Central-peripheral synchronous regulation low-intensity focused ultrasound stimulation method

Abstract

The invention discloses a center-periphery synchronous regulation low-intensity focused ultrasound stimulation method and system, and the method comprises the steps: calculating the functional connection strength of a center-periphery target based on the functional magnetic resonance imaging and joint MRI data of the brain of a patient, and determining a focus and a target based on the functional connection strength; calculating the focus and target distance of the joint and the focus and target distance of the transcranial low-intensity focused ultrasound in real time; calculating the target depth of the joint and the target depth of the transcranial low-intensity focused ultrasound based on the joint MRI data; the ultrasonic beam is controlled to be aligned with the target spot of the joint and the target spot of the transcranial low-intensity focused ultrasound; acquiring a feedback condition in a stimulation process; and adjusting the parameters based on the feedback condition. The feedback condition in the stimulation process can be obtained in real time, tissue response monitoring is achieved so as to evaluate the stimulation effect, and in addition, stimulation parameters can be dynamically adjusted based on the feedback condition.

Description

Technical Field

[0001] This invention belongs to the field of medical device technology, specifically relating to a method and system for central-peripheral synchronous modulation of low-intensity focused ultrasound stimulation. Background Technology

[0002] Low-intensity focused ultrasound (LIFOUS) focuses ultrasound energy onto target tissue, generating mechanical and thermal effects to stimulate tissue repair and regeneration. In recent years, it has shown great potential in the field of central nervous system stimulation. LIFUS offers higher spatial resolution (millimeter-level) and deeper penetration, enabling non-invasive targeted modulation of deep brain regions. Unlike traditional high-intensity focused ultrasound (HIFU), LIFUS typically uses an intensity below 1 W / cm², causing no tissue damage and instead stimulating cellular activity through mechanical stress. LIFUS has been shown to promote chondrocyte proliferation, increase collagen synthesis, and reduce inflammatory responses, demonstrating broad application prospects.

[0003] Despite the many theoretical advantages of LIFUS technology, it still has the following shortcomings in practical applications: (1) lack of synchronization between central and peripheral regulation: existing systems cannot achieve synchronous and precise regulation of the central nervous system and peripheral tissues; (2) lack of neuro-joint feedback mechanism: lack of multimodal feedback system for evaluating central-peripheral interaction; (3) lack of real-time feedback: tissue response cannot be monitored in real time during stimulation, making it difficult to evaluate stimulation effect and adjust stimulation parameters. Summary of the Invention

[0004] To overcome the above-mentioned technical defects, the present invention provides a method and system for central-peripheral synchronous modulation of low-intensity focused ultrasound stimulation, which can improve the reliability of creep intensity prediction.

[0005] This invention is achieved through the following scheme: A method for centrally and peripherally synchronized low-intensity focused ultrasound stimulation includes: Based on the patient's functional magnetic resonance imaging (fMRI) and joint MRI data, the functional connectivity strength of central-peripheral targets was calculated, and the focal point and target were determined based on the functional connectivity strength. Real-time calculation of the focal and target distances of joints and transcranial low-intensity focused ultrasound. Based on joint MRI data, the target depth of the joint and the target depth of transcranial low-intensity focused ultrasound were calculated. Controlling the ultrasound beam to align with the target points of the joint and the target points of transcranial low-intensity focused ultrasound; Obtain feedback during the stimulation process; Based on the feedback, the parameters were adjusted.

[0006] As a further improvement of the present invention, the step of calculating the functional connectivity strength of central-peripheral target points based on functional magnetic resonance imaging (fMRI) and joint MRI data of the patient's brain includes: Obtain functional magnetic resonance imaging (fMRI) data of the patient's brain and joints; Based on functional magnetic resonance imaging and joint MRI data, a 3D convolutional neural network was used to reconstruct a three-dimensional model of the motor cortex and joint cavity. Identify regulatory target areas from three-dimensional models of the cortex and joint cavity based on symptom networks; Identify joint lesion areas from the target area of ​​regulation; Calculate the functional connectivity strength between the central nervous system and peripheral target sites.

[0007] As a further improvement of the present invention, the focal distance between the joint and the target point, as well as the focal distance between the transcranial low-intensity focused ultrasound, are simultaneously displayed.

