Lung biopsy sampling needle stabilizing method and system based on dynamic respiration compensation

Abstract

The invention relates to the technical field of medical instrument control, in particular to a lung biopsy sampling needle stabilizing method and system based on dynamic respiration compensation. The stabilizing method comprises the following steps: acquiring a respiratory movement signal of a patient in real time through a multi-source biosensor, wherein the multi-source biosensor comprises an optical fiber strain sensor array, a diaphragm myoelectricity sensor and a respiratory airflow sensor which are deployed at the chest and abdomen. According to the method, millimeter-microsecond-level accurate and stable control over the lung biopsy sampling process is achieved by establishing a cooperation mechanism of a breathing phase-displacement nonlinear mapping model and a macro-micro composite driving framework. The biological rationality of displacement prediction is improved through deep coupling of multi-mode breathing signals, and the chest wall non-uniform deformation gradient is analyzed in space based on cross topology deployment of an optical fiber strain sensor in an intercostal space area in combination with cross-scale feature fusion of myoelectricity and airflow sensing.

Description

Technical Field

[0001] The present invention relates to the technical field of medical device control, and specifically to a method and system for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation. Background Art

[0002] In CT-guided percutaneous lung biopsy, the spatial position drift of the puncture target caused by respiratory movement is the core challenge affecting the sampling accuracy. Existing respiratory compensation techniques mainly estimate the target displacement through chest wall movement extrapolation or airflow integration models, and their limitations are as follows: On the one hand, due to the strong non-linear coupling characteristics of the thoracic-lung tissue dynamics system, such as the synergistic effect of diaphragmatic contraction and intercostal elastic deformation, traditional linear extrapolation models are difficult to capture the viscoelastic potential field differences between the inhalation and exhalation phases.

[0003] On the other hand, the transient jump of respiratory movement acceleration, such as coughing and apnea conversion, will significantly change the tissue strain distribution, and a single sensing modality cannot decompose the frequency variation characteristics of each component of the displacement vector in real time.

[0004] In addition, the coupling interference between the rigid structure dynamics lag of the current driving platform and physiological high-frequency tremors results in a millisecond-level desynchronization between the compensation action and the true respiratory phase, thereby inducing accidental contact between the tip of the puncture needle and high-risk anatomical structures. Especially when the needle tip crosses the tissue interface with different viscoelastic moduli, the control system lacks an adaptive adjustment mechanism for gain rigidity, which is likely to cause iatrogenic injuries such as tissue tearing or blood vessel penetration.

[0005] Although existing methods introduce safety monitoring strategies, there is a delay of hundreds of milliseconds in the decision-making loop of impedance feature recognition and pose adjustment, which cannot meet the safety response threshold requirements under rapidly changing respiratory states. Therefore, how to construct a real-time prediction model that can simultaneously couple the time-varying characteristics of respiratory dynamics and tissue viscoelastic parameters, and generate a non-linear phase synchronization compensation control law based on this, has become a technical bottleneck for improving the spatial accuracy of lung biopsy sampling. Summary of the Invention

[0006] The present disclosure proposes a method and system for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation, aiming to overcome at least one defect existing in the prior art.

[0007] To achieve the above object, the technical solutions disclosed by the present invention are as follows: According to one aspect of the present disclosure, a method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation is provided. The steps of the stabilization method include: The respiratory motion signals of the patient are collected in real time through a multi-source biosensor, and the multi-source biosensor includes a fiber optic strain sensor array, a diaphragm electromyogram sensor, and a respiratory airflow sensor deployed on the chest and abdomen. The fiber optic strain sensor covers the 4th - 8th intercostal space area in a cross - cross grid topology; A respiratory phase - displacement non - linear mapping model is established. After removing the electromyogram noise from the respiratory motion signals through wavelet packet decomposition, a three - dimensional respiratory motion vector sequence is fused and generated. The displacement compensation reference point is set as the corresponding body surface position where the puncture target is vertically projected onto the chest wall; According to the instantaneous change rate and acceleration spectrum characteristics of the three - dimensional respiratory motion vector, a feed - forward compensation control law is dynamically constructed, and a puncture needle spatial pose adjustment instruction is generated through a variable - gain proportional - integral - derivative controller; The compensation action is executed through a macro - micro composite drive platform. The macro - micro composite drive platform includes a six - degree - of - freedom macro - motion platform driven by a voice coil motor cascaded with a nano - positioning platform driven by a piezoelectric ceramic, and full - range compensation of respiratory displacement is achieved through cascaded control; Based on the actual displacement data of the puncture needle feedback by the optical encoder and the target compensation amount, closed - loop error correction is performed. At the same time, the contact state between the needle tip and the tissue is monitored by combining an impedance detection module. When the change gradient of the contact force exceeds a preset safety threshold, an emergency stop protocol is triggered.

[0008] Furthermore, the construction process of the respiratory phase - displacement non - linear mapping model includes: Perform phase - space reconstruction on the three - dimensional respiratory motion vector sequence, select the time delay τ = 0.2T - 0.3T, where T is the average respiratory cycle, and the embedding dimension m = 6 - 8; Calculate the predicted trajectory of the displacement compensation reference point. The calculation method of the predicted trajectory is: , where Φ(·) is the chaotic system evolution operator trained by a radial basis function neural network, X(t) represents the respiratory motion displacement vector at time t, τ represents the time variable in the integration process, Δt represents the prediction time interval, ∇ 2 Ψ represents the Laplace operator of the tissue visco - elastic potential field, X d is the ideal target coordinate, and α, β, and γ are the weight parameters of the respiratory amplitude, frequency variation coefficient, and thoracic rigidity factor respectively.

[0009] Furthermore, the parameter adjustment of the variable - gain proportional - integral - derivative controller follows the following time - domain - frequency - domain hybrid tuning criterion: Define a gain adjustment factor to respond in real time to the acceleration change of respiratory motion; Online optimize the controller parameters through an impedance frequency modulation equation: , where, Kp (t) represents the proportion gain adjusted in real time, κ(t) represents the gain adjustment factor, and K p0 represents the reference proportion coefficient, μ represents the error accumulation compensation gain, represents the integral term of historical error with exponential decay, e(τ) represents the displacement error signal at time τ, ω c represents the Hilbert instantaneous frequency estimate of the respiratory fundamental frequency; K i (t) represents the integral gain adjusted in real time, ζ represents the dynamic estimated value of the damping ratio, ω n represents the natural frequency of the system, represents the time change rate of the contact force F(t) between the needle tip and the tissue; K d (t) represents the differential gain adjusted in real time, represents the reference differential gain term, represents the logarithmic adjustment term of the error change rate.

[0010] Furthermore, the steps of the cascade control include: The macro motion platform is responsible for compensating the respiratory motion component with large low-frequency displacements, and adopts a position-current double-loop control structure to ensure motion stiffness; The nano-positioning platform compensates the physiological tremor component with high-frequency and small amplitudes, and integrates an inverse hysteresis feedforward compensation algorithm to suppress the nonlinear hysteresis effect of the piezoelectric ceramic; A kinematic coupling error model is established, and the motion synchronization between the macro motion platform and the nano-positioning platform is realized through a coordinate transformation matrix, which is used to ensure the pose accuracy of the puncture needle.

[0011] Furthermore, the monitoring steps of the impedance detection module include: Apply a swept-frequency electrical excitation signal to the puncture needle and collect the admittance spectrum between the needle body and the tissue; Identify the tissue type in contact with the needle tip based on the characteristic parameters of the admittance spectrum. When the characteristic impedance of high-risk tissues such as blood vessels and pulmonary bullae is detected, a path correction instruction is generated; The emergency stop protocol includes the millisecond-level response lock of the electromagnetic brake, the release of the adsorption force of the negative pressure suction system, and the dangerous navigation prompt of the augmented reality interface, which is used to ensure operation safety.

[0012] Furthermore, the steps of the stabilization method further include: Under the CT image navigation coordinate system, the spatial registration between the respiratory compensation system and the medical image is realized through the rigid body transformation matrix of the body surface marking points. The process of the spatial registration includes marking point recognition, coordinate transformation calculation, and dynamic error correction to ensure that the positioning accuracy of the puncture target is synchronized with the respiratory motion in real time.

[0013] Furthermore, the fiber optic strain sensor array covers the 4th - 8th intercostal space region in a cross - crossed grid topology. The spacing between the sensing optical fibers along the costal arch direction and the parasternal line direction meets the spatial resolution requirements, and millimeter - level accuracy chest wall deformation measurement is achieved through optical frequency domain reflectometry technology.

[0014] Furthermore, a patient - specific pulmonary finite element model is constructed based on preoperative CT data. Through multi - physical field coupling simulation, a respiration phase - tissue deformation mapping atlas is generated to calibrate the phase window for safe puncture and the dangerous tissue area, providing a preoperative planning basis for the dynamic compensation strategy.

