulation mode and cyclic stimulation mode), one or more of the upper and lower limits controlled by the doctor (the range adjustable by the doctor) and the upper and lower limits controlled by the patient (the range adjustable by the patient).

[0098] During the process of doctors programming patients, visual programming can be used to help doctors better understand the situation of electrodes in the patient's body. That is, through three-dimensional imaging of the patient's brain tissue and electrodes, doctors can more quickly determine the electrode positions and electrode combinations that need to deliver pulses. Therefore, in the three-dimensional imaging process, whether it is the imaging of the patient's own brain tissue, the imaging of electrodes implanted in the patient's brain, and the display of the electrode stimulation field range, there are high requirements. Therefore, image processing in the above imaging process is a problem that needs to be solved urgently.

[0099] Based on the above problems, the present invention proposes a processing method and related devices for electric field domain models applied to implantable medical devices, aiming to establish an accurate and complete electric field domain model under the condition of sparse data.

[0100] refer to Figure 1 The processing system of the electric field action domain model of the present invention includes: a collector and a first processor. Furthermore, the processing system of the present invention may also include: a display.

[0101] The collector of the present invention is connected to the implantable medical device and is configured to collect medical imaging data of the patient. The collector can also be connected to an external program-controlled device, so that when the doctor performs program-controlled operation, the output instructions of the program-controlled device (i.e., stimulation parameters and corresponding stimulation electrodes) can be obtained, and the collected information can be sent to the first processor.

[0102] In some other embodiments, the collector and the first processor are both functional modules integrated into the programmable device (mainly the doctor's programmable device), so that when the programmable device outputs the programmable instructions, it can promptly and quickly model the scope of the stimulation electric field, and assist the doctor in performing precise programmable treatment.

[0103] Specifically, the medical imaging data includes: electrode parameters, stimulation parameters delivered to the patient, electric field parameters corresponding to the stimulation parameters delivered to the patient, and the biological tissue environment in which the stimulation electrodes (i.e., electrode contacts) are located. It should be noted that the "electrodes" mentioned below can be more understood as electrode contacts, that is, metal materials that can release electric fields, such as ring metals or sheet metals.

[0104] When applied, the electrode parameters include: electrode coordinates, physical state of the electrode (such as position, shape, integrity, etc.) and electrical properties (such as impedance, polarization effect, etc.).

[0105] The stimulation parameters include at least the amplitude value of the stimulation pulse, and may also include the frequency and pulse width of the stimulation pulse. The electric field parameters are based on the stimulation parameters delivered to the patient, and the collector can measure and record the corresponding electric field distribution characteristics, for example: the electric field parameters may include electric field strength, directionality, and penetration depth. The stimulation parameters and their biological tissue environment include but are not limited to the type of tissue (such as gray matter, white matter), density, water content, temperature, and the presence of pathological changes (such as inflammation, scar formation).

[0106] In actual application, in order to ensure the safety and effectiveness of treatment, especially for pulses with small amplitudes, there are often unreliable situations such as electric field collapse, so the stimulation parameters of the present invention are less than or equal to the preset stimulation parameter threshold. The preset stimulation parameter threshold is less than or equal to 4V, that is, the present application preferably adapts to the small amplitude electric field environment, which can ensure the reliability and accuracy of the small amplitude stimulation electric field display.

[0107] Specifically, since different diseases may require different stimulation intensities, factors such as the patient's age (old, middle-aged, young), severity of the disease, and physical condition may affect the tolerance and effect of the stimulation. In addition, the specific position of the electrode in the body will affect the electric field distribution and stimulation efficiency. Differences in brain regions, local blood flow, cerebrospinal fluid circulation, etc. will all affect the stimulation effect. Therefore, it can be flexibly adjusted according to actual conditions, usually between 2 and 3V.

[0108] In some preferred embodiments, the preset stimulation parameter threshold is set based on the biological tissue environment where the stimulation electrode is located, and is generally set to less than 2V.

