Target spot optimization method, system and equipment and storage medium
By acquiring the target device and head model, calculating the intensity of the induced electric field in the cerebral cortex, and determining the optimal device pose, the problem of inaccurate device positioning in existing technologies is solved, achieving precise target positioning and improved treatment efficacy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-12
- Publication Date
- 2026-03-13
AI Technical Summary
In existing technologies, the device positioning methods for transcranial magnetic stimulation rely on cranial bony landmarks and physician experience, resulting in significant differences in intervention effects and making it impossible to guarantee precise consistency for each treatment.
By acquiring the target device model and the head model of the object being detected, the intensity of the induced electric field in the cerebral cortex under different poses on the target intervention target area is calculated. The target device pose when the induced electric field in the cerebral cortex is optimal is determined, and precise positioning is achieved using a spherical or custom target area.
It improves the therapeutic effect of transcranial magnetic stimulation, ensures the precise placement of the device in the target area of the brain, reduces noise interference, and improves the safety and consistency of treatment.
Smart Images
Figure CN121662277A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of medical imaging technology, and in particular to target optimization methods, systems, devices and storage media. Background Technology
[0002] Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that temporarily activates or inhibits neurons in a localized area of the cerebral cortex by generating an electrical current. During the intervention, the target device must be accurately positioned at a specific location on the patient's head, and a preset stimulation intensity must be applied to ensure that the current effectively acts on the target cerebral cortex area.
[0003] In related technologies, the placement and angle of the target device are mainly determined by the bony landmarks of the subject's skull and the doctor's experience, while the stimulation intensity is based on the motor threshold measurement of the hand motor area. However, the intervention effect of relying solely on the bony landmarks of the skull and the doctor's experience varies greatly among different patients, and the accuracy and consistency of each treatment cannot be guaranteed.
[0004] Therefore, existing technologies lack a more precise device positioning method to improve the overall efficacy of interventions. Summary of the Invention
[0005] To address or partially address the problems existing in related technologies, this application provides a target optimization method, system, device, and storage medium for accurately locating the target device of the detection object, thereby improving the efficacy of the entire intervention process.
[0006] The first aspect of this application provides a target optimization method, comprising:
[0007] Obtain the target device model;
[0008] Obtain the head model of the object to be detected, and determine the target intervention point region on the head model;
[0009] Calculate the intensity of the induced electric field in the cerebral cortex at the target intervention target area when the target device is in different poses;
[0010] The target device pose when the cerebral cortex-induced electric field is optimal is taken as the target pose of the target device.
[0011] Optionally, the target intervention target area is a spherical target intervention target area, and determining the target intervention target area on the head model includes:
[0012] Obtain the initial target points of the object being detected;
[0013] Using the initial target point as the center and a preset distance as the radius of the sphere, a spherical region is obtained, which is then used as the target intervention target region.
[0014] Optionally, the target intervention target region is a custom target intervention target region, and determining the target intervention target region on the head model includes:
[0015] Obtain the initial target points of the object being detected;
[0016] The region within the preset range of the initial target point is selected as the target intervention target region.
[0017] Optionally, calculating the intensity of the induced electric field in the cerebral cortex at different poses of the target device on the target intervention target area includes:
[0018] Based on the rotation angle of the target device, the target intervention target area is divided into multiple target intervention sub-regions in equal proportions;
[0019] Electromagnetic field simulation calculations were performed on each target intervention sub-region to obtain the intensity of the induced electric field in the cerebral cortex corresponding to each target intervention sub-region.
[0020] Optionally, the division of the target intervention target area into multiple target intervention sub-regions proportionally based on the rotation angle of the target device includes:
[0021] The target intervention area is divided into grids to obtain multiple grid intersection points;
[0022] The grid intersections are used as candidate action points; wherein each candidate action point includes the pitch angle, yaw angle and roll angle corresponding to the current target device pose.
[0023] The region corresponding to the heading angle is divided proportionally based on a preset division rule, and at least one preset angle is taken for the pitch angle and the roll angle to obtain the angle range of the corresponding target intervention sub-region.