[0015] According to another aspect of the present disclosure, a lung biopsy sampling needle stabilization system based on dynamic respiration compensation is provided for implementing the stabilization method as described above. The stabilization system includes: A multi - modal respiration signal acquisition module, including a fiber optic strain sensor array, a diaphragm electromyography sensor, and a respiratory airflow sensor, for real - time acquisition of chest and abdominal respiration movement signals to achieve multi - dimensional perception of respiratory movement; A respiration - displacement conversion processor, integrating a wavelet packet denoising module and a non - linear mapping algorithm, fuses the acquired respiration signals to generate a three - dimensional respiration movement vector sequence, and establishes a dynamic mapping model between the respiration phase and tissue displacement to provide motion prediction data for compensation control; A variable - structure controller, including a variable - gain PID main control unit, dynamically adjusts control parameters based on the instantaneous characteristics of the respiration movement vector to generate pose adjustment instructions for the puncture needle to adapt to the time - varying characteristics of the respiratory movement; A macro - micro composite drive platform for compensating for low - frequency large displacements and high - frequency micro - amplitude movements, and achieving precise compensation of the full - range respiration displacement through a kinematic coupling mechanism; A multi - parameter feedback system, integrating an optical encoder, a six - dimensional force sensor, and an impedance detection module, real - time feedbacks the actual displacement, contact force, and tissue impedance of the puncture needle to form a closed - loop control to correct the compensation error; A medical image registration unit, which realizes spatial registration with CT images through coordinate transformation of body surface marking points, for ensuring the positioning accuracy of the puncture target and dynamic synchronization with the respiratory movement; A safety monitoring module, including an emergency stop execution mechanism and a dangerous path planning algorithm. When abnormal contact force or high - risk tissue characteristics are detected, it triggers rapid braking and provides a needle - retracting navigation to ensure operation safety.

[0016] Furthermore, the cascade control of the macro - micro composite drive platform is set as: The macro - motion platform adopts a Stewart parallel mechanism configuration, with a millimeter - level stroke and low - frequency response ability, for compensating the overall chest wall displacement caused by respiratory movement; The nano-positioning platform is based on a piezoelectric ceramic stack structure, with nano-scale resolution and high-frequency response capabilities, and is used to compensate for physiological tremors and tissue micro-deformations; The kinematic coupling mechanism combines a rigid connection component with a flexible shock-absorbing unit to reduce the dynamic coupling interference between the two platforms, so as to ensure the collaborative accuracy of the puncture needle pose adjustment.

[0017] The beneficial effects of the present invention are: By establishing a collaborative mechanism of a breathing phase-displacement non-linear mapping model and a macro-micro composite drive architecture, the present invention realizes millimeter-microsecond level precise and stable control during the lung biopsy sampling process. Specifically: The deep coupling of multi-modal breathing signals improves the biological rationality of displacement prediction. Based on the cross-shaped topological deployment of fiber optic strain sensors in the intercostal region, the cross-scale feature fusion of combined electromyogram and airflow sensing is used to resolve the non-uniform deformation gradient of the chest wall in space, such as the non-linear displacement coupling between the precordial area and under the costal arch, and the myoelectric noise interference on the main displacement frequency is eliminated through wavelet packet decomposition. The three-dimensional breathing motion vector sequence can characterize the changes in the tissue viscoelastic potential field at different breathing depths, such as the enhancement of the thoracic cavity rigidity factor at the end of inspiration, and an anatomical drive mechanism is established for the feedforward compensation control law.

[0018] Furthermore, the chaos operator of the non-linear mapping model breaks through the prediction bottleneck of the traditional extrapolation method. Using the tissue evolution operator Φ(·) trained by a radial basis function neural network, the breathing amplitude weight parameter α and the thoracic cavity rigidity factor γ are embedded in the integration process of the viscoelastic potential field (∇ 2 Ψ), so that the displacement prediction value X(t+Δt) not only covers the phase continuity of the breathing cycle, but also quantifies the non-linear modulation effect of the target attraction on the displacement trajectory. The model introduces the frequency variation coefficient through the β term and still maintains the smooth convergence characteristic of the prediction trajectory when the fundamental breathing frequency mutates, fundamentally suppressing the divergence error of the traditional linear model at the phase jump point.

[0019] Furthermore, the frequency-time domain hybrid tuning mechanism of variable gain PID realizes micro-loss compensation control. Through the real-time response of the gain adjustment factor κ(t) to the breathing acceleration, the proportional gain K p (t) automatically enhances the control stiffness in the state of sudden cough; at the same time, the contact force differential term (∂F / ∂t) in the integral gain K i (t) actively attenuates the integral effect when the needle tip contacts high-risk tissues such as blood vessels to avoid over-puncturing; the error change rate logarithmic adjustment term (log[1+log(1+∣det / dt∣)]) of the differential gain K d (t) suppresses noise amplification during high-frequency tremors and improves the damping performance during low-frequency large displacements. This control law enables the puncture needle pose adjustment to have both millimeter-level tracking accuracy and soft tissue adaptability.

[0020] Furthermore, the kinematic coupling mechanism of the macro-micro platform decouples the full-frequency motion compensation. The Stewart mechanism of the macro-motion platform compensates for the breathing fundamental frequency displacement through position-current double-loop control, while the nano-positioning platform eliminates physiological micro-tremors above 50 Hz with inverse hysteresis feedforward. The two achieve pose synchronization through the kinematic registration matrix of the rigid-flexible hybrid connection mechanism, forming a stable "dynamic zero point" for the puncture needle in three-dimensional space, and maintaining a millimeter-level relative position constancy with the target projection position even under highly variable breathing states.

[0021] Furthermore, the closed-loop safety mechanism of impedance detection and emergency stop protocol uses the phase angle mutation of the characteristic frequency band in the tissue impedance characteristics based on the swept-frequency admittance spectrum to identify high-risk tissues in real time, such as blood vessels and large pulmonary bullae, triggering the electromagnetic brake to achieve millisecond-level mechanical locking; simultaneously releasing the negative pressure adsorption force to avoid secondary damage, and mapping it to the augmented reality interface through the spatial registration coordinate system to generate a needle withdrawal path navigation, upgrading the safety monitoring from passive response to active avoidance, and reducing the risks of pneumothorax and bleeding. Description of the Drawings

[0022] Figure 1 It is a flowchart of the lung biopsy sampling needle stabilization method in an embodiment of the present invention; Figure 2 It is a schematic diagram of multi-sensor data fusion processing in an embodiment of the present invention; Figure 3 It is a schematic diagram of the respiratory displacement prediction space phase in an embodiment of the present invention; Figure 4 It is a schematic diagram of variable-gain PID tuning in an embodiment of the present invention; Figure 5 It is a schematic diagram of the dual-platform cascade control effect and time-frequency residual error analysis in an embodiment of the present invention; Figure 6 It is a schematic diagram of the characteristic impedance spectrum of tissue and the contact force safety monitoring mechanism in an embodiment of the present invention; Figure 7 It is a schematic diagram of three-dimensional dynamic error distribution and rigid body transformation of body surface marker points in an embodiment of the present invention; Figure 8 It is a schematic diagram of the safe puncture phase window and dangerous tissue area in an embodiment of the present invention; Figure 9 It is a schematic diagram of the real-time change trend of displacement tracking accuracy and error envelope in an embodiment of the present invention. Detailed Embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] The present invention provides the following preferred embodiments: Embodiment 1: To solve the problem of target displacement caused by respiratory movement during lung puncture biopsy, this embodiment proposes a method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation. Through an optical fiber strain sensor array deployed on the chest and abdomen, combined with a cross-cross grid topology covering the 4th - 8th intercostal space area, respiratory dynamics characteristics are collected in real time. This sensing system works in cooperation with a diaphragm electromyogram sensor and a respiratory airflow sensor to form a multi-source biological signal acquisition network, ensuring spatio-temporal synchronous monitoring of the respiratory phase and tissue displacement. As Figure 1 shown, the flow of the sampling needle stabilization method is as follows: S100: Real-time collect the respiratory movement signals of the patient through a multi-source biological sensor. The multi-source biological sensor includes an optical fiber strain sensor array, a diaphragm electromyogram sensor, and a respiratory airflow sensor deployed on the chest and abdomen, where the optical fiber strain sensor covers the 4th - 8th intercostal space area in a cross-cross grid topology.

[0025] S200: Establish a respiratory phase-displacement non-linear mapping model. After removing the electromyogram noise from the respiratory movement signals through wavelet packet decomposition, fuse them to generate a three-dimensional respiratory movement vector sequence, where the displacement compensation reference point is set as the corresponding body surface position of the puncture target projected vertically onto the chest wall.

