Feedback method and system based on miniature catheter tail end force

Abstract

The invention relates to the technical field of medical treatment, in particular to a feedback method and system based on miniature catheter end force, and the method comprises the following steps: obtaining three-dimensional data of a blood vessel of a patient through CT scanning, carrying out the preprocessing of the three-dimensional data, and carrying out the feature extraction of the preprocessed three-dimensional data, and generating a processed feature data set; a contact force signal S is collected through a force sensor at the tail end of a catheter, wavelet transformation is conducted on the collected contact force signal, and the collected contact force signal is decomposed into approximation coefficients and detail coefficients under different resolutions; performing Kalman filtering on the detail coefficient Dj to obtain a filtered estimation signal, and taking the filtered estimation signal as a filtered detail coefficient; reconstructing the approximation coefficient Aj and the filtered detail coefficient D'j into a time domain signal S 'based on wavelet inverse transformation, and transmitting the time domain signal to a catheter control system; fusing the CT image, the real-time force signal and the catheter position information to obtain a fused signal; and the catheter control precision can be improved.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a feedback method and system based on the end force of a micro-catheter. Background Art

[0002] In the field of minimally invasive medicine, microcatheters are increasingly used. Surgery usually requires extremely high precision and safety, so it is particularly important to accurately control the end of the microcatheter. Traditional catheter control systems mainly rely on manual operation by doctors, but this method is often limited by the doctor's experience and visual feedback during surgery, making it difficult to achieve ideal control accuracy and safety.

[0003] In order to improve the accuracy and safety of catheter control, researchers have begun to explore the use of advanced signal processing technology to optimize catheter control systems in recent years. However, existing signal processing methods, such as Fourier transform, although they perform well in processing stationary signals, have great limitations in processing non-stationary signals, especially signals such as human body waveforms that change frequently in the time dimension. Fourier transform cannot provide local information of the signal in the time domain, so it is difficult to accurately capture the dynamic changes caused by the movement of the catheter in the human body.

[0004] In addition, although some studies have tried to apply filtering technology to catheter control systems to eliminate noise and interference, these methods are often too simple and cannot effectively cope with complex noise environments and dynamic changes in human systems. Therefore, in actual surgery, these methods still find it difficult to provide stable and reliable catheter control.

[0005] To this end, we propose a feedback method and system based on the end force of a micro-catheter to solve the above problems. Summary of the invention

[0006] The object of the present invention is to provide a method and system for feedback based on the force at the end of a micro-catheter to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a method and system for feedback based on the end force of a micro-catheter, the method comprising the following steps:

[0008] S1: Acquire the three-dimensional data of the patient's blood vessels through CT scanning, preprocess the three-dimensional data, and extract features from the preprocessed three-dimensional data to generate a processed feature data set;

[0009] S2: The contact force signal S is collected by the force sensor at the end of the catheter, and the collected contact force signal is subjected to wavelet transformation to be decomposed into approximate coefficients and detail coefficients at different resolutions;

[0010] S3: detail coefficient Dj Perform Kalman filtering to obtain a filtered estimation signal, and use the filtered estimation signal as a filtered detail coefficient;

[0011] S4: Based on the inverse wavelet transform, the approximate coefficient A j and the filtered detail coefficient D j ' is reconstructed into a time domain signal S', and the time domain signal is transmitted to the catheter control system;

[0012] S5: fusing the CT image, the real-time force signal and the catheter position information to obtain a fusion signal, and transmitting the fusion signal to the catheter control system;

[0013] S6: Acquire the time domain signal and the fusion signal, determine the adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjust the parameters of the catheter control system based on the feedback mechanism.

[0014] Preferably, the step of collecting the contact force signal S through the force sensor at the end of the catheter, performing wavelet transform on the collected contact force signal, and decomposing it into approximate coefficients and detail coefficients at different resolutions includes: installing a force sensor at the end or proximal end of the catheter, and collecting the contact force signal S(t) through the force sensor, wherein t represents time. The signal S(t) contains the force magnitude and direction information when the catheter contacts the blood vessel wall. The collected analog signal is converted into a digital signal through an analog-to-digital converter (ADC); the collected digital signal is initially filtered and normalized to make its amplitude range uniform; the appropriate wavelet basis function is selected according to the signal characteristics, and the number of wavelet decomposition layers n is determined according to the signal frequency range and resolution requirements; the preprocessed contact force signal S(t) is subjected to discrete wavelet transform (WT) to decompose it into approximate coefficients and detail coefficients at different resolutions. The corresponding formula is WT(S) = {Aj, Dj|j = 1, 2, 3…n}, where Aj is the approximate coefficient of the jth layer, and Dj is the detail coefficient of the jth layer. Each layer of decomposition divides the signal into approximate coefficients and detail coefficients. The approximate coefficient continues to be decomposed until the set number of decomposition layers n is reached.

