Transcranial magnetic stimulation treatment parameter quantification method and system based on electric field intensity calibration

Abstract

The present application relates to the technical field of biomedical engineering, and a transcranial magnetic stimulation treatment parameter quantification method and system based on electric field intensity calibration, comprising: using an image unit to obtain a medical image set of a patient's head, performing multi-tissue segmentation on the medical image set through a medical image processing unit to obtain multiple tissue layer spatial positions, assigning electric conductivity parameters to the multiple tissue layer spatial positions using an electromagnetic simulation unit to obtain multiple tissue layer electric conductivities, simulating multiple reference field strengths using the multiple tissue layer electric conductivities and a pre-constructed coil electromagnetic field model, adjusting the configuration current of a TMS coil at a preset treatment site using the multiple reference field strengths to obtain a target point field strength and a configuration current, and retrieving a treatment intensity from a preset current intensity mapping table according to the peak current. The present application can achieve accurate quantification of transcranial magnetic stimulation treatment parameters.

Description

Technical Field

[0001] This invention relates to the field of biomedical engineering technology, and in particular to a method and system for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration. Background Technology

[0002] Currently, in clinical practice, the selection of transcranial magnetic stimulation (TMS) intensity mainly relies on the motor threshold as a benchmark. However, this traditional method has significant limitations: First, the motor threshold can only reflect the local excitability characteristics of the motor cortex and cannot fully characterize the spatial differences in electric field distribution among different brain regions. Second, due to the complexity of the anatomical structure of different brain regions, especially the differences in cortical depth and geometry, the electric field distribution produced by the same stimulation intensity varies significantly in different brain regions.

[0003] In clinical practice, different brain regions are selected as treatment targets for different conditions. Generally, relatively low stimulation intensities are used for the superficial cortex, while higher intensities are required for the deep cortex. This intensity selection method, based on a percentage of the motor threshold, relies primarily on empirical judgment and lacks precise quantitative evidence.

[0004] Therefore, how to upgrade the traditional experience-driven treatment model to a data-driven precision treatment model has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method for quantifying transcranial magnetic stimulation (TMS) therapy parameters based on electric field strength calibration, and a computer-readable storage medium. Its main purpose is to achieve accurate quantification of TMS therapy parameters.

[0006] To achieve the above objectives, the present invention provides a method for quantifying transcranial magnetic stimulation (TMS) therapeutic parameters based on electric field strength calibration, comprising:

[0007] Step A: Receive treatment intensity determination instruction, and confirm the treatment intensity determination environment based on the treatment intensity determination instruction. The treatment intensity determination environment includes MRI equipment and TMS equipment. The MRI equipment includes an individual detection unit and an imaging unit. The TMS equipment includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength through the working current.

[0008] Step B: When the individual detection unit detects a patient, the imaging unit is used to acquire a set of medical images of the patient's head. The medical image processing unit then performs multi-tissue segmentation on the medical image set to obtain the spatial locations of multiple tissue layers.

[0009] Step C: Use the electromagnetic simulation unit to assign conductivity parameters to the spatial locations of multiple tissue layers to obtain multiple tissue layer conductivity. Use the multiple tissue layer conductivity and the pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, where the tissue layer conductivity corresponds one-to-one with the reference field strength.

[0010] Step D: Adjust the working current of the TMS coil at the preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current. Update the configuration current to the pre-built current database to obtain the updated database. Obtain the field strength status based on the target field strength, where the field strength status is a normal state or an abnormal state.

[0011] Step E: When the field strength is in a normal state, extract the peak current from the updated database, and retrieve the treatment intensity from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity, thus realizing the method for quantifying transcranial magnetic stimulation treatment parameters based on electric field strength calibration.

[0012] Optionally, the step of performing multi-tissue segmentation on the medical image set through the medical image processing unit to obtain multiple tissue layer spatial locations includes:

[0013] Medical images are extracted sequentially from the medical image set, and the following operations are performed on the extracted medical images:

[0014] The medical image is segmented using a medical image processing unit to obtain a segmented image set, wherein the segmented image set includes multiple segmented images;

[0015] Obtain a standard tissue image set, wherein the standard tissue image set includes multiple standard tissue images, extract standard tissue images sequentially from the standard tissue image set, and perform the following operations on the extracted standard tissue images:

[0016] Segmented images are extracted sequentially from the segmented image set, and the following operations are performed on the extracted segmented images:

[0017] Calculate the similarity between the segmented image and the standard tissue image to obtain the single tissue similarity. Summarize the single tissue similarities to obtain the single tissue similarity set. The single tissue similarity set includes multiple single tissue similarities, and each single tissue similarity corresponds one-to-one with the segmented image.

[0018] Extract the single tissue similarity with the highest similarity from the single tissue similarity set to obtain the maximum similarity. Use the segmented image corresponding to the maximum similarity as the target image and obtain the tissue plane position based on the target image.

[0019] Summarize the organizational plane locations to obtain a set of organizational plane locations; summarize the organizational plane location sets to obtain multiple sets of organizational plane locations.

[0020] Multiple tissue layer spatial locations were identified based on multiple sets of planar locations of tissue layers.

[0021] Optionally, the step of segmenting the medical image using a medical image processing unit to obtain a segmented image set includes:

[0022] Obtain the initial side length, calculate the initial area based on the initial side length, and use the initial area to extract multiple sub-images from the medical image;

[0023] Standard tissue images are sequentially extracted from the standard tissue image set, and the following operations are performed on the extracted standard tissue images:

[0024] Extract sub-images sequentially from multiple sub-images, and perform the following operations on the extracted sub-images:

[0025] Calculate the similarity between the sub-image and the standard tissue image to obtain the sub-image similarity. Summarize the sub-image similarities to obtain multiple sub-image similarities. Extract the maximum sub-image similarity among the multiple sub-image similarities to obtain the maximum sub-image similarity.

[0026] Obtain the sub-image similarity threshold, compare the maximum sub-image similarity with the sub-image similarity threshold, and if the maximum sub-image similarity is greater than the sub-image similarity threshold, then the sub-image corresponding to the maximum sub-image similarity is selected as the filter image.

[0027] Summarize the filtered images to obtain a filtered image set, count the number of filtered images in the filtered image set, and obtain the filtered statistics.

[0028] The number of standard tissue images in the standard tissue images is counted to obtain the standard statistical quantity. The size of the selected statistical quantity is compared with the standard statistical quantity. If the selected statistical quantity is equal to the standard statistical quantity, the selected image set is used as the segmentation image set.

[0029] Otherwise, the initial side length is increased by a preset unit length to obtain an updated side length. The updated side length is then used as the initial side length, and the process returns to the step of calculating the initial area based on the initial side length, until the number of filtered statistics equals the standard number of statistics. The filtered image set is then used as the segmented image set.

[0030] Optionally, obtaining the tissue planar location based on the target image includes:

[0031] Calculate the grayscale value of the target image to obtain a grayscale image, and perform denoising processing on the grayscale image to obtain a low-noise image;

[0032] An initial edge sensitivity is obtained, and the edges of tissue layers in a low-noise image are detected based on the edge sensitivity to obtain a binary image, which includes the edges and the background.

[0033] If a closed region is observed in the binary image based on the edge, and if no closed region is found, the initial edge sensitivity is increased by a preset unit value to obtain an updated edge sensitivity. The updated edge sensitivity is then used as the initial edge sensitivity, and the process returns to the step of detecting the edge of the tissue layer in the low-noise image based on the edge sensitivity to obtain the binary image, until a closed region is found in the binary image.

[0034] Otherwise, establish a Cartesian coordinate system for the binary image, and determine multiple edge coordinates on the edge according to the preset number of coordinates. The edge coordinates include the edge x-coordinate and the edge y-coordinate.

[0035] The center position of the closed region is calculated based on multiple edge coordinates to obtain the tissue plane position. The tissue plane position includes the horizontal and vertical coordinates of the plane, and the calculation method is as follows:

[0036]

[0037] in, The horizontal coordinate of the plane representing the position of the tissue. The vertical coordinate of the plane representing the position of the tissue. This indicates that there is a total of [number] edge coordinates among the multiple edge coordinates. Each edge coordinate, Indicates the first The x-coordinates of the edges, express Each edge's ordinate.

[0038] Optionally, the simulation of multiple reference field strengths using the conductivity of multiple tissue layers and a pre-constructed coil electromagnetic field model includes:

[0039] Multiple initial field strengths are determined from a preset field strength table based on multiple tissue conductivity parameters, wherein each initial field strength corresponds one-to-one with the spatial location of the tissue layer.

[0040] Initial field strengths are extracted sequentially from multiple initial field strengths, and the following operations are performed on the extracted initial field strengths:

[0041] Obtain the stimulation interval and number of stimulations, and identify multiple stimulation moments based on the number of stimulations and stimulation interval;

[0042] In the coil electromagnetic field model, the TMS coil is used to apply the initial field strength to the corresponding tissue layer spatial position at multiple stimulation times, and the generated MEP is observed to obtain multiple MEP amplitudes.