[0026] S300: Dynamically construct a feedforward compensation control law according to the instantaneous change rate and acceleration spectrum characteristics of the three-dimensional respiratory movement vector, and generate a puncture needle spatial pose adjustment instruction through a variable gain proportional-integral-differential controller.

[0027] S400: Drive a six-degree-of-freedom parallel mechanism to perform a compensation action. This mechanism includes a nano-positioning platform driven by a piezoelectric ceramic and a macro-motion platform driven by a voice coil motor, and realizes full-range compensation of respiratory displacement through cascade control.

[0028] S500: Perform closed-loop error correction based on the actual displacement data of the puncture needle feedback by an optical encoder and the target compensation amount. At the same time, combine an impedance detection module to monitor the contact state between the needle tip and the tissue, and trigger an emergency stop protocol when the change gradient of the contact force exceeds a preset safety threshold.

[0029] Further, a respiration phase-displacement nonlinear mapping model is established to preprocess the original signal. High-frequency noise introduced by the electromyography sensor is filtered through wavelet packet decomposition technology, and the respiratory fundamental frequency and its harmonic components are retained, as Figure 2 shown. The processed multi-source signals are fused to generate a three-dimensional respiratory motion vector sequence. The setting of the displacement compensation reference point follows the anatomical projection principle: taking the vertical projection position of the puncture target on the chest wall as the origin of the coordinate system, a body surface-target association mapping is established, as shown in the appendix Figure 3 shown. This mapping model decomposes the complex respiratory displacement into motion components in three orthogonal directions.

[0030] It should be understood that based on the real-time dynamic characteristics of this vector sequence, a feedforward compensation control law is constructed in this embodiment. By analyzing the instantaneous change rate and acceleration spectrum characteristics of the three-dimensional vector, the nonlinear dynamic mode of respiratory motion is extracted. These characteristic parameters are input into a variable-gain proportional-integral-derivative controller to generate spatial pose adjustment instructions for the puncture needle. The core lies in adjusting the control stiffness in real time according to the change of respiratory acceleration.

[0031] The innovative design of the actuator is further elaborated. The six-degree-of-freedom parallel mechanism adopts a composite structure of a piezoelectric ceramic-driven nano-positioning platform and a voice coil motor-driven macro-moving platform. The nano-positioning platform is responsible for compensating high-frequency micro-vibrations above 50 Hz, and the macro-moving platform processes large-range displacements of the respiratory fundamental frequency. The two achieve full-range displacement compensation through a cascade control strategy: the macro-moving platform first performs rough positioning, and then the nano-platform performs fine correction, as shown in the Figure 4 displacement tracking process shown.

[0032] The implementation method of the closed-loop safety mechanism also needs to be specifically described. The optical encoder continuously monitors the actual displacement of the puncture needle. After comparing it with the target compensation amount generated by the nonlinear mapping model, real-time error correction is performed. The impedance detection module running in parallel analyzes the change of the electrical characteristics at the needle tip-tissue interface to identify abnormal contact states. When the change gradient of the contact force exceeds the preset safety threshold, an emergency stop protocol is triggered to immediately freeze all moving parts, as shown in the Figure 6 safety mechanism trigger logic shown.

[0033] Through the technical solution of this embodiment, the following collaborative control mechanism is realized: the multi-source sensing network provides the biomechanical feature analysis of respiratory motion; the nonlinear mapping model establishes an anatomy-driven feedforward compensation; the macro-micro composite actuator realizes full-frequency domain displacement suppression. The collaborative effect among subsystems enables the puncture needle to maintain a stable dynamic zero point under respiratory interference, creating a mechanical environment benchmark for precise puncture.

[0034] Embodiment 2: Aiming at the problem that the strong non - linear coupling characteristics of the thoracic - lung tissue in respiratory motion lead to insufficient prediction accuracy of traditional extrapolation models, the construction process of the respiratory phase - displacement non - linear mapping model is deeply designed in this embodiment. This model captures the dynamic characteristics of respiratory motion through phase - space reconstruction technology and introduces a chaotic system evolution operator to analyze the time - varying influence of the tissue visco - elastic potential field.

[0035] Specifically, phase - space reconstruction is performed on the three - dimensional respiratory motion vector sequence generated by multi - source biosensors. The time - delay parameter τ is selected in the range of 0.2T to 0.3T, where T is the average respiratory cycle of the patient. This range can cover the phase - change critical points of the diaphragm contraction and thoracic deformation within a single respiratory cycle. At the same time, the embedding dimension m is set to 6 to 8 dimensions. This dimension configuration can effectively characterize the dynamic characteristics of the multi - degree - of - freedom coupling motion of the chest cavity, such as the non - uniform strain - field distribution generated by the coordinated contraction of the intercostal muscle groups. It should be understood that while retaining the topological structure of the respiratory motion trajectory, the reconstructed phase - space transforms the non - stationary characteristics of chest - wall displacement into a deterministic evolution process on a high - dimensional manifold.

[0036] Furthermore, calculate the predicted trajectory of the displacement compensation reference point. The calculation method of the predicted trajectory is: , where Φ(·) is a chaotic system evolution operator trained by a radial basis function neural network, X(t) represents the respiratory motion displacement vector at time t, τ represents the time variable in the integration process, Δt represents the prediction time interval, ∇ 2 Ψ represents the Laplace operator of the tissue visco - elastic potential field, X d is the ideal target coordinate, and α, β, γ are the weight parameters of the respiratory amplitude, frequency variation coefficient, and thoracic cavity rigidity factor respectively. It can be understood that the predicted trajectory of the displacement compensation reference point is generated by the chaotic system evolution operator Φ(·). This operator is trained based on a radial basis function neural network, and its input quantity is the composite integral term of the Laplace operator ∇²Ψ of the tissue visco - elastic potential field and the target attracting potential. Among them, the Laplace term of the visco - elastic potential field quantifies the gradient distribution characteristics of the strain energy of the thoracic and abdominal tissues, such as the hysteresis effect during the transition between the inhalation and exhalation phases; the exponentially decaying target attracting potential reflects the constraint effect of the ideal target coordinate on the respiratory displacement trajectory, and its decay rate is regulated by the thoracic cavity rigidity factor γ; the weight parameter group (α, β, γ) is respectively associated with the respiratory amplitude, frequency variation coefficient, and thoracic biomechanical properties, and the three together constitute the coupling function of respiratory motion and tissue deformation.

[0037] It can be understood that the compensation mechanism of the β term for the sudden change of the respiratory fundamental frequency enables the model to maintain the smooth convergence of the predicted trajectory under physiological tremor interferences such as coughing or breath - holding states. For example, Figure 4 the displacement tracking error shown avoids divergence at the phase - jump point.

[0038] Furthermore, through the non-linear approximation ability of the neural network, the integral process transforms the chaotic characteristics of the respiratory system into a deterministic prediction of the displacement vector. As Figure 3 shown in the phase space reconstruction diagram reveals the inherent dynamic order of respiratory movement, and its low-dimensional manifold structure verifies the model's analytical ability for the characteristics of physiological non-equilibrium states.

[0039] In this embodiment, the phase space reconstruction parameter setting overcomes the limitation of the traditional linear model in characterizing the non-uniform deformation of the chest wall; the chaotic evolution operator realizes the dynamic fusion of viscoelastic parameters and spatial constraints through the potential field integral mechanism; the weight parameter group constructs an adaptive mapping bridge between respiratory physiological characteristics and tissue mechanical responses. Furthermore, the anatomical rationality of respiratory displacement prediction is improved. The model transforms microscopic mechanisms such as the enhanced rigidity effect during diaphragmatic contraction and the viscous dissipation at the lung lobe sliding interface into macroscopic displacement correction terms, providing a kinematic benchmark based on biophysical principles for the subsequent feedforward control law.

[0040] Embodiment 3: To solve the problem of insufficient compensation accuracy of traditional fixed-parameter controllers in dynamic respiratory scenarios, this embodiment deeply optimizes the parameter adjustment mechanism of the variable-gain proportional-integral-derivative controller. By constructing a tuning criterion that couples the dynamic correlation of time-domain errors and frequency-domain characteristics, the control parameters are adaptively matched to the respiratory movement and tissue interaction state.

[0041] The parameter adjustment of the controller uses the acceleration characteristics of respiratory movement as a dynamic trigger condition, and defines a gain adjustment factor κ(t) to capture the instantaneous changes of respiratory movement in real time. It should be understood that the acceleration signal of chest wall displacement during respiration contains key information such as respiratory phase transition and amplitude mutation. As a non-linear mapping link, the output value of κ(t) dynamically increases with the increase of the absolute value of the acceleration signal, thus establishing a direct correlation channel between respiratory dynamic characteristics and control gain. This design enables the controller to quickly enhance the control stiffness during the inspiratory acceleration period or sudden movement states such as coughing, avoiding the out-of-control of the needle tip displacement caused by the intensification of respiratory movement.