[0015] Preferably, the detail coefficient D jPerforming Kalman filtering to obtain a filtered estimated signal, and the steps of using the filtered estimated signal as the filtered detail coefficient include: extracting the detail coefficient of a certain layer from the result of wavelet transform; initializing the state vector, state transfer matrix, observation matrix, process noise covariance matrix and observation noise covariance matrix of the Kalman filter according to prior knowledge and signal characteristics; predicting the future state of the signal according to the prior model, and predicting the error covariance matrix of the state estimation; calculating the Kalman gain using the observation noise covariance matrix and the prediction error covariance matrix, and updating the state estimation according to the detail coefficient of the current layer and the predicted state in combination with the Kalman gain to obtain the filtered estimated signal, and using the state estimation after Kalman filtering as the filtered detail coefficient.

[0016] Preferably, the approximate coefficient A is converted based on the inverse wavelet transform j and the filtered detail coefficient D j The step of reconstructing the time domain signal S′ includes: extracting the approximate coefficient A from the result of wavelet transform j and the detail coefficient D after Kalman filtering or other denoising methods j′ , where j′ represents different decomposition levels, and the wavelet basis functions used for inverse wavelet transform are determined; the lowest frequency approximate coefficients are obtained, and the lowest frequency approximate coefficients are reconstructed into a rough version of the time domain signal using inverse wavelet transform. For each detail coefficient level, the inverse wavelet transform is used to combine it with the current reconstructed signal to restore the high-frequency components of the signal layer by layer, and the reconstruction results of all levels are combined to obtain the time domain signal S′.

[0017] Preferably, the step of fusing the CT image, the real-time force signal and the catheter position information to obtain a fused signal comprises: obtaining the position information of the catheter in the three-dimensional space to obtain the catheter position information, setting initial weights for the CT image, the real-time force signal and the catheter position information at the same time point, and for each time point or data point, calculating the weighted sum of the CT image, the real-time force signal and the catheter position information according to the weights to obtain the fused signal, and the corresponding calculation formula is: Among them, M i is the i-th modal data, w i is the weight of the i-th modal data, and k is the total number of modes.

[0018] Preferably, the steps of acquiring the time domain signal and the fusion signal, determining the adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjusting the parameters of the catheter control system based on the feedback mechanism include: acquiring the time domain signal and the fusion signal, extracting features of the preprocessed time domain signal, and determining the adjustment direction of the catheter control system based on the features of the time domain signal; parsing the fusion signal to extract information directly related to the surgical operation, and formulating a preliminary catheter control strategy based on the fusion signal and the surgical goal; combining the analysis results of the time domain signal and the fusion signal to determine the target adjustment strategy for the catheter control system; designing a closed-loop feedback system to compare the output of the catheter control system with the desired target; generating an error signal based on the comparison result, and adjusting the parameters of the catheter control system according to the size and direction of the error signal.

[0019] A micro-catheter end force-based feedback system, applied to any of the micro-catheter end force-based feedback methods described above, comprising:

[0020] A data acquisition module is used to obtain three-dimensional data of the patient's blood vessels through CT scanning, pre-process the three-dimensional data, and extract features from the pre-processed three-dimensional data to generate a processed feature data set;

[0021] A first processing module is used to collect a contact force signal S through a force sensor at the end of the catheter, perform a wavelet transform on the collected contact force signal, and decompose it into approximate coefficients and detail coefficients at different resolutions;

[0022] The second processing module is used to process the detail coefficient D j Perform Kalman filtering to obtain a filtered estimation signal, and use the filtered estimation signal as a filtered detail coefficient;

[0023] The third processing module is used to convert the approximate coefficient A into j and the filtered detail coefficient D j ' is reconstructed into a time domain signal S', and the time domain signal is transmitted to the catheter control system;

[0024] A fusion module, used for fusing the CT image, the real-time force signal and the catheter position information to obtain a fusion signal, and transmitting the fusion signal to the catheter control system;