[0024] Optionally, before determining the target intervention point region, the method further includes:
[0025] The head model is binarized based on a preset expected stimulus threshold.
[0026] The region exceeding the preset expected stimulation threshold is designated as the target intervention region.
[0027] Optionally, before calculating the intensity of the induced electric field in the cerebral cortex, the method further includes: recording the target device pose and stimulus intensity data of the detection object during the target intervention process based on navigation records, and applying the stimulus intensity data corresponding to the target device pose to the electromagnetic field simulation process.
[0028] A second aspect of this application provides a target optimization system, comprising:
[0029] The first acquisition module acquires the target device model;
[0030] The second acquisition module acquires the head model of the object to be detected and determines the target intervention point region on the head model.
[0031] The calculation module calculates the intensity of the induced electric field in the cerebral cortex at the target intervention target area when the target device is in different poses.
[0032] The target device positioning module uses the target device pose when the cerebral cortex induced electric field is optimal as the target pose of the target device.
[0033] A third aspect of this application provides an electronic device, comprising:
[0034] Processor; and
[0035] A memory that stores executable code, which, when executed by the processor, causes the processor to perform the method described above.
[0036] A fourth aspect of this application provides a non-transitory machine-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method described above.
[0037] Therefore, the target optimization method, system, device, and storage medium provided in this application acquire a head model of the test subject and determine the target intervention target area on the head model, so that the target device can accurately determine the brain region that needs intervention in the test subject. Furthermore, by simulating and calculating the intensity of the induced electric field in the cerebral cortex at different poses on the target intervention target area, and obtaining the optimal target device placement pose, the therapeutic effect of the target intervention is improved.
[0038] Among them, using a spherical target intervention area as the target intervention area can precisely control the target area to the brain region of the subject that needs intervention, thereby improving the stimulation accuracy and treatment effect.
[0039] Selecting custom target areas for intervention allows for precise control over the brain regions targeted for intervention, and provides greater flexibility by allowing for the customization of stimulation areas.
[0040] Furthermore, when calculating the electric field strength, the present invention divides the target intervention target area into multiple target intervention areas proportionally based on the rotation angle of the target device, making the calculation of the target area more accurate.
[0041] Based on the above method, the present invention establishes a correspondence between the pitch angle, yaw angle and roll angle corresponding to the target device pose and the target area after proportional division, so that the adjustment of the target device pose can obtain a sufficiently accurate electric field effect.
[0042] In this invention, a binarization algorithm is further used to remove non-target stimulus regions on the head model, reducing noise interference in practical applications and making the determined target region more accurate.
[0043] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0044] The above and other objects, features and advantages of this application will become more apparent from the following description of exemplary embodiments of this application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of this application.
[0045] Figure 1 This is a flowchart illustrating the target optimization method in an embodiment of this application.
[0046] Figure 2 This is a schematic diagram of the head model shown in the embodiments of this application.
[0047] Figure 3 This is a schematic diagram of the structure of the intervention target region and non-target region shown in the embodiments of this application.
[0048] Figure 4 This is a flowchart illustrating the target optimization method in an embodiment of this application.
[0049] Figure 5 This is a schematic diagram of the target optimization system shown in the embodiments of this application.
[0050] Figure 6 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application. Detailed Implementation
[0051] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.
[0052] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.
[0053] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.
[0054] Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique. Before intervention, an electromagnetic device is placed above the scalp, generating a rapidly changing magnetic field. This magnetic field penetrates the scalp and skull, inducing electrical currents in the cerebral cortex, thereby modulating the activity of local neurons. TMS is widely used in researching brain function and treating various neurological and psychiatric disorders, such as depression, anxiety, migraines, and chronic pain.
[0055] Please see Figure 1 , Figure 1 This is a flowchart illustrating the target optimization method in an embodiment of this application.
[0056] This application provides a target optimization method, including:
[0057] S100, Obtain the target device model.