[0042] Furthermore, the adjustment of the proportional gain K p (t) combines the reference proportional coefficient and the integral term of historical error with exponential decay. The specific expression is: , where K p0 serves as the reference proportional coefficient to provide the basic control stiffness, and μ is the error accumulation compensation gain, which is used to adjust the contribution of historical errors to the current gain. In particular, the exponential decay function in the integral term uses the estimated value ω of the Hilbert instantaneous frequency of the respiratory fundamental frequency cAs an attenuation factor, the recent displacement error e(τ) has a higher weight on the current gain, while the effect of the long-term error decays exponentially over time. This design not only retains the correction effect of the recent error, but also avoids the interference of outdated data on the current control decision, especially when the respiratory rate fluctuates, ω c Real-time updates ensure that the decay rate is synchronized with the respiratory cycle, thereby improving the timeliness of error compensation.

[0043] Furthermore, the integral gain K i The optimization of (t) introduces the coupling constraint between the system dynamic parameters and the contact force change rate, and its expression is: , where K i (t) represents the real-time adjusted integral gain, ζ represents the dynamic estimation value of the damping ratio, ω n represents the natural frequency of the system, It represents the time rate of change of the contact force F(t) between the needle tip and the tissue. When the puncture needle contacts tissues with different elastic moduli, such as when it enters the blood vessel wall from the lung parenchyma, The controller will exhibit a significant gradient change. At this point, by subtracting this rate of change, it actively reduces the integral gain to suppress excessive cumulative position adjustments and avoid the risk of needle penetration due to sudden changes in tissue impedance. This design, which directly embeds biomechanical signals into control parameters, achieves deep coupling between the control strategy and the tissue interaction state.

[0044] Furthermore, the differential gain K d The adjustment of (t) adopts a logarithmic adjustment mechanism based on the error change rate, which is as follows: ,in, represents the reference differential gain term, Represents the logarithmic adjustment term of the error change rate. p (t) and the system natural frequency ω n The ratio of is used as the reference differential gain term to ensure that the intensity of the differential action matches the dynamic change of the proportional gain; and the logarithmic adjustment term The magnitude of the error rate of change is nonlinearly scaled: when de(t) / dt is small, the logarithmic term slowly increases the differential gain to enhance the response to small displacement changes. When de(t) / dt is large, the saturation characteristic of the logarithmic function prevents excessive amplification of the differential gain, thereby suppressing high-frequency noise interference with the control output. This design establishes an adaptive adjustment mechanism between low-frequency displacement changes caused by respiratory motion and high-frequency noise caused by tissue tremor, balancing the system's dynamic response capability and anti-interference performance.

[0045] It can be understood that the above parameter adjustment criteria capture the transient characteristics of respiratory acceleration in real time through κ(t) and cRealize the frequency-domain tracking of the respiratory cycle. By perceiving the mechanical mutation of the tissue interface, a three-dimensional optimization system of time-domain error accumulation, frequency-domain period adaptation, and force feedback constraint is formed. The parameters do not act independently, but through K p (t)'s dynamic amplitude as a bridge to organically integrate the respiratory motion characteristics, historical error information, and current contact force state, enabling the controller to maintain stable control performance in complex scenarios such as respiratory frequency fluctuations and tissue impedance changes.

[0046] Through this embodiment, the variable-gain proportional-integral-differential controller no longer relies on preset fixed parameters, but constructs a parameter optimization framework with biomechanical adaptability through multiple mechanisms such as frequency weighting of the time-domain integral term, force feedback correction of the integral term, and logarithmic smoothing of the differential term. This design enables the sampling needle to adjust the control stiffness in real time during dynamic breathing, ensuring both the tracking accuracy of respiratory displacement and avoiding the risk of tissue damage caused by parameter lag, providing a more reliable stability control scheme for lung biopsy operations.

[0047] Embodiment 4: To solve the problem of precise compensation of multi-scale displacement components during dynamic breathing, this embodiment refines the design of the motion compensation mechanism for the cascade control structure, such as Figure 5 shown, realizing the differential processing of low-frequency large displacements and high-frequency micro-amplitude motions through a hierarchical control strategy, and combining kinematic coupling modeling to ensure the multi-platform motion coordination.

[0048] Specifically, the cascade control architecture includes a collaborative working system of a macro motion platform and a nano-positioning platform. Among them, the macro motion platform undertakes the compensation task of the low-frequency large-displacement respiratory motion component, and its control structure adopts a position-current double-loop design: the outer loop is the position loop, which collects platform displacement data in real time through a high-precision encoder and generates a speed command based on the proportional-integral control algorithm; the inner loop is the current loop, which uses the feedback signal of the current sensor of the servo motor and quickly responds to the torque demand through the combination of feedforward compensation and PID regulation. It should be understood that this double-loop structure can effectively suppress the low-frequency chest wall displacement during the respiratory cycle by enhancing the position tracking stiffness of the system, providing a basic stable support for the puncture needle.

[0049] Furthermore, the nano-positioning platform is used to compensate for the high-frequency and micro-amplitude physiological tremor component, and its actuator adopts a piezoelectric ceramic drive unit. Considering that the nonlinear hysteresis effect of piezoelectric ceramics will significantly affect the positioning accuracy, in this embodiment, the control input signal is pre-distorted through a pre-generated inverse hysteresis model function, so as to form a linear mapping relationship between the actual displacement output and the command signal. Specifically, the controller first calculates the theoretical drive voltage according to the target displacement, and then corrects the voltage signal through the hysteresis inverse model to compensate for the displacement hysteresis phenomenon generated during the polarization process of the piezoelectric ceramics, thereby controlling the positioning error within the sub-micron level. This design effectively solves the nonlinear distortion problem of traditional piezoelectric drive systems at high-frequency response and ensures accurate tracking of physiological tremors.

[0050] To achieve the motion synchronization of the two-stage platform, the system establishes a kinematic coupling error model and maps the motion state of the macro-motion platform to the control coordinate system of the nano-positioning platform through a coordinate transformation matrix. The specific process is as follows: First, define the spatial transformation relationship between the global coordinate system and the local coordinate systems of each platform, use a laser rangefinder to measure the position deviation of the macro-motion platform in real time, and convert this deviation into a compensation command for the nano-platform through a homogeneous transformation matrix. It should be noted that an inertial force coupling compensation term is introduced during the coordinate transformation process to correct the additional displacement error caused by dynamic coupling during the motion of the two-stage platform. For example, when the macro-motion platform performs a rapid translation, the nano-platform will detect the minute deformation caused by the inertial force and adjust the control command in real time through the coupling model to eliminate the influence of mechanical vibration transmission between the platforms. This error correction mechanism ensures the pose consistency of the puncture needle during the coordinated motion of the two-stage platform and avoids the tip positioning deviation caused by asynchronous motion.

[0051] It can be understood that the division of labor and cooperation between the macro-motion platform and the nano-positioning platform form a multi-scale motion compensation system. The former provides sufficient motion stiffness through a double-loop control structure to solve the main displacement compensation of respiratory motion; the latter achieves accurate tracking of high-frequency tremors with the help of an inverse hysteresis algorithm and eliminates the dynamic interaction error between the two-stage platforms through a kinematic coupling model. This hierarchical control strategy is not a simple functional division, but an organic combination of control loop design, nonlinear compensation algorithm, and coordinate transformation mechanism, constructing a full-frequency motion compensation ability covering the frequency range from 0.1 Hz to 100 Hz. The subsystems form a closed-loop correction through real-time data interaction to ensure that the pose error of the puncture needle is always within a controllable range under complex scenarios such as respiratory frequency fluctuations and changes in the amplitude of physiological tremors.

[0052] Through this embodiment, the cascaded control structure achieves precise decoupling compensation for multiple components of respiratory motion. The low-frequency displacement is processed by the high-stiffness control loop of the macro-motion platform, the high-frequency tremor is suppressed by the nonlinear compensation algorithm of the nano-platform, and the kinematic coupling model ensures the collaborative accuracy of the two-stage platform. This not only improves the system's response ability to different characteristic motion components but also reduces the load of a single platform through the optimization of the control architecture, providing hierarchical technical support for the stable control of the puncture needle during lung biopsy operations.

[0053] Embodiment 5: To solve the problem of real-time identification and safe response to the interface of high-risk tissues during puncture, this embodiment specifically designs the monitoring mechanism and emergency stop protocol of the impedance detection module, realizes tissue type identification through swept-frequency electrical excitation signals and admittance spectrum analysis, and constructs an operation risk prevention and control system by combining multi-modal safety measures.