[0025] The feedback adjustment module is used to obtain the time domain signal and the fusion signal, determine the adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjust the parameters of the catheter control system based on the feedback mechanism.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] .The three-dimensional data of the patient's blood vessels is obtained through CT scanning, and preprocessing and feature extraction are performed to generate a feature data set that reflects the morphology and structure of the blood vessels. At the same time, the contact force signal is collected by the force sensor at the end of the catheter, and wavelet transform and Kalman filter processing are performed to effectively remove noise and interference and improve the accuracy and reliability of the signal. The processed data provides a more accurate control basis for the catheter control system, thereby improving the accuracy of catheter control.

[0028] 2. Fuse the CT image, real-time force signal and catheter position information to obtain a fused signal containing rich information. This fused signal can more comprehensively reflect the actual situation during the operation and provide doctors with more intuitive surgical guidance. At the same time, the catheter control system adjustment strategy determined based on the time domain signal and the fused signal can adjust the position and posture of the catheter in real time, avoiding surgical risks caused by improper operation and enhancing the safety of the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0030] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0031] Figure 2 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Example

[0034] See also Figure 1 to Figure 2 The present invention provides a feedback method and system technical solution based on the end force of a micro-catheter: A feedback method and system based on the end force of a micro-catheter comprises the following steps:

[0035] S1: Acquire the three-dimensional data of the patient's blood vessels through CT scanning, preprocess the three-dimensional data, and extract features from the preprocessed three-dimensional data to generate a processed feature data set;

[0036] Specifically, the three-dimensional data of the patient's blood vessels are obtained through CT scanning; Gaussian filtering is used to remove noise in the CT image, and the clarity of the blood vessel boundary is improved through image enhancement technology; the threshold segmentation algorithm is used to extract the blood vessel area and generate a three-dimensional blood vessel model; the convolution kernel is used to extract the local features of the blood vessels (such as blood vessel diameter and curvature); the global features (such as blood vessel branching structure) are extracted through statistical methods; and the preprocessed feature data set D is generated: D = f p (x), for intraoperative navigation and signal analysis;

[0037] Among them, D contains key anatomical information, such as vascular location, organ boundaries, etc., and removes irrelevant information that may interfere with subsequent signal analysis. In addition, the preprocessing stage also includes data normalization operations to ensure the consistency of CT images in spatial resolution and intensity distribution. Finally, a feature construction algorithm is used to generate a data representation that matches subsequent catheter navigation and signal analysis. This process can use convolution kernels to extract three-dimensional local features and use statistical methods to extract global features. The final output feature data set D can be used for real-time processing during surgery;

[0038] S2: The contact force signal S is collected by the force sensor at the end of the catheter, and the collected contact force signal is subjected to wavelet transformation to be decomposed into approximate coefficients and detail coefficients at different resolutions;

[0039] The contact force signal S is collected by the force sensor at the end of the catheter, and the collected contact force signal is subjected to wavelet transformation to be decomposed into approximate coefficients and detail coefficients at different resolutions. The steps include: installing a force sensor at the end or proximal end of the catheter, and collecting the contact force signal S(t) through the force sensor, where t represents time. The signal S(t) contains the force magnitude and direction information when the catheter contacts the blood vessel wall, and the collected analog signal is converted into a digital signal through an analog-to-digital converter (ADC); the collected digital signal is preliminarily filtered, and the signal is normalized to make its amplitude range uniform; a suitable wavelet basis function is selected according to the signal characteristics, and the number of wavelet decomposition layers n is determined according to the signal frequency range and resolution requirements; the preprocessed contact force signal S(t) is subjected to discrete wavelet transformation (WT), and it is decomposed into approximate coefficients and detail coefficients at different resolutions, and the corresponding formula is WT(S)={A j ,D j |j=1,2,3…n}, where A j is the approximation coefficient of the jth layer, D j The detail coefficient of the jth layer, each layer of decomposition divides the signal into approximate coefficients and detail coefficients, and the approximate coefficients continue to be decomposed until the set decomposition layer number n is reached;

[0040] It should be noted that the selection criteria for selecting appropriate wavelet basis functions according to signal characteristics are: first, the time-frequency localization characteristics of the wavelet basis functions; second, the matching degree between the wavelet basis functions and the signal characteristics;