[0043] The number of MEP amplitudes that are greater than or equal to a preset amplitude threshold is counted among multiple MEP amplitudes to obtain the qualified statistical quantity, and the size of the qualified statistical quantity is compared with the preset statistical quantity threshold.

[0044] If the number of qualified statistics is greater than or equal to the statistical number threshold, the initial field strength is reduced by a preset unit field strength, and the current operation sequence is recorded to obtain the first updated field strength and the first operation sequence.

[0045] Using the first updated field strength as the initial field strength, the process returns to the step of applying the initial field strength to the corresponding tissue layer spatial position at multiple stimulation moments using the TMS coil in the electromagnetic field model of the coil, and observing the generated MEP to obtain multiple MEP amplitudes, until the number of qualified statistics is less than or equal to the statistical number threshold, and the initial field strength corresponding to the first operation sequence is used as the reference field strength.

[0046] Otherwise, the initial field strength is increased by a preset unit field strength, and the current operation sequence is recorded to obtain the second updated field strength and the second operation sequence.

[0047] Using the second updated field strength as the initial field strength, return to the step of applying the initial field strength to the corresponding tissue layer spatial position at multiple stimulation times using the TMS coil in the electromagnetic field model of the coil, and observe the generated MEP to obtain multiple MEP amplitudes until the number of qualified statistics is greater than or equal to the statistical number threshold, and use the second updated field strength corresponding to the second operation sequence as the reference field strength.

[0048] By summing up the reference field strengths, multiple reference field strengths are obtained.

[0049] Optionally, the step of adjusting the operating current of the TMS coil at a preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current includes:

[0050] The target reference field strength is determined from multiple reference field strengths based on the treatment site, and the target initial field strength is determined from multiple initial field strengths based on the treatment site.

[0051] The operating current of the TMS coil is adjusted based on the target initial field strength and the target reference field strength to obtain the target node, wherein the target node includes the target field strength and the configuration current.

[0052] Optionally, adjusting the operating current of the TMS coil based on the target initial field strength and the target reference field strength to obtain the target node includes:

[0053] The difference between the initial field strength of the target and the reference field strength of the target is calculated to obtain the target deviation;

[0054] Obtain the historical deviation sequence, update the target deviation to the historical deviation sequence to obtain the updated deviation sequence, and accumulate all the updated deviations in the updated deviation sequence to obtain the deviation integral;

[0055] Obtain the circuit schematic of the TMS coil, determine the model of the digital-to-analog converter and the circuit resistance based on the circuit schematic, obtain the datasheet of the digital-to-analog converter based on the model of the digital-to-analog converter, and retrieve the resolution and reference voltage from the datasheet;

[0056] The configuration current is calculated based on the target deviation, deviation integral, circuit resistance, resolution, and reference voltage.

[0057] The current is applied to the TMS coil to obtain the target field strength.

[0058] Optionally, the configuration current is calculated based on the target deviation, deviation integral, circuit resistance, resolution, and reference voltage, and the calculation method is as follows:

[0059]

[0060] in, This indicates the configured current. This indicates that there are a total of [number] errors in the updated bias sequence. One update deviation, This represents the preset scaling factor. This indicates the target deviation. Indicates the th in the update bias sequence One update deviation, This represents the preset integral coefficient. This represents the integral of the deviation. This indicates the resolution. This refers to the reference voltage. This indicates the resistance of the circuit. Indicates the job identifier. Indicates the scale identifier. Indicates the integral identifier. Indicates a reference identifier.

[0061] Optionally, the step of obtaining the field strength state based on the target field strength includes:

[0062] Obtain the error range, and determine the field strength range based on the error range and the reference field strength. The field strength range includes the upper limit and the lower limit of the field strength.

[0063] When the target field strength is greater than the lower limit of the field strength but less than the upper limit of the field strength, record the current time to obtain the starting time.

[0064] Obtain the detection duration and number of detections, and identify multiple detection moments within the detection duration based on the number of detections;

[0065] The detection times are extracted sequentially from the plurality of detection times, and the following judgment is performed at the extracted detection times:

[0066] If the target field strength is greater than the lower limit of the field strength and less than the upper limit of the field strength, then the detection is in a normal state; otherwise, the detection is in an abnormal state. The detection state is determined by the detection state or the detection abnormal state.

[0067] Summarize the detection status to obtain multiple detection statuses;

[0068] The number of abnormal states detected in multiple detection states is counted to obtain the abnormality count. If the abnormality count is equal to 0, the field strength state is confirmed as a normal state; otherwise, the field strength state is confirmed as an abnormal state.

[0069] To achieve the above objectives, the present invention also provides a transcranial magnetic stimulation therapy parameter quantification system based on electric field strength calibration, comprising:

[0070] The treatment intensity environment confirmation module is used in step A: receiving a treatment intensity determination instruction and confirming the treatment intensity determination environment based on the treatment intensity determination instruction. The treatment intensity determination environment includes an MRI device and a TMS device. The MRI device includes an individual detection unit and an imaging unit. The TMS device includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength through a working current.

[0071] The medical image acquisition and processing module is used in step B: when the individual detection unit detects a patient, the imaging unit is used to acquire a set of medical images of the patient's head, and the medical image processing unit performs multi-tissue segmentation on the medical image set to obtain the spatial locations of multiple tissue layers.

[0072] The electromagnetic simulation calculation module is used in step C: using the electromagnetic simulation unit to assign conductivity parameters to the spatial locations of multiple tissue layers, obtaining multiple tissue layer conductivity, and using the multiple tissue layer conductivity and the pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, wherein the tissue layer conductivity corresponds one-to-one with the reference field strength;

[0073] The treatment intensity determination module is used in step D: adjusting the working current of the TMS coil at the preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current, updating the configuration current to the pre-built current database to obtain the updated database, and obtaining the field strength status based on the target field strength, wherein the field strength status is a normal state or an abnormal state.

[0074] Step E: When the field strength is in a normal state, extract the peak current from the updated database, and retrieve the treatment intensity from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity, thus realizing the method for quantifying transcranial magnetic stimulation treatment parameters based on electric field strength calibration.

[0075] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0076] A memory that stores at least one instruction; and a processor that executes the instruction stored in the memory to implement the above-described method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration.

[0077] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration.

[0078] To address the problems described in the background art, this invention includes the following steps: Step A: Receiving a treatment intensity determination command, and confirming the treatment intensity determination environment based on the command. This environment includes an MRI device and a TMS device. The MRI device includes an individual detection unit and an imaging unit. The TMS device includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength via a working current. Step B: When the individual detection unit detects a patient, the imaging unit acquires a set of medical images of the patient's head. The medical image processing unit performs multi-tissue segmentation on the medical image set to obtain multiple spatial locations of tissue layers. This invention, by introducing multi-tissue segmentation and a similarity-adaptive screening mechanism, achieves accurate identification and localization of multiple tissue locations on the patient's head. Simultaneously, by dynamically adjusting the segmentation ratio and edge sensitivity, it improves the accuracy and robustness of the tissue layer spatial locations. Step C: The electromagnetic simulation unit assigns conductivity parameters to multiple tissue layer spatial locations to obtain multiple tissue layer conductivity values. Multiple reference field strengths are simulated using these multiple tissue layer conductivity values ​​and a pre-constructed coil electromagnetic field model. In this invention, the conductivity of the tissue layer corresponds one-to-one with the reference field strength. It is evident that by assigning conductivity parameters to different tissue layers and dynamically adjusting the field strength using a coil electromagnetic field model, the invention achieves adaptive optimization of electromagnetic stimulation intensity. Step D: The working current of the TMS coil at the preset treatment site is adjusted using multiple reference field strengths to obtain the target field strength and configuration current. The configuration current is updated to the pre-built current database to obtain the updated database. The field strength state is obtained based on the target field strength, where the field strength state is either normal or abnormal. This demonstrates that the invention introduces adaptive current adjustment based on electric field simulation. The mechanism enables precise current configuration and real-time field strength stability monitoring of the TMS coil at individualized treatment sites. Step E: When the field strength is normal, the peak current is extracted from the updated database, and the treatment intensity is retrieved from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity. This realizes a method for quantifying transcranial magnetic stimulation (TMS) treatment parameters based on electric field strength calibration. It is evident that this invention, by introducing a treatment intensity quantification mechanism based on electric field simulation, achieves a mapping from physical electric field strength to clinical treatment intensity. Therefore, this invention can achieve precise quantification of TMS treatment parameters. Attached Figure Description

[0079] Figure 1 This is a flowchart illustrating a method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration, according to an embodiment of the present invention.

[0080] Figure 2 This is a functional block diagram of a transcranial magnetic stimulation therapy parameter quantification system based on electric field strength calibration provided in an embodiment of the present invention;

[0081] Figure 3 This is a schematic diagram of an electronic device for implementing the method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration, according to an embodiment of the present invention.