[0054] The core monitoring steps of the impedance detection module start with applying a swept-frequency electrical excitation signal to the puncture needle, which is injected into the tissue through a microelectrode array integrated in the insulation layer of the needle body. As the needle tip contacts tissues with different physiological characteristics, such as lung parenchyma, blood vessels, and pulmonary bullae, the admittance spectrum between the needle body and the tissue will show characteristic differences: blood vessel tissue exhibits low impedance and high capacitive reactance characteristics due to the rich electrolyte solution, while pulmonary bullae tissue shows high impedance and low loss factor characteristics due to air filling. The system collects the amplitude and phase information of the admittance spectrum through a lock-in amplifier, extracts characteristic parameters including equivalent resistance, capacitive reactance, and tangent of loss angle, and real-time identifies the type of tissue currently contacted based on the support vector machine classification model. It should be understood that the frequency coverage range of the swept-frequency signal is calibrated according to the impedance characteristics of biological tissues to ensure that the admittance differences at different tissue interfaces can be effectively distinguished, thus providing a reliable basis for path correction.

[0055] When the detection module identifies the characteristic impedance of high-risk tissues, such as the impedance modulus of blood vessels being lower than the preset threshold and the tangent of loss angle of pulmonary bullae being less than the critical value, the path correction mechanism is immediately triggered. This mechanism includes three levels of response measures: First, the control algorithm calculates the distance between the current position of the needle tip and the boundary of the high-risk tissue based on real-time impedance data, generates a path offset instruction based on gradient descent, and guides the puncture needle to adjust its direction along the minimum invasion path; Second, if the gradient change rate of the detected tissue interface exceeds the safety threshold, indicating that the needle tip is approaching the tissue edge, the emergency stop protocol is activated. This protocol includes three collaborative working units: The electromagnetic brake receives a millisecond-level response instruction and locks the feeding mechanism of the puncture needle through electromagnetic force to avoid out-of-control penetration caused by inertial displacement; The negative pressure suction system synchronously releases the current adsorption force to eliminate the risk of tissue traction caused by the pressure difference; The augmented reality interface real-time marks the boundary of the dangerous area, provides visual navigation prompts for the operator, and assists in judging subsequent operation strategies.

[0056] It is understandable that there is a strict signal processing logic between impedance detection and the emergency response mechanism. The high signal-to-noise ratio acquisition of the swept-frequency electrical excitation ensures the accuracy of tissue characteristic parameters. The real-time computing ability of the classification model meets the real-time requirements of clinical operations, with a response delay less than 20 ms. The parallel execution of multi-modal safety measures forms a multi-dimensional protection system of physical braking, force feedback control, and visual cues. In particular, the electromagnetic brake adopts a normally closed design and automatically locks when the system is powered off, avoiding safety failures caused by power failures. The adsorption force release process of the negative pressure suction system includes an exponential decay curve control to prevent tissue damage caused by sudden force unloading. These design details jointly ensure the reliability and robustness of the safety control strategy.

[0057] Through this embodiment, the impedance detection module not only realizes the real-time identification of tissue types, but also constructs a closed-loop safety control chain from risk detection to response execution through deep linkage with the actuator and the human-machine interface. The application of swept-frequency electrical excitation avoids the risk of tissue damage in radioactive detection or optical imaging. The non-invasive characteristics of admittance spectroscopy ensure the safety of the detection process. The multi-mechanism collaboration of the emergency stop protocol sets up multiple protection barriers for the puncture operation. This design enables the system to respond at the early stage of contacting high-risk tissues, effectively reducing the occurrence probability of complications such as vascular injury and alveolar rupture during lung biopsy.

[0058] Embodiment Six: To solve the problem of the attenuation of puncture target positioning accuracy caused by respiratory movement, this embodiment conducts a detailed design on the spatial registration mechanism of the CT image navigation and respiratory compensation system. Through the rigid body transformation matrix calculation and dynamic error correction of the body surface marker points, high-precision alignment between the medical image space and the real-time operation space is achieved.

[0059] The registration process is based on the CT image navigation coordinate system. First, an array of marker points with optical characteristics is pasted on the patient's body surface, usually 3 - 5 spherical markers. The spatial distribution of the marker points satisfies the geometric constraint conditions for rigid body transformation calculation. During the operation, the binocular vision camera is used to collect the marker point images in real time. The image processing algorithm is used to identify the sub-pixel level position coordinates of the marker points, and the three-dimensional coordinates of the marker points in the camera coordinate system are calculated in combination with the camera calibration parameters. It should be understood that the material and shape of the marker points are specially designed to reflect infrared light of a specific wavelength, avoiding environmental light interference and ensuring stable identification during respiratory movement.

[0060] Next, the system realizes the coordinate transformation from the camera coordinate system to the CT image coordinate system through a rigid body transformation matrix. This matrix contains a rotation component R and a translation component T, and its solution process uses the Iterative Closest Point (ICP) algorithm: First, the virtual positions of the marker points in the preoperative CT image are matched with the intraoperative real-time measurement positions, and the parameters of R and T are optimized by minimizing the root mean square error to obtain the initial registration transformation relationship. Considering the displacement of the body surface marker points caused by respiratory movement, which is mainly manifested as the periodic undulation of the chest wall, a dynamic error correction module is introduced in the registration process: This module monitors the movement trajectory of the marker points through an acceleration sensor, establishes a phase model of the respiratory cycle and the displacement of the marker points, predicts the position offset of the marker points in the CT coordinate system in real time, and compensates and updates the rigid body transformation matrix. For example, when the marker points move upward with the chest wall during inspiration, the system predicts the current displacement according to the historical respiratory waveform data and corrects the translation component T in the coordinate transformation to ensure that the position coordinates of the puncture target are always synchronized with the real-time respiratory phase.

[0061] To verify the registration accuracy, the system has a built-in error calibration mechanism. In the preoperative planning stage, the registration error at different respiratory phases is tested through a phantom model, and an error compensation lookup table is established; during the operation, the static positions of the marker points are regularly collected, and the real-time error is calculated by comparing with the CT image coordinates, and the parameters of the phase model are dynamically adjusted. This closed-loop calibration mechanism effectively corrects the non-rigid body motion differences between the displacement of the marker points and the movement of internal organs. For example, the displacement of lung tissue caused by diaphragmatic movement and the displacement of body surface marker points have a non-linear coupling, ensuring that the positioning error of the puncture target remains stable during the respiratory cycle.

[0062] It can be understood that, as Figure 7 and Figure 9 shown, spatial registration is not a one-time coordinate transformation, but a dynamic process that includes dynamic tracking of marker points, rigid body transformation solution, respiratory phase compensation, and real-time error calibration. The body surface marker points, as the bridge between the external reference system and the internal anatomical structure, directly affect the accuracy of the target coordinate transformation; and the real-time update of the rigid body transformation matrix solves the problem of accuracy degradation of traditional static registration methods in dynamic respiratory scenarios by embedding respiratory movement parameters into the coordinate transformation process. In particular, the establishment of the phase model takes into account individual respiratory pattern differences, such as the different movement characteristics of thoracic breathing and abdominal breathing, and improves the generalization ability of the registration model by using an adaptive filtering algorithm to learn the specific respiratory movement law of the patient in real time.

[0063] Through this embodiment, the CT image navigation and respiratory compensation system achieves the guarantee of spatial consistency from preoperative planning to intraoperative operation: the dynamic recognition of body surface marker points provides a real-time motion reference, the rigid body transformation matrix calculation establishes a mathematical mapping between the image space and the operation space, and the respiratory phase compensation and error correction eliminate the registration deviation caused by physiological motion, enabling the puncture target to be adjusted in real time with respiratory motion, ensuring that the position of the needle tip always maintains spatial correspondence with the target area in the CT image during the needle insertion process, and providing technical support for the precise positioning of lung biopsy operations.

[0064] Embodiment Seven: To solve the problem of high-precision real-time measurement of chest wall deformation during respiratory motion, this embodiment refines the layout design and measurement principle of the fiber optic strain sensor array, and realizes motion perception with millimeter-level resolution through a cross-cross grid topology and optical frequency domain reflectometry technology, providing basic data support for dynamic respiratory compensation.

[0065] Specifically, the fiber optic strain sensor array takes the 4th - 8th intercostal spaces as the core coverage area and adopts an orthogonal cross-cross grid topology: sensing optical fibers are laid along the costal arch direction (transverse) and the parasternal line direction (longitudinal), and the spacing between the two is optimized according to the spatial resolution requirements to ensure that the distance between adjacent sensor nodes is not greater than twice the target measurement accuracy to meet the accuracy of interpolation calculation. It should be understood that the selection of the intercostal space area is based on the main occurrence site of chest wall deformation during respiratory motion. The rib motion in this area has a significant coupling effect with diaphragmatic contraction, and its deformation signal can effectively reflect the displacement trend of lung tissue. The sensing optical fiber uses single-mode polarization-maintaining optical fiber, with a polyimide stress-sensitive layer coated on the surface, and is fixed on a flexible substrate through an ultraviolet curing process to ensure good adhesion to the chest wall skin and reduce motion artifacts.