[0008] As a further improvement of the present invention, the step of controlling the ultrasound beam to align with the target point of the joint and the target point of the transcranial low-intensity focused ultrasound includes: The phased array transducer is controlled to adjust the ultrasonic beam to align with the target. The head-joint motion synchronization prediction model is invoked to compensate for focus shift.

[0009] As a further improvement of the present invention, the step of obtaining feedback during the stimulation process includes: Ultrasonic elastography was used to obtain information on changes in tissue stiffness. Monitor the pressure within the joint; Real-time acquisition of the rate of change of blood oxygen level dependent on signals; Real-time acquisition of electroencephalograms (EEGs).

[0010] As a further improvement of the present invention, the step of adjusting parameters based on feedback includes: Based on the feedback, a real-time correlation curve between central activation and peripheral input intensity was constructed. Calculate the positive feedback gain coefficient; When the set time is reached, calculate the central component and the peripheral component; When the composite index increases to the set value, the co-optimization model of central and peripheral stimulation parameters is invoked to adjust the parameters.

[0011] As a further improvement of the present invention, the present invention also includes: Early warnings will be issued based on feedback.

[0012] As a further improvement of the present invention, the present invention also includes the following steps: Generate a report on the central-peripheral synergistic effect.

[0013] The present invention also provides a central-peripheral synchronous modulation low-intensity focused ultrasound system, comprising: The multimodal optical navigation and positioning module is used to calculate the functional connectivity strength of central-peripheral targets based on the patient's functional magnetic resonance imaging and joint MRI data, and to determine the focal point and target based on the functional connectivity strength. The multimodal optical navigation and positioning module is also used to calculate the focal and target distances of joints and transcranial low-intensity focused ultrasound in real time. The multimodal optical navigation and positioning module is also used to calculate the target depth of the joint and the target depth of transcranial low-intensity focused ultrasound based on joint MRI data. The dual-target synchronous control module is used to control the alignment of the ultrasound beam with the target point of the joint and the target point of the transcranial low-intensity focused ultrasound. A neuro-articular multimodal feedback module is used to acquire feedback during the stimulation process; The intelligent collaborative control module is used to adjust parameters based on feedback.

[0014] The present invention also provides a central-peripheral synchronous modulation low-intensity focused ultrasound system, comprising: a processor and a memory, wherein the memory stores at least one instruction, the at least one instruction being loaded and executed by the processor to realize the central-peripheral synchronous modulation low-intensity focused ultrasound stimulation method as described above.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: based on the functional magnetic resonance imaging and joint MRI data of the patient's brain, the focal-to-target distance of the joint and the focal-to-target distance of transcranial low-intensity focused ultrasound are calculated in real time, realizing the synchronous regulation of the central and peripheral systems. At the same time, the feedback during the stimulation process is acquired in real time, enabling the monitoring of tissue response to evaluate the stimulation effect. In addition, the stimulation parameters can be dynamically adjusted based on the feedback. Attached Figure Description The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a flowchart of the central-peripheral synchronous modulation low-intensity focused ultrasound stimulation method described in Example 1. Detailed Implementation

[0016] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0017] It should be noted that similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, the sequence numbers of each step are merely used to distinguish between steps and do not imply that each step must be strictly performed in the order of the numbers.

[0018] Example 1 This invention discloses a method for central-peripheral synchronous modulation of low-intensity focused ultrasound stimulation, such as... Figure 1 As shown, it includes: S1. Multimodal imaging technology is used to simultaneously acquire high-resolution functional magnetic resonance imaging (fMRI) and joint MRI data of the patient's brain. Based on the high-resolution fMRI and joint MRI data of the patient's brain, the functional connectivity strength of the central-peripheral target is calculated. Based on the functional connectivity strength, the focal point and target are determined.

[0019] First, functional magnetic resonance imaging (fMRI) of the patient's brain and joint MRI data were acquired to provide basic data support for subsequent analysis. Based on the fMRI and joint MRI data, a 3D convolutional neural network was used to reconstruct a three-dimensional model of the motor cortex and joint cavity, constructing a three-dimensional visualization model that includes the central nervous system and peripheral joint structures. Subsequently, dual-target identification was performed: based on the symptom network, regulatory target areas, such as the subthalamic nucleus, were identified from the three-dimensional model of the cortex and joint cavity; joint lesion areas, such as cartilage damage areas, were identified from the regulatory target areas. This process can be achieved through AI algorithms. Finally, correlation analysis was performed: the functional connectivity strength between the central and peripheral targets was calculated.