[0082] Explanation of reference numerals in the attached figures:

[0083] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0084] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0085] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0086] This application provides a method for quantifying transcranial magnetic stimulation (TMS) therapy parameters based on electric field strength calibration. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for quantifying TMS therapy parameters based on electric field strength calibration can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0087] Reference Figure 1 The diagram shown is a flowchart illustrating a method for quantifying transcranial magnetic stimulation (TMS) treatment parameters based on electric field strength calibration, according to an embodiment of the present invention. In this embodiment, the method for quantifying TMS treatment parameters based on electric field strength calibration includes:

[0088] S1, Step A: Receive treatment intensity determination instruction, and confirm the treatment intensity determination environment based on the treatment intensity determination instruction. The treatment intensity determination environment includes an MRI device and a TMS device. The MRI device includes an individual detection unit and an imaging unit. The TMS device includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength through the working current.

[0089] It should be explained that the treatment intensity determination instruction is issued by the person who wants to determine the intensity of transcranial magnetic stimulation (TMS) treatment. TMS is a non-invasive brain stimulation technique that stimulates specific areas of the cerebral cortex by generating brief magnetic field pulses, thereby affecting neural activity. The treatment intensity determination environment is a necessary working environment for TMS treatment. The MRI device is an instrument that uses a powerful magnetic field to generate high-resolution images of the human skull structure. The individual detection unit is a device for identifying and locating patients, and the imaging unit is a device for acquiring medical images. The TMS device is a device that uses a rapidly changing magnetic field to generate an induced electric field in a specific area of ​​the brain, thereby achieving neural modulation and functional stimulation. The TMS coil is a device for generating magnetic field pulses, which can generate a magnetic field through current control, thereby providing non-invasive stimulation to the cerebral cortex. The medical image processing unit is computer software for processing, analyzing, and interpreting medical image data, and the electromagnetic simulation unit is computer software for simulating and calculating electromagnetic field distribution.

[0090] For example, Xiao Zhang, as the attending physician in charge of transcranial magnetic stimulation therapy, issues a treatment intensity determination instruction in order to help a patient with depression treat their symptoms, and confirms the treatment intensity determination environment to assist Xiao Zhang in carrying out the treatment.

[0091] S2, Step B: When the individual detection unit detects a patient, the imaging unit is used to acquire a set of medical images of the patient's head, and the medical image processing unit performs multi-tissue segmentation on the medical image set to obtain the spatial locations of multiple tissue layers.

[0092] It should be understood that the medical image set is a collection of images used for medical diagnosis and treatment, including frontal, left, and top views, providing visualization information of the internal structure and lesion areas of the patient's skull. The spatial location of the tissue layer is the center of a spatial region corresponding to a specific physiological tissue layer, including but not limited to, the scalp, skull, cerebrospinal fluid, gray matter, or white matter, identified in the segmented images. Because different patients have different skull structures, it is necessary to accurately distinguish the spatial locations of different tissue layers such as the scalp, skull, cerebrospinal fluid, gray matter, and white matter in the patient's skull before performing transcranial magnetic stimulation (TMS) treatment. Therefore, the medical image processing unit performs multi-tissue segmentation on the medical image set to obtain multiple tissue layer spatial locations, including:

[0093] Medical images are extracted sequentially from the medical image set, and the following operations are performed on the extracted medical images:

[0094] The medical image is segmented using a medical image processing unit to obtain a segmented image set, wherein the segmented image set includes multiple segmented images;

[0095] Obtain a standard tissue image set, wherein the standard tissue image set includes multiple standard tissue images, extract standard tissue images sequentially from the standard tissue image set, and perform the following operations on the extracted standard tissue images:

[0096] Segmented images are extracted sequentially from the segmented image set, and the following operations are performed on the extracted segmented images:

[0097] Calculate the similarity between the segmented image and the standard tissue image to obtain the single tissue similarity. Summarize the single tissue similarities to obtain the single tissue similarity set. The single tissue similarity set includes multiple single tissue similarities, and each single tissue similarity corresponds one-to-one with the segmented image.

[0098] Extract the single tissue similarity with the highest similarity from the single tissue similarity set to obtain the maximum similarity. Use the segmented image corresponding to the maximum similarity as the target image and obtain the tissue plane position based on the target image.

[0099] Summarize the organizational plane locations to obtain a set of organizational plane locations; summarize the organizational plane location sets to obtain multiple sets of organizational plane locations.

[0100] Multiple tissue layer spatial locations were identified based on multiple sets of planar locations of tissue layers.

[0101] It should be understood that the segmented image set is a collection of images containing multiple segmentation results obtained after processing medical images. The spatial location of the tissue layer describes the three-dimensional location of the tissue layer within the patient's brain. The standard tissue image set refers to a pre-established image set containing multiple standard tissue structures, used as a reference template for comparison with the segmented images. The single-tissue similarity is a quantitative indicator used to measure the similarity between a segmented image and its corresponding standard tissue image in terms of grayscale distribution or texture features. The maximum similarity is the highest single-tissue similarity value in the calculated single-tissue similarity set. The target image is the segmented image with the highest similarity calculated with the standard tissue image in the segmented image set, i.e., the segmented image with the maximum similarity. The tissue plane location is the coordinate of the specific tissue layer determined in the two-dimensional plane of the medical image. Optionally, feature matching methods can be used to calculate the similarity between the segmented image and the standard tissue image, which is existing technology and will not be elaborated here. Adaptive thresholding algorithms can be used to segment medical images, which is existing technology and will not be elaborated here.

[0102] It should be understood that the segmentation of the medical image needs to consider the proportion of the segmented image within the overall medical image. If the segmented image is too small, larger tissue layers may not be identifiable, and the image processing speed will be reduced. Conversely, if the segmented image is too large, multiple tissue layers may be identified within it. Therefore, selecting an appropriate proportion for medical image segmentation is crucial. The segmentation of the medical image using the medical image processing unit to obtain a segmented image set includes:

[0103] Obtain the initial side length, calculate the initial area based on the initial side length, and use the initial area to extract multiple sub-images from the medical image;

[0104] Standard tissue images are sequentially extracted from the standard tissue image set, and the following operations are performed on the extracted standard tissue images:

[0105] Extract sub-images sequentially from multiple sub-images, and perform the following operations on the extracted sub-images:

[0106] Calculate the similarity between the sub-image and the standard tissue image to obtain the sub-image similarity. Summarize the sub-image similarities to obtain multiple sub-image similarities. Extract the maximum sub-image similarity among the multiple sub-image similarities to obtain the maximum sub-image similarity.

[0107] Obtain the sub-image similarity threshold, compare the maximum sub-image similarity with the sub-image similarity threshold, and if the maximum sub-image similarity is greater than the sub-image similarity threshold, then the sub-image corresponding to the maximum sub-image similarity is selected as the filter image.

[0108] Summarize the filtered images to obtain a filtered image set, count the number of filtered images in the filtered image set, and obtain the filtered statistics.

[0109] The number of standard tissue images in the standard tissue images is counted to obtain the standard statistical quantity. The size of the selected statistical quantity is compared with the standard statistical quantity. If the selected statistical quantity is equal to the standard statistical quantity, the selected image set is used as the segmentation image set.

[0110] Otherwise, the initial side length is increased by a preset unit length to obtain an updated side length. The updated side length is then used as the initial side length, and the process returns to the step of calculating the initial area based on the initial side length, until the number of filtered statistics equals the standard number of statistics. The filtered image set is then used as the segmented image set.

[0111] Understandably, the initial side length is the initial boundary length of the image region set for cropping sub-images when segmenting a medical image, used to determine the spatial size and segmentation ratio of each sub-image. The initial area is the size of the two-dimensional region used to crop sub-images of the medical image, calculated based on the initial side length. The sub-image is a smaller image block cropped from the medical image according to the initial area or updated area. The sub-image similarity is a quantitative indicator used to measure the degree of matching between a sub-image and a corresponding standard tissue image in terms of structure, grayscale, or texture features. The maximum sub-image similarity is the sub-image similarity with the highest value among the sub-image similarities calculated between multiple sub-images and a certain standard tissue image. The sub-image similarity threshold is a preset similarity threshold used to determine whether a sub-image is sufficiently close to the standard tissue image. The selected image is a sub-image whose similarity to the standard tissue image reaches or exceeds the sub-image similarity threshold among multiple sub-images. The selected statistical count is the total number of selected images in the selected image set that meet the sub-image similarity threshold condition. The standard statistical count is the total number of standard tissue images contained in the standard tissue image set. The updated side length is the new side length obtained by adjusting the initial side length based on the feedback from the segmentation results.

[0112] It should be explained that obtaining the tissue planar location based on the target image includes:

[0113] Calculate the grayscale value of the target image to obtain a grayscale image, and perform denoising processing on the grayscale image to obtain a low-noise image;

[0114] An initial edge sensitivity is obtained, and the edges of tissue layers in a low-noise image are detected based on the edge sensitivity to obtain a binary image, which includes the edges and the background.

[0115] If a closed region is observed in the binary image based on the edge, and if no closed region is found, the initial edge sensitivity is increased by a preset unit value to obtain an updated edge sensitivity. The updated edge sensitivity is then used as the initial edge sensitivity, and the process returns to the step of detecting the edge of the tissue layer in the low-noise image based on the edge sensitivity to obtain the binary image, until a closed region is found in the binary image.