[0066] Furthermore, the measurement process realizes distributed strain sensing based on optical frequency domain reflectometry (OFDR) technology: the swept-frequency light emitted by a broadband light source enters the sensing optical fiber through a coupler. The backward Rayleigh scattered light in the optical fiber forms a time-delay signal according to the distance. The interference spectrum is collected by a high-speed spectrometer, and the strain distribution at each position is calculated by Fourier transform. Specifically, the system performs time-frequency analysis on the strain data of each grid node, separates the periodic component of respiratory motion and random noise, and updates the three-dimensional deformation field on the chest wall surface in real time through the Kalman filter algorithm. It should be noted that the millimeter-level spatial resolution of the OFDR technology benefits from the high-frequency stability of the swept-frequency light source and the high sampling rate of the spectrometer, which can capture the subtle displacements in the intercostal space area, and the minimum measurable deformation is <5μm.

[0067] Furthermore, the sensor array's signal processing process includes two key steps: temperature compensation and motion decoupling. Temperature drift is monitored in real time by a built-in platinum resistance temperature sensor, and a polynomial fitting algorithm is used to correct for temperature-induced changes in the fiber's refractive index. Motion decoupling utilizes the rigid body motion assumption of the chest wall surface, separating the overall translational component from the local deformation component through principal component analysis to extract effective signals related to lung tissue displacement. This design enables the sensor array to not only measure the overall chest wall fluctuation but also identify localized non-uniform deformations, such as abnormal unilateral rib movement, providing more accurate motion characteristic parameters for the respiratory compensation system.

[0068] Understandably, the advantage of the cross-grid topology lies in achieving full coverage of the two-dimensional deformation field: the transverse optical fiber captures the opening and closing movement of the ribs, while the longitudinal optical fiber monitors the anterior-posterior displacement of the sternum. The strain data of both are synthesized through tensor analysis to synthesize the principal strain direction of the chest wall surface, thereby inferring the displacement vector of the lung tissue. This multi-dimensional strain measurement avoids the information loss problem of single-point sensors. Combined with the distributed sensing capabilities of OFDR technology, a dynamic monitoring network covering the main areas of respiratory movement is constructed. The flexible substrate design of the sensor array is compatible with the body curvature of patients of different body sizes, ensuring signal stability in different body positions such as side-lying and supine, expanding clinical application scenarios.

[0069] This embodiment uses a fiber optic strain sensor array to measure chest wall deformation with high precision and high temporal and spatial resolution. The orthogonal grid layout ensures the integrity of the deformation signal, OFDR technology provides distributed sensing with millimeter-level accuracy, and the signal processing algorithm eliminates environmental interference and extracts the effective motion component. This design provides real-time, reliable motion input data for the dynamic breathing compensation system, enabling the system to accurately track chest wall motion and providing a direct position reference for needle position adjustment, thus ensuring the stability and safety of lung biopsy procedures from a sensory perspective.

[0070] Example 8: To address the problem of predicting individual differences in lung tissue deformation during respiratory movement, this example constructs a patient-specific lung finite element model based on preoperative CT data, and generates a respiratory phase-tissue deformation mapping map through multi-physics field coupling simulation, providing an individualized preoperative planning basis for the dynamic compensation strategy and improving the safety and accuracy of puncture path design.

[0071] The model construction process begins with the three-dimensional reconstruction of preoperative high-resolution CT images. The anatomical structures such as the lung parenchyma, ribs, and blood vessels are extracted using threshold segmentation and region growing algorithms, and a physical model is generated through reverse engineering software. Regarding the biomechanical properties of lung tissue, tetrahedral elements are used for finite element mesh generation. For the lung parenchyma, a hyperelastic material constitutive model such as the Mooney-Rivlin model is used, and a linear elastic model is used for the blood vessel wall. Contact boundary conditions are set for the pleural layer to simulate tissue sliding during the breathing process. It should be understood that the assignment of material parameters is adjusted in combination with the patient's age, gender, and pulmonary function test data. For example, the elastic modulus of the lung parenchyma in patients with chronic obstructive pulmonary disease is appropriately reduced to reflect the differences in tissue mechanical properties under pathological conditions.

[0072] Furthermore, the multi-physics field coupling simulation process integrates the physiological driving mechanism of respiratory motion. A diaphragmatic displacement load is applied at the bottom of the model, and a pressure load that changes with the respiratory phase is applied to the chest wall surface, which is obtained by fitting the historical data of the fiber optic strain sensor array, to simulate the expansion and contraction process of lung tissue during inhalation and exhalation phases. The explicit dynamics algorithm is used for simulation solving, and the time step is set to 5 ms to ensure capturing the dynamic stress distribution during the respiratory cycle. Through simulation calculations at different respiratory phases, displacement fields, strain energy density distributions, and blood vessel path change data of each anatomical structure are generated to construct a deformation database containing multiple key respiratory phase points.

[0073] As Figure 8 shown, based on the simulation results, the phase window for safe puncture and the dangerous tissue regions are calibrated by the system. The safe phase window is defined as the phase interval where the deformation rate of lung tissue is less than 5% and the displacement trajectory of blood vessels is relatively stable, which is determined by calculating the tissue displacement gradient and the blood vessel curvature change rate at each phase point. The dangerous tissue regions include pulmonary bullae, blood vessel-dense areas, etc., and their boundaries are marked with color coding in the deformation atlas, and at the same time, the change rules of the spatial positions of these regions at different respiratory phases are recorded. It should be noted that the calibration of dangerous regions is combined with the puncture contraindication criteria in clinical guidelines. For example, the region within 3 mm of the main pulmonary artery is defined as an absolute contraindication area, and the boundary of the contraindication area that changes with the respiratory phase is updated in real time through an interpolation algorithm.

[0074] It is understandable that the advantage of patient-specific models is that they take into account the differences in individual anatomical structures and mechanical properties: there are significant differences in the rib morphology, lung lobe distribution, and vascular shape of different patients. Traditional general models are difficult to accurately predict their respiratory deformation, while individualized modeling based on CT data can accurately reflect these differences. Multi-physics field coupling simulation not only simulates the passive displacement of lung tissue, but also considers biomechanical factors such as respiratory muscle contraction and pleural friction, making the deformation map closer to the real physiological process. This preoperative planning method provides prior knowledge for intraoperative dynamic compensation strategies. For example, a lower compensation gain can be used within the safe phase window, and the control accuracy can be automatically improved when approaching the dangerous phase, thereby achieving personalized risk control.

[0075] This embodiment combines patient-specific finite element models with multi-physics simulation technology to construct a complete prediction system from anatomical structure to biomechanical response. Precise modeling of preoperative CT data ensures model authenticity. Deformation maps generated by coupled simulation provide a quantitative relationship between respiratory phase and tissue displacement. Calibration of the safe phase window and risk zone provides clear constraints for puncture path design. This design elevates preoperative planning from static anatomical positioning to dynamic physiological process prediction, providing a scientific basis for parameter optimization and path adjustment of intraoperative dynamic compensation strategies, effectively reducing the risk of puncture caused by respiratory motion.

[0076] Example 9: To solve the problem of systematic integration of multi-component compensation for respiratory motion during lung biopsy, this example provides a lung biopsy sampling needle stabilization system based on dynamic respiratory compensation. The hardware architecture and module coordination mechanism of the stabilization system are designed in detail. Through the organic combination of modules such as multimodal signal acquisition, variable structure control, and a composite drive platform, a closed-loop control system covering respiratory motion perception, prediction, compensation, and feedback is constructed.

[0077] Specifically, the core component of the stabilization system is a multimodal respiratory signal acquisition module, comprising a fiber optic strain sensor array, a diaphragm electromyography sensor, and a respiratory airflow sensor. The fiber optic strain sensor array, as shown, covers the intercostal space of the chest wall, collecting real-time chest wall surface deformation signals. The diaphragm electromyography sensor, using a three-lead surface electrode array, monitors diaphragm electrical activity to reflect changes in respiratory driving force. The respiratory airflow sensor collects tidal volume signals through a nasal airflow mask, providing a direct indicator of respiratory phase. The signals from these three sensors are time-aligned using a synchronized clock and input into a respiratory-displacement conversion processor. This processor integrates a wavelet packet noise reduction module to filter high-frequency noise and baseline drift. It then fuses the multimodal signals using a nonlinear mapping algorithm, such as a neural network regression model, to generate a respiratory motion vector sequence containing three-dimensional translation and rotational components. A dynamic mapping model between respiratory phase and lung tissue displacement is then established to predict the positional offset of the puncture target at different phases.