[0109] The first processor of the present invention is the processor of the doctor user end, and realizes a stable and low-energy connection with the collector through Bluetooth technology. However, due to the bandwidth limitation of Bluetooth technology, the device does not directly support uploading data to the server, but chooses to process small amounts of data in real time at the front end.

[0110] When applied, the storage space of the first processor is less than or equal to the preset space. In order to maximize the utilization efficiency of the storage space and ensure efficient operation even when processing multiple patient data, the present invention limits the storage space occupied by the medical imaging data of each patient to less than or equal to half of the quotient of the preset space and the preset number of patients.

[0111] In actual application, only when the medical imaging data occupies a small space can the doctor's first processor be able to store enough patients. Therefore, a large-step data acquisition method can be used, which not only avoids the operation bottleneck caused by insufficient storage space, but also realizes rapid data acquisition and imaging. However, the large-step data acquisition method causes the collected medical imaging data to be sparsely distributed in a point-like manner. The large-step data acquisition method can collect the potential at each position in the electric field with a large minimum accuracy, so that the smaller potential at the edge of the electric field may not be fully recognized, resulting in inaccurate electric field acquisition at the edge, resulting in collapse. In particular, for small pulse amplitudes, the electric field range itself is relatively small. In the case of loss in edge acquisition, the loss of the entire electric field display will be more obvious, affecting the accurate display of the electric field. In some other embodiments, different acquisition steps are set for different pulse amplitudes. For example, the acquisition step for a large pulse amplitude is smaller than the acquisition step for a small pulse amplitude. This can also achieve targeted acquisition, taking into account the fact that edge defects have little effect on the electric field range corresponding to large pulse amplitudes, and also ensuring that smaller pulse amplitudes accurately display the electric field.

[0112] The first processor of the present invention is also configured to: use a preset simulation algorithm to perform electric field simulation based on the stimulation parameters delivered to the patient and the information of the biological tissue environment in which the stimulation electrodes are located, obtain electric field point cloud data, and determine the three-dimensional empty network based on the data size of the electric field point cloud data.

[0113] Among them, the spacing between adjacent grid points of the three-dimensional empty network is less than or equal to the acquisition step of the medical imaging data. In some other embodiments, it can also be greater than the acquisition step of the medical imaging data. When applied, the first processor of the present invention integrates an electric field simulation function, and uses a preset simulation algorithm to perform electric field simulation based on the stimulation parameters delivered to the patient (such as current intensity, frequency, pulse width, etc.) and the biological tissue environment (such as brain structure, tissue conductivity, etc.) where the stimulation electrode is located. Information to obtain an electric field model. The electric field simulation process not only takes into account the stimulation parameters, the physical position and shape of the electrode, but also deeply analyzes the electrical properties of the biological tissue. Uniform sampling is performed on the electric field model to generate electric field point cloud data, that is, sparse point cloud data P1. The electric field point cloud data consists of a series of data points representing the distribution of electric field intensities at different positions, which constitute the initial representation of the electric field stimulation domain.

[0114] Specifically, in order to obtain electric field point cloud data, professional simulation software (such as Comsol) and patients' medical imaging data (such as CT or MRI scan images) can be used to simulate the electric field distribution of electrodes in the brain through modeling and calculation.

[0115] In practice, according to the data size and distribution characteristics of the electric field point cloud data, a three-dimensional spatial network is determined as the basic framework for subsequent processing. The electric field parameters (such as electric field strength, electric field direction, etc.) are associated with each voxel in the three-dimensional spatial network, thereby realizing the spatial representation of the electric field parameters. Specifically, the three-dimensional spatial network is composed of a series of adjacent grid points, and the spacing between the grid points can be flexibly adjusted according to the acquisition step of the medical imaging data. Furthermore, the three-dimensional spatial network is a 50x60x100 voxel grid, in which each voxel represents a small three-dimensional space area, and the spacing can be less than, equal to or greater than the acquisition step of the medical imaging data to adapt to different accuracy and processing requirements.

[0116] The first processor is configured to: map the electric field parameters into a three-dimensional empty network to obtain first voxel grid data.