[0041] Specifically, during the operation, the contact force signal S is collected by the force sensor at the end of the catheter; the collected force signal is subjected to wavelet transformation and decomposed into approximate coefficients (low-frequency part) and detail coefficients (high-frequency part): WT(S) = {A j ,D j |j=1,2,3…n}, where A j is the approximate coefficient (low-frequency part), which indicates the overall trend of the signal, D j is the detail coefficient (high frequency part), which indicates the local variation characteristics of the signal, n is the number of decomposition levels; approximation coefficient A j Used to capture the overall trend of force; detail factor D j Used to analyze the local variation characteristics of force;

[0042] By selecting the appropriate wavelet basis function, a balance can be achieved between the time domain and the frequency domain. The goal of this stage is to lay the foundation for signal denoising and feature extraction while reducing computational complexity. In intraoperative applications, decomposition at different scales can more intuitively show the changes in key components in the signal, which helps to accurately locate the catheter and analyze the lesion area;

[0043] Approximation coefficient A j :Used to capture the overall trend of the contact force signal and reflect the main changes in the contact force between the catheter and the blood vessel wall. It can be used to analyze the overall movement state of the catheter (such as propulsion and rotation).

[0044] Detail coefficient D j , used to analyze the local variation characteristics of the contact force signal, reflecting the tiny fluctuations when the catheter contacts the blood vessel wall. It can be used to detect abnormal force signals (such as collision and friction).

[0045] Detail coefficient denoising: high frequency detail coefficient D j Perform threshold denoising to remove noise interference. Denoising methods include hard threshold and soft threshold.

[0046] Feature extraction: From the approximation coefficient A j And the detail coefficient D after denoising j Extract key features: Approximate coefficient features: mean value and trend change of force. Detailed coefficient features: frequency of force fluctuation and amplitude change;

[0047] S3: detail coefficient D j Perform Kalman filtering to obtain a filtered estimation signal, and use the filtered estimation signal as a filtered detail coefficient;

[0048] Detail coefficient Dj Performing Kalman filtering to obtain a filtered estimated signal, and using the filtered estimated signal as the filtered detail coefficient comprises the following steps: extracting the detail coefficient of a certain layer from the result of wavelet transform; initializing the state vector, state transfer matrix, observation matrix, process noise covariance matrix and observation noise covariance matrix of the Kalman filter according to prior knowledge and signal characteristics; predicting the future state of the signal according to the prior model, and predicting the error covariance matrix of the state estimation; calculating the Kalman gain using the observation noise covariance matrix and the prediction error covariance matrix, and updating the state estimation according to the detail coefficient of the current layer and the predicted state in combination with the Kalman gain to obtain the filtered estimated signal, and using the state estimation after the Kalman filter as the filtered detail coefficient;

[0049] Specifically, let D j is the detail coefficient of a certain layer, and the estimated signal after filtering is expressed as D j ′,D j ′=KF(D j ), where KF represents the Kalman filter function; the future state of the signal is predicted according to the prior model, the prediction result is adjusted according to the actual observation value, the signal is optimally estimated, and the estimated signal after filtering is obtained; the detail coefficient D after filtering j ′Retains the local characteristics of the signal while reducing noise interference;

[0050] The filtering process is divided into two steps: first, predict the future state of the signal based on the prior model (i.e., the state transfer matrix); second, adjust the prediction results based on the actual observed values ​​to obtain a more accurate estimate. In intraoperative applications, high-frequency components are usually associated with noise, so Kalman filtering is mainly used to optimize high-frequency signals to reduce the impact of false signals on catheter control;

[0051] S4: Based on the inverse wavelet transform, the approximate coefficient A j and the filtered detail coefficient D j ' is reconstructed into a time domain signal S', and the time domain signal is transmitted to the catheter control system;

[0052] Based on the inverse wavelet transform, the approximate coefficient A j and the filtered detail coefficient D j The step of reconstructing the time domain signal S′ includes: extracting the approximate coefficient A from the result of wavelet transform j and the detail coefficient D after Kalman filtering or other denoising methods j′, where j′ represents different decomposition levels, and the wavelet basis functions used for inverse wavelet transform are determined; the lowest frequency approximate coefficients are obtained, and the lowest frequency approximate coefficients are reconstructed into a rough version of the time domain signal using inverse wavelet transform. For each detail coefficient level, the inverse wavelet transform is used to combine it with the current reconstructed signal to restore the high-frequency components of the signal layer by layer, and the reconstruction results of all levels are combined to obtain the time domain signal S′;