[0078] Furthermore, the variable-structure controller includes a variable-gain PID main control unit, whose control parameters are adjusted in real time according to the instantaneous characteristics of the respiratory motion vector. For example, when the respiratory frequency is detected to increase, the proportional coefficient is automatically increased to improve the system response speed; if the displacement amplitude exceeds the preset threshold, the differential preview algorithm is activated to suppress overshoot. The pose adjustment instructions output by the controller include translation amounts (X / Y / Z axes) and rotation angles (pitch / yaw / roll), which are respectively sent to the two-stage actuators of the macro-micro composite drive platform.

[0079] Furthermore, the macro-micro composite drive platform adopts a cascaded structure. The macro drive platform is a six-degree-of-freedom Stewart parallel mechanism driven by a voice coil motor, with a stroke of 100 mm and a response bandwidth of 0-10 Hz, responsible for compensating for the large low-frequency displacements caused by respiratory motion, with a period of 0.5-5 s and an amplitude of 5-30 mm; the nano-positioning platform is based on a piezoelectric ceramic stack structure, with a resolution of up to 5 nm and a response frequency up to 500 Hz, used to compensate for physiological tremors (5-50 Hz) and tissue micro-deformations (<50 μm). The two platforms are connected by a kinematic coupling mechanism, which includes a rigid adapter and a flexible hinge. The former ensures structural stability, and the latter isolates the vibration transmission of the macro drive platform and reduces dynamic coupling interference. It should be understood that the control input of the drive platform is converted through a coordinate transformation matrix, and the pose instructions in the global coordinate system are decomposed into the local coordinate system motion components of the two platforms to achieve coordinated compensation of the full-range displacement.

[0080] Furthermore, the multi-parameter feedback system constitutes a key link in the closed-loop control. The optical encoder real-time feedbacks the displacement of the macro drive platform, the capacitance sensor built into the piezoelectric ceramic monitors the micro-displacement of the nano platform, the six-axis force sensor detects the contact force between the puncture needle and the tissue, and the impedance detection module collects the tissue admittance spectrum as before. These feedback signals are fused by a Kalman filter to generate a compensation error correction amount, and the controller output instructions are adjusted in real time. The medical image registration unit aligns the CT image coordinate system with the system operation coordinate system through the rigid body transformation matrix of the body surface marker points, ensuring that the puncture target coordinates are dynamically updated with respiratory motion. The safety monitoring module integrates an emergency stop actuator and a dangerous path planning algorithm. When the contact force exceeds the safety threshold or the characteristics of high-risk tissues are detected, the brake is immediately triggered and a retraction path is generated, and at the same time, the operator is prompted through the haptic feedback handle.

[0081] It is understandable that real-time communication is achieved between modules via a high-speed data bus, and the overall system control cycle is set to 1ms to ensure timely response to high-frequency respiratory components. Multimodal signal acquisition avoids the measurement blind spots of a single sensor, the variable structure control strategy adapts to the time-varying characteristics of respiratory motion, the cascade design of the composite drive platform achieves hierarchical compensation of multi-scale displacements, and the feedback system and safety module ensure the stability and safety of the operation process. This systematic architecture is not a simple stacking of components, but through the deep integration of signal flow, control flow, and energy flow, it forms a closed-loop control system from perception to execution, effectively addressing the complexity and uncertainty of respiratory motion.

[0082] This embodiment demonstrates the engineering integration of respiratory compensation technology in the stabilization system. Multimodal sensing provides comprehensive motion information, variable structure control addresses time-varying parameter adjustment, a composite drive platform achieves full-band displacement compensation, and feedback and safety modules provide a fault-tolerant mechanism. This design provides an automated, high-precision, stable control platform for lung biopsy procedures. Its modular architecture supports configuration and functional expansion in clinical applications, ensuring the safety and accuracy of the puncture process at a system level.

[0083] Example 10: In order to solve the dynamic coupling problem of multi-scale displacement compensation in respiratory motion, this embodiment refines the mechanical structure and control strategy of the macro-micro composite drive platform. Through the cascade design of the Stewart parallel mechanism and the piezoelectric ceramic stack structure, combined with the rigid-flexible coupling mechanism, the decoupling compensation of low-frequency large displacement and high-frequency micro-motion is realized, ensuring the coordinated accuracy of the puncture needle posture adjustment.

[0084] Specifically, the macro-motion platform adopts a six-degree-of-freedom Stewart parallel mechanism configuration, consisting of an upper platform, a lower platform and six retractable drive rods. Each drive rod is equipped with a voice coil motor and a ball screw transmission pair, with a linear travel of ±50mm and a positioning accuracy of 0.1mm. The kinematic feature of this configuration is that the spatial posture command is decomposed into the length change of each drive rod through an inverse solution algorithm such as the closed-loop vector method. The high stiffness characteristics of its parallel structure can effectively resist the low-frequency interference force generated by respiratory movement, such as the inertial force of chest wall movement. It should be understood that the direct drive method of the voice coil motor avoids the clearance error of the gear transmission, and cooperates with the position feedback of the high-precision grating scale to ensure that the displacement tracking error of the macro-motion platform in the frequency range of 0.5-10Hz is less than 5μm, meeting the needs of low-frequency large displacement compensation.

[0085] Furthermore, the nano-positioning platform is constructed based on a piezoelectric ceramic stack structure. It uses three layers of orthogonally arranged piezoelectric ceramic wafers, with each layer containing 8 parallel stacks. The nano-scale displacement output is achieved through the inverse piezoelectric effect. The displacement range of this platform is ±10μm, the resolution reaches 5nm, and the resonant frequency exceeds 500Hz. It can respond to physiological tremors and tissue micro-deformations, such as the peristalsis of the lung surface during breathing. Aiming at the hysteresis non-linearity problem of piezoelectric ceramics, the control circuit integrates a feed-forward inverse model compensation module. The driving voltage is pre-distorted through the inverse function of the Preisach model, and the hysteresis error is suppressed within 1%. The displacement of the platform is detected by a capacitive sensor, which real-time feedbacks the nano-scale displacement signal to form a closed-loop control to improve the positioning accuracy.

[0086] Furthermore, the cascading of the macro-micro platform is realized through a kinematic coupling mechanism, which includes a rigid adapter plate and a flexible shock-absorbing unit: The rigid adapter plate is made of titanium alloy and is fixed to the upper surface of the macro-moving platform by bolts, providing a stable mounting base for the nano-platform; The flexible shock-absorbing unit consists of four cross-shaped spring plates, which are evenly distributed between the adapter plate and the nano-platform. Its designed stiffness shows high rigidity in the low-frequency band to transmit the low-frequency displacement of the macro-moving platform, and shows low stiffness in the high-frequency band to isolate the vibration noise of the macro-moving platform. It should be noted that the dynamic parameters of the coupling mechanism are optimized through finite element simulation to ensure that its resonance frequency is far from the working frequency bands of the two platforms, avoiding the resonance effect from affecting the compensation accuracy.

[0087] Furthermore, at the control strategy level, the cascaded system adopts a master-slave control architecture. The macro-moving platform receives low-frequency pose commands from the variable structure controller, while the nano-platform receives high-frequency compensation commands. The control signals of the two are frequency decoupled through a Butterworth filter. The kinematic coupling error correction module monitors the displacement consistency of the two platforms in real time: Through a six-dimensional acceleration sensor installed on the base of the puncture needle, the interference displacement of the nano-platform caused by the inertial force generated during the movement of the macro-moving platform is detected. After calculation by the dynamic model, a compensation command is generated to adjust the control input of the nano-platform to eliminate the influence of coupling vibration. For example, when the macro-moving platform makes a rapid translation, the elastic deformation of the flexible unit causes a parasitic displacement of the nano-platform, and this module pre-corrects it through a feed-forward compensation algorithm to ensure that the actual displacement of the puncture needle is consistent with the commanded displacement.

[0088] It can be understood that the parallel structure of the Stewart mechanism and the stacked design of the piezoelectric ceramics form complementary motion characteristics: the former provides the low-frequency compensation ability with large stroke and low stiffness, the latter realizes the high-frequency response with micro-displacement and high bandwidth, and the flexible coupling mechanism decouples the dynamic interaction of the two-stage platform at the mechanical level. This design not only meets the compensation requirements of the multi-scale components of the breathing motion, but also solves the common problems of vibration transmission and synchronization error in the cascaded system through the collaborative optimization of the control algorithm and the mechanical structure. In terms of parameter matching, the maximum speed of the macro-motion platform and the maximum response speed of the nano-platform cover the typical speed ranges of the breathing motion and physiological tremors, ensuring the real-time performance of dynamic compensation.

[0089] Through this embodiment, the macro-micro composite drive platform realizes the hardware support for multi-scale motion compensation: the six-degree-of-freedom motion ability of the Stewart mechanism adapts to the three-dimensional displacement of the chest wall, the nano-level resolution of the piezoelectric ceramic platform processes high-frequency micro-amplitude motions, the rigid-flexible combination design of the coupling mechanism reduces dynamic interference, and the frequency decoupling and error correction of the control strategy ensure the collaborative accuracy. The engineering implementation of this cascaded structure provides an execution platform with both large stroke and high precision for the stable control of the puncture needle in lung biopsy operations. The deep integration of its mechanical design and control algorithm effectively improves the compensation ability of the system for complex breathing motions.