[0117] When applied, in order to improve the spatial resolution and continuity of the electric field point cloud data, an interpolation algorithm (such as linear interpolation, bilinear interpolation, nearest neighbor interpolation, etc.) can be used to calculate the electric field intensity in the corresponding voxel according to each voxel position and the surrounding electric field point cloud data to obtain the first voxel grid data.

[0118] In practical application, each voxel in the first voxel grid data contains information about the electric field strength. In addition, the voxel grid data V1 not only contains information about the electric field strength, but also maintains the spatial structure and continuity of the data, which facilitates subsequent data processing and analysis.

[0119] The first processor of the present invention is configured to: perform compensation processing on the first voxel grid data using electrode parameters to obtain second voxel grid data.

[0120] First, the first processor uses a preset simulation algorithm to perform electrode simulation according to electrode parameters to obtain electrode point cloud data. Specifically, the electrode implantation trajectory of the patient's brain is determined according to the electrode parameters, and the electrode parameters include electrode coordinates; the electrode implantation trajectory is fused according to the electrode configuration parameters to obtain an electrode model to obtain electrode point cloud data for each electrode patch. Among them, the electrode configuration parameters include at least the length of the electrode patch, the radius of the electrode patch, and the distance between the electrode patches.

[0121] When applied, the first processor is configured to execute steps A1 to A3.

[0122] Step A1: Determine the implantation trajectory of the electrode 11 in the brain based on the patient's MRI or CT scan data and electrode parameters (such as electrode coordinates, implantation depth, direction, etc.).

[0123] Step A2: Using the electrode configuration parameters (electrode sheet length, radius, spacing, etc.), construct an electrode model through 3D modeling software (such as Blender, SolidWorks) or programming (such as Python combined with OpenGL).

[0124] Step A3: uniformly sample the electrode model to generate electrode point cloud data P2. The electrode point cloud data represents the shape and position of the electrode sheet and the area that may be affected by the electric field.

[0125] Next, the three-dimensional empty grid is used for data mapping.

[0126] When applied, the first processor maps the electrode point cloud data into the three-dimensional empty grid to obtain the electrode voxel grid data; and maps the electric field point cloud data into the three-dimensional empty grid to obtain the first voxel grid data.

[0127] Then, the first processor performs compensation processing on the first voxel grid data according to the electrode voxel grid data to obtain the second voxel grid data. Specifically, the first processor determines the boundary voxel range of the electric field according to the first voxel grid data, and determines the boundary voxel range of the electrode according to the electrode voxel grid data, and then fuses the boundary voxel range of the electric field and the boundary voxel range of the electrode to obtain the second voxel grid data.

[0128] When applied, the first processor is configured to execute steps B1 to B3.

[0129] Step B1: Analyze the first voxel grid data to determine the boundary voxel range of the electric field, that is, the boundary of the area where the electric field intensity changes significantly.

[0130] Step B2: Analyze the electrode voxel grid data to determine the boundary voxel range of the electrode. The boundary voxel range of the electrode usually corresponds to the physical size and position of the electrode sheet.

[0131] Step B3: By using interpolation or weighted averaging, the electrode voxel grid data is used to compensate for the missing or sparse part of the first voxel grid data (i.e., the area not covered by the electrode sheet), and the second voxel grid data is obtained to make the electric field intensity distribution in each voxel more reasonable and accurate. For example, the boundary voxel range of the electric field and the boundary voxel range of the electrode are combined to ensure that the combined area can completely cover the electrode sheet and its affected area.

[0132] Furthermore, it is also necessary to consider the interaction between the electric field and the electrodes, such as the attenuation of the electric field intensity and the focusing effect of the electrodes on the electric field.

[0133] In actual application, the first processor is configured to verify and optimize the second voxel grid data. Specifically, the compensated second voxel grid data can be verified by comparison with actual measurement data, expert evaluation, or cross-validation with other simulation results. Then, based on the verification results, the compensation algorithm is adjusted and optimized to improve the rationality and accuracy of the electric field intensity distribution in each voxel in the second voxel grid data.