[0058] In this embodiment, the target device can be a device for generating a magnetic field and stimulating the cerebral cortex, or a device for generating low-current continuous stimulation to change the excitability of neurons, thereby modulating brain oscillation activity. In practical applications, the target device, such as a magnetic stimulation coil, induces current on the head of the test subject by applying a magnetic field, thereby activating or inhibiting neuronal activity in specific areas of the brain.
[0059] Understandably, the target device model can be a key model describing the output characteristics of the TMS device. This model can record parameters such as the target device's structural composition and electromagnetic properties to accurately simulate the output characteristics of the target device under different operating conditions and the induced electric field it generates on the cerebral cortex. For example, electromagnetic field simulation software such as SimNIBS and Ansys can be used to model the target device and calculate the magnetic field distribution and induced electric field intensity generated by the target device under different conditions.
[0060] S101. Obtain the head model of the object to be detected, and determine the target intervention point region on the head model.
[0061] In this embodiment of the application, the head model of the object being detected can be constructed using structural MRI image data.
[0062] See Figure 2 , Figure 2 This is a schematic diagram of the head model shown in the embodiments of this application.
[0063] in, Figure 2 (a) An individual head model constructed based on individual MRI structural images, including structures such as skin, skull, cerebrospinal fluid, cerebral gray matter, cerebral white matter, ventricles, and cerebellum. Figure 2 (b) is Figure 2 (a) Sagittal section, in which the internal structure of the individual head model can be seen. Figure 2 (c) is a schematic diagram of the target induced electric field calculation. Based on the general target point selection, the induced electric field calculation results are obtained in the dorsolateral prefrontal cortex of the brain. Among them, 201 is the region with a large target induced electric field intensity, and 202 has a weaker electric field intensity relative to 201.
[0064] In this embodiment, during the process of acquiring MRI data to construct a head model, acquiring T1-weighted images is essential. T1 images are an imaging modality in Magnetic Resonance Imaging (MRI) used to describe the longitudinal relaxation time of tissues. Relaxation time refers to the time required for protons in tissue to return to equilibrium after being excited by a radiofrequency pulse. This technique is particularly useful for generating high-contrast images of human tissues. In MRI, the T1 value of tissue affects the brightness and contrast of the image. Tissues with shorter T1 values appear brighter in the image, while tissues with longer T1 values appear darker. This contrast helps doctors diagnose different lesions and tissue types. T1 images provide clear brain anatomy, including showing differences in gray matter, white matter, and cerebrospinal fluid, enabling comprehensive detection of the basic structural information of the brain.
[0065] Optional image acquisition also includes T2 images, DTI (Diffusion Tensor Imaging) images, and fMRI (functional Magnetic Resonance Imaging) images.
[0066] T2 images are used to describe the lateral relaxation time of tissues and are used to examine the internal structures and tissue conditions of the human body. T2 images are mainly used to observe the water content and spin velocity of tissues, thereby identifying lesions and abnormalities in different tissues, and providing better contrast between gray and white matter, enhancing the resolution of different brain tissues.
[0067] DTI images are imaging modalities used to measure the diffusion of water molecules in biological tissues. DTI is primarily used in neuroscience and medicine, providing information about the structure and microstructure of brain white matter.
[0068] fMRI images are used to detect areas of brain activity, providing information about brain function. By reflecting brain activity during specific tasks or at rest, they help identify functional targets.
[0069] S102. Calculate the intensity of the induced electric field in the cerebral cortex on the target intervention target area when the target device is in different poses.
[0070] In the embodiments of this application, the intensity of the induced electric field in the cerebral cortex can refer to the electric field intensity generated by the target device in a specific area of the cerebral cortex of the detection object under different positions and angles. By determining the optimal pose of the target device, the optimal stimulation effect can be achieved, ensuring the effectiveness and accuracy of the intervention.