[0053] Specifically, obtain the approximate coefficient A j and the filtered detail coefficient D j ′, based on the inverse wavelet transform, the approximate coefficient A j and the filtered detail coefficient D j Reconstructed into a time domain signal S', the corresponding signal reconstruction formula is, S' = WT -1 (A j ,D j ′), where WT -1 Represents the inverse wavelet transform. Usually, the reconstruction process starts from the highest-frequency detail coefficient (if there are multiple levels), but in fact, from the perspective of multi-scale decomposition of the wavelet transform, the reconstruction should start from the lowest-frequency approximate coefficient, and then add the detail coefficient layer by layer. For the sake of logical clarity, this application is described in the order of reconstruction; the lowest-frequency approximate coefficient is reconstructed into a rough version of the time domain signal using the inverse wavelet transform. This step is equivalent to reconstructing the low-frequency component of the signal; for each level of detail coefficients (from high frequency to low frequency, or according to the specific decomposition order), the inverse wavelet transform is used to combine it with the current reconstructed signal to restore the high-frequency components of the signal layer by layer. This step is achieved by convolving the detail coefficients with the corresponding wavelet function and summing them, thereby gradually refining the reconstructed signal; the reconstruction results of all levels are combined to obtain the final reconstructed signal S′. Ensure that all frequency components of the signal are accurately restored;

[0054] The inverse wavelet transform is used to reconstruct the filtered approximate coefficient Aj and detail coefficient Dj′ into the time domain signal S′. The corresponding formula is: S′=WT -1 (A j ,D j ′), where WT -1Represents inverse wavelet transform; the reconstructed signal S′ is transmitted to the catheter control system for real-time adjustment of catheter movement; this process ensures that the denoised signal has high fidelity in the time domain and is suitable for real-time transmission and analysis during surgery. The key to signal reconstruction is to maintain the temporal characteristics of the original signal while minimizing the damage to its structure caused by noise. The reconstructed signal S′S' is transmitted to the catheter control system for guiding catheter movement and position adjustment. The catheter control system adjusts the movement and position of the catheter in real time according to the received reconstructed time domain signal, achieving the ultimate goal of signal processing, i.e. guiding the movement of the catheter during surgical operations;

[0055] S5: fusing the CT image, the real-time force signal and the catheter position information to obtain a fusion signal, and transmitting the fusion signal to the catheter control system;

[0056] The step of fusing the CT image, the real-time force signal and the catheter position information to obtain the fused signal includes: obtaining the position information of the catheter in the three-dimensional space to obtain the catheter position information, setting initial weights for the CT image, the real-time force signal and the catheter position information at the same time point, and for each time point or data point, calculating the weighted sum of the CT image, the real-time force signal and the catheter position information according to the weights to obtain the fused signal. The corresponding calculation formula is: Among them, M i is the i-th modal data, wi is the weight of the i-th modal data, and k is the total number of modalities;

[0057] Specifically, the weighted fusion process itself helps to reduce the interference of false signals, because the modal data with lower weights has less impact on the fusion results. In addition, additional signal processing techniques (such as threshold setting, signal morphology analysis, etc.) can be used to further eliminate false signals, effectively fuse CT images, real-time force signals and catheter position information, and obtain fusion signals with high credibility and accuracy;

[0058] Specifically, it fuses CT images, real-time force signals, and catheter position information; performs weighted fusion of multimodal data to eliminate false signals, and dynamically adjusts weights according to the surgical scenario to ensure the accuracy and reliability of the fusion results; through the weighted fusion algorithm, the system can combine the advantages of multiple information sources, eliminate the interference of false signals, and improve the credibility and accuracy of the signal. In addition, the fusion algorithm can also dynamically adjust the weights through a learning mechanism to ensure optimal performance in different surgical scenarios;

[0059] S6: acquiring a time domain signal and a fusion signal, determining an adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjusting parameters of the catheter control system based on a feedback mechanism;