[0116] Otherwise, establish a Cartesian coordinate system for the binary image, and determine multiple edge coordinates on the edge according to the preset number of coordinates. The edge coordinates include the edge x-coordinate and the edge y-coordinate.

[0117] The center position of the closed region is calculated based on multiple edge coordinates to obtain the tissue plane position. The tissue plane position includes the horizontal and vertical coordinates of the plane, and the calculation method is as follows:

[0118]

[0119] in, The horizontal coordinate of the plane representing the position of the tissue. The vertical coordinate of the plane representing the position of the tissue. This indicates that there is a total of [number] edge coordinates among the multiple edge coordinates. Each edge coordinate, Indicates the first The x-coordinates of the edges, express Each edge's ordinate.

[0120] Understandably, transcranial magnetic stimulation (TMS) therapy requires finding the precise location of the tissue layer and applying an electric field at that location to complete the treatment. This necessitates identifying the contour of the tissue layer in medical images and determining its center location based on the contour.

[0121] Furthermore, the grayscale image is an image containing only brightness information and no color information. Optionally, a weighted average method can be used to calculate the grayscale image, which will not be elaborated here. The low-noise image is an image that has undergone denoising processing and whose noise interference has been effectively suppressed. Optionally, methods including but not limited to Gaussian blur and median filtering can be used to obtain the low-noise image, which will not be elaborated here. The binary image is an image composed only of black and white pixel values. Optionally, an edge detection algorithm can be used to obtain the binary image, which will not be elaborated here. The initial edge sensitivity is an initial threshold parameter set before edge detection is performed, used to control the algorithm's response to changes in image grayscale. The closed region is a closed graphical region in the binary image formed by continuous edges, connected end-to-end without breaks. The unit value is a fixed increment used to gradually increase or decrease the initial edge sensitivity during parameter adjustment. The updated edge sensitivity is a new threshold obtained by adjusting the initial edge sensitivity based on whether the current binary image forms a closed region. The number of coordinates is the total number of edge points selected on the closed edge. The edge coordinates are the planar coordinates of the points representing the edge positions in the binary image.

[0122] For example, suppose the medical image has 512 pixels. The standard tissue image set includes scalp images, skull images, cerebrospinal fluid images, gray matter images, and white matter images, with a standard statistical count of 5 and an initial side length of 64 pixels. The initial area can be calculated to be 4096 pixels. Assuming an adaptive thresholding algorithm is used, a total of 2048 sub-images are extracted from the front-view medical image. When calculating the sub-image similarity related to gray matter, the resulting sub-image similarity set is {(sub-image 1, similarity: 0.75), (sub-image 2, similarity: 0.82), (sub-image 3, similarity: 0.6)}. 5), ..., (sub-image 2048, similarity: 0.11), assuming a similarity of 0.82 is the maximum sub-image similarity, which is greater than the sub-image similarity threshold of 0.8, then sub-image 2 is used as the screening image. After performing the same steps on each standard tissue image in the standard tissue image set, assuming the obtained screening statistics are 3, which is less than the standard statistics, the initial side length is increased to 84 pixels, and sub-images are re-extracted from the medical images until the screening statistics are 5, which is equal to the standard statistics. The screening image set is then used as the segmentation image set.

[0123] Furthermore, with an initial side length of 84 pixels, the resulting segmented image set contains 512 segmented images. Assuming that during the extraction of the spatial location of the gray matter tissue layer, among the 512 single-tissue similarities, (segmented image 28, single-tissue similarity: 0.91) has the highest similarity to the standard gray matter image, then segmented image 28 is taken as the target image. Assuming that after grayscale conversion and denoising of segmented image 28, the resulting binary image can detect a closed region, and assuming the number of coordinates is set to 4, then the coordinates of the four points in the Cartesian coordinate system are identified as A(1,1), B(4,1), C(4,3), and D(1,3). Based on the coordinates of these four points, the tissue plane position can be calculated as (…). 2.5, : 2.0), this position coordinate is the front view in the medical image set. Similarly, the tissue plane position of the left view is obtained as ( 2.0 :1.5), the location of the tissue plane in the top view is ( 2.5, The final spatial location of the gray matter tissue layer was determined to be (2.5, 2.0, 1.5). This embodiment of the invention, by introducing a multi-tissue segmentation and similarity adaptive filtering mechanism, achieves accurate identification and localization of multiple tissue locations in the patient's skull. Simultaneously, by dynamically adjusting the segmentation ratio and edge sensitivity, the accuracy and robustness of the tissue layer spatial location are improved.

[0124] S3, Step C: Use the electromagnetic simulation unit to assign conductivity parameters to the spatial locations of multiple tissue layers to obtain multiple tissue layer conductivity. Use the multiple tissue layer conductivity and the pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, where the tissue layer conductivity corresponds one-to-one with the reference field strength.

[0125] Understandably, the multi-tissue layer conductivity is a conductivity parameter assigned to each tissue layer based on its location in the patient's head (such as scalp, skull, cerebrospinal fluid, gray matter, white matter, etc.), used to describe the tissue layer's ability to conduct electric fields. The reference field strength is a reference electric field strength calculated based on the tissue layer conductivity and a pre-constructed TMS coil electromagnetic field model. In transcranial magnetic stimulation simulations, different tissues (such as scalp, skull, gray matter, and white matter) have different current conduction capabilities, and their conductivity directly affects the distribution of the electromagnetic field within the tissue. By assigning precise conductivity parameters to each tissue layer and calculating them in conjunction with the coil's electromagnetic field model, the reference field strength for the corresponding tissue layer can be obtained, ensuring that the simulation results accurately reflect the response of each tissue to the TMS electric field.

[0126] It should be explained that the simulation of multiple reference field strengths using the conductivity of multiple tissue layers and a pre-constructed coil electromagnetic field model includes:

[0127] Multiple initial field strengths are determined from a preset field strength table based on multiple tissue conductivity parameters, wherein each initial field strength corresponds one-to-one with the spatial location of the tissue layer.

[0128] Initial field strengths are extracted sequentially from multiple initial field strengths, and the following operations are performed on the extracted initial field strengths:

[0129] Obtain the stimulation interval and number of stimulations, and identify multiple stimulation moments based on the number of stimulations and stimulation interval;

[0130] In the coil electromagnetic field model, the TMS coil is used to apply the initial field strength to the corresponding tissue layer spatial position at multiple stimulation times, and the generated MEP is observed to obtain multiple MEP amplitudes.

[0131] The number of MEP amplitudes that are greater than or equal to a preset amplitude threshold is counted among multiple MEP amplitudes to obtain the qualified statistical quantity, and the size of the qualified statistical quantity is judged to be greater than or equal to the preset statistical quantity threshold.

[0132] If the number of qualified statistics is greater than or equal to the statistical number threshold, the initial field strength is reduced by a preset unit field strength, and the current operation sequence is recorded to obtain the first updated field strength and the first operation sequence.

[0133] Using the first updated field strength as the initial field strength, the process returns to the step of applying the initial field strength to the corresponding tissue layer spatial position at multiple stimulation moments using the TMS coil in the electromagnetic field model of the coil, and observing the generated MEP to obtain multiple MEP amplitudes, until the number of qualified statistics is less than or equal to the statistical number threshold, and the initial field strength corresponding to the first operation sequence is used as the reference field strength.

[0134] Otherwise, the initial field strength is increased by a preset unit field strength, and the current operation sequence is recorded to obtain the second updated field strength and the second operation sequence.

[0135] Using the second updated field strength as the initial field strength, return to the step of applying the initial field strength to the corresponding tissue layer spatial position at multiple stimulation times using the TMS coil in the electromagnetic field model of the coil, and observe the generated MEP to obtain multiple MEP amplitudes until the number of qualified statistics is greater than or equal to the statistical number threshold, and use the second updated field strength corresponding to the second operation sequence as the reference field strength.

[0136] By summing up the reference field strengths, multiple reference field strengths are obtained.

[0137] It should be understood that the coil electromagnetic field model is a mathematical or computational model used to simulate the spatial electromagnetic field distribution generated by the TMS coil under different operating currents, and can be used to predict the electric field strength and evoked potentials when each tissue layer is stimulated. The field strength table is a pre-established data table used to query the initial electric field strength under different tissue layers with corresponding conductivity. The initial field strength is the initial reference electric field strength obtained from the preset field strength table based on the tissue layer conductivity. The stimulation interval is the time interval between two adjacent transcranial magnetic stimulations, and the stimulation count is the total number of transcranial magnetic stimulations applied to a specific tissue layer. The stimulation time is the specific time point at which transcranial magnetic stimulation is applied. The MEP amplitude refers to the peak amplitude of the spatial evoked potentials in the tissue layer after transcranial magnetic stimulation. The amplitude threshold is an amplitude threshold used to determine whether the MEP has reached an effective response. The statistical quantity threshold is a quantity threshold used to determine whether the number of effective MEP responses meets the standard. The unit field strength is a fixed increment or decrement used to adjust the initial field strength. The first updated electric field strength is obtained by reducing the initial electric field strength by a unit when the initial electric field strength is determined to be too high using the MEP response. The first operation sequence records the execution order of the operations to reduce the initial electric field strength, which is used to determine the corresponding first updated electric field strength. The second updated electric field strength is obtained by increasing the initial electric field strength by a unit when the initial electric field strength is determined to be too low using the MEP response. The second operation sequence records the execution order of the operations to increase the initial electric field strength, which is used to determine the corresponding second updated electric field strength.