[0134] The first processor of the present invention is configured to construct an electric field domain model corresponding to the stimulation parameters according to the second voxel grid data. The second voxel grid data contains key information such as the electric field strength and direction, and is usually stored in a three-dimensional array, where each voxel represents a small cube in space and contains relevant parameters of the electric field.

[0135] When applied, the first processor is configured with a high-performance computing unit and a graphics processing unit (GPU) to support complex 3D reconstruction and rendering tasks. In addition, the first processor is also integrated with a user interaction interface to allow users to make visual adjustments to the model.

[0136] In practical applications, the voxel grid data V2 is processed using the Marching Cubes algorithm (or other advanced algorithms such as Surface Nets, Sparse Voxel Octrees, etc.). The Marching Cubes algorithm traverses the voxel grid and determines whether each voxel belongs to the model surface based on the electric field strength threshold of each voxel. Then, according to the electric field direction and strength of adjacent voxels, the position and shape of the model surface are determined by linear interpolation. After being processed by the Marching Cubes algorithm, a three-dimensional model composed of triangular facets is obtained, namely the electric field domain model. The electric field domain model reflects the spatial distribution of the electric field domain.

[0137] like Figure 5The above is an example schematic diagram for small-amplitude electric field compensation in an embodiment of the present application, wherein, in view A of the figure, at least one electrode contact 21 is provided on the electrode wire 11. When a small-amplitude stimulation pulse is delivered to the electrode contact 21, due to the acquisition accuracy and the small electric field range, the acquired electric field domain 31 will have a certain deviation from the actual range, and collapse will occur. Taking the light gray ellipse in view A as an example, it is a schematic diagram of the acquired electric field (in actual sampling, the electric field range should be irregular, and the ellipse is just a schematic diagram). The cross-section of the electrode contact 21 is a rectangle, such as the black part in view A. In theory, the electric field generated by the electrical stimulation delivered by the electrode contact 21 should cover the electrode contact 21, but after actual sampling, it is found that the light gray electric field range does not completely cover the electrode contact 21, so an electric field collapse occurs, resulting in inaccurate display of the small-amplitude electric field model, affecting the reliability and accuracy of the doctor's programming.

[0138] like Figure 5 View B in FIG. 1 is the electric field domain 31 after electric field compensation. It can be seen that the electric field domain 31 completely covers the electrode contacts, thereby improving the reliability and accuracy of the electric field display of small-amplitude stimulation pulses.

[0139] In some embodiments, each triangular facet in the electric field scope model carries color coding information of the electric field strength so as to intuitively display the change of the electric field strength during visualization. By setting a color mapping tool, the user is allowed to customize the color scheme according to different ranges of electric field strength. For example, a low-intensity electric field can be mapped to blue, a medium-intensity electric field to green, and a high-intensity electric field to red, thereby enhancing the readability of the electric field distribution. In some preferred embodiments, in order to improve the visual effect of the electric field scope model, the first processor is also configured with a smoothness adjustment function. By applying Laplacian smoothing or other smoothing algorithms, the jagged edges on the surface of the electric field scope model can be reduced, making the electric field scope model smoother and continuous. Further, the number and intensity of smoothing iterations can be adjusted as needed to achieve the best visual effect.

[0140] The first processor of the present invention is further configured to: render the electric field domain model into the nucleus model and / or the fiber model to obtain a three-dimensional image, and refer to Figure 6 .

[0141] When used, high-resolution MRI or DTI imaging technology is used to obtain the patient's brain structure data, and professional image processing software (such as FSL, SPM, etc.) is used for segmentation and reconstruction to obtain a three-dimensional model of neural nuclei and fibers containing anatomical structures.

[0142] In actual application, the coordinate system of the electric field domain model is aligned with the coordinate system of the neural nucleus / fiber model to ensure the consistency of the two in spatial position. Specifically, it is achieved through image registration technology, using methods such as feature point matching or mutual information maximization to ensure seamless docking between models. Then, the electric field domain model is embedded into the neural nucleus and fiber model using three-dimensional modeling software (such as Blender, Maya, etc.) or a dedicated medical image processing platform. Furthermore, the color coding of the electric field domain model can be adjusted according to the electric field strength to intuitively display the spatial variation of the electric field strength. Then, a graphics rendering engine (such as Unity, Unreal Engine or OpenGL) is used to render the fused three-dimensional model into a realistic and interactive three-dimensional image. Furthermore, during the rendering process, factors such as lighting, shadows, and materials can be considered to enhance the realism of the image.

