Multi-modal nerve regeneration ultrasonic parameter optimization method and system

Abstract

The invention relates to the technical field of medical instruments, in particular to a multi-mode nerve regeneration ultrasonic parameter optimization method and system, and the method comprises the steps: obtaining electrophysiological signals (action potential, conduction velocity and sensory / motion threshold) and metabolic signals (oxygenation index, oxygen content and hydration) of nervous tissue; judging a neural restoration stage (demyelination, axon rupture / reconnection, remyelination, outer diameter and conduction velocity stage), and determining CMAP, CV and SNAm weight parameters according to the restoration stage and the ratio of the electrophysiological signal to a normal value. By combining weight parameters and metabolic signals, dynamically optimizing ultrasonic treatment parameters (frequency, intensity, duty ratio and time) and monitoring the signals in real time, maintaining the parameters if the electrophysiological signals are recovered, and adjusting the parameters if the electrophysiological signals are reduced, so that accurate neural restoration is realized, the treatment parameters can be accurately adjusted according to individual differences of patients and a neural restoration dynamic process, and the accuracy of neural restoration is improved. The pertinence and the effectiveness of treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and particularly relates to a method and system for optimizing multi-modal nerve regeneration ultrasound parameters, which are particularly suitable for accurately regulating ultrasound parameters during the treatment of peripheral nerve lesions. Background Art

[0002] Peripheral nerve lesions are common neurological diseases, and the causes include trauma, inflammation, metabolic disorders, immune diseases, etc. Currently, the clinical treatment of peripheral nerve lesions mainly adopts methods such as drug treatment, physical therapy, and surgical treatment. Among them, as a non-invasive physical therapy means, ultrasound therapy has gradually attracted attention in the treatment of peripheral nerve lesions due to its advantages such as promoting nerve regeneration, improving local blood circulation, and reducing inflammatory reactions.

[0003] However, traditional ultrasound therapy devices have some obvious deficiencies: on the one hand, most devices use fixed parameters for treatment and cannot adjust parameters according to individual patient differences and the dynamic process of nerve repair; on the other hand, there is a lack of real-time monitoring and feedback mechanisms for treatment effects, resulting in a blind treatment process and difficult-to-guarantee effects. In addition, the setting of traditional ultrasound therapy parameters mainly relies on the empirical judgment of therapists and lacks a scientific quantitative optimization method.

[0004] With the development of medical technology, electrophysiological detection technology provides an important means for nerve function assessment, and at the same time, metabolic-related signal monitoring technology has become increasingly mature. However, there is currently a lack of a method and system that can integrate multi-modal biological signals to achieve precise optimization of ultrasound therapy parameters, which severely limits the application effect of ultrasound therapy in peripheral nerve lesions. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for optimizing multi-modal nerve regeneration ultrasound parameters. By obtaining the electrophysiological signals and metabolic-related signals of nerve tissues, judging the stage of nerve repair, determining weight parameters, and dynamically optimizing ultrasound therapy parameters, precise treatment of peripheral nerve lesions can be achieved, and the treatment effect can be improved.

[0006] The present invention discloses a method for optimizing multi-modal nerve regeneration ultrasound parameters, including:

[0007] Obtaining the electrophysiological signals and metabolic-related signals of nerve tissues, wherein the electrophysiological signals include the magnitude of action potential, conduction velocity, sensory threshold, and motor threshold, and the metabolic-related signals include tissue oxygenation index, tissue oxygen content, and hydration data;

[0008] Based on the electrophysiological signals, judging the stage of nerve repair, wherein the stage of nerve repair includes the demyelination stage, axon breakage and reconnection stage, axon remyelination stage, axon outer diameter stage, and conduction velocity stage;

[0009] Determine the weight parameters according to the stage of the nerve repair and the ratio of the electrophysiological signal to the electrophysiological signal in the normal state, wherein the weight parameters include the CMAP value weight, the CV value weight, and the SNAm value weight;

[0010] Dynamically optimize the ultrasound treatment parameters based on the weight parameters and the metabolism-related signals, wherein the ultrasound treatment parameters include frequency, intensity, duty cycle, and treatment time;

[0011] Monitor the electrophysiological signal and the metabolism-related signal in real time. When the electrophysiological signal shows a recovery trend, maintain the current ultrasound treatment parameters. When the electrophysiological signal shows a decreasing trend, adjust the ultrasound treatment parameters.

[0012] Preferably, the judgment of the stage of nerve repair includes:

[0013] Compare the CMAP value in the electrophysiological signal with the CMAP value in the normal state. When the CMAP value is 0.3 to 0.5 times the CMAP value in the normal state, it is judged as the demyelination stage;

[0014] When the CMAP value is 0.8 to 1 times the CMAP value in the normal state, it is judged as the axon breakage and reconnection stage;

[0015] When the CMAP value is 1.5 to 2 times the CMAP value in the normal state, it is judged as the axon remyelination stage;

[0016] When the CMAP value is 1 to 1.2 times the CMAP value in the normal state, it is judged as the axon outer diameter stage;

[0017] When the CMAP value is 1.2 to 1.4 times the CMAP value in the normal state, it is judged as the conduction velocity stage.

[0018] Preferably, the determination of the weight parameters includes:

[0019] When it is judged as the demyelination stage, adjust the CMAP value weight to 0.3 to 0.5 times the normal state, adjust the CV value weight to 0.4 to 0.8 times the normal state, and adjust the SNAm value weight to 0.3 to 0.5 times the normal state;

[0020] When it is judged as the axon breakage and reconnection stage, adjust the CMAP value weight to 0.8 to 1 times the normal state, adjust the CV value weight to 0.7 to 1 times the normal state, and adjust the SNAm value weight to 0.4 to 0.7 times the normal state;

[0021] When it is determined to be the axon remyelination stage, adjust the weight of the CMAP value to 1.5 to 2 times that of the normal state, adjust the weight of the CV value to 1.3 to 1.8 times that of the normal state, and adjust the weight of the SNAm value to 0.7 to 1 times that of the normal state.

[0022] Preferably, the dynamically optimized ultrasound treatment parameters include:

[0023] According to the weight parameters, select one treatment mode from the nerve regeneration mode, the injury repair mode, the inhibitory mode, or the demyelination mode;

[0024] Among them, the nerve regeneration mode uses low-intensity intermittent wave stimulation, the injury repair mode uses low-intensity continuous wave treatment, the inhibitory mode uses high-intensity continuous wave treatment, and the demyelination mode uses high-intensity intermittent wave treatment.

[0025] Preferably, select the treatment mode according to the type of neuropathy, including:

[0026] When the type of neuropathy is immune neuropathy, select the parameter combination of low frequency 1 MHz, pulse wave 0.3 W / cm², and duty cycle 30%;

[0027] When the type of neuropathy is metabolic neuropathy, select the parameter combination of medium frequency 3 MHz and continuous wave 0.5 W / cm²;

[0028] When the type of neuropathy is compressive neuropathy, select the parameter combination of focused mode 0.8 W / cm² and local vibration assistance.