[0020] The aforementioned symptom network is built by collecting clinical symptoms (such as joint pain scores and types of movement disorders), brain region activation data from functional magnetic resonance imaging (fMRI), and anatomical structure data from joint MRI of patients with similar diseases (such as osteoarthritis with motor dysfunction), creating a multi-center database. The specific steps for building the symptom network are as follows: 1. Network model construction: Using brain functional areas (such as the M1 area of ​​the motor cortex and the subthalamic nucleus) and joint anatomical structures (such as cartilage and ligaments) as nodes, and the correlation strength between symptoms and regions as edges, the weights are calculated based on the correlation between clinical symptoms and brain / joint regions (such as the Pearson correlation coefficient between pain scores and thalamic activation, and a GNN network model is constructed).

[0021] 2. 3D model reconstruction and feature extraction: The patient's brain fMRI and joint MRI were fused using spatial registration technology. A 3D convolutional neural network was used to reconstruct a 3D "functional-anatomical" hybrid model of the motor cortex and joint cavity, achieving multimodal image fusion. The cortical model marked functional activation hotspots with fMRI signal intensity greater than a threshold, while the joint model marked lesion areas such as cartilage defects and ligament injuries on MRI (e.g., high signal areas on T2-weighted images). In the semantic segmentation and feature enhancement stage, the 3D model was automatically segmented, and quantitative features such as activation intensity of cortical functional areas and volume / location of joint lesion areas were extracted to provide data support for target area identification.

[0022] 3. Target region inference and selection based on symptom networks: First, symptom input is matched with the network. The patient's specific symptoms are input (e.g., "limited left knee flexion and extension + weakened motor cortex activation"). The symptom network then uses the following logical reasoning: it calculates the matching degree between the symptoms and each node in the network, identifying the brain regions (e.g., motor cortex) and joint regions (e.g., knee cartilage) corresponding to key symptoms. Combining functional activation hotspots and lesion areas in the 3D model, it filters out candidate target areas with the highest correlation to the symptoms (e.g., M1 area of ​​the motor cortex and knee cartilage injury area). Simultaneously, an attention mechanism is introduced to optimize target area weights. Candidate target areas are weighted and fused based on anatomical location and functional correlation; for example, brain regions closest to the lesion area with a functional connectivity strength > 0.6 are given higher weights. Finally, the central and peripheral regulatory target areas are determined.

[0023] 4. Target validation and model iteration: First, real-time feedback verification is performed: the identified target area is initially stimulated, and its effectiveness is verified using feedback data such as electroencephalography (EEG) and ultrasound elastography. If the feedback does not meet expectations (e.g., no improvement in joint pressure fluctuations), the association weights of nodes in the symptom network are adjusted, and the target area is re-identified. Subsequently, reinforcement learning iterative optimization is implemented: based on the verification results of multiple patients, reinforcement learning (RL) algorithms are used to optimize the symptom network model. For example, if the effectiveness rate of target area stimulation for a certain type of symptom reaches 80%, the connection weights between that symptom and the corresponding node are strengthened to improve subsequent identification accuracy.

[0024] The “symptom-brain region-joint” association network constructed using graph neural networks can screen out regions closely related to patient symptoms from three-dimensional models of the cortex and joint cavity as potential regulatory targets.

[0025] Functional connectivity strength directly reflects the neural functional association between the brain's central nervous system (such as the motor cortex) and peripheral joint lesion areas, and is the core basis for determining the location of "dual targets." If the functional connectivity strength is low, it indicates a weak association between the two, and the target selection may need to be readjusted. Functional connectivity strength also serves as a reference indicator for real-time feedback, helping to determine whether the stimulus accurately targets the associated area. The comparison of intensity before and after stimulation directly reflects the synergistic effect, providing data for subsequent generation of central-peripheral synergistic effect reports.

[0026] S2. Real-time calculation of the focal and target distance of the joint, and the focal and target distance of transcranial low-intensity focused ultrasound.

[0027] Before performing this step in real time, the dual-probe optical navigation system needs to be initialized: device registration is performed by attaching infrared reflective marker balls to the joint LIFUS probe and the transcranial LIFUS probe, and spatially registering them with the MRI model using an optical tracking system, with a registration error ≤0.3mm. Patient positional changes, such as joint flexion and extension movements, are then dynamically calibrated.