[0060] The steps of acquiring time domain signals and fusion signals, determining an adjustment strategy for a catheter control system based on the time domain signals and the fusion signals, and adjusting the parameters of the catheter control system based on a feedback mechanism include: acquiring time domain signals and fusion signals, extracting features of the preprocessed time domain signals, and determining an adjustment direction for the catheter control system based on the features of the time domain signals; parsing the fusion signals to extract information directly related to the surgical operation, and formulating a preliminary catheter control strategy based on the fusion signals and the surgical objectives; combining the analysis results of the time domain signals and the fusion signals to determine a target adjustment strategy for the catheter control system; designing a closed-loop feedback system to compare the output of the catheter control system with the desired target; generating an error signal based on the comparison result, and adjusting the parameters of the catheter control system based on the size and direction of the error signal;

[0061] Specifically, the sensors are used to collect physical quantities such as force, displacement, and speed during the operation of the catheter in real time. These physical quantities change with time to form time domain signals. The collected time domain signals are preprocessed by filtering, denoising, and other preprocessing operations to improve the accuracy and reliability of the signals. Multimodal data such as CT images, real-time force signals, and catheter position information are weighted fused to obtain fused signals. The fused signals should comprehensively reflect the key information during the operation of the catheter, such as anatomical structure, tissue hardness, and catheter position. The preprocessed time domain signals are analyzed to identify abnormalities or key events during the operation of the catheter, such as sudden increase in force and sudden change in displacement. According to the characteristics of the time domain signals, the adjustment direction of the catheter control system is preliminarily determined, such as slowing down the speed and increasing the strength. The fused signals are analyzed to extract information directly related to the surgical operation, such as the relative position of the catheter and the surrounding tissues, and the distribution of tissue hardness. Combined with the fused signals and surgical objectives, a detailed catheter control strategy is formulated, such as adjusting the catheter path and avoiding sensitive areas. The analysis results of the time domain signals and fused signals are combined, and the factors such as surgical safety, efficiency, and accuracy are comprehensively considered to determine the final catheter control system adjustment strategy. Design a closed-loop feedback system to compare the output of the catheter control system (such as catheter position, speed, etc.) with the desired target. Based on the comparison results, an error signal is generated as the basis for adjusting the parameters of the catheter control system. Adjust the parameters of the catheter control system based on the size and direction of the error signal and the pre-set control algorithm (such as PID control, adaptive control, etc.). Parameter adjustment should ensure that the catheter can smoothly and accurately track the desired path while avoiding unnecessary damage to surrounding tissues; the adjustment strategy for the catheter control system can be determined based on the time domain signal and the fusion signal, and the system parameters can be adjusted in real time through the feedback mechanism to achieve precise control of catheter operation and safe and efficient surgical procedures;

[0062] Specifically, real-time feedback signals are obtained through force sensors and position sensors; the rotation, advancement speed and bending angle of the catheter are adjusted in combination with preset path planning and real-time feedback signals; a safety force threshold is set to detect and warn abnormal force values ​​in real time; flexible contact of the catheter is achieved through active force control, resistance compensation and steady-state force maintenance mechanisms; based on the reconstructed signals and fusion results, the catheter system needs to be fine-tuned in real time to adapt to the dynamically changing environment during surgery. The parameters are adjusted through the feedback mechanism to optimize the end motion mode of the catheter. Specifically, the feedback signal is obtained through the sensor, and the control algorithm adjusts the rotation, advancement speed and bending angle of the catheter in combination with the preset path planning and real-time calculated signal characteristics, thereby ensuring the precise positioning and safety of the catheter.

[0063] A micro-catheter end force-based feedback system, applied to any of the micro-catheter end force-based feedback methods described above, comprising:

[0064] A data acquisition module is used to obtain three-dimensional data of the patient's blood vessels through CT scanning, pre-process the three-dimensional data, and extract features from the pre-processed three-dimensional data to generate a processed feature data set;

[0065] A first processing module is used to collect a contact force signal S through a force sensor at the end of the catheter, perform a wavelet transform on the collected contact force signal, and decompose it into approximate coefficients and detail coefficients at different resolutions;

[0066] The second processing module is used to process the detail coefficient D j Perform Kalman filtering to obtain a filtered estimation signal, and use the filtered estimation signal as a filtered detail coefficient;

[0067] The third processing module is used to convert the approximate coefficient A into j and the filtered detail coefficient D j ' is reconstructed into a time domain signal S', and the time domain signal is transmitted to the catheter control system;

[0068] A fusion module, used for fusing the CT image, the real-time force signal and the catheter position information to obtain a fusion signal, and transmitting the fusion signal to the catheter control system;

[0069] The feedback adjustment module is used to obtain the time domain signal and the fusion signal, determine the adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjust the parameters of the catheter control system based on the feedback mechanism.