[0029] Preferably, before obtaining the electrophysiological signals and metabolism-related signals of the nerve tissue, it further includes:

[0030] Judge the type of nerve in the treatment area, which is divided into deep nerves and superficial nerves;

[0031] For deep nerves, measure and record the distal electrophysiological signals, set the current stimulation threshold, perform single-pulse or continuous wave stimulation, record the propagation speed of the electrophysiological signals after stimulation, and determine the ultrasonic focusing depth according to the propagation speed;

[0032] For superficial nerves, record the skin resistance value of the treatment area, measure and obtain the muscle resistance value of the treatment area, and use the skin resistance value and the muscle resistance value as auxiliary treatment parameters.

[0033] Preferably, the dynamically optimized ultrasound treatment parameters include:

[0034] Determine the treatment frequency and total treatment course according to the degree of nerve defect. When the degree of nerve defect exceeds 10%, adjust the treatment frequency to more than 5 times per week, and extend the total treatment course to 6 to 8 weeks;

[0035] When the degree of nerve defect is less than 10%, adjust the treatment frequency to no more than 3 times per week, and the total treatment course is 4 to 6 weeks.

[0036] Preferably, the real-time monitoring of the electrophysiological signal and the metabolism-related signal further includes:

[0037] Monitoring the temperature of the treatment site. When the temperature is 40 to 41 °C, reduce the output intensity; when the temperature is 30 to 40 °C, reduce the pulse repetition frequency; when the temperature is less than 30 °C, increase the output intensity;

[0038] Monitoring the tissue impedance. When the tissue impedance mutates, pause the treatment and re-collect the impedance data;

[0039] Monitoring abnormal signals. When measurement abnormalities occur in two consecutive detections, stop the treatment.

[0040] Preferably, among the ultrasonic treatment parameters:

[0041] When low-frequency ultrasound is selected, the frequency range is 0.8 to 1.5 MHz, which is suitable for deep nerves with a penetration depth of 3 to 5 cm;

[0042] When medium-frequency ultrasound is selected, the frequency range is 3.0 to 3.5 MHz, which is suitable for superficial nerves with a penetration depth of 1 to 2 cm;

[0043] When the continuous wave mode is selected, the output intensity is 0.1 to 0.5 W / cm², which is suitable for the repair of chronic nerve injuries;

[0044] When the pulsed wave mode is selected, the output intensity is 0.5 to 1.0 W / cm², and the duty cycle is 20% to 50%, which is suitable for the acute inflammation period.

[0045] The multi-modal nerve regeneration ultrasonic parameter optimization system includes:

[0046] The multi-modal signal acquisition subsystem is used to acquire the electrophysiological signal and the metabolism-related signal of nerve tissue. Among them, the electrophysiological signal includes the action potential magnitude, conduction velocity, sensory threshold, and motor threshold, and the metabolism-related signal includes the tissue oxygenation index, tissue oxygen content, and hydration data;

[0047] The nerve repair status evaluation subsystem is used to judge the stage of nerve repair based on the electrophysiological signal. The stage of nerve repair includes the demyelination stage, axon breakage and reconnection stage, axon remyelination stage, axon outer diameter stage, and conduction velocity stage; and determine the weight parameters according to the stage of nerve repair and the ratio of the electrophysiological signal to the electrophysiological signal in the normal state. Among them, the weight parameters include the CMAP value weight, CV value weight, and SNAm value weight;

[0048] A treatment parameter optimization subsystem, which is used to dynamically optimize ultrasonic treatment parameters based on the weight parameters and the metabolism-related signals, wherein the ultrasonic treatment parameters include frequency, intensity, duty cycle, and treatment time;

[0049] An ultrasonic wave emission and control subsystem, which is used to output ultrasonic waves according to the ultrasonic treatment parameters, and to monitor the electrophysiological signals and the metabolism-related signals in real time. When the electrophysiological signals show a recovery trend, the current ultrasonic treatment parameters are maintained. When the electrophysiological signals show a decreasing trend, the ultrasonic treatment parameters are adjusted;

[0050] An ultrasonic wave transducer and transmission subsystem, which is used to convert electrical signals into ultrasonic waves and transmit them to the targeted treatment area, including an ultrasonic array probe module and an acoustic wave transmission medium module.

[0051] The present invention has the following beneficial effects:

[0052] 1. It realizes the adaptive optimization of ultrasonic parameters based on multi-modal biological signals, enabling the treatment parameters to be accurately adjusted according to the individual differences of patients and the dynamic process of nerve repair, and improving the pertinence and effectiveness of treatment.

[0053] 2. It establishes a precise recognition mechanism for five stages of nerve repair. Through the analysis of electrophysiological signals, it realizes the accurate evaluation of the nerve repair process, providing a scientific basis for the optimization of ultrasonic parameters.

[0054] 3. It constructs a dynamic adjustment framework for treatment parameters based on weight parameters, realizing the accurate matching of ultrasonic energy and nerve repair requirements, and avoiding the problems of insufficient or excessive energy.

[0055] 4. It designs a differential processing strategy for deep and shallow nerves, solves the technical problems of treating nerves at different depths, and improves the targeting and treatment efficiency of ultrasonic energy.

[0056] 5. It establishes a perfect safety monitoring mechanism. Through temperature monitoring, impedance monitoring, and abnormal signal detection, it ensures the safety of the treatment process and reduces the risk of adverse reactions. Description of the Drawings

[0057] Figure 1 It is a flowchart of the multi-modal nerve regeneration ultrasonic parameter optimization method of the present invention;

[0058] Figure 2 It is a flowchart for judging five stages of nerve repair in the present invention;

[0059] Figure 3 It is a flowchart for optimizing ultrasonic treatment parameters based on weight parameters in the present invention;

[0060] Figure 4It is the structural block diagram of the multi-modal nerve regeneration ultrasound parameter optimization system of the present invention;

[0061] Figure 5 It is the structural block diagram of the multi-modal signal acquisition subsystem in the present invention;

[0062] Figure 6 It is the schematic structural diagram of the ultrasonic transducer and transmission subsystem in the present invention. Detailed implementation manners

[0063] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to illustrate the present invention more clearly and are not used to limit the protection scope of the present invention.

[0064] Referring to Figure 1 The multi-modal nerve regeneration ultrasound parameter optimization method provided by the present invention includes the following steps:

[0065] Step S1: Obtain the electrophysiological signals and metabolism-related signals of nerve tissues.

[0066] Among them, the electrophysiological signals include the size of the action potential (CMAP value), conduction velocity (CV value), sensory threshold, and motor threshold. In a preferred embodiment of the present invention, the CMAP value is obtained by a surface electromyogram acquisition device, the sampling frequency is 20 kHz, and the sampling time is 100 ms to ensure that the muscle action potential can be completely captured; the CV value is measured by two-point stimulation of the nerve, and the calculation method is the distance between two points divided by the time difference of the action potential conduction between the two-point stimulations; the sensory threshold and motor threshold are determined by gradually increasing the stimulation intensity until the minimum stimulation intensity when a sensory or muscle contraction response appears.

[0067] The metabolism-related signals include tissue oxygenation index, tissue oxygen content, and hydration data. Preferably, the tissue oxygenation index and oxygen content are collected by near-infrared spectroscopy technology, and the sampling frequency is 10 Hz; the hydration data is obtained by multi-frequency bioelectrical impedance analysis technology, and the sampling frequency is 1 Hz.

[0068] Step S2: Based on the electrophysiological signals, judge the stage of nerve repair.