[0028] The infrared optical positioning system continuously monitors and updates the probe position, enabling dynamic real-time tracking. Simultaneously, it calculates the distance between the focal point and target point for both joint-based and transcranial LIFUS. The navigation interface synchronously displays the target point (red), focal point (green), and distance (numerical value), ensuring the stimulation path aligns with the planned path.

[0029] S3. Based on joint MRI data, calculate the target depth of the joint and the target depth of transcranial low-intensity focused ultrasound (TIG). The distance from the cartilage injury zone to the probe is 3.2 cm. This step optimizes the focal length by controlling the phased array transducer to adjust the ultrasound beam to align with the target. The phased array transducer allows for continuous adjustment of the focal length from 3 to 5 cm. Furthermore, the output intensity needs to be adjusted based on individual patient differences, such as tissue thickness and lesion severity, to 0.5-3.0 W / cm².

[0030] S4. To control the ultrasound beam to align with the target point of the joint and the target point of the transcranial low-intensity focused ultrasound, the focus is always aligned with the target point by adjusting the beam direction of the phased array transducer in real time. Considering that joint movement will cause the focus to shift, the head-joint movement synchronization prediction model needs to be called to compensate for the focus shift, so that the error is <0.3mm.

[0031] Head-joint motion synchronization prediction models can be achieved through the following techniques: The system uses an infrared optical positioning system to collect six-degree-of-freedom motion data of the head and joints in real time, such as three-dimensional position and three-dimensional rotation. The accuracy of the system used in this embodiment is ≤0.1mm. Based on historical motion data, a prediction model is trained to predict the motion trajectory within the next 0.5-1 seconds. The phased array transducer is controlled to adjust the direction and focal length of the sound beam in real time to compensate for the focus shift caused by motion.

[0032] Specifically, infrared reflective marker balls with a diameter of 3mm are installed in the head and joint stimulation areas. The optical tracking system synchronously collects the motion trajectory of the head (such as skull markers) and joints (such as inner and outer knee markers) at a frequency of 100Hz, and obtains six-dimensional motion parameters including translation and rotation. The translation parameters include X-axis displacement, Y-axis displacement, and Z-axis displacement, and the rotation parameters include pitch angle, yaw angle, and roll angle.

[0033] Kalman filtering algorithm is used to remove noise from motion data, and coordinate transformation is used to map the motion data of marker points to the ultrasound focusing coordinate system to ensure consistency with the spatial position of the target point.

[0034] Analyze historical motion data to extract periodic motion features (such as the amplitude and frequency of joint flexion and extension) and sudden motion features (such as involuntary convulsions of patients), and identify motion patterns (such as uniform motion, accelerated motion, and random motion) through long short-term memory networks.

[0035] Differentiated prediction algorithms are employed for different motion patterns: for example, Fourier series is used to fit the motion trajectory for periodic motion to predict the motion parameters for the next cycle; for non-periodic motion, a gated recurrent unit neural network is used to predict displacement and angle changes within the next second based on the motion data of the most recent 500ms; for sudden motion, a threshold trigger mechanism is set up, and when the motion speed exceeds 0.5cm / s, an emergency prediction model is activated to prioritize the safety of the focal point. Every 100ms, the actual motion data is compared with the prediction results, and the model parameters are updated through a Bayesian optimization algorithm to keep the prediction error within 0.2mm.

[0036] Based on the predicted motion parameters, such as a joint flexion of 10°, the focal offset is calculated, such as an X-axis offset of 0.3mm and a Z-axis offset of 0.2mm. Combined with the target depth, such as a joint target depth of 3cm, the adjustment parameters of the phased array transducer are derived using geometric transformation formulas, thereby calculating the compensation amount. Simultaneously, angle adjustments are also implemented. The formula for angle adjustment is: Δθ = arctan(offset / target depth), for example, with an offset of 0.3mm and a depth of 3cm, Δθ ≈ 0.57°. The formula for focal length adjustment is: Δf = target depth + the component of the offset in the depth direction, for example, with a Z-axis offset of 0.2mm, Δf = 3.002cm.

[0037] After obtaining the adjustment parameters, the timing of the phased array transducer elements is driven to adjust the beam direction and focal length in real time. For example, when a flexion movement of the joint is predicted, the beam is deflected 0.5° in the opposite direction of the movement 0.5 seconds in advance, while the focal length is increased by 0.1cm to counteract the expected focus shift.