[0138] For example, assume the patient's head consists of three layers of tissue: scalp, gray matter, and white matter, with conductivities of 0.465 S / m, 0.276 S / m, and 0.126 S / m, respectively. Initial field strengths are obtained from a pre-defined field strength table: 100 V / m for the scalp, 120 V / m for gray matter, and 110 V / m for white matter. Taking gray matter as an example, the initial field strength of 120 V / m corresponds to five stimuli, with a stimulation interval of 50 ms, and the stimulation times are 0 ms, 50 ms, 100 ms, 150 ms, and 200 ms. After applying TMS stimulation to the coil electromagnetic field model, the MEP amplitudes are obtained as 0.6 mV, 0.7 mV, 0.8 mV, 0.55 mV, and 0.65 mV, respectively. The number of valid statistical counts is 5, exceeding the statistical threshold of 3. Therefore, the initial field strength is too high and needs to be reduced by 5 V / m, resulting in a first updated field strength of 115 V / m. The operation sequence is recorded as the first time. When the voltage was further reduced to 110V / m, the MEP amplitudes were 0.50mV, 0.60mV, 0.70mV, 0.45mV, and 0.55mV, respectively. The number of qualified statistical values ​​was 4, which was greater than the statistical threshold. When the voltage was further reduced to 105V / m, the MEP amplitudes were 0.45mV, 0.55mV, 0.65mV, 0.40mV, and 0.50mV, respectively. The number of qualified statistical values ​​was 2, which was less than the statistical threshold. Therefore, the gray mass reference field strength was determined to be 110V / m from the previous step. With an initial scalp field strength of 100 V / m, the MEP amplitudes were 0.3 mV, 0.35 mV, 0.4 mV, 0.25 mV, and 0.3 mV. Since the number of valid statistical counts was 0 (less than the statistical threshold), the field strength needed to be increased by 5 V / m. Iterating to 120 V / m, the MEP amplitudes were 0.5 mV, 0.55 mV, 0.6 mV, 0.45 mV, and 0.5 mV. The number of valid statistical counts was 3 (equal to the statistical threshold), and the baseline scalp field strength was determined to be 120 V / m. Similarly, the baseline field strength for white matter was also determined to be 120 V / m. Finally, the baseline field strengths for the three tissue layers were: scalp 120 V / m, gray matter 110 V / m, and white matter 120 V / m. This embodiment of the invention achieves adaptive optimization of electromagnetic stimulation intensity by assigning conductivity parameters to different tissue layers and dynamically adjusting the field strength using a coil electromagnetic field model.

[0139] S4, Step D: Adjust the working current of the TMS coil at the preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current. Update the configuration current to the pre-built current database to obtain the updated database. Obtain the field strength status based on the target field strength, where the field strength status is a normal state or an abnormal state.

[0140] Understandably, the electric field strength generated by the TMS coil is produced by adjusting the current flowing through the coil, while also referencing a simulated reference field strength to prevent the generation of an electric field harmful to the human body. Therefore, the process of adjusting the working current of the TMS coil at a preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current includes:

[0141] The target reference field strength is determined from multiple reference field strengths based on the treatment site, and the target initial field strength is determined from multiple initial field strengths based on the treatment site.

[0142] The operating current of the TMS coil is adjusted based on the target initial field strength and the target reference field strength to obtain the target node, wherein the target node includes the target field strength and the configuration current.

[0143] It should be explained that the treatment site is a specific tissue layer location on the patient's head where the TMS coil acts to generate an electromagnetic induction effect. The target field strength is the actual electric field strength generated by the TMS coil at the treatment site. The configuration current is the current value set and input into the TMS coil to generate the target field strength. The current database is a core data set used to store and manage the information of each treatment site, stimulation parameter, and corresponding configuration current in the transcranial magnetic stimulation system. The target reference field strength is a standard electric field strength value determined for a specific treatment site that can safely and effectively induce a neural response. The target initial field strength is an initial electric field strength value selected from the field strength table based on the tissue conductivity parameter for a specific treatment site.

[0144] Furthermore, the step of adjusting the operating current of the TMS coil based on the target initial field strength and the target reference field strength to obtain the target node includes:

[0145] The difference between the initial field strength of the target and the reference field strength of the target is calculated to obtain the target deviation;

[0146] Obtain the historical deviation sequence, update the target deviation to the historical deviation sequence to obtain the updated deviation sequence, and accumulate all the updated deviations in the updated deviation sequence to obtain the deviation integral;

[0147] Obtain the circuit schematic of the TMS coil, determine the model of the digital-to-analog converter and the circuit resistance based on the circuit schematic, obtain the datasheet of the digital-to-analog converter based on the model of the digital-to-analog converter, and retrieve the resolution and reference voltage from the datasheet;

[0148] The configuration current is calculated based on the target deviation, deviation integral, circuit resistance, resolution, and reference voltage.

[0149] The current is applied to the TMS coil to obtain the target field strength.

[0150] It should be understood that the historical deviation sequence is a sequence formed by storing the historical set of differences between the target initial field strength and the target reference field strength, stored by the TMS device for recording and analyzing the effects of multiple field strength adjustments, and then sorting them chronologically. The deviation integral is the integral obtained by summing the target deviations from each iteration. The circuit diagram is a schematic diagram of the electrical connections and signal transmission paths between the electronic components in the TMS coil. The digital-to-analog converter model number is the model number of the electronic component used to convert digital control signals into analog voltage signals, and the circuit resistor is an electronic component used in the circuit to limit current, divide voltage, or stabilize signals. The datasheet is the official technical document provided by the electronic component manufacturer, which details the component's performance parameters, operating conditions, electrical characteristics, and application instructions. The resolution is the minimum distinguishable output value of the digital-to-analog converter. The reference voltage is the reference voltage used for full-scale output in the digital-to-analog converter.

[0151] Furthermore, the configuration current is calculated based on the target deviation, deviation integral, circuit resistance, resolution, and reference voltage, and the calculation method is as follows:

[0152]

[0153] in, This indicates the configured current. This indicates that there are a total of [number] errors in the updated bias sequence. One update deviation, This represents the preset scaling factor. This indicates the target deviation. Indicates the th in the update bias sequence One update deviation, This represents the preset integral coefficient. This represents the integral of the deviation. This indicates the resolution. This refers to the reference voltage. This indicates the resistance of the circuit. Indicates the job identifier. Indicates the scale identifier. Indicates the integral identifier. Indicates a reference identifier.

[0154] It should be explained that the proportional coefficient is an adjustment coefficient used to linearly amplify the target deviation, and the integral coefficient is a coefficient used to adjust the cumulative amount of historical deviation.

[0155] For example, in a TMS treatment targeting the left motor cortex of a patient, the target reference field strength at the treatment site is 100V / m. After repeatedly adjusting the operating current of the TMS coil, the initial target field strength is obtained as 97V / m, and the historical deviation sequence is {+10V / m, +5V / m}. Integrating the target deviation with the historical deviation, the deviation integral is obtained as 10 + 5 + 3 = 18V / m. Assuming a proportionality coefficient... =0.5A / (V / m), integral coefficient =0.1A / (V / m), DAC resolution =16-bit, reference voltage =2.5V, circuit resistance =10Ω, the calculated configuration current is: =[(0.5 10 0.1 18) 65536] (2.5 10) 2.59 10 -5A, when this configured current is input into the TMS coil, the actual target field strength generated is close to 100V / m, thereby ensuring that the treatment site receives safe and effective electric field stimulation. At the same time, the configured current is updated to the current database for subsequent treatment or system optimization.

[0156] Understandably, when the target field strength reaches the allowable error range near the reference field strength, it does not mean that the target field strength tuning is complete. It is still necessary to observe for a period of time whether the target field strength can stabilize within the allowable error range. Therefore, the process of obtaining the field strength state based on the target field strength includes:

[0157] Obtain the error range, and determine the field strength range based on the error range and the reference field strength. The field strength range includes the upper limit and the lower limit of the field strength.

[0158] When the target field strength is greater than the lower limit of the field strength but less than the upper limit of the field strength, record the current time to obtain the starting time.

[0159] Obtain the detection duration and number of detections, and identify multiple detection moments within the detection duration based on the number of detections;

[0160] The detection times are extracted sequentially from the plurality of detection times, and the following judgment is performed at the extracted detection times:

[0161] If the target field strength is greater than the lower limit of the field strength and less than the upper limit of the field strength, then the detection is in a normal state; otherwise, the detection is in an abnormal state. The detection state is determined by the detection state or the detection abnormal state.