[0070] The CT data of the patient's blood vessels are collected and three-dimensional reconstruction is completed. Using an improved force feedback system, the signal is transmitted to the robot main controller after analog-to-digital conversion and filtering. Multi-modal information fusion is used to eliminate false signals. At the same time, a safety force threshold is set, and the movement mode of the microcatheter tip is adjusted according to the force feedback information.

[0071] First, the doctor collects the CT data of the patient's blood vessels before the operation and completes the three-dimensional reconstruction; during the operation, the force feedback system collects the contact force and direction data through the force sensor at the end or proximal end of the catheter, and transmits the signal to the robot main controller after analog-to-digital conversion and filtering to ensure the real-time and accuracy of the force information; the force data is integrated with the image and position information to eliminate errors and correct false signals, and at the same time, a safety force threshold is set to detect and warn abnormal force values ​​in real time to ensure the safety and accuracy of the operation. In the process of eliminating errors, wavelet transform and Kalman filtering are used; the system actively adjusts the movement of the catheter according to the feedback force, realizes flexible contact through active force control, resistance compensation and steady-state force maintenance mechanism, and transmits real-time force information to the doctor through tactile or visual feedback.

[0072] Compared with the traditional Fourier transform for digital signal processing, through the method of the present invention, doctors can use wavelet transform to accurately process and transmit the changes of human body waveform in the time dimension, and Kalman filtering can help doctors consider the impact of changes in the human body system on blood vessels. This impact can more scientifically correct the errors caused by information detection during the dynamic changes of the human body.

[0073] In addition, the present invention proposes a new digital signal processing method based on wavelet transform and Kalman filter algorithm. The use of wavelet transform helps to capture the local characteristics of the signal, especially in the part with higher frequency of change; the use of Kalman filter algorithm can optimize signal estimation by considering the characteristics of process noise and measurement noise, and provide doctors in actual surgery with scientific navigation based on the physiological activities in the patient's body.

[0074] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0075] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A feedback method based on the end force of a micro-catheter, characterized in that: The following steps are involved: S1: Acquire the three-dimensional data of the patient's blood vessels through CT scanning, preprocess the three-dimensional data, and extract features from the preprocessed three-dimensional data to generate a processed feature data set; S2: The contact force signal S is collected by the force sensor at the end of the catheter, and the collected contact force signal is subjected to wavelet transformation to be decomposed into approximate coefficients and detail coefficients at different resolutions; S3: detail coefficient D j Perform Kalman filtering to obtain a filtered estimation signal, and use the filtered estimation signal as a filtered detail coefficient; S4: Based on the inverse wavelet transform, the approximate coefficient A j and the filtered detail coefficient D j ' is reconstructed into a time domain signal S', and the time domain signal is transmitted to the catheter control system; S5: fusing the CT image, the real-time force signal and the catheter position information to obtain a fusion signal, and transmitting the fusion signal to the catheter control system; S6: Acquire the time domain signal and the fusion signal, determine the adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjust the parameters of the catheter control system based on the feedback mechanism.

2. A micro-catheter end force feedback method according to claim 1, characterized in that: The step of collecting the contact force signal S through the force sensor at the end of the catheter, performing wavelet transform on the collected contact force signal, and decomposing it into approximate coefficients and detail coefficients at different resolutions includes: installing a force sensor at the end or proximal end of the catheter, and collecting the contact force signal S(t) through the force sensor, wherein t represents time. The signal S(t) contains the force magnitude and direction information when the catheter contacts the blood vessel wall, and converting the collected analog signal into a digital signal through an analog-to-digital converter (ADC); performing preliminary filtering on the collected digital signal, and normalizing the signal to make its amplitude range uniform; selecting a suitable wavelet basis function according to the signal characteristics, and determining the number of layers n of the wavelet decomposition according to the signal frequency range and resolution requirements; performing discrete wavelet transform (WT) on the preprocessed contact force signal S(t), and decomposing it into approximate coefficients and detail coefficients at different resolutions, and the corresponding formula is WT(S)={A j ,D j |j=1,2,3…n}, where A j is the approximation coefficient of the jth layer, D j The detail coefficient of the jth layer, each layer of decomposition divides the signal into approximate coefficients and detail coefficients, and the approximate coefficients continue to be decomposed until the set decomposition layer number n is reached.

