Voltage control method and system for electroporation ablation based on pulse sequence

Abstract

The invention discloses an electroporation ablation voltage control method and system based on a pulse sequence, and relates to key technologies such as intelligent target area identification, electric field modeling, dose control, real-time monitoring and feedback. Tumors are accurately identified and treatment is planned through the AI technology, electric field distribution is optimized to ensure the accurate effect of electroporation pulses, pulse parameters are dynamically adjusted through closed-loop control, and treatment effect evaluation is provided through the sensor fusion technology. The system integrates data analysis and machine learning, intelligently adjusts treatment strategies, realizes personalized treatment and complication prediction, and improves the treatment effect and patient safety. The system is suitable for precise tumor treatment, and healthy tissues are protected while the curative effect is ensured.

Description

Technical Field

[0001] The present invention relates to the field of medical equipment technology, and in particular to a voltage control method and system for electroporation ablation based on a pulse sequence, which is suitable for accurately positioning and treating diseases such as tumors, and in particular, realizing precise control of the target area during electroporation treatment while maximally protecting surrounding healthy tissues from damage. Background Art

[0002] Traditional tumor treatments, such as surgical resection, chemotherapy, and radiotherapy, are often accompanied by certain side effects and risks. For example, surgery may lead to wound infection, and chemotherapy and radiotherapy may damage normal tissue. As a new tumor treatment technology, electroporation ablation creates temporary pores in the cell membrane to allow the leakage of internal cell substances, leading to cell death. It is particularly suitable for tumors that are difficult to remove surgically or are insensitive to chemotherapy and radiotherapy. However, the traditional electroporation ablation method has the following technical problems:

[0003] (1) Inaccurate target identification: Traditional methods rely on the doctor’s experience and manual operation. The identification and positioning of tumor areas are not accurate enough, which can easily cause accidental damage to healthy tissues.

[0004] (2) Uneven distribution of electric field: The electrode layout and pulse parameter selection are not optimized, resulting in uneven distribution of the electric field in the target area, affecting the treatment effect.

[0005] (3) The treatment process is uncontrollable: There is a lack of real-time monitoring and feedback mechanism, and the pulse parameters cannot be dynamically adjusted according to the treatment progress and tissue response, which affects dose control and treatment safety.

[0006] (4) Difficulty in adjusting treatment strategies: The lack of an intelligent decision-making system based on data analysis makes it difficult to adjust strategies based on individual patient differences and treatment outcomes. Summary of the invention

[0007] The present invention provides a voltage control method and system for electroporation ablation based on a pulse sequence, so as to solve the problem of how to accurately identify and apply pulse voltage to the tumor area during electroporation ablation treatment, while minimizing the impact on surrounding healthy tissues, thereby achieving personalized treatment and precise dose control.

[0008] In order to solve the above problems, the present invention provides a voltage control method and system for electroporation ablation based on a pulse sequence, comprising:

[0009] A voltage control method for electroporation ablation based on a pulse sequence, comprising:

[0010] S100, integrating target recognition and electric field modeling, uses AI technology to automatically identify target areas in medical images, such as tumors, and distinguish healthy tissues to establish a three-dimensional electric field distribution model;

[0011] S200, combining electric field optimization and dose control to form a closed-loop control system, dynamically adjusting pulse parameters to optimize electric field distribution and ensure that the target area receives an accurate dose;

[0012] S300, integrates treatment effect evaluation with sensor technology to monitor electric field strength, tissue status and dose distribution in real time, and provide feedback for dose control;

[0013] S400 integrates information from other modules, conducts comprehensive analysis, guides the adjustment of treatment strategies, and achieves personalized treatment.

[0014] As a preferred solution, step S100 includes:

[0015] S110, use deep learning and computer vision technology to identify the target area and ensure accurate identification of the target area;

[0016] S120, establishing an electric field distribution model according to the position, shape and size of the target area in combination with parameters such as tissue conductivity;

[