[0143] The display of the present invention is configured to display an electric field domain model and a three-dimensional image, wherein the three-dimensional image can display the complete electric field domain.

[0144] When used, the display of the present invention has ultra-high-definition resolution, which can clearly display every detail of the electric field action model and the three-dimensional image of the brain, ensuring that doctors can accurately judge the relative position relationship between the electric field action domain and the brain structure.

[0145] In actual application, the display allows doctors to adjust the transparency of the three-dimensional brain image so that they can view the deep relationship between the electric field domain model and the internal structure of the brain when necessary. Furthermore, the display is equipped with a touch screen and operating software, and doctors can dynamically interact with the three-dimensional image through touch operations such as rotation, zooming, and translation, so as to observe the interaction between the electric field domain and the brain structure from different angles and levels.

[0146] The processing method of the electric field domain model of the present invention is implemented based on the above-mentioned processing system. By combining multiple steps such as simulation software, point cloud data processing, electrode model construction and three-dimensional reconstruction algorithm, effective compensation for sparse data and model optimization are achieved, providing more reliable technical support for remote programming and visual programming.

[0147] refer to Figure 2 The processing method of the present invention includes: step SS1 to step SS4. Further, the processing method of the present invention may also include: step SS5.

[0148] Step SS1: Acquire medical imaging data of the patient. The medical imaging data includes: electrode parameters and electric field parameters corresponding to the stimulation parameters delivered to the patient. The medical imaging data also includes: the stimulation parameters delivered to the patient and the biological tissue environment where the stimulation electrodes are located.

[0149] Step SS2: Map the electric field parameters into the three-dimensional empty network to obtain the first voxel grid data.

[0150] The method for determining the three-dimensional empty network in step SS2 includes: step SS21-step SS22.

[0151] Step SS21: Using a preset simulation algorithm, perform electric field simulation based on the stimulation parameters delivered to the patient and the information of the biological tissue environment where the stimulation electrodes are located to obtain electric field point cloud data.

[0152] Step SS22: Determine the three-dimensional empty network according to the data size of the electric field point cloud data.

[0153] Step SS3: Compensate the first voxel grid data using the electrode parameters to obtain second voxel grid data.

[0154] The method for obtaining the second voxel grid data in step SS3 includes: steps SS31 to SS34.

[0155] Step SS31: Using a preset simulation algorithm, perform electrode simulation according to electrode parameters to obtain electrode point cloud data.

[0156] The electrode simulation according to the electrode parameters in step SS31 includes: step SS311 - step SS312.

[0157] Step SS311: determining the electrode implantation trajectory of the patient's brain according to the electrode parameters, where the electrode parameters include electrode coordinates;

[0158] Step SS312: The implantation trajectory of the electrode 11 is fused according to the electrode configuration parameters to obtain an electrode model to obtain electrode point cloud data of each electrode sheet. The electrode configuration parameters include at least the length of the electrode sheet, the radius of the electrode sheet and the distance between the electrode sheets.

[0159] Step SS32: Map the electric field point cloud data into a three-dimensional empty grid to obtain first voxel grid data.

[0160] Step SS33: Map the electrode point cloud data into a three-dimensional empty grid to obtain electrode voxel grid data.

[0161] Step SS34: Compensate the first voxel grid data according to the electrode voxel grid data to obtain second voxel grid data.

[0162] The compensation process in step SS34 includes: step SS341 to step SS343.

[0163] Step SS341: Determine the boundary voxel range of the electric field based on the first voxel grid data.

[0164] Step SS342: Determine the boundary voxel range of the electrode based on the electrode voxel grid data.

[0165] Step SS343: Fusing the boundary voxel range of the electric field and the boundary voxel range of the electrode to obtain second voxel grid data.