[0038] After compensation, the focal distance is monitored in real time. If the offset is still >0.3mm, secondary compensation is triggered: recalculate the prediction error → correct the model parameters → readjust the acoustic beam until the deviation between the focal point and the target point is ≤0.3mm. Steps S2 to S4 above, using target depth as the anatomical reference, focal distance as real-time feedback, and ultrasound beam control as the execution means, form a precise and organic whole through three-dimensional spatial positioning, dynamic error compensation, and multimodal data fusion. MRI anatomical data and real-time optical navigation data are converted into executable acoustic beam control parameters, achieving millimeter-level synchronous focusing of central and peripheral dual targets, providing technical support for precise stimulation of low-intensity focused ultrasound. S5. Obtain feedback during the stimulation process to prepare for subsequent dynamic adjustments to the stimulation parameters. Specific feedback can be obtained through the following methods: Peripheral monitoring: Ultrasonic elastography is used to acquire changes in tissue stiffness; a miniature pressure sensor (accuracy ±0.5 mmHg) is embedded in the stimulation probe to monitor intra-articular pressure and detect the mechanical response during stimulation. When the intra-articular pressure exceeds or falls below a set threshold of 2 mmHg, an early warning of intra-articular pressure fluctuation is triggered, tissue stiffness abrupt change is detected, and an alarm for the rate of change of elastic modulus is established.

[0039] Central nervous system monitoring: Real-time acquisition of blood oxygen level-dependent signal change rate, real-time acquisition of electroencephalogram to achieve high-frequency oscillation analysis, and immediate detection of epileptiform discharges.

[0040] S6. Adjust parameters based on feedback. By establishing a neural-peripheral feedback loop, in conjunction with the aforementioned steps, a closed-loop control is formed, consisting of "dual-target localization - synchronous stimulation - multidimensional feedback - dynamic adjustment".

[0041] First, based on the feedback, a real-time correlation curve between central activation and peripheral input intensity is constructed; the positive feedback gain coefficient is calculated; then, when the set time is reached, the central and peripheral components are calculated; when the comprehensive index increases by 15%, the collaborative optimization model of central and peripheral stimulation parameters is invoked to adjust the parameters.

[0042] The specific implementation process of the collaborative optimization model for central and peripheral stimulation parameters is as follows: Feedback includes central and peripheral feedback. Central feedback includes the rate of change of oxygen-dependent (BOLD) signals and the high-frequency oscillation power of electroencephalograms (EEGs). Peripheral feedback includes changes in tissue stiffness and the amplitude of intra-articular pressure fluctuations as observed in ultrasound elastography. A deep autoencoder is used to map multimodal data to a unified feature space to eliminate dimensional differences. For example, the rate of change of BOLD signals and joint pressure fluctuations are normalized to the [0,1] interval. Attention mechanisms are used for weighted fusion to highlight key feedback features. For instance, when abnormal EEG discharges are observed, central feedback is given higher weight. Based on the fused feedback data, the central-peripheral synergistic effect comprehensive index (CEI) is calculated: CEI = α × central activation + β × peripheral responsiveness + γ × functional connectivity enhancement. Here, α, β, and γ are weighting coefficients, which can be determined through training with historical case data. Central activation is calculated from the coordination of the rate of change of BOLD signals and EEG oscillations, while peripheral responsiveness is determined from the improvement rate of tissue stiffness and joint pressure stability.

[0043] With the objectives of maximizing the synergistic effect comprehensive index (CEI), minimizing stimulus intensity fluctuations, balancing the energy distribution between central and peripheral stimuli, and defining and optimizing the parameter search space, a synergistic optimization model is constructed: First, the parameter search space needs to be defined, including: defining adjustable parameters and their ranges; central stimulation parameters: transcranial ultrasound intensity (0.5-3.0 W / cm²), pulse frequency (1-1000 Hz), duty cycle (10%-90%); peripheral stimulation parameters: joint ultrasound intensity (0.3-2.5 W / cm²), depth of focus (3-10 cm), scanning mode (fixed-point / linear scan). Peripheral stimulation parameters: joint ultrasound intensity (0.3-2.5 W / cm²), depth of focus (3-10 cm), scanning mode (fixed-point / linear scan).