[0162] Summarize the detection status to obtain multiple detection statuses;

[0163] The number of abnormal states detected in multiple detection states is counted to obtain the abnormality count. If the abnormality count is equal to 0, the field strength state is confirmed as a normal state; otherwise, the field strength state is confirmed as an abnormal state.

[0164] It should be explained that the error range is a numerical interval used to evaluate the allowable deviation between the target field strength and the reference field strength. The field strength range is a numerical interval of allowable electric field strength variation determined by calculating the sum of the reference field strength and the error range. The upper limit of the field strength is the maximum allowable value of the target field strength, and the lower limit of the field strength is the minimum allowable value of the target field strength. The start time is the time point recorded when the target field strength first enters and stabilizes within the field strength range. The detection duration is the total time from the start of detection to the end of detection, and the number of detections is the total number of times the target field strength is judged within the entire detection duration. The detection time is a time point evenly selected according to the number of detections within the detection duration. The detection state is the result determined at each detection time based on whether the target field strength is within the field strength range.

[0165] For example, assuming a reference field strength of 120V / m and an error range of ±4%, the upper limit of the field strength is 124.8V / m and the lower limit is 115.2V / m. After adjusting the operating current of the TMS coil, when the field strength at the target point is measured to be 118V / m, the field strength at the target point is within the allowable range. This moment is recorded as the starting moment. =0s. Then, the detection duration was set to 12s and the number of detections to 6. Based on the number of detections, a detection time sequence {2s, 4s, 6s, 8s, 10s, 12s} was generated at equal intervals within the detection duration. At each detection time, the target field strength was monitored in real time, and the obtained field strength measurements were (t=2s: 119.6V / m), (t=4s: 121.2V / m), (t=6s: 117.8V / m), (t=8s: 125.3V / m), (t=10s: 120.1V / m), and (t=12s: 118.5V / m). Based on the upper and lower limits of the field strength, the detection state sequence was determined to be {normal, normal, normal, abnormal, normal, normal}. Further counting of abnormal states yielded an abnormal count of 1. Since the abnormal count was not equal to 0, the field strength state of the current treatment phase was determined to be abnormal. This invention, through the introduction of an adaptive current adjustment mechanism based on electric field simulation, achieves precise current configuration and real-time field strength stability monitoring of the TMS coil at individualized treatment sites.

[0166] S5, Step E: When the field strength is in a normal state, extract the peak current from the updated database, and retrieve the treatment intensity from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity, thus realizing the method for quantifying transcranial magnetic stimulation treatment parameters based on electric field strength calibration.

[0167] Understandably, the peak current is the largest of the multiple configured currents generated by the TMS coil to achieve the target field strength. The current intensity mapping table is a data table that establishes a correspondence between the operating current values ​​of the TMS coil and the corresponding percentage treatment intensity. The treatment intensity is the percentage treatment intensity that the TMS device can perform.

[0168] It should be explained that using treatment intensity as a percentage is the practice of clinicians, while electric field intensity is the physical benchmark of the output intensity of TMS equipment. Therefore, it is necessary to organically combine clinical experience with physical quantification to convert the target field intensity into treatment intensity.

[0169] For example, taking the treatment of depression as an example, the dorsolateral prefrontal cortex is the target location. By adjusting the operating current of the TMS coil, the target field strength at the target location is made equal to the reference field strength (66V / m, tolerance ±2%). The peak current during the current adjustment process is found to be 3120A. By consulting the preset "current-intensity" mapping table, the corresponding treatment intensity is determined to be 40%. This value is the final treatment intensity parameter adopted by the TMS device. This embodiment of the invention introduces a treatment intensity quantification mechanism based on electric field simulation, realizing the mapping from physical electric field intensity to clinical treatment intensity.

[0170] To address the problems described in the background art, this invention includes the following steps: Step A: Receiving a treatment intensity determination command, and confirming the treatment intensity determination environment based on the command. This environment includes an MRI device and a TMS device. The MRI device includes an individual detection unit and an imaging unit. The TMS device includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength via a working current. Step B: When the individual detection unit detects a patient, the imaging unit acquires a set of medical images of the patient's head. The medical image processing unit performs multi-tissue segmentation on the medical image set to obtain multiple spatial locations of tissue layers. This invention, by introducing multi-tissue segmentation and a similarity-adaptive screening mechanism, achieves accurate identification and localization of multiple tissue locations on the patient's head. Simultaneously, by dynamically adjusting the segmentation ratio and edge sensitivity, it improves the accuracy and robustness of the tissue layer spatial locations. Step C: The electromagnetic simulation unit assigns conductivity parameters to multiple tissue layer spatial locations to obtain multiple tissue layer conductivity values. Multiple reference field strengths are simulated using these multiple tissue layer conductivity values ​​and a pre-constructed coil electromagnetic field model. In this invention, the conductivity of the tissue layer corresponds one-to-one with the reference field strength. It is evident that by assigning conductivity parameters to different tissue layers and dynamically adjusting the field strength using a coil electromagnetic field model, the invention achieves adaptive optimization of electromagnetic stimulation intensity. Step D: The working current of the TMS coil at the preset treatment site is adjusted using multiple reference field strengths to obtain the target field strength and configuration current. The configuration current is updated to the pre-built current database to obtain the updated database. The field strength state is obtained based on the target field strength, where the field strength state is either normal or abnormal. This demonstrates that the invention introduces adaptive current adjustment based on electric field simulation. The mechanism enables precise current configuration and real-time field strength stability monitoring of the TMS coil at individualized treatment sites. Step E: When the field strength is normal, the peak current is extracted from the updated database, and the treatment intensity is retrieved from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity. This realizes a method for quantifying transcranial magnetic stimulation (TMS) treatment parameters based on electric field strength calibration. It is evident that this invention, by introducing a treatment intensity quantification mechanism based on electric field simulation, achieves a mapping from physical electric field strength to clinical treatment intensity. Therefore, this invention can achieve precise quantification of TMS treatment parameters.

[0171] like Figure 2 The diagram shown is a functional block diagram of a transcranial magnetic stimulation therapy parameter quantification system based on electric field strength calibration provided in an embodiment of the present invention.

[0172] The transcranial magnetic stimulation (TMS) treatment parameter quantification system 100 based on electric field strength calibration described in this invention can be installed in an electronic device. Depending on the functions implemented, the TMS treatment parameter quantification system 100 may include a treatment intensity environment confirmation module 101, a medical image acquisition and processing module 102, an electromagnetic simulation calculation module 103, and a treatment intensity determination module 104. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0173] The treatment intensity environment confirmation module 101 is used in step A: receiving a treatment intensity determination instruction and confirming the treatment intensity determination environment based on the treatment intensity determination instruction. The treatment intensity determination environment includes an MRI device and a TMS device. The MRI device includes an individual detection unit and an imaging unit. The TMS device includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength through a working current.

[0174] The medical image acquisition and processing module 102 is used in step B: when the individual detection unit detects a patient, the imaging unit is used to acquire a set of medical images of the patient's head, and the medical image processing unit performs multi-tissue segmentation on the medical image set to obtain the spatial locations of multiple tissue layers.

[0175] The electromagnetic simulation calculation module 103 is used in step C: using the electromagnetic simulation unit to assign conductivity parameters to the spatial positions of multiple tissue layers, obtaining multiple tissue layer conductivity, and using the multiple tissue layer conductivity and the pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, wherein the tissue layer conductivity corresponds one-to-one with the reference field strength;

[0176] The treatment intensity determination module 104 is used in step D: adjusting the working current of the TMS coil at the preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current, updating the configuration current to the pre-built current database to obtain the updated database, and obtaining the field strength status based on the target field strength, wherein the field strength status is a normal state or an abnormal state.

[0177] Step E: When the field strength is in a normal state, extract the peak current from the updated database, and retrieve the treatment intensity from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity, thus realizing the method for quantifying transcranial magnetic stimulation treatment parameters based on electric field strength calibration.

[0178] In detail, the modules in the transcranial magnetic stimulation therapy parameter quantification system 100 based on electric field strength calibration described in this embodiment of the invention employ the same methods as described above. Figure 1 The method described herein is the same as the method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration, and can produce the same technical effect, so it will not be elaborated here.

[0179] like Figure 3 The diagram shown is a schematic representation of an electronic device for implementing a method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration, according to an embodiment of the present invention.

[0180] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration.

[0181] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a transcranial magnetic stimulation therapy parameter quantification method program based on electric field strength calibration, but also to temporarily store data that has been output or will be output.

[0182] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a method for quantifying transcranial magnetic stimulation treatment parameters based on electric field strength calibration) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0183] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0184] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0185] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0186] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0187] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0188] The program for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0189] Step A: Receive treatment intensity determination instruction, and confirm the treatment intensity determination environment based on the treatment intensity determination instruction. The treatment intensity determination environment includes MRI equipment and TMS equipment. The MRI equipment includes an individual detection unit and an imaging unit. The TMS equipment includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength through the working current.