[0166] Step SS4: constructing an electric field domain model corresponding to the stimulation parameters according to the second voxel grid data.

[0167] Step SS5: Render the electric field domain model into the nucleus model and / or the fiber model to obtain a three-dimensional image.

[0168] The specific implementation of the processing method of the present invention can refer to the relevant program configuration of the first processor, which will not be further described here.

[0169] The implementation of specific functions of each functional module of the processing device of the electric field action domain model of the present invention is based on the above-mentioned processing method.

[0170] refer to Figure 3 The processing device of the present invention includes: an acquisition module, a mapping module, a compensation module and an imaging module.

[0171] The acquisition module is used to obtain the patient's medical imaging data, which includes: electrode parameters and electric field parameters corresponding to the stimulation parameters delivered to the patient; the mapping module is used to map the electric field parameters to a three-dimensional empty network to obtain first voxel grid data; the compensation module is used to compensate the first voxel grid data using the electrode parameters to obtain second voxel grid data; the imaging module is used to construct an electric field domain model corresponding to the stimulation parameters based on the second voxel grid data.

[0172] refer to Figure 4 The medical device of the present invention comprises a second processor, a memory and a computer program stored in the memory. The second processor executes the computer program to implement the steps of the above-mentioned method for processing the electric field domain model. Although the embodiments of the present invention have been shown and described above, it can be understood that the above-mentioned embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above-mentioned embodiments within the scope of the invention without departing from the principles and purpose of the present invention, and all these changes should fall within the scope of protection of the claims of the present invention.

Claims

1. A processing system for an electric field domain model, characterized in that: Application in implantable medical devices; The processing system of the electric field domain model includes: A collector configured to collect medical imaging data of a patient, wherein the medical imaging data includes: electrode parameters and electric field parameters corresponding to stimulation parameters delivered to the patient; The first processor is configured to map the electric field parameters into a three-dimensional empty network to obtain first voxel grid data; use electrode parameters to compensate the first voxel grid data to obtain second voxel grid data; and construct an electric field domain model corresponding to the stimulation parameters based on the second voxel grid data.

2. The electric field domain model processing system according to claim 1, characterized in that: The first processor is further configured to: The electric field domain model is rendered into a nucleus model and / or a fiber model to obtain a three-dimensional image.

3. The electric field domain model processing system according to claim 2, characterized in that: The processing system of the electric field domain model also includes: A display is configured to display the electric field domain model and / or the three-dimensional image.

4. The electric field domain model processing system according to claim 1, characterized in that: The stimulation parameter is less than or equal to a preset stimulation parameter threshold.

5. The electric field domain model processing system according to claim 1, characterized in that: The stimulation parameters include at least the amplitude value of the stimulation pulse.

6. The electric field domain model processing system according to claim 4, characterized in that: The preset stimulation parameter threshold is set based on the biological tissue environment where the stimulation electrode is located.

7. The electric field domain model processing system according to claim 4, characterized in that: The preset stimulation parameter threshold is less than or equal to 4V.

8. The electric field domain model processing system according to claim 1, characterized in that: The medical imaging data also includes: stimulation parameters delivered to the patient and the biological tissue environment in which the stimulation electrodes are located; The first processor is also configured to use a preset simulation algorithm to perform electric field simulation based on the stimulation parameters delivered to the patient and information about the biological tissue environment in which the stimulation electrodes are located, so as to obtain electric field point cloud data.

9. The electric field domain model processing system according to claim 8, characterized in that: The first processor is further configured to: Using the preset simulation algorithm, perform electrode simulation according to electrode parameters to obtain electrode point cloud data; Determining a three-dimensional empty network according to the data size of the electric field point cloud data; Mapping the electric field point cloud data to the three-dimensional empty grid to obtain first voxel grid data; Mapping the electrode point cloud data to the three-dimensional empty grid to obtain electrode voxel grid data; The first voxel grid data is compensated according to the electrode voxel grid data to obtain second voxel grid data.