[0044] In the initial stage, a genetic algorithm is used for global search to quickly narrow down the parameter range. The fine-tuning stage switches to a sequential quadratic programming algorithm for refined searching near local optima. In the real-time update stage, the optimization process is re-initialized every 500ms based on the latest feedback data to adapt to dynamic changes in the patient's condition. The optimized parameters (e.g., transcranial intensity 1.8 W / cm², joint intensity 1.2 W / cm²) are converted into control signals for the phased array transducer, including element excitation timing and focusing delay parameters.

[0045] After adjusting the parameters, monitor the feedback data for the next period and calculate the improvement of the comprehensive index CEI: If the comprehensive index CEI improves by ≥15%, the parameter adjustment is retained; if the comprehensive index CEI does not change significantly or decreases, the rollback mechanism is activated to restore the previous set of effective parameters and re-optimize.

[0046] For example, the set time can be five minutes, the set value can be 15%, and the central component can be calculated as follows: brain region activation growth rate × neural oscillation coordination; the peripheral component can be calculated as follows: motor control precision × sensory input intensity.

[0047] S7. Generate a central-peripheral synergistic effect report. The report may include the following: recording the temporal relationship of dual-target stimulation parameters; generating a heatmap of central-peripheral response correlation; and outputting personalized subsequent stimulation suggestions. It may also include: central effect assessment, peripheral effect assessment, synergistic effect assessment, quantitative brain function remodeling indicators, and joint function improvement parameters. Central effect assessment includes: the rate of change in motor cortex activation volume; peripheral effect assessment includes: the degree of improvement in cartilage elastic modulus; synergistic effect assessment includes: the enhancement of neural-joint functional connectivity; quantitative brain function remodeling indicators can be the activation volume growth rate; and joint function improvement parameters can be motor control precision.

[0048] In summary, this embodiment has the following beneficial effects: Based on various neural network algorithms, collaborative modeling of central nervous system functional zones and peripheral anatomical structures and intelligent target matching are achieved. Based on fMRI / EEG (central feedback) and ultrasound elastography / mechanical monitoring (peripheral feedback), a report on central-peripheral synergistic effects is generated; A closed-loop control of "central intervention - peripheral reinforcement - feedback regulation" is achieved, and an integrated stimulation of "central regulation - peripheral reinforcement - closed-loop optimization" is constructed, providing a solution for motor dysfunction accompanied by neurological diseases. Example 2 This embodiment provides a central-peripheral synchronous modulation low-intensity focused ultrasound system, including: a multimodal optical navigation and positioning module, a dual-target synchronous modulation module, a neuro-joint multimodal feedback module, and an intelligent collaborative control module; the multimodal optical navigation and positioning module is used to calculate the functional connectivity strength of the central-peripheral target points based on the patient's functional magnetic resonance imaging and joint MRI data, determine the focal point and target point based on the functional connectivity strength, and is used to calculate the focal point and target point distance of the joint and the focal point and target point distance of the transcranial low-intensity focused ultrasound in real time, and to calculate the target point depth of the joint and the target point depth of the transcranial low-intensity focused ultrasound based on the joint MRI data; The dual-target synchronous control module is used to control the ultrasound beam to align with the target point of the joint and the target point of the transcranial low-intensity focused ultrasound; the neuro-joint multimodal feedback module is used to acquire feedback during the stimulation process; and the intelligent collaborative control module is used to adjust parameters based on the feedback.

[0049] In summary, this embodiment has the following technical effects: A hybrid optical-electromagnetic navigation system is used to achieve sub-millimeter (≤0.5mm) synchronous localization of brain regions and joint targets, breaking through the limitations of traditional single-target stimulation; Employing a central-peripheral coordinated phased array system, the parameters for transcranial stimulation (adjustable from 1 to 5 cm) and joint stimulation (adjustable from 3 to 10 cm) are independently optimized to meet the precise control requirements of different tissue depths.

[0050] Example 3 This embodiment discloses a central-peripheral synchronous modulation low-intensity focused ultrasound system, including a processor and a memory. The memory stores at least one instruction, which is loaded and executed by the processor to implement the central-peripheral synchronous modulation low-intensity focused ultrasound stimulation method in Embodiment 1. For specific implementation details of this embodiment, please refer to Embodiments 1 and 2, which will not be repeated here.