[0190] Step B: When the individual detection unit detects a patient, the imaging unit is used to acquire a set of medical images of the patient's head. The medical image processing unit then performs multi-tissue segmentation on the medical image set to obtain the spatial locations of multiple tissue layers.

[0191] Step C: Use the electromagnetic simulation unit to assign conductivity parameters to the spatial locations of multiple tissue layers to obtain multiple tissue layer conductivity. Use the multiple tissue layer conductivity and the pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, where the tissue layer conductivity corresponds one-to-one with the reference field strength.

[0192] Step D: Adjust the working current of the TMS coil at the preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current. Update the configuration current to the pre-built current database to obtain the updated database. Obtain the field strength status based on the target field strength, where the field strength status is a normal state or an abnormal state.

[0193] Step E: When the field strength is in a normal state, extract the peak current from the updated database, and retrieve the treatment intensity from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity, thus realizing the method for quantifying transcranial magnetic stimulation treatment parameters based on electric field strength calibration.

[0194] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0195] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0196] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0197] Step A: Receive treatment intensity determination instruction, and confirm the treatment intensity determination environment based on the treatment intensity determination instruction. The treatment intensity determination environment includes MRI equipment and TMS equipment. The MRI equipment includes an individual detection unit and an imaging unit. The TMS equipment includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength through the working current.

[0198] Step B: When the individual detection unit detects a patient, the imaging unit is used to acquire a set of medical images of the patient's head. The medical image processing unit then performs multi-tissue segmentation on the medical image set to obtain the spatial locations of multiple tissue layers.

[0199] Step C: Use the electromagnetic simulation unit to assign conductivity parameters to the spatial locations of multiple tissue layers to obtain multiple tissue layer conductivity. Use the multiple tissue layer conductivity and the pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, where the tissue layer conductivity corresponds one-to-one with the reference field strength.

[0200] Step D: Adjust the working current of the TMS coil at the preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current. Update the configuration current to the pre-built current database to obtain the updated database. Obtain the field strength status based on the target field strength, where the field strength status is a normal state or an abnormal state.

[0201] Step E: When the field strength is in a normal state, extract the peak current from the updated database, and retrieve the treatment intensity from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity, thus realizing the method for quantifying transcranial magnetic stimulation treatment parameters based on electric field strength calibration.

[0202] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0203] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0204] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0205] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for quantifying transcranial magnetic stimulation (TMS) therapeutic parameters based on electric field strength calibration, characterized in that, The method includes: Step A: Receive treatment intensity determination instruction, and confirm the treatment intensity determination environment based on the treatment intensity determination instruction. The treatment intensity determination environment includes MRI equipment and TMS equipment. The MRI equipment includes an individual detection unit and an imaging unit. The TMS equipment includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength through the working current. Step B: When the individual detection unit detects a patient, the imaging unit is used to acquire a set of medical images of the patient's head. The medical image processing unit then performs multi-tissue segmentation on the medical image set to obtain the spatial locations of multiple tissue layers. Step C: Use the electromagnetic simulation unit to assign conductivity parameters to the spatial locations of multiple tissue layers to obtain multiple tissue layer conductivity. Use the multiple tissue layer conductivity and the pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, where the tissue layer conductivity corresponds one-to-one with the reference field strength. The method utilizes the conductivity of multiple tissue layers and a pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, including: Multiple initial field strengths are determined from a preset field strength table based on multiple tissue conductivity parameters, wherein each initial field strength corresponds one-to-one with the spatial location of the tissue layer. Initial field strengths are extracted sequentially from multiple initial field strengths, and the following operations are performed on the extracted initial field strengths: Obtain the stimulation interval and number of stimulations, and identify multiple stimulation moments based on the number of stimulations and stimulation interval; In the coil electromagnetic field model, the TMS coil is used to apply the initial field strength to the corresponding tissue layer spatial position at multiple stimulation times, and the generated MEP is observed to obtain multiple MEP amplitudes. The number of MEP amplitudes that are greater than or equal to a preset amplitude threshold is counted among multiple MEP amplitudes to obtain the qualified statistical quantity, and the size of the qualified statistical quantity is judged to be greater than or equal to the preset statistical quantity threshold. If the number of qualified statistics is greater than or equal to the statistical number threshold, the initial field strength is reduced by a preset unit field strength, and the current operation sequence is recorded to obtain the first updated field strength and the first operation sequence. Using the first updated field strength as the initial field strength, the process returns to the step of applying the initial field strength to the corresponding tissue layer spatial position at multiple stimulation moments using the TMS coil in the electromagnetic field model of the coil, and observing the generated MEP to obtain multiple MEP amplitudes, until the number of qualified statistics is less than or equal to the statistical number threshold, and the initial field strength corresponding to the first operation sequence is used as the reference field strength. Otherwise, the initial field strength is increased by a preset unit field strength, and the current operation sequence is recorded to obtain the second updated field strength and the second operation sequence. Using the second updated field strength as the initial field strength, return to the step of applying the initial field strength to the corresponding tissue layer spatial position at multiple stimulation times using the TMS coil in the electromagnetic field model of the coil, and observe the generated MEP to obtain multiple MEP amplitudes until the number of qualified statistics is greater than or equal to the statistical number threshold, and use the second updated field strength corresponding to the second operation sequence as the reference field strength. By summing up the reference field strengths, multiple reference field strengths are obtained; Step D: Adjust the working current of the TMS coil at the preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current. Update the configuration current to the pre-built current database to obtain the updated database. Obtain the field strength status based on the target field strength, where the field strength status is a normal state or an abnormal state. The method of adjusting the working current of the TMS coil at a preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current includes: The target reference field strength is determined from multiple reference field strengths based on the treatment site, and the target initial field strength is determined from multiple initial field strengths based on the treatment site. The operating current of the TMS coil is adjusted based on the target initial field strength and the target reference field strength to obtain the target node, wherein the target node includes the target field strength and the configuration current. The process of adjusting the operating current of the TMS coil based on the target initial field strength and the target reference field strength to obtain the target node includes: The difference between the initial field strength of the target and the reference field strength of the target is calculated to obtain the target deviation; Obtain the historical deviation sequence, update the target deviation to the historical deviation sequence to obtain the updated deviation sequence, and accumulate all the updated deviations in the updated deviation sequence to obtain the deviation integral; Obtain the circuit schematic of the TMS coil, determine the model of the digital-to-analog converter and the circuit resistance based on the circuit schematic, obtain the datasheet of the digital-to-analog converter based on the model of the digital-to-analog converter, and retrieve the resolution and reference voltage from the datasheet; The configuration current is calculated based on the target deviation, deviation integral, circuit resistance, resolution, and reference voltage. The configuration current is applied to the TMS coil to obtain the target field strength; Step E: When the field strength is in a normal state, extract the peak current from the updated database, and retrieve the treatment intensity from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity, thus realizing the method for quantifying transcranial magnetic stimulation treatment parameters based on electric field strength calibration.

2. The method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration as described in claim 1, characterized in that, The step of performing multi-tissue segmentation on the medical image set using a medical image processing unit to obtain multiple tissue layer spatial locations includes: Medical images are extracted sequentially from the medical image set, and the following operations are performed on the extracted medical images: The medical image is segmented using a medical image processing unit to obtain a segmented image set, wherein the segmented image set includes multiple segmented images; Obtain a standard tissue image set, wherein the standard tissue image set includes multiple standard tissue images, extract standard tissue images sequentially from the standard tissue image set, and perform the following operations on the extracted standard tissue images: Segmented images are extracted sequentially from the segmented image set, and the following operations are performed on the extracted segmented images: Calculate the similarity between the segmented image and the standard tissue image to obtain the single tissue similarity. Summarize the single tissue similarities to obtain the single tissue similarity set. The single tissue similarity set includes multiple single tissue similarities, and each single tissue similarity corresponds one-to-one with the segmented image. Extract the single tissue similarity with the highest similarity from the single tissue similarity set to obtain the maximum similarity. Use the segmented image corresponding to the maximum similarity as the target image and obtain the tissue plane position based on the target image. Summarize the organizational plane locations to obtain a set of organizational plane locations; summarize the organizational plane location sets to obtain multiple sets of organizational plane locations. Multiple tissue layer spatial locations were identified based on multiple sets of planar locations of tissue layers.

3. The method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration as described in claim 2, characterized in that, The step of segmenting the medical image using a medical image processing unit to obtain a segmented image set includes: Obtain the initial side length, calculate the initial area based on the initial side length, and use the initial area to extract multiple sub-images from the medical image; Standard tissue images are sequentially extracted from the standard tissue image set, and the following operations are performed on the extracted standard tissue images: Extract sub-images sequentially from multiple sub-images, and perform the following operations on the extracted sub-images: Calculate the similarity between the sub-image and the standard tissue image to obtain the sub-image similarity. Summarize the sub-image similarities to obtain multiple sub-image similarities. Extract the maximum sub-image similarity among the multiple sub-image similarities to obtain the maximum sub-image similarity. Obtain the sub-image similarity threshold, compare the maximum sub-image similarity with the sub-image similarity threshold, and if the maximum sub-image similarity is greater than the sub-image similarity threshold, then the sub-image corresponding to the maximum sub-image similarity is selected as the filter image. Summarize the filtered images to obtain a filtered image set, count the number of filtered images in the filtered image set, and obtain the filtered statistics. The number of standard tissue images in the standard tissue images is counted to obtain the standard statistical quantity. The size of the selected statistical quantity is compared with the standard statistical quantity. If the selected statistical quantity is equal to the standard statistical quantity, the selected image set is used as the segmentation image set. Otherwise, the initial side length is increased by a preset unit length to obtain an updated side length. The updated side length is then used as the initial side length, and the process returns to the step of calculating the initial area based on the initial side length, until the number of filtered statistics equals the standard number of statistics. The filtered image set is then used as the segmented image set.