[0071] In the embodiments of this application, the target intervention target area can be a specific area on the cerebral cortex that receives the target stimulus in the intervention plan. By defining the target intervention target area, precise localization can be achieved to achieve the expected neural detection effect.
[0072] Understandably, in practical applications, such as for depression and anxiety disorders, there are specific target intervention areas corresponding to the existing pathology.
[0073] In the embodiments of this application, selecting appropriate intervention target areas during target planning can effectively improve treatment efficacy and reduce side effects. The following describes two types of target intervention areas.
[0074] In this embodiment of the application, when the target intervention target area is a spherical target intervention target area, determining the target intervention target area on the head model includes:
[0075] Obtain the initial target point of the object to be detected.
[0076] Using the initial target point as the center and a preset distance as the radius of the sphere, a spherical region is obtained, which is then used as the target intervention target region.
[0077] In this embodiment, when the target intervention region is defined as a spherical target intervention region, the coordinates of the initial target point are used as the center of the sphere. The initial target point can be a three-dimensional point coordinate in a three-dimensional head model, representing the target point of the planned target stimulus for the detection object. A spherical region is obtained around this center at a preset distance. The generated spherical target intervention region includes the initial target point and all cortical regions and related brain tissues within a preset distance surrounding it.
[0078] In another embodiment, when the target intervention target region is a custom target intervention target region, determining the target intervention target region on the head model includes:
[0079] Obtain the initial target points of the object being detected;
[0080] The region within the preset range of the initial target point is selected as the target intervention target region.
[0081] In this embodiment, an initial target point is determined based on the head model of the target object. To specify the intervention area more flexibly, a continuous region around the target point can be selected as the target area for intervention. This region can be customized within a preset range to ensure that all important brain structures near the target point are included.
[0082] In this embodiment of the application, before determining the target intervention target area, the method further includes:
[0083] The head model is binarized based on a preset expected stimulus threshold.
[0084] The region exceeding the preset expected stimulation threshold is designated as the target intervention region.
[0085] See Figure 3 , Figure 3 This is a schematic diagram of the structure of the intervention target region and non-target region shown in the embodiments of this application.
[0086] Based on the above embodiments, according to the determined custom target intervention target area, the custom target intervention target area can be described as a binary mask on the cerebral cortex. This is used to convert the target area on the head model into a binary image, wherein the custom target intervention target area 301 is marked as "1" and the non-target area 302 is marked as "0".
[0087] Understandably, in actual targeted interventions, in order to ensure the safety and effectiveness of the treatment, it is necessary to consider before the intervention that some brain regions of the subject are areas of undesirable stimulation or that the scalp surface has abundant peripheral nerves, so as to avoid adverse reactions in the subject.
[0088] In addition, different non-target areas can be selected as target exclusion areas for different pathological conditions, which will not be elaborated on here.
[0089] S103. The target device pose when the cerebral cortex induced electric field is optimal is taken as the target pose of the target device.
[0090] In this embodiment, by acquiring the head model of the test subject and determining the target intervention area on the head model, the target device can accurately determine the brain region that needs intervention in the test subject. Furthermore, by simulating and calculating the intensity of the induced electric field in the cerebral cortex at different poses on the target intervention area, and by obtaining the optimal target device placement pose, the therapeutic effect of the target intervention is improved.
[0091] In this embodiment of the application, calculating the intensity of the induced electric field in the cerebral cortex when the target device is in different poses on the target intervention target area includes:
[0092] Based on the rotation angle of the target device, the target intervention target area is divided into multiple target intervention sub-regions in equal proportions;
[0093] Electromagnetic field simulation calculations were performed on each target intervention sub-region to obtain the intensity of the induced electric field in the cerebral cortex corresponding to each target intervention sub-region.
[0094] Optionally, the step of dividing the target intervention target area into multiple target intervention areas proportionally based on the rotation angle of the target device includes:
[0095] The target intervention area is divided into grids to obtain multiple grid intersection points;
[0096] The grid intersections are used as candidate action points; wherein each candidate action point includes the pitch angle, yaw angle and roll angle corresponding to the current target device pose.