3. A micro-catheter end force feedback method according to claim 1, characterized in that: The detail coefficient D j Performing Kalman filtering to obtain a filtered estimated signal, and the steps of using the filtered estimated signal as the filtered detail coefficient include: extracting the detail coefficient of a certain layer from the result of wavelet transform; initializing the state vector, state transfer matrix, observation matrix, process noise covariance matrix and observation noise covariance matrix of the Kalman filter according to prior knowledge and signal characteristics; predicting the future state of the signal according to the prior model, and predicting the error covariance matrix of the state estimation; calculating the Kalman gain using the observation noise covariance matrix and the prediction error covariance matrix, and updating the state estimation according to the detail coefficient of the current layer and the predicted state in combination with the Kalman gain to obtain the filtered estimated signal, and using the state estimation after Kalman filtering as the filtered detail coefficient.

4. The micro-catheter end force feedback method according to claim 1, characterized in that: The approximate coefficient A is obtained by inverse wavelet transform. j and the filtered detail coefficient D j The step of reconstructing the time domain signal S′ includes: extracting the approximate coefficient A from the result of wavelet transform j and the detail coefficient D after Kalman filtering or other denoising methods j′ , where j′ represents different decomposition levels, and the wavelet basis functions used for inverse wavelet transform are determined; the lowest frequency approximate coefficients are obtained, and the lowest frequency approximate coefficients are reconstructed into a rough version of the time domain signal using inverse wavelet transform. For each detail coefficient level, the inverse wavelet transform is used to combine it with the current reconstructed signal to restore the high-frequency components of the signal layer by layer, and the reconstruction results of all levels are combined to obtain the time domain signal S′.

5. The micro-catheter end force feedback method according to claim 1, characterized in that: The step of fusing the CT image, the real-time force signal and the catheter position information to obtain a fused signal includes: obtaining the position information of the catheter in the three-dimensional space to obtain the catheter position information, setting initial weights for the CT image, the real-time force signal and the catheter position information at the same time point, and for each time point or data point, calculating the weighted sum of the CT image, the real-time force signal and the catheter position information according to the weights to obtain the fused signal. The corresponding calculation formula is: Among them, M i is the i-th modal data, w i is the weight of the i-th modal data, and k is the total number of modes.

6. The micro-catheter end force feedback method according to claim 1, characterized in that: The steps of acquiring the time domain signal and the fusion signal, determining the adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjusting the parameters of the catheter control system based on the feedback mechanism include: acquiring the time domain signal and the fusion signal, extracting features of the preprocessed time domain signal, and determining the adjustment direction of the catheter control system based on the features of the time domain signal; parsing the fusion signal to extract information directly related to the surgical operation, and formulating a preliminary catheter control strategy based on the fusion signal and the surgical goal; combining the analysis results of the time domain signal and the fusion signal to determine the target adjustment strategy of the catheter control system; designing a closed-loop feedback system to compare the output of the catheter control system with the expected target; generating an error signal based on the comparison result, and adjusting the parameters of the catheter control system according to the size and direction of the error signal.

7. A micro-catheter end force-based feedback system, applied to the micro-catheter end force-based feedback method as claimed in any one of claims 1 to 6, characterized in that: include: A data acquisition module is used to obtain three-dimensional data of the patient's blood vessels through CT scanning, pre-process the three-dimensional data, and extract features from the pre-processed three-dimensional data to generate a processed feature data set; A first processing module is used to collect a contact force signal S through a force sensor at the end of the catheter, perform a wavelet transform on the collected contact force signal, and decompose it into approximate coefficients and detail coefficients at different resolutions; The second processing module is used to process the detail coefficient D j Perform Kalman filtering to obtain a filtered estimation signal, and use the filtered estimation signal as a filtered detail coefficient; The third processing module is used to convert the approximate coefficient A into j and the filtered detail coefficient D j ' is reconstructed into a time domain signal S', and the time domain signal is transmitted to the catheter control system; A fusion module, used for fusing the CT image, the real-time force signal and the catheter position information to obtain a fusion signal, and transmitting the fusion signal to the catheter control system; The feedback adjustment module is used to obtain the time domain signal and the fusion signal, determine the adjustment strategy for the catheter control system based on the time domain signal and the fusion signal, and adjust the parameters of the catheter control system based on the feedback mechanism.

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