10. The electric field domain model processing system according to claim 9, characterized in that: The first processor is further configured to perform electrode simulation through the following procedure: determining an electrode implantation trajectory in the patient's brain according to electrode parameters, wherein the electrode parameters include electrode coordinates; The electrode implantation trajectory is fused according to the electrode configuration parameters to obtain an electrode model to obtain electrode point cloud data of each electrode sheet. The electrode configuration parameters at least include the electrode sheet length, the electrode sheet radius and the distance between the electrode sheets.

11. The electric field domain model processing system according to claim 9, characterized in that: The first processor is further configured to perform compensation processing on the first voxel grid data by the following procedure: Determining a boundary voxel range of the electric field according to the first voxel grid data; Determining a boundary voxel range of the electrode according to the electrode voxel grid data; The boundary voxel range of the electric field and the boundary voxel range of the electrode are fused to obtain second voxel grid data.

12. A method for processing an electric field domain model, characterized in that: The processing method comprises: Acquiring medical imaging data of the patient, the medical imaging data including: electrode parameters and electric field parameters corresponding to stimulation parameters delivered to the patient; Mapping the electric field parameters into the three-dimensional empty network to obtain first voxel grid data; Using the electrode parameters to perform compensation processing on the first voxel grid data to obtain second voxel grid data; An electric field domain model corresponding to the stimulation parameters is constructed according to the second voxel grid data.

13. The method for processing the electric field domain model according to claim 12, characterized in that: The processing method also includes: The electric field domain model is rendered into a nucleus model and / or a fiber model to obtain a three-dimensional image.

14. The method for processing the electric field domain model according to claim 12, characterized in that: The medical imaging data also includes: stimulation parameters delivered to the patient and the biological tissue environment in which the stimulation electrodes are located; The method for determining the three-dimensional empty network includes: Using a preset simulation algorithm, an electric field simulation is performed according to the stimulation parameters delivered to the patient and the information of the biological tissue environment where the stimulation electrode is located, so as to obtain electric field point cloud data; A three-dimensional empty network is determined according to the data size of the electric field point cloud data.

15. The method for processing the electric field domain model according to claim 14, characterized in that: The method for obtaining the second voxel grid data comprises: Using the preset simulation algorithm, perform electrode simulation according to electrode parameters to obtain electrode point cloud data; Mapping the electric field point cloud data to the three-dimensional empty grid to obtain first voxel grid data; Mapping the electrode point cloud data to the three-dimensional empty grid to obtain electrode voxel grid data; The first voxel grid data is compensated according to the electrode voxel grid data to obtain second voxel grid data.

16. The electric field domain model processing system according to claim 15, characterized in that: The electrode simulation according to the electrode parameters comprises: determining an electrode implantation trajectory in the patient's brain according to electrode parameters, wherein the electrode parameters include electrode coordinates; The electrode implantation trajectory is fused according to the electrode configuration parameters to obtain an electrode model to obtain electrode point cloud data of each electrode sheet. The electrode configuration parameters at least include the electrode sheet length, the electrode sheet radius and the distance between the electrode sheets.

17. The electric field domain model processing system according to claim 15, characterized in that: The compensation process includes: Determining a boundary voxel range of the electric field according to the first voxel grid data; Determining a boundary voxel range of the electrode according to the electrode voxel grid data; The boundary voxel range of the electric field and the boundary voxel range of the electrode are fused to obtain second voxel grid data.

18. A processing device for an electric field domain model, characterized in that: The processing device comprises: An acquisition module, used to acquire medical imaging data of a patient, wherein the medical imaging data includes: electrode parameters and electric field parameters corresponding to stimulation parameters delivered to the patient; A mapping module, used for mapping the electric field parameters into the three-dimensional empty network to obtain first voxel grid data; A compensation module, used for performing compensation processing on the first voxel grid data using the electrode parameters to obtain second voxel grid data; An imaging module is used to construct an electric field domain model corresponding to the stimulation parameters according to the second voxel grid data.

19. A medical device comprising a second processor, a memory and a computer program stored in the memory, characterized in that: The second processor executes the computer program to implement the steps of the method for processing the electric field domain model according to any one of claims 12 to 17.

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