[0069] As Figure 2 shown, the present invention divides the nerve repair process into five stages: demyelination stage, axon breakage and reconnection stage, axon remyelination stage, axon outer diameter stage, and conduction velocity stage.

[0070] The specific judgment method is as follows: Compare the obtained CMAP value with the CMAP value in the normal state. When the CMAP value is 0.3 to 0.5 times the CMAP value in the normal state, it is judged as the demyelination stage. In actual applications, the normal CMAP value usually takes the CMAP value of the same nerve on the healthy side as a reference, or uses the normal reference value for this gender and age group. For example, for the median nerve, the normal CMAP value is in the range of 10 - 20 mV. When the measured CMAP value is in the range of 3 - 10 mV, it can be judged as the demyelination stage.

[0071] When the CMAP value is 0.8 to 1 times the CMAP value in the normal state, it is judged as the axon rupture and reconnection stage. The recovery of the CMAP value at this stage indicates that the axons have begun to reconnect, but the function has not been fully restored.

[0072] When the CMAP value is 1.5 to 2 times the CMAP value in the normal state, it is judged as the axon remyelination stage. At this stage, the CMAP value exceeds the normal value because the number of regenerated axons increases, but the myelin sheath is not yet fully mature, resulting in certain incoordination in the conduction of nerve impulses, manifested as increased excitability.

[0073] When the CMAP value is 1 to 1.2 times the CMAP value in the normal state, it is judged as the axon outer diameter stage. At this time, the axon structure has basically recovered, but the diameter has not reached the optimal state.

[0074] When the CMAP value is 1.2 to 1.4 times the CMAP value in the normal state, it is judged as the conduction velocity stage. This stage is mainly the optimization process of nerve conduction velocity.

[0075] Preferably, in addition to the CMAP value, the CV value can also be used as an auxiliary judgment basis. When the CV value is 0.4 to 0.8 times the CV value in the normal state, it supports the judgment of the demyelination stage; when the CV value recovers to 0.7 to 1 times the normal state, it supports the judgment of the axon rupture and reconnection stage; when the CV value is 1.3 to 1.8 times the normal state, it supports the judgment of the axon remyelination stage.

[0076] The judgment of the nerve repair stage uses a stage discrimination function, and its mathematical expression is:

[0077] ,

[0078] Among them, is the nerve repair stage (PhaseStage); is the stage index, corresponding to five repair stages respectively; is the number of electrophysiological indicators; is the weight coefficient of the is the measured value of the th electrophysiological index; is the normal reference value of the th electrophysiological index; is the characteristic function of the th electrophysiological index, which maps the ratio of the measured value of the index to the normal value to the characteristic space of each stage. The characteristic function is defined as:

[0079] ,

[0080] where is the characteristic mean value of the th index in the th stage; is the standard deviation of the th index in the th stage.

[0081] For the CMAP value, the characteristic mean values of each stage are respectively set as: 0.4 in the demyelination stage (i.e., 0.4 times the normal value), 0.9 in the axon breakage and reconnection stage, 1.75 in the axon remyelination stage, 1.1 in the axon outer diameter stage, and 1.3 in the conduction velocity stage. These parameter values are obtained based on the statistical analysis of a large amount of clinical data and have good discriminant efficacy.

[0082] In practical applications, the normal CMAP value usually takes the CMAP value of the homonymous nerve on the healthy side as a reference, or adopts the normal reference value of this gender and age group. For example, for the median nerve, the normal CMAP value is in the range of 10 - 20 mV. When the measured CMAP value is in the range of 3 - 10 mV, it can be judged as the demyelination stage.

[0083] Step S3: Determine the weight parameters according to the stage of the nerve repair and the ratio of the electrophysiological signal to the electrophysiological signal in the normal state.

[0084] The weight parameters include the CMAP value weight, the CV value weight, and the SNAm value weight, where the SNAm value is the amplitude of the sensory nerve action potential.

[0085] Specifically, when it is judged as the demyelination stage, adjust the CMAP value weight to 0.3 to 0.5 times that of the normal state, adjust the CV value weight to 0.4 to 0.8 times that of the normal state, and adjust the SNAm value weight to 0.3 to 0.5 times that of the normal state. These parameter settings are based on the neuropathological characteristics of the demyelination stage. At this time, myelin sheath damage leads to nerve conduction blockage, and low-intensity stimulation is required to avoid further damage.

[0086] When it is determined to be the axon breakage and reconnection stage, adjust the weight of the CMAP value to 0.8 to 1 times the normal state, adjust the weight of the CV value to 0.7 to 1 times the normal state, and adjust the weight of the SNAm value to 0.4 to 0.7 times the normal state. In this stage, the parameter settings are aimed at promoting axon reconnection, but the stimulation intensity still needs to be carefully controlled.

[0087] When it is determined to be the axon remyelination stage, adjust the weight of the CMAP value to 1.5 to 2 times the normal state, adjust the weight of the CV value to 1.3 to 1.8 times the normal state, and adjust the weight of the SNAm value to 0.7 to 1 times the normal state. The purpose of the parameter settings in this stage is to promote myelination, and the stimulation intensity can be appropriately increased.

[0088] Preferably, the weight of the CMAP value in the axon outer diameter stage is adjusted to 1 to 1.2 times the normal state, the weight of the CV value is adjusted to 1.2 to 1.4 times the normal state, and the weight of the SNAm value is adjusted to 0.8 to 1.2 times the normal state.

[0089] The weight of the CMAP value in the conduction velocity stage is adjusted to 1.2 to 1.4 times the normal state, the weight of the CV value is adjusted to 1.3 to 1.6 times the normal state, and the weight of the SNAm value is adjusted to 1 to 1.3 times the normal state.

[0090] The precise setting of these weight parameters is crucial for the precise optimization of ultrasound treatment parameters. For example, in the axon remyelination stage, higher CMAP value weight and CV value weight will cause the system to select higher ultrasound intensity and frequency to promote the myelination process.

[0091] The weight parameters include the CMAP value weight, the CV value weight, and the SNAm value weight, where the SNAm value is the amplitude of the sensory nerve action potential. The weight parameters are calculated using a stage-adaptive weight function:

[0092] ,

[0093] where, is the weight parameter of the th electrophysiological index; is the basic weight coefficient of the index; is the measured value of the index; is the normal reference value of the index; is the stage adjustment coefficient of the th index corresponding to the current repair stage .

[0094] For the CMAP value weight, its stage adjustment coefficient Set as: demyelination stage 0.8, axon breakage and reconnection stage 1.0, axon remyelination stage 1.2, axon outer diameter stage 1.0, conduction velocity stage 0.9. For the CV value weight, its stage adjustment coefficient Set as: demyelination stage 1.1, axon breakage and reconnection stage 0.9, axon remyelination stage 1.3, axon outer diameter stage 1.1, conduction velocity stage 1.4. For the SNAm value weight, its stage adjustment coefficient Set as: demyelination stage 0.9, axon breakage and reconnection stage 0.8, axon remyelination stage 1.0, axon outer diameter stage 1.1, conduction velocity stage 1.2. For example, for the median nerve in the axon remyelination stage, if the measured CMAP value is 30 mV (the normal reference value is 15 mV), then the CMAP value weight is calculated as: . This indicates that the CMAP value weight of this nerve is 2.4 times that of the normal state, and the system will adjust the ultrasonic treatment parameters accordingly to increase the treatment intensity to promote myelination.