[0051] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method of centrally-peripherally synchronized regulation of low intensity focused ultrasound stimulation, characterized in that, The method comprises the following steps: Based on the functional magnetic resonance imaging of the patient's brain and the MRI data of the joint, the functional connection strength of the central-peripheral target point is calculated, and the focus and target point are determined based on the functional connection strength; Real-time calculation of the distance between the focus and the target point of the joint, the distance between the focus and the target point of the transcranial low-intensity focused ultrasound; Based on the MRI data of the joint, the depth of the target point of the joint and the depth of the target point of the transcranial low-intensity focused ultrasound are calculated; Control the sound beam to align the target point of the joint and the target point of the transcranial low-intensity focused ultrasound; Obtain the feedback during the stimulation process; Adjust the parameters based on the feedback.

2. The method of claim 1, wherein the low intensity focused ultrasound stimulation is synchronized with the central-peripheral modulation. The step of calculating the functional connection strength of the central-peripheral target point based on the functional magnetic resonance imaging of the patient's brain and the MRI data of the joint comprises the following steps: Obtain the functional magnetic resonance imaging of the patient's brain and the MRI data of the joint; Based on the functional magnetic resonance imaging and the MRI data of the joint, a 3D convolutional neural network is used to reconstruct a motor cortex and joint cavity three-dimensional model; Identify the regulatory target area from the cortical and joint cavity three-dimensional model based on the symptom network; Identify the joint lesion area from the regulatory target area; Calculate the functional connection strength of the central-peripheral target point.

3. The method of claim 1, wherein the low intensity focused ultrasound stimulation is synchronized with the central-peripheral modulation. Synchronously display the distance between the focus and the target point of the joint and the distance between the focus and the target point of the transcranial low-intensity focused ultrasound.

4. The method of central-peripheral synchronized regulation low intensity focused ultrasound stimulation according to claim 1, wherein, The step of controlling the sound beam to align the target point of the joint and the target point of the transcranial low-intensity focused ultrasound comprises the following steps: Control the phased array transducer to adjust the sound beam to align the target point; Call the head-joint motion synchronization prediction model to compensate for the focus offset.

5. The method of central-peripheral synchrony regulation low intensity focused ultrasound stimulation according to claim 1, wherein, The step of obtaining the feedback during the stimulation process comprises the following steps: Use ultrasonic elastography to obtain the change of tissue stiffness; Monitor the pressure in the joint; Real-time acquisition of blood oxygen level dependent signal change rate; Real-time acquisition of electroencephalogram.

6. The method of central-peripheral synchronized regulation low intensity focused ultrasound stimulation according to claim 1, wherein, The step of adjusting the parameters based on the feedback comprises the following steps: Based on the feedback, a central activation-input strength real-time correlation curve is constructed; Calculate the positive feedback gain coefficient; When the set time is reached, calculate the central component and the peripheral component; When the comprehensive index improves the set value, call the synergistic optimization model of the central and peripheral stimulation parameters to adjust the parameters.

7. The method of central-peripheral synchrony regulation low intensity focused ultrasound stimulation according to claim 1, wherein, It also comprises the following steps: Based on the feedback, a warning is given.

8. The method of claim 7, wherein the low intensity focused ultrasound stimulation is synchronized with the central-peripheral modulation. It also comprises the following steps: Generate a central-peripheral synergistic effect report.

9. A central-peripheral synchronized regulation low intensity focused ultrasound system, characterized in that, The method comprises the following steps: A multi-modal optical navigation positioning module is used to calculate the functional connection strength of the central-peripheral target point based on the functional magnetic resonance imaging of the patient's brain and the MRI data of the joint, and to determine the focus and target point based on the functional connection strength; The multi-modal optical navigation positioning module is also used to real-time calculate the distance between the focus and the target point of the joint and the distance between the focus and the target point of the transcranial low-intensity focused ultrasound; The multi-modal optical navigation positioning module is also used to calculate the depth of the target point of the joint and the depth of the target point of the transcranial low-intensity focused ultrasound based on the MRI data of the joint; A dual-target synchronous regulation module is used to control the sound beam to align the target point of the joint and the target point of the transcranial low-intensity focused ultrasound; A neural-joint multi-modal feedback module is used to obtain the feedback during the stimulation process; An intelligent synergistic control module is used to adjust the parameters based on the feedback.

10. A central-peripheral synchronized regulation low intensity focused ultrasound system, characterized in that, The method comprises the following steps: a processor and a memory having stored therein at least one instruction, the at least one instruction being loaded and executed by the processor to implement the method of hub-periphery synchronized regulation of low intensity focused ultrasound stimulation as claimed in any one of claims 1 to 8.

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