[0097] The region corresponding to the heading angle is divided proportionally based on a preset division rule, and at least one preset angle is taken for the pitch angle and the roll angle to obtain the angle range of the corresponding target intervention region.
[0098] In practical applications, based on the location and range of the intervention target area of the custom target intervention target area, the scalp point closest to the custom target intervention target area is taken as the center point, and a 2mm*2mm grid that can cover the target area is generated. Then, multiple grid intersections are obtained as candidate positions, such as a 6*6 grid. Each candidate position will serve as the base point for subsequent simulation calculations.
[0099] For each candidate position, a grid search is performed on the three three-dimensional attitude angles of the target device, including pitch angle, yaw angle and roll angle. Specifically, an angle point is selected every 2 degrees for pitch angle and roll angle, and 5 angles are selected for each direction. The yaw angle is divided into 24 equal parts evenly in 360 degrees. It can be seen that in the above embodiment, each candidate position has a total of 600 angle combinations for subsequent electric field simulation calculation.
[0100] Electric field simulation calculations are performed on all angle combinations for each candidate location to evaluate the electric field distribution of each angle group in the custom target intervention target area and the electric field energy distribution in the exclusion area. The angle combination with the highest electric field intensity in the custom target intervention target area and the lowest electric field energy in the exclusion area is ultimately selected as the optimal angle for target intervention.
[0101] In this embodiment of the application, before calculating the intensity of the induced electric field in the cerebral cortex, the method further includes: recording the target device pose and stimulation intensity data of the detection object during the target intervention process according to the target navigation record, and applying the stimulation intensity data corresponding to the target device pose to the electromagnetic field simulation process.
[0102] In this embodiment, the target navigation device can be a device capable of providing precise target device location and attitude information, and recording the target location, state, and intensity of the applied stimulus during stimulation. The recorded information includes the target device location, state, and stimulus intensity for each stimulus during motor threshold (MT) measurement.
[0103] Understandably, during targeted therapy, motor threshold (MT) measurement is used to describe the minimum stimulus intensity required to stimulate the cerebral cortex. In this embodiment, the process of measuring and determining the motor threshold includes:
[0104] A target device is precisely located on the subject's head using a target navigation device to stimulate the hand motor cortex. During stimulation, the position, orientation, and stimulation intensity of the target device are recorded and adjusted to ensure precise stimulation of the hand motor cortex. Then, the muscle responses of the hand are monitored. A standard measurement method is to determine the subject's motor threshold as the stimulation intensity output by the current TMS device when 5 out of every 10 stimuli elicit an electromyographic response of 50 microvolts (µV).
[0105] Based on this, in this embodiment, a target device model and a head model of the target object are obtained, and specific target points are selected for simulation calculation on the cerebral cortex. The electric field intensity at the target point is compared and converted with the induced electric field intensity in the hand motor area. This is to ensure that the target point can receive several similar effects to the hand motor area, thus guaranteeing the effectiveness and safety of the target stimulation.
[0106] This scheme utilizes an optimization method to select the optimal combination of target device poses, ensuring that as much target device energy as possible can be applied to the target intervention area. Specific implementation schemes include:
[0107] In one embodiment, when the stimulation intensity of the target device is constant, the one with the largest average electric field intensity in the preset target area is optimal.
[0108] In another embodiment, according to electromagnetic field theory, the intensity of the induced electric field in the cerebral cortex is proportional to the output intensity of the TMS stimulator. Therefore, a fixed output intensity of the TMS stimulator can be used for calculation during simulation. In practical applications, the ratio of the average induced electric field intensity in the preset target area to the average intensity outside the preset target area should be maximized, that is, the stronger the field intensity inside the target area, the better, and the weaker the field intensity outside the target area, the better.
[0109] See Figure 4 , Figure 4 This is a flowchart illustrating the target optimization method in an embodiment of this application.