[0095] Step S4: Dynamically optimize the ultrasonic treatment parameters based on the weight parameters and the metabolism-related signals.

[0096] As Figure 3 shown, the ultrasonic treatment parameters include frequency, intensity, duty cycle, and treatment time. The optimization process first selects an appropriate treatment mode according to the weight parameters, and then sets detailed parameters for the specific mode.

[0097] Specifically, according to the weight parameters, the system selects one treatment mode from the nerve regeneration mode, injury repair mode, inhibitory mode, or demyelination mode.

[0098] The nerve regeneration mode uses low-intensity intermittent wave stimulation, which is especially suitable for patients with sensory nerve and motor nerve dysfunction. In this mode, ultrasonic waves are emitted intermittently. For example, it works for 5 seconds and rests for 1 second. This pulse mode can avoid nerve adaptation and maintain treatment sensitivity.

[0099] The injury repair mode uses low-intensity continuous wave treatment, which is suitable for patients with nerve defects or neuropathic pain. The continuous wave mode provides continuous biological stimulation, which helps to promote nerve tissue repair and reduce the inflammatory response.

[0100] The inhibitory mode uses high-intensity continuous wave treatment, which is suitable for patients with inhibitory neuropathy or pain. High-intensity ultrasonic waves can temporarily inhibit nerve conduction to achieve an analgesic effect.

[0101] The demyelination mode uses high-intensity intermittent wave treatment, which is suitable for patients with multiple sclerosis or nerve segment defects. The energy stimulation in this mode helps to promote myelination.

[0102] After selecting the treatment mode, the system further optimizes the parameter combination according to the type of neuropathy. When the type of neuropathy is immune neuropathy (such as GBS, CIDP), the parameter combination of low frequency 1 MHz, pulse wave 0.3 W / cm², and duty cycle 30% is selected. Immune neuropathy is mainly manifested as myelin sheath damage. Low frequency ultrasound can better reach deep into nerve tissues, while low-intensity pulse waves can gently promote immune regulation and avoid overstimulation to further activate the immune response.

[0103] When the type of neuropathy is metabolic neuropathy (such as diabetic peripheral neuropathy), the parameter combination of medium frequency 3 MHz and continuous wave 0.5 W / cm² is selected. Metabolic neuropathy is usually accompanied by microcirculation disorders. Medium frequency ultrasound can effectively improve local blood flow, and the continuous wave mode helps to maintain this improvement effect.

[0104] When the type of neuropathy is compressive neuropathy (such as carpal tunnel syndrome), the parameter combination of focused mode 0.8 W / cm² and local vibration assistance is selected. The focused mode can accurately concentrate ultrasonic energy on the compressed nerve segment, while local vibration assistance helps to relieve adhesion of surrounding tissues and reduce compression.

[0105] In addition, the present invention also considers the influence of the degree of nerve defect on the treatment frequency and total treatment course. When the degree of nerve defect exceeds 10%, the treatment frequency is adjusted to more than 5 times per week, and the total treatment course is extended to 6 to 8 weeks; when the degree of nerve defect is less than 10%, the treatment frequency is adjusted to no more than 3 times per week, and the total treatment course is 4 to 6 weeks. The degree of nerve defect can be evaluated by the percentage of amplitude decrease or the percentage of conduction velocity decrease in electrophysiological examinations.

[0106] The optimization of ultrasonic parameters adopts a multi-parameter collaborative regulation algorithm:

[0107] ,

[0108] where, is the optimized value of the th ultrasonic parameter; is the reference value of the th ultrasonic parameter; is the parameter adjustment sensitivity, is the influence coefficient of the th electrophysiological index on the th ultrasonic parameter; is the weight parameter of the th electrophysiological index; is the weight parameter value under normal conditions (usually 1.0); is the treatment mode adjustment factor. For the ultrasonic frequency parameter, its reference value Determined according to the nerve depth: The deep nerves (3 - 5 cm) are set to 1.0 MHz, and the superficial nerves (1 - 2 cm) are set to 3.0 MHz. Influence coefficient Set as: (Effect of CMAP value on frequency), (Effect of CV value on frequency), (Effect of SNAm value on frequency). For the ultrasonic intensity parameter, its reference value Determined according to the treatment mode: The nerve regeneration mode is set to 0.3 W / cm 2 , the injury repair mode is set to 0.4 W / cm 2 , the inhibitory mode is set to 0.7 W / cm², and the demyelination mode is set to 0.6 W / cm². Influence coefficient Is: 、 、 . For the treatment time parameter, its reference value Determined according to the degree of nerve defect: Mild injury is set to 7 minutes, moderate injury is set to 10 minutes, and severe injury is set to 13 minutes. The degree of nerve defect is evaluated by the deviation degree of electrophysiological indexes from the normal value:

[0109] ,

[0110] Among them, Is the degree of nerve defect (NeuralDeficit), and the value range is 0 - 1; 、 、SNAm are the measured values respectively; 、 、 Are the normal reference values respectively.

[0111] Step S5: Real-time monitor the electrophysiological signal and the metabolism-related signal. When the electrophysiological signal shows a recovery trend, maintain the current ultrasonic treatment parameter. When the electrophysiological signal shows a decreasing trend, adjust the ultrasonic treatment parameter.

[0112] The present invention establishes a closed-loop feedback control mechanism, and dynamically adjusts the ultrasonic treatment parameter by real-time monitoring the changes of the electrophysiological signal and the metabolism-related signal. Specifically, when it is monitored that the CMAP value, the CV value or the SNAm value shows an upward trend and the upward amplitude exceeds 5%, the system judges it as a recovery trend and maintains the current ultrasonic parameter for continuous treatment; when it is monitored that these indexes show a downward trend and the downward amplitude exceeds 5%, the system judges it as a decreasing trend and needs to adjust the ultrasonic parameter.

[0113] During the adjustment process, when the electrophysiological signal shows a decreasing trend, the system first increases the ultrasound intensity by 10% - 20% of the current intensity, and at the same time decreases the treatment frequency by about 10% to avoid overstimulation. If the signal continues to decline after adjustment, consider switching the treatment mode or pausing the treatment for evaluation.

[0114] Meanwhile, the present invention also establishes a perfect safety monitoring mechanism. The system monitors the temperature of the treatment site. When the temperature is 40 to 41 °C, it automatically reduces the output intensity to 70% of the original setting; when the temperature is 30 to 40 °C, it reduces the pulse repetition frequency to 80% of the original setting; when the temperature is less than 30 °C, it appropriately increases the output intensity, but does not exceed the safety upper limit.

[0115] In addition, the system also monitors the tissue impedance. When the tissue impedance mutates (the change rate exceeds 20%), it pauses the treatment for 3 seconds and re-collects the impedance data; when two consecutive abnormal signals (such as waveform distortion or abnormal amplitude fluctuation) are detected, it automatically stops the treatment and requires the operator to re-evaluate the patient's condition.