4. The method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration as described in claim 3, characterized in that, The step of obtaining the tissue plane location based on the target image includes: Calculate the grayscale value of the target image to obtain a grayscale image, and perform denoising processing on the grayscale image to obtain a low-noise image; An initial edge sensitivity is obtained, and the edges of tissue layers in a low-noise image are detected based on the edge sensitivity to obtain a binary image, which includes the edges and the background. If a closed region is observed in the binary image based on the edge, and if no closed region is found, the initial edge sensitivity is increased by a preset unit value to obtain an updated edge sensitivity. The updated edge sensitivity is then used as the initial edge sensitivity, and the process returns to the step of detecting the edge of the tissue layer in the low-noise image based on the edge sensitivity to obtain the binary image, until a closed region is found in the binary image. Otherwise, establish a Cartesian coordinate system for the binary image, and determine multiple edge coordinates on the edge according to the preset number of coordinates. The edge coordinates include the edge x-coordinate and the edge y-coordinate. The center position of the closed region is calculated based on multiple edge coordinates to obtain the tissue plane position. The tissue plane position includes the horizontal and vertical coordinates of the plane, and the calculation method is as follows: in, The horizontal coordinate of the plane representing the position of the tissue. The vertical coordinate of the plane representing the position of the tissue. This indicates that there is a total of [number] edge coordinates among the multiple edge coordinates. Each edge coordinate, Indicates the first The x-coordinates of the edges, express Each edge's ordinate.

5. The method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration as described in claim 4, characterized in that, The configuration current is calculated based on the target deviation, deviation integral, circuit resistance, resolution, and reference voltage, and the calculation method is as follows: in, This indicates the configured current. This indicates that there are a total of [number] errors in the updated bias sequence. One update deviation, This represents the preset scaling factor. This indicates the target deviation. Indicates the th in the update bias sequence One update deviation, This represents the preset integral coefficient. This represents the integral of the deviation. This indicates the resolution. This refers to the reference voltage. This indicates the resistance of the circuit. Indicates the job identifier. Indicates the scale identifier. Indicates the integral identifier. Indicates a reference identifier.

6. The method for quantifying transcranial magnetic stimulation therapy parameters based on electric field strength calibration as described in claim 5, characterized in that, The method of obtaining the field strength state based on the target field strength includes: Obtain the error range, and determine the field strength range based on the error range and the reference field strength. The field strength range includes the upper limit and the lower limit of the field strength. When the target field strength is greater than the lower limit of the field strength but less than the upper limit of the field strength, record the current time to obtain the starting time. Obtain the detection duration and number of detections, and identify multiple detection moments within the detection duration based on the number of detections; The detection times are extracted sequentially from the plurality of detection times, and the following judgment is performed at the extracted detection times: If the target field strength is greater than the lower limit of the field strength and less than the upper limit of the field strength, then the detection is in a normal state; otherwise, the detection is in an abnormal state. The detection state is determined by the detection state or the detection abnormal state. Summarize the detection status to obtain multiple detection statuses; The number of abnormal states detected in multiple detection states is counted to obtain the abnormality count. If the abnormality count is equal to 0, the field strength state is confirmed as a normal state; otherwise, the field strength state is confirmed as an abnormal state.

7. A transcranial magnetic stimulation (TMS) therapy parameter quantification system based on electric field strength calibration, characterized in that, The system includes: The treatment intensity environment confirmation module is used in step A: receiving a treatment intensity determination instruction and confirming the treatment intensity determination environment based on the treatment intensity determination instruction. The treatment intensity determination environment includes an MRI device and a TMS device. The MRI device includes an individual detection unit and an imaging unit. The TMS device includes a medical image processing unit, an electromagnetic simulation unit, and a TMS coil. The TMS coil controls the electric field strength through a working current. The medical image acquisition and processing module is used in step B: when the individual detection unit detects a patient, the imaging unit is used to acquire a set of medical images of the patient's head, and the medical image processing unit performs multi-tissue segmentation on the medical image set to obtain the spatial locations of multiple tissue layers. The electromagnetic simulation calculation module is used in step C: using the electromagnetic simulation unit to assign conductivity parameters to the spatial locations of multiple tissue layers, obtaining multiple tissue layer conductivity, and using the multiple tissue layer conductivity and the pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, wherein the tissue layer conductivity corresponds one-to-one with the reference field strength; The method utilizes the conductivity of multiple tissue layers and a pre-constructed coil electromagnetic field model to simulate multiple reference field strengths, including: Multiple initial field strengths are determined from a preset field strength table based on multiple tissue conductivity parameters, wherein each initial field strength corresponds one-to-one with the spatial location of the tissue layer. Initial field strengths are extracted sequentially from multiple initial field strengths, and the following operations are performed on the extracted initial field strengths: Obtain the stimulation interval and number of stimulations, and identify multiple stimulation moments based on the number of stimulations and stimulation interval; In the coil electromagnetic field model, the TMS coil is used to apply the initial field strength to the corresponding tissue layer spatial position at multiple stimulation times, and the generated MEP is observed to obtain multiple MEP amplitudes. The number of MEP amplitudes that are greater than or equal to a preset amplitude threshold is counted among multiple MEP amplitudes to obtain the qualified statistical quantity, and the size of the qualified statistical quantity is compared with the preset statistical quantity threshold. If the number of qualified statistics is greater than or equal to the statistical number threshold, the initial field strength is reduced by a preset unit field strength, and the current operation sequence is recorded to obtain the first updated field strength and the first operation sequence. Using the first updated field strength as the initial field strength, the process returns to the step of applying the initial field strength to the corresponding tissue layer spatial position at multiple stimulation moments using the TMS coil in the electromagnetic field model of the coil, and observing the generated MEP to obtain multiple MEP amplitudes, until the number of qualified statistics is less than or equal to the statistical number threshold, and the initial field strength corresponding to the first operation sequence is used as the reference field strength. Otherwise, the initial field strength is increased by a preset unit field strength, and the current operation sequence is recorded to obtain the second updated field strength and the second operation sequence. Using the second updated field strength as the initial field strength, return to the step of applying the initial field strength to the corresponding tissue layer spatial position at multiple stimulation times using the TMS coil in the electromagnetic field model of the coil, and observe the generated MEP to obtain multiple MEP amplitudes until the number of qualified statistics is greater than or equal to the statistical number threshold, and use the second updated field strength corresponding to the second operation sequence as the reference field strength. By summing up the reference field strengths, multiple reference field strengths are obtained; The treatment intensity determination module is used in step D: adjusting the working current of the TMS coil at the preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current, updating the configuration current to the pre-built current database to obtain the updated database, and obtaining the field strength status based on the target field strength, wherein the field strength status is a normal state or an abnormal state. The method of adjusting the working current of the TMS coil at a preset treatment site using multiple reference field strengths to obtain the target field strength and configuration current includes: The target reference field strength is determined from multiple reference field strengths based on the treatment site, and the target initial field strength is determined from multiple initial field strengths based on the treatment site. The operating current of the TMS coil is adjusted based on the target initial field strength and the target reference field strength to obtain the target node, wherein the target node includes the target field strength and the configuration current. The process of adjusting the operating current of the TMS coil based on the target initial field strength and the target reference field strength to obtain the target node includes: The difference between the initial field strength of the target and the reference field strength of the target is calculated to obtain the target deviation; Obtain the historical deviation sequence, update the target deviation to the historical deviation sequence to obtain the updated deviation sequence, and accumulate all the updated deviations in the updated deviation sequence to obtain the deviation integral; Obtain the circuit schematic of the TMS coil, determine the model of the digital-to-analog converter and the circuit resistance based on the circuit schematic, obtain the datasheet of the digital-to-analog converter based on the model of the digital-to-analog converter, and retrieve the resolution and reference voltage from the datasheet; The configuration current is calculated based on the target deviation, deviation integral, circuit resistance, resolution, and reference voltage. The configuration current is applied to the TMS coil to obtain the target field strength; Step E: When the field strength is in a normal state, extract the peak current from the updated database, and retrieve the treatment intensity from the preset current intensity mapping table based on the peak current. Otherwise, return to step D until the target field strength equals the reference field strength to obtain the treatment intensity, thus realizing the method for quantifying transcranial magnetic stimulation treatment parameters based on electric field strength calibration.

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