[0090] Although the present invention has been specifically described above with reference to the preferred embodiments of the present invention, it should be understood that the present invention is not limited to the embodiments described above.

Claims

1. A method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation, characterized in that, The steps of the stabilization method include: Real-time collecting the respiratory motion signals of the patient through a multi-source biosensor, where the multi-source biosensor includes an optical fiber strain sensor array, a diaphragm electromyogram sensor, and a respiratory airflow sensor deployed on the chest and abdomen. Among them, the optical fiber strain sensor covers the 4th - 8th intercostal space area in a cross-cross grid topology; Establishing a respiratory phase-displacement non-linear mapping model. After removing the electromyogram noise from the respiratory motion signals through wavelet packet decomposition, a three-dimensional respiratory motion vector sequence is fused and generated. Among them, the displacement compensation reference point is set as the corresponding body surface position where the puncture target is vertically projected onto the chest wall; According to the instantaneous change rate and acceleration spectrum characteristics of the three-dimensional respiratory motion vector, a feedforward compensation control law is dynamically constructed, and a puncture needle spatial pose adjustment instruction is generated through a variable gain proportional-integral-derivative controller; Performing a compensation action through a macro-micro composite drive platform. The macro-micro composite drive platform includes a six-degree-of-freedom macro motion platform driven by a voice coil motor and a nano-positioning platform driven by a piezoelectric ceramic in cascade, and full-range compensation of respiratory displacement is achieved through cascade control; Based on the actual displacement data of the puncture needle feedback by the optical encoder and the target compensation amount, closed-loop error correction is performed. At the same time, the impedance detection module is combined to monitor the contact state between the needle tip and the tissue. When the change gradient of the contact force exceeds the preset safety threshold, an emergency stop protocol is triggered; 2. The method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation according to claim 1, wherein The construction process of the respiratory phase-displacement non-linear mapping model includes: Performing phase space reconstruction on the three-dimensional respiratory motion vector sequence, and selecting a time delay τ = 0.2T - 0.3T, where T is the average respiratory cycle, and the embedding dimension m = 6 - 8; Calculating the prediction trajectory of the displacement compensation reference point, and the calculation method of the prediction trajectory is: , where Φ(·) is the evolution operator of the chaotic system trained by the radial basis function neural network, X(t) represents the displacement vector of the respiratory motion at time t, τ represents the time variable in the integration process, Δt represents the prediction time interval, and ∇ 2 Ψ represents the Laplace operator of the tissue viscoelastic potential field, and X d is the ideal target coordinate, and α, β, and γ are the weight parameters of the respiratory amplitude, the coefficient of variation of the frequency, and the thoracic rigidity factor, respectively.

3. The method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation according to claim 1, wherein The parameter adjustment of the variable gain proportional-integral-derivative controller follows the following time-domain-frequency-domain hybrid tuning criterion: Defining a gain adjustment factor to respond to the acceleration change of respiratory motion in real time; Online optimizing the controller parameters through an impedance frequency modulation equation: , where K p (t) represents the proportion gain adjusted in real time, κ(t) represents the gain adjustment factor, and K p0 represents the reference proportion coefficient, μ represents the error accumulation compensation gain, represents the integral term of historical error with exponential decay, e(τ) represents the displacement error signal at time τ, and ω c represents the Hilbert instantaneous frequency estimate of the fundamental respiratory frequency; K i (t) represents the integral gain adjusted in real time, ζ represents the dynamic estimate of the damping ratio, and ω n represents the natural frequency of the system, represents the time change rate of the contact force F(t) between the needle tip and the tissue; K d (t) represents the differential gain adjusted in real time, represents the reference differential gain term, represents the logarithmic adjustment term of the error change rate.

4. The method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation according to claim 3, wherein The steps of the cascade control include: The macro motion platform is responsible for compensating the respiratory motion components with low frequency and large displacement, and adopts a position-current double-loop control structure to ensure motion stiffness; The nano-positioning platform compensates for the physiological tremor components with high frequency and small amplitude, and integrates an inverse hysteresis feedforward compensation algorithm to suppress the non-linear hysteresis effect of the piezoelectric ceramic; Establishing a kinematic coupling error model, and realizing the motion synchronization of the macro motion platform and the nano-positioning platform through a coordinate transformation matrix, which is used to ensure the pose accuracy of the puncture needle; 5. The method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation according to claim 1, characterized in that The monitoring steps of the impedance detection module include: Applying a swept-frequency electrical excitation signal to the puncture needle and collecting the admittance spectrum between the needle body and the tissue; Identifying the tissue type in contact with the needle tip based on the characteristic parameters of the admittance spectrum. When the characteristic impedance of a high-risk tissue is detected, a path correction instruction is generated; The emergency stop protocol includes the millisecond-level response locking of the electromagnetic brake, the release of the adsorption force of the negative pressure suction system, and the dangerous navigation prompt of the augmented reality interface, which are used to ensure operation safety; 6. The method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation according to claim 5, characterized in that The steps of the stabilization method further include: In the CT image navigation coordinate system, spatial registration of the respiratory compensation system and medical images is achieved through the rigid body transformation matrix of body surface marker points. The process of the spatial registration includes marker point recognition, coordinate transformation calculation, and dynamic error correction to ensure that the positioning accuracy of the puncture target is synchronized with respiratory motion in real time.

7. The method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation according to claim 1, wherein The fiber optic strain sensor array covers the 4th - 8th intercostal space area in a cross - cross grid topology. The spacing between the sensing optical fibers along the costal arch direction and the parasternal line direction meets the spatial resolution requirements, and millimeter - level accuracy chest wall deformation measurement is achieved through optical frequency domain reflectometry.

8. The method for stabilizing a lung biopsy sampling needle based on dynamic respiratory compensation according to claim 1, wherein Based on preoperative CT data, a patient - specific pulmonary finite element model is constructed. Through multi - physical field coupling simulation, a respiratory phase - tissue deformation mapping atlas is generated to calibrate the phase window for safe puncture and the dangerous tissue area, providing a preoperative planning basis for the dynamic compensation strategy.

9. A lung biopsy sampling needle stabilization system based on dynamic respiratory compensation for implementing the stabilization method according to any one of claims 1-8, characterized in that, The stabilization system includes: A multi - modal respiratory signal acquisition module, including a fiber optic strain sensor array, a diaphragm electromyography sensor, and a respiratory airflow sensor, which is used to collect chest and abdominal respiratory motion signals in real time to achieve multi - dimensional perception of respiratory motion; A respiration - displacement conversion processor, integrating a wavelet packet denoising module and a non - linear mapping algorithm, which fuses the collected respiratory signals to generate a three - dimensional respiratory motion vector sequence and establishes a dynamic mapping model between respiratory phase and tissue displacement, providing motion prediction data for compensation control; A variable - structure controller, including a variable - gain PID main control unit, which dynamically adjusts control parameters based on the instantaneous characteristics of the respiratory motion vector to generate pose adjustment instructions for the puncture needle to adapt to the time - varying characteristics of respiratory motion; A macro - micro composite drive platform, which is used to compensate for low - frequency large displacements and high - frequency micro - amplitude motions, and achieves precise compensation of the full - range respiratory displacement through a kinematic coupling mechanism; A multi - parameter feedback system, integrating an optical encoder, a six - dimensional force sensor, and an impedance detection module, which real - time feedbacks the actual displacement, contact force, and tissue impedance of the puncture needle to form a closed - loop control to correct the compensation error; A medical image registration unit, which achieves spatial registration with CT images through coordinate transformation of body surface marker points, and is used to ensure that the positioning accuracy of the puncture target is dynamically synchronized with respiratory motion; A safety monitoring module, including an emergency stop execution mechanism and a dangerous path planning algorithm. When abnormal contact force or high - risk tissue characteristics are detected, it triggers rapid braking and provides needle - retraction navigation to ensure operation safety.

10. The lung biopsy sampling needle stabilization system based on dynamic respiratory compensation according to claim 9, wherein, The cascade control of the macro - micro composite drive platform is set as: The macro - motion platform adopts a Stewart parallel mechanism configuration, with a millimeter - level stroke and low - frequency response ability, which is used to compensate for the overall chest wall displacement caused by respiratory motion; The nano - positioning platform is based on a piezoelectric ceramic stack structure, with nano - level resolution and high - frequency response ability, which is used to compensate for physiological tremors and tissue micro - deformations; The kinematic coupling mechanism combines a rigid connection component and a flexible damping unit to reduce the dynamic coupling interference between the two platforms to ensure the cooperative accuracy of puncture needle pose adjustment.

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