[0116] The present invention establishes a closed-loop feedback control mechanism, which dynamically adjusts the ultrasound treatment parameters by real-time monitoring the changes of electrophysiological signals and metabolism-related signals. The signal change trend judgment adopts time series analysis:

[0117] ,

[0118] wherein, is the relative change rate of the th index at time , with the unit of percentage; is the index value at time , is the index value at the previous time point; is the monitoring time interval, usually set to 30 seconds. When , it is judged as an upward trend of the index; when , it is judged as a downward trend of the index. Among them, and are the upward and downward judgment thresholds respectively, which are set to +5% and -5% respectively based on clinical experience. In practical applications, when it is monitored that the CMAP value, CV value or SNAm value shows an upward trend (the change rate is greater than +5%) and lasts for two monitoring cycles, the system judges it as a recovery trend and continues the treatment with the current ultrasound parameters; when it is monitored that these indicators show a downward trend (the change rate is less than -5%) and lasts for two monitoring cycles, the system judges it as a decreasing trend and needs to adjust the ultrasound parameters.

[0119] The parameter adjustment strategy adopts a feedback control algorithm:

[0120] ,

[0121] Among them, is the adjusted value of the th ultrasonic parameter at the next time point; is the current parameter value; is the parameter adjustment coefficient; is the th feedback weight of the index; is the index change rate. For the ultrasonic intensity parameter, its adjustment coefficient is set to 0.5, indicating that when the index shows a downward trend, the intensity will increase, and the increase amplitude is about half of the index decline amplitude; when the index shows an upward trend, the intensity can be appropriately reduced to avoid over-stimulation.

[0122] Referring to Figure 1 and Figure 6 , in this embodiment, before obtaining the electrophysiological signal and metabolic-related signal of the nerve tissue, the present invention further includes the steps of judging the nerve type in the treatment area and determining the targeted treatment area.

[0123] Specifically, first judge the type of nerve in the treatment area, which is divided into deep nerves and superficial nerves. For nerves in parts such as the leg and trunk, such as the sciatic nerve, it is usually judged as a deep nerve; for nerves in parts such as the wrist and ankle, such as the median nerve and the common peroneal nerve, it is usually judged as a superficial nerve.

[0124] For deep nerves, the following method is used to determine the targeted treatment area: First, measure and record the electrophysiological signal at the distal end of the nerve to obtain baseline data; then set the current stimulation threshold, usually 2 - 5 mA, and perform single-pulse or continuous-wave stimulation; record the propagation speed of the electrophysiological signal after stimulation, and the typical value is in the range of 40 - 60 m / s; according to the propagation speed and the propagation speed of sound waves in the tissue (about 1540 m / s), calculate the time delay required for ultrasonic focusing, so as to determine the ultrasonic focusing depth.

[0125] For superficial nerves, the following method is used to determine the targeted treatment area: Take the skin surface of the nerve in the treatment area as the detection point, and record the skin resistance value of the treatment area, and the normal range is 1 - 10 kΩ; measure and obtain the muscle resistance value of the treatment area, which is usually in the range of 0.5 - 2 kΩ; use the skin resistance value and the muscle resistance value as auxiliary treatment parameters for ultrasonic coupling and energy transfer optimization.

[0126] Preferably, the muscle resistance value includes the muscle superficial layer resistance value and the muscle deep layer resistance value, where the muscle superficial layer resistance value is the skin resistance value of the treatment area and the skin resistance value of the muscle, and the muscle deep layer resistance value is the skin resistance value of the muscle and the insulation resistance value of the bone. These detailed impedance data help to construct a tissue sound conduction model and improve the accuracy of ultrasonic energy transfer.

[0127] For deep nerves, an ultrasound focusing localization algorithm is used to determine the targeted treatment area:

[0128] ,

[0129] where, is the ultrasound focusing depth, with the unit of mm; is the average propagation speed of ultrasonic waves in tissues, and the typical value is 1540 m / s; is the nerve impulse conduction speed, with the unit of m / s, which is obtained through electrophysiological measurement; is the length of the target nerve segment, with the unit of mm. In applications, for example, for a patient with sciatic nerve injury, the measured nerve conduction speed is 45 m / s and the length of the target nerve segment is 50 mm, then the calculated ultrasound focusing depth is: mm. The system adjusts the focusing parameters of the ultrasound probe accordingly to ensure that the ultrasound energy acts precisely on the target nerve tissue. For superficial nerves, a multi-layer tissue impedance model is used to optimize energy transfer:

[0130] ,

[0131] where, is the energy transferred to the nerve tissue; is the initial ultrasound energy; is the attenuation coefficient of the th layer of tissue, which is related to tissue impedance; is the thickness of the th layer of tissue; is the number of tissue layers.

[0132] Tissue attenuation coefficient and tissue impedance are related as follows:

[0133] ,

[0134] where, is the proportionality coefficient, with a value of approximately is the tissue impedance, with the unit of is the ultrasound frequency, with the unit of .

[0135] In applications, for example, for a patient with median nerve injury, the measured impedance of the skin layer is 5 kΩ, the impedance of the subcutaneous tissue is 2 kΩ, and the impedance of the muscle layer is 1 kΩ. When using 3 MHz ultrasound, the system can automatically adjust the initial ultrasound energy according to the multi-layer tissue attenuation model to ensure that the effective energy can be accurately transferred to the median nerve.

[0136] Refer to Figures 4 to 6, the present invention also provides a multi-modal nerve regeneration ultrasound parameter optimization system, including a multi-modal signal acquisition subsystem 10, a nerve repair status evaluation subsystem 20, a treatment parameter optimization subsystem 30, an ultrasonic wave emission and control subsystem 40, and an ultrasonic wave transducer and transmission subsystem 50.

[0137] The multi-modal signal acquisition subsystem 10 is used to acquire the electrophysiological signals and metabolism-related signals of nerve tissues. This subsystem includes an electrophysiological signal acquisition module 11, a metabolism-related signal acquisition module 12, and a tissue impedance measurement module 13.

[0138] The multi-modal signal acquisition subsystem 10 is used to acquire the electrophysiological signals and metabolism-related signals of nerve tissues. This subsystem uses a signal processing algorithm for noise reduction and enhancement, and the expression for improving the signal-to-noise ratio is:

[0139] ,

[0140] where, is the signal-to-noise ratio after processing; is the original signal-to-noise ratio; is the filtering gain. For electrophysiological signals, a band-pass filter is used, with a frequency range of 20 Hz - 2 kHz and a gain of approximately 15 dB. The nerve repair status evaluation subsystem 20 is based on a multi-feature fusion algorithm, comprehensively analyzes the electrophysiological signal features, and judges the nerve repair stage:

[0141] ,

[0142] where, is the finally judged nerve repair stage; is the stage judged based on the th feature set; is the credibility weight of the th feature set; is the number of feature sets. In practical applications, the system usually uses three feature sets: time-domain features (such as amplitude, latency), frequency-domain features (such as spectral distribution), and time-frequency features (such as wavelet coefficients), with weights of 0.5, 0.3, and 0.2 respectively.

[0143] The electrophysiological signal acquisition module 11 adopts a multi-channel electrode configuration and can simultaneously collect parameters such as CMAP value, CV value, sensory threshold, and motor threshold. In practical applications, the sampling frequency of this module is 20 kHz, the resolution is 16 bits, and the signal-to-noise ratio is greater than 80 dB, ensuring the accuracy of electrophysiological signal acquisition.

[0144] The metabolism-related signal acquisition module 12 uses near-infrared spectroscopy and photoplethysmography techniques to collect tissue oxygenation index, tissue oxygen content, and hydration data. The working wavelength range of this module is 700 - 900 nm, and the sampling frequency is 10 Hz, enabling non-invasive monitoring of tissue metabolic status.