[0110] The method includes:
[0111] Step 401: Establish the target device model;
[0112] Step 402: Acquire individual MRI data;
[0113] Step 403: Establish the finite element model;
[0114] Step 404: Determine the target area for intervention in a spherical target; or, determine the target area for intervention in a custom target.
[0115] Step 405: Perform target electromagnetic field simulation;
[0116] Step 406: Implement the intervention using the optimal target device pose.
[0117] Corresponding to the aforementioned application function implementation method embodiments, this application also provides a target optimization system, electronic device, and corresponding embodiments.
[0118] See Figure 5 , Figure 5 This is a schematic diagram of the target optimization system shown in the embodiments of this application.
[0119] This application embodiment illustrates a target optimization system, including:
[0120] The first acquisition module 51 acquires the target device model;
[0121] The second acquisition module 52 acquires the head model of the detection object and determines the target intervention point region on the head model;
[0122] Calculation module 53 calculates the intensity of the induced electric field in the cerebral cortex on the target intervention target area when the target device is in different poses;
[0123] The target device positioning module 54 uses the target device pose when the cerebral cortex induced electric field is optimal as the target pose of the target device.
[0124] The second acquisition module is used for:
[0125] The target intervention target area is a spherical target intervention target area. Determining the target intervention target area on the head model includes:
[0126] Obtain the initial target points of the object being detected;
[0127] Using the initial target point as the center and a preset distance as the radius of the sphere, a spherical region is obtained, which is then used as the target intervention target region.
[0128] The second acquisition module is also used for:
[0129] The target intervention target area is a custom target intervention target area. Determining the target intervention target area on the head model includes:
[0130] Obtain the initial target points of the object being detected;
[0131] The region within the preset range of the initial target point is selected as the target intervention target region.
[0132] The calculation module is used for:
[0133] Based on the rotation angle of the target device, the target intervention target area is divided into multiple target intervention sub-regions in equal proportions;
[0134] Electromagnetic field simulation calculations were performed on each target intervention sub-region to obtain the intensity of the induced electric field in the cerebral cortex corresponding to each target intervention sub-region.
[0135] Optionally, the division of the target intervention target area into multiple target intervention sub-regions proportionally based on the rotation angle of the target device includes:
[0136] The target intervention area is divided into grids to obtain multiple grid intersection points;
[0137] The grid intersections are used as candidate action points; wherein each candidate action point includes the pitch angle, yaw angle and roll angle corresponding to the current target device pose.
[0138] The region corresponding to the heading angle is divided proportionally based on a preset division rule, and at least one preset angle is taken for the pitch angle and the roll angle to obtain the angle range of the corresponding target intervention region.
[0139] Optionally, before determining the target intervention point region, the method further includes:
[0140] The head model is binarized based on a preset expected stimulus threshold.
[0141] The region exceeding the preset expected stimulation threshold is designated as the target intervention region.
[0142] Optionally, before calculating the intensity of the induced electric field in the cerebral cortex, the method further includes: recording the target device pose and stimulus intensity data of the detected object during the target intervention process according to the target navigation record, and applying the stimulus intensity data corresponding to the target device pose to the electromagnetic field simulation process.
[0143] It is evident that by acquiring the head model of the test subject and determining the target intervention area on the head model, the target device can accurately determine the brain region that needs intervention in the test subject. Furthermore, by simulating and calculating the intensity of the induced electric field in the cerebral cortex under different poses on the target intervention area, and by obtaining the optimal placement pose of the target device, the therapeutic effect of the target intervention can be improved.
[0144] Among them, using a spherical target intervention area as the target intervention area can precisely control the target area to the brain region of the subject that needs intervention, thereby improving the stimulation accuracy and treatment effect.
[0145] Selecting custom target areas for intervention allows for precise control over the brain regions targeted for intervention, and provides greater flexibility by allowing for the customization of stimulation areas.
[0146] Furthermore, when calculating the electric field strength, the present invention divides the target intervention target area into multiple target intervention areas proportionally based on the rotation angle of the target device, making the calculation of the target area more accurate.