[0145] The tissue impedance measurement module 13 uses multi-frequency bioelectrical impedance analysis technology to measure skin resistance value, superficial muscle resistance value, and deep muscle resistance value. The working frequency range of this module is 5 - 100 kHz, and the measurement accuracy is ±2%, capable of providing detailed tissue impedance information to assist in optimizing ultrasonic energy transfer.

[0146] The nerve repair status assessment subsystem 20 is used to determine the stage of nerve repair and calculate weight parameters based on electrophysiological signals. This subsystem includes a nerve repair stage identification module and a weight parameter calculation module.

[0147] The nerve repair stage identification module realizes the accurate identification of the five-stage nerve repair process. Based on pattern recognition algorithms, this module matches electrophysiological signals with preset stage feature templates to determine the current nerve repair stage. The accuracy rate of the module exceeds 90%, and the specificity is greater than 85%, capable of reliably identifying different nerve repair stages.

[0148] The weight parameter calculation module calculates the weights of CMAP value, CV value, and SNAm value based on the nerve repair stage and electrophysiological signals. This module uses an adaptive weight allocation algorithm to dynamically adjust the weight coefficients of each parameter according to the characteristics of nerve repair at different stages, providing a scientific basis for optimizing ultrasonic parameters.

[0149] The treatment parameter optimization subsystem 30 is used to dynamically optimize ultrasonic treatment parameters based on weight parameters and metabolism-related signals. This subsystem includes a parameter adjustment decision module, a treatment mode selection module, and a parameter precise calculation module.

[0150] The treatment parameter optimization subsystem 30 uses an adaptive parameter mapping algorithm to generate an optimal treatment parameter combination according to the nerve repair stage and weight parameters:

[0151] ,

[0152] where, is the parameter vector, including frequency, intensity, duty cycle, and treatment time; is the parameter mapping matrix, with a dimension of , trained from a large amount of clinical data; is the weight vector, including the weights of CMAP value, CV value, and SNAm value; b is the reference parameter vector.

[0153] The parameter adjustment decision-making module formulates a parameter adjustment strategy based on the changing trends of electrophysiological signals and metabolic signals. This module adopts a fuzzy logic control algorithm, generates parameter adjustment instructions according to the direction and amplitude of signal changes, and realizes precise regulation of treatment parameters.

[0154] The treatment mode selection module selects a suitable treatment mode according to the type of neuropathy and repair needs. This module has four built-in preset treatment modes: nerve regeneration mode, injury repair mode, inhibitory mode, and demyelination mode. Each mode corresponds to a specific parameter combination and control strategy.

[0155] The parameter precise calculation module precisely calculates four key parameters: frequency, intensity, duty cycle, and treatment time. This module calculates the optimal parameter combination through an optimization algorithm based on the treatment mode and weight parameters, ensuring the precise matching of ultrasonic energy and nerve repair needs.

[0156] The ultrasonic emission and control subsystem 40 is used to output ultrasonic waves according to ultrasonic treatment parameters and real-time monitor electrophysiological signals and metabolism-related signals. This subsystem includes a multi-modal ultrasonic signal generation module, an ultrasonic focusing control module, and a safety monitoring and protection module.

[0157] The ultrasonic emission and control subsystem 40 is responsible for generating an accurate ultrasonic waveform according to the optimized parameters. The waveform generation uses digital synthesis technology:

[0158] ,

[0159] where, is the ultrasonic signal; is the amplitude, determined by the intensity parameter; is the frequency; is the time; is the modulation function. For the continuous wave mode , for the pulsed wave mode is the periodic rectangular function, and its duty cycle is determined by the treatment parameters. The ultrasonic transducer and transmission subsystem 50 includes an impedance matching optimization algorithm to ensure efficient energy transmission:

[0160] ,

[0161] where, is the energy transmission efficiency, in percentage; is the acoustic impedance of the transducer; is the acoustic impedance of the tissue. The system adjusts the composition of the coupling medium to make and as close as possible to achieve an energy transmission efficiency of more than 90%.

[0162] In actual clinical applications, for example, for a patient with diabetic peripheral neuropathy, the system first collects electrophysiological signals. The measured CMAP value is 0.7 times the normal value, and the CV value is 0.6 times the normal value, indicating the axon breakage and reconnection stage. Based on this, the weight parameters are calculated: the weight of the CMAP value is 0.7, and the weight of the CV value is 0.54 (0.6×0.9, considering the stage adjustment coefficient). The system selects the injury repair mode, sets the ultrasonic frequency to 1.2 MHz (suitable for superficial nerves), the intensity to 0.38 W / cm², the duty cycle to 40%, and the single treatment time to 9 minutes. During the treatment process, the system monitors the electrophysiological signals every 30 seconds. When it is detected that the CMAP value increases continuously twice and the increase amplitude exceeds 5%, the current parameters are maintained for continuous treatment; when the temperature approaches 41°C, the output intensity is automatically reduced to 0.3 W / cm² to ensure the safety and effectiveness of the treatment.

[0163] The multi-modal ultrasonic signal generation module generates various types of ultrasonic signals, including low-intensity pulsed waves (60 Hz), low acoustic impedance continuous waves, and high acoustic impedance continuous waves (1 - 2 MHz). The signal purity of this module is greater than 95%, and the harmonic distortion is less than 3%, ensuring the quality of the ultrasonic signals.

[0164] The ultrasonic focusing control module realizes the precise targeted positioning of ultrasonic waves, including a deep nerve targeting unit and a superficial nerve targeting unit. The focusing accuracy of this module is better than ±2 mm, and it can accurately concentrate the ultrasonic energy on the target nerve tissue.

[0165] The safety monitoring and protection module ensures the safety of the treatment process, including a temperature monitoring unit, an impedance monitoring unit, and an anomaly detection unit. The temperature measurement accuracy of this module is ±0.2°C, and the impedance measurement accuracy is ±5%, enabling it to detect safety risks in a timely manner and take corresponding measures.

[0166] The ultrasonic transducer and transmission subsystem 50 is used to convert electrical signals into ultrasonic waves and transmit them to the targeted treatment area. This subsystem includes an ultrasonic array probe module 51 and an acoustic wave transmission medium module 52.

[0167] The ultrasonic array probe module 51 includes an ultrasonic transducer, an ultrasonic wafer, and a focusing module, which realizes the generation and focusing of ultrasonic waves. The ultrasonic transducer converts electrical signals into mechanical vibrations, with frequencies of 60 Hz / 1 MHz / 2 MHz; the diameter of the ultrasonic wafer is 0.5 - 10 cm, generating ultrasonic waves with specific frequencies; the length of the focusing module is 0.5 - 10 cm, adjustable, and consists of a sound-transmitting soft pad, an ultrasonic energy-concentrating shell, and the targeted treatment area.

[0168] The acoustic wave transmission medium module 52 optimizes the propagation of ultrasonic waves in tissues, including a medium selection unit and an impedance matching unit. The medium selection unit selects a suitable acoustic wave transmission medium (such as normal saline, water, or distilled water, etc.) according to the treatment site; the impedance matching unit automatically adjusts the output power according to the tissue impedance to improve the signal transmission efficiency, and the impedance matching accuracy is better than 90%.

[0169] In practical applications, the components of the system work together. The multi-modal signal acquisition subsystem 10 first acquires various biological signals of nerve tissues, the nerve repair state evaluation subsystem 20 analyzes these signals and judges the nerve repair stage, the treatment parameter optimization subsystem 30 optimizes the ultrasonic parameters according to the evaluation results, the ultrasonic wave emission and control subsystem 40 outputs ultrasonic waves according to the optimized parameters, and the ultrasonic wave transducer and transmission subsystem 50 accurately transmits the ultrasonic energy to the targeted treatment area. The whole process forms a closed-loop control to achieve the precision, personalization, and safety of ultrasonic treatment.

[0170] Refer to Figure 4 and Figure 5 , in this embodiment, the ultrasonic treatment parameters of the multi-modal nerve regeneration ultrasonic parameter optimization system are specifically as follows:

[0171] When low-frequency ultrasonic waves are selected, the frequency range is 0.8 to 1.5 MHz, which is suitable for deep nerves with a penetration depth of 3 to 5 cm. Low-frequency ultrasonic waves have a longer wavelength and stronger penetration ability, and can effectively reach deep tissues. For example, for the sciatic nerve (typical depth is about 4 cm), the preferred frequency is 1 MHz; for the femoral nerve (typical depth is about 3 cm), the preferred frequency is 1.2 MHz.

[0172] The relationship between frequency and penetration depth can be expressed as:

[0173] ,

[0174] where is the maximum effective penetration depth, in cm; is the ultrasonic frequency, in MHz; is the tissue correlation coefficient, about 4.5 for muscle tissue. According to this formula, the maximum effective penetration depth of 1 MHz ultrasonic waves is about 4.5 cm, which is suitable for the sciatic nerve; the maximum effective penetration depth of 1.2 MHz ultrasonic waves is about 3.75 cm, which is suitable for the femoral nerve.

[0175] When medium-frequency ultrasonic waves are selected, the frequency range is 3.0 to 3.5 MHz, which is suitable for superficial nerves with a penetration depth of 1 to 2 cm. Medium-frequency ultrasonic waves have moderate penetration ability but high energy concentration, and are suitable for precise treatment of superficial nerves. For example, for the wrist segment of the median nerve (typical depth is about 1 cm), the preferred frequency is 3.3 MHz; for the elbow segment of the ulnar nerve (typical depth is about 1.5 cm), the preferred frequency is 3.0 MHz.

[0176] The relationship between the output intensity parameter and the treatment mode is as follows:

[0177]

[0178] Among them, is the optimized output intensity, with the unit of W / cm 2 , is the basic intensity value, which is 0.3 W / cm for the continuous wave mode 2 , and 0.6 W / cm² for the pulsed wave mode; is the stage adjustment coefficient, with a value of 0.5; PS is the nerve repair stage (1 - 5).

[0179] When the continuous wave mode is selected, the output intensity is 0.1 to 0.5 W / cm², which is suitable for the repair of chronic nerve injuries. The continuous wave provides continuous biological stimulation, which helps to promote the long - term repair process. For chronic lesions such as diabetic peripheral neuropathy, the recommended intensity is 0.3 W / cm²; for chronic inflammatory demyelinating polyneuropathy, the recommended intensity is 0.2 W / cm².

[0180] When the pulsed wave mode is selected, the output intensity is 0.5 to 1.0 W / cm², and the duty cycle is 20% to 50%, which is suitable for the acute inflammation period. The pulsed wave can provide intermittent high - intensity stimulation, while avoiding heat accumulation and reducing the inflammatory response. For acute neuritis, the recommended intensity is 0.6 W / cm² and the duty cycle is 30%; for the acute stage of traumatic nerve injury, the recommended intensity is 0.8 W / cm² and the duty cycle is 20%.

[0181] For the pulsed wave mode, the duty cycle is calculated by the formula:

[0182] ,

[0183] Among them, is the duty cycle, with a range of 20% - 50%; and are the minimum and maximum duty cycle values respectively, set as 20% and 50%; is the CMAP value weight; is the upper limit of the CMAP value weight, usually set as 2.0.

[0184] Preferably, the single - treatment time range is 5 to 15 minutes per target area, depending on the degree of nerve injury and treatment goals. For mild injuries, 5 - 8 minutes are usually selected; for moderate injuries, 8 - 12 minutes are selected; for severe injuries, 12 - 15 minutes are selected.

[0185] The single - treatment time is adjusted according to the severity of nerve injury:

[0186] ,

[0187] Among them, is the single treatment time, with the unit of minute; is the basic treatment time, set to 5 minutes; is the time adjustment range, set to 10 minutes; is the degree of nerve defect, ranging from 0 to 1.

[0188] The total treatment course length is determined according to the degree of nerve defect. When the degree of nerve defect exceeds 10% (usually manifested as a decrease in amplitude or a decrease in conduction velocity by more than 10% of the normal value in electrophysiological examination), the treatment frequency is adjusted to more than 5 times per week, and the total treatment course is extended to 6 to 8 weeks; when the degree of nerve defect is less than 10%, the treatment frequency is adjusted to no more than 3 times per week, and the total treatment course is 4 to 6 weeks.

[0189] In practical applications, these parameters will be finely adjusted according to individual patient differences and real-time monitoring feedback. For example, for a patient with moderate diabetic peripheral neuropathy (nerve defect degree ND = 0.4), the system calculates that the single treatment time is 9 minutes (5 + 10×0.4). The injury repair mode is selected for treatment, with medium-frequency ultrasound (3.2 MHz), medium intensity (0.35 W / cm²) and continuous wave form, and the treatment is performed 4 times per week for a total treatment course of 6 weeks. As the patient's condition improves, the system will automatically adjust the parameters to gradually optimize the treatment effect.

[0190] These parameter settings are based on a large amount of clinical practice and research data, and have been proven to have good safety and effectiveness. In specific applications, the system will make fine adjustments within the above range according to individual patient differences and treatment responses to achieve true personalized treatment. The present invention realizes the precise dynamic regulation of ultrasonic treatment parameters by acquiring multi-dimensional biological signals of nerve tissue, establishing a nerve repair stage recognition mechanism, and constructing a treatment parameter optimization framework based on weight parameters, providing a new technical path for the treatment of peripheral nerve diseases.

[0191] It should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. Method for optimizing multimodal nerve regeneration ultrasound parameters, characterized in that, Including: Obtaining electrophysiological signals and metabolism-related signals of nerve tissue, wherein the electrophysiological signals include action potential magnitude, conduction velocity, sensory threshold, and motor threshold, and the metabolism-related signals include tissue oxygenation index, tissue oxygen content, and hydration data; Based on the electrophysiological signals, determining the stage of nerve repair, wherein the stage of nerve repair includes demyelination stage, axon breakage and reconnection stage, axon remyelination stage, axon outer diameter stage, and conduction velocity stage; According to the stage of nerve repair and the ratio of the electrophysiological signals to the electrophysiological signals in the normal state, determining weight parameters, wherein the weight parameters include CMAP value weight, CV value weight, and SNAm value weight; Based on the weight parameters and the metabolism-related signals, dynamically optimizing ultrasound treatment parameters, wherein the ultrasound treatment parameters include frequency, intensity, duty cycle, and treatment time; Real-time monitoring the electrophysiological signals and the metabolism-related signals, when the electrophysiological signals show a recovery trend, maintaining the current ultrasound treatment parameters, and when the electrophysiological signals show a decreasing trend, adjusting the ultrasound treatment parameters.

2. The multimodal nerve regeneration ultrasound parameter optimization method according to claim 1, characterized in that The determining the stage of nerve repair includes: Comparing the CMAP value in the electrophysiological signals with the CMAP value in the normal state, when the CMAP value is 0.3 to 0.5 times the CMAP value in the normal state, it is determined as the demyelination stage; When the CMAP value is 0.8 to 1 times the CMAP value in the normal state, it is determined as the axon breakage and reconnection stage; When the CMAP value is 1.5 to 2 times the CMAP value in the normal state, it is determined as the axon remyelination stage; When the CMAP value is 1 to 1.2 times the CMAP value in the normal state, it is determined as the axon outer diameter stage; When the CMAP value is 1.2 to 1.4 times the CMAP value in the normal state, it is determined as the conduction velocity stage.

3. The multimodal nerve regeneration ultrasound parameter optimization method according to claim 1, characterized in that The determining the weight parameters includes: When it is determined as the demyelination stage, adjusting the CMAP value weight to 0.3 to 0.5 times the normal state, adjusting the CV value weight to 0.4 to 0.8 times the normal state, and adjusting the SNAm value weight to 0.3 to 0.5 times the normal state; When it is determined as the axon breakage and reconnection stage, adjusting the CMAP value weight to 0.8 to 1 times the normal state, adjusting the CV value weight to 0.7 to 1 times the normal state, and adjusting the SNAm value weight to 0.4 to 0.7 times the normal state; When it is determined as the axon remyelination stage, adjusting the CMAP value weight to 1.5 to 2 times the normal state, adjusting the CV value weight to 1.3 to 1.8 times the normal state, and adjusting the SNAm value weight to 0.7 to 1 times the normal state.

4. The multi-modal nerve regeneration ultrasound parameter optimization method according to claim 1, wherein The dynamically optimizing the ultrasound treatment parameters includes: According to the weight parameters, selecting one treatment mode from nerve regeneration mode, injury repair mode, inhibitory mode, or demyelination mode; Among them, the nerve regeneration mode adopts low-intensity intermittent wave stimulation, the injury repair mode adopts low-intensity continuous wave treatment, the inhibitory mode adopts high-intensity continuous wave treatment, and the demyelination mode adopts high-intensity intermittent wave treatment.

5. The multimodal nerve regeneration ultrasound parameter optimization method according to claim 4, wherein Select the treatment mode according to the type of neuropathy, including: When the type of neuropathy is immune neuropathy, select the parameter combination of low frequency 1 MHz, pulse wave 0.3 W / cm², and duty cycle 30%; When the type of neuropathy is metabolic neuropathy, select the parameter combination of medium frequency 3 MHz and continuous wave 0.5 W / cm²; When the type of neuropathy is compressive neuropathy, select the parameter combination of focusing mode 0.8 W / cm² and local vibration assistance.

6. The multimodal nerve regeneration ultrasonic parameter optimization method according to claim 1, wherein Before obtaining the electrophysiological signals and metabolism-related signals of nerve tissue, it also includes: Judging the type of nerve in the treatment area, divided into deep nerves and superficial nerves; For deep nerves, measure and record the distal electrophysiological signals, set the current stimulation threshold, perform single-pulse or continuous wave stimulation, record the propagation speed of the electrophysiological signals after stimulation, and determine the ultrasonic focusing depth according to the propagation speed; For superficial nerves, record the skin resistance value of the treatment area, measure and obtain the muscle resistance value of the treatment area, and use the skin resistance value and the muscle resistance value as auxiliary treatment parameters.

7. The multimodal nerve regeneration ultrasound parameter optimization method according to claim 1, wherein The dynamic optimization of ultrasonic treatment parameters includes: Determine the treatment frequency and total course of treatment according to the degree of nerve defect. When the degree of nerve defect exceeds 10%, adjust the treatment frequency to more than 5 times per week, and extend the total course of treatment to 6 to 8 weeks; When the degree of nerve defect is less than 10%, adjust the treatment frequency to no more than 3 times per week, and the total course of treatment is 4 to 6 weeks.

8. The multimodal nerve regeneration ultrasound parameter optimization method according to claim 1, wherein The real-time monitoring of the electrophysiological signals and the metabolism-related signals also includes: Monitor the temperature of the treatment site. When the temperature is 40 to 41 °C, reduce the output intensity; when the temperature is 30 to 40 °C, reduce the pulse repetition frequency; when the temperature is less than 30 °C, increase the output intensity; Monitor the tissue impedance. When the tissue impedance mutates, pause the treatment and re-collect the impedance data; Monitor abnormal signals. When measurement abnormalities occur twice in a row, stop the treatment.

9. The multimodal nerve regeneration ultrasound parameter optimization method according to claim 1, wherein, Among the ultrasonic treatment parameters: When low-frequency ultrasound is selected, the frequency range is 0.8 to 1.5 MHz, which is suitable for deep nerves with a penetration depth of 3 to 5 cm; When medium-frequency ultrasound is selected, the frequency range is 3.0 to 3.5 MHz, which is suitable for superficial nerves with a penetration depth of 1 to 2 cm; When the continuous wave mode is selected, the output intensity is 0.1 to 0.5 W / cm², which is suitable for the repair of chronic nerve injuries; When the pulse wave mode is selected, the output intensity is 0.5 to 1.0 W / cm², and the duty cycle is 20% to 50%, which is suitable for the acute inflammation period.

10. A multi-modal nerve regeneration ultrasound parameter optimization system for performing the multi-modal nerve regeneration ultrasound parameter optimization method according to any one of claims 1-9, characterized in that, Including: A multi-modal signal acquisition subsystem for obtaining electrophysiological signals and metabolism-related signals of nerve tissue. Among them, the electrophysiological signals include the magnitude of action potential, conduction speed, sensory threshold, and motor threshold, and the metabolism-related signals include tissue oxygenation index, tissue oxygen content, and hydration data; A nerve repair status assessment subsystem, which is used to judge the stage of nerve repair based on the electrophysiological signal. The stage of nerve repair includes the demyelination stage, the axon breakage and reconnection stage, the axon remyelination stage, the axon outer diameter stage, and the conduction velocity stage; and determines the weight parameters according to the stage of nerve repair and the ratio of the electrophysiological signal to the electrophysiological signal in the normal state, wherein the weight parameters include the CMAP value weight, the CV value weight, and the SNAm value weight; A treatment parameter optimization subsystem, which is used to dynamically optimize the ultrasound treatment parameters based on the weight parameters and the metabolism-related signal, wherein the ultrasound treatment parameters include frequency, intensity, duty cycle, and treatment time; An ultrasonic wave emission and control subsystem, which is used to output ultrasonic waves according to the ultrasound treatment parameters and monitor the electrophysiological signal and the metabolism-related signal in real time. When the electrophysiological signal shows a recovery trend, the current ultrasound treatment parameters are maintained. When the electrophysiological signal shows a decreasing trend, the ultrasound treatment parameters are adjusted; An ultrasonic wave conversion and transmission subsystem, which is used to convert an electrical signal into an ultrasonic wave and transmit it to the targeted treatment area, including an ultrasonic array probe module and a sound wave transmission medium module.

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