[0147] Based on the above method, the present invention establishes a correspondence between the pitch angle, yaw angle and roll angle corresponding to the target device pose and the target area after proportional division, so that the adjustment of the target device pose can obtain a sufficiently accurate electric field effect.
[0148] In this invention, a binarization algorithm is further used to remove non-target stimulus regions on the head model, reducing noise interference in practical applications and making the determined target region more accurate.
[0149] Figure 6 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application.
[0150] See Figure 6 The electronic device 600 includes a memory 610 and a processor 620.
[0151] The processor 620 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.
[0152] Memory 610 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by the processor 620 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives). System memory may be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 610 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, memory 610 may include a removable storage device that is readable and / or writable, such as a laser disc (CD), a read-only digital multifunction optical disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, an ultra-high density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.
[0153] The memory 610 stores executable code, which, when processed by the processor 620, can cause the processor 620 to execute part or all of the methods described above.
[0154] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.
[0155] Alternatively, this application may be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) thereon, which, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.
[0156] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.
Claims
1. A target optimization method, characterized in that, include: Obtain the target device model; Obtain the head model of the object to be detected, and determine the target intervention point region on the head model; Calculate the intensity of the induced electric field in the cerebral cortex at the target intervention target area when the target device is in different poses; The target device pose when the cerebral cortex-induced electric field is optimal is taken as the target pose of the target device.
2. The method according to claim 1, characterized in that, The target intervention target area is a spherical target intervention target area, and the determination of the target intervention target area on the head model includes: Obtain the initial target points of the object being detected; Using the initial target point as the center and a preset distance as the radius of the sphere, a spherical region is obtained, which is then used as the target intervention target region.
3. The method according to claim 1, characterized in that, The target intervention target region is a custom target intervention target region. Determining the target intervention target region on the head model includes: Obtain the initial target points of the object being detected; The region within the preset range of the initial target point is selected as the target intervention target region.
4. The method according to claim 1, characterized in that, The calculation of the induced electric field intensity of the cerebral cortex in the target intervention target area when the target device is in different poses includes: Based on the rotation angle of the target device, the target intervention target area is divided into multiple target intervention sub-regions in equal proportions; Electromagnetic field simulation calculations were performed on each target intervention sub-region to obtain the intensity of the induced electric field in the cerebral cortex corresponding to each target intervention sub-region.
5. The method according to claim 4, characterized in that, The target intervention target area is divided into multiple target intervention sub-regions proportionally based on the rotation angle of the target device, including: The target intervention area is divided into grids to obtain multiple grid intersection points; The grid intersections are used as candidate action points; wherein each candidate action point includes the pitch angle, yaw angle and roll angle corresponding to the current target device pose; The region corresponding to the heading angle is divided proportionally based on a preset division rule, and at least one preset angle is taken for the pitch angle and the roll angle to obtain the angle range of the corresponding target intervention sub-region.
6. The method according to claim 1, characterized in that, Before determining the target intervention area, the process also includes: The head model is binarized based on a preset expected stimulus threshold. The region exceeding the preset expected stimulation threshold is designated as the target intervention region.
7. The method according to claim 1, characterized in that, Before calculating the intensity of the induced electric field in the cerebral cortex, the method further includes: recording the target device pose and stimulus intensity data of the detection object during the target intervention process based on navigation records, and applying the stimulus intensity data corresponding to the target device pose to the electromagnetic field simulation process.
8. A target optimization system, characterized in that, include: The first acquisition module acquires the target device model; The second acquisition module acquires the head model of the object to be detected and determines the target intervention point region on the head model. The calculation module calculates the intensity of the induced electric field in the cerebral cortex at the target intervention target area when the target device is in different poses. The TMS device positioning module uses the target device pose when the cerebral cortex induced electric field is optimal as the target pose of the target device.
9. An electronic device, characterized in that, include: processor; as well as A memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the method as described in any one of claims 1-7.
10. A computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method as described in any one of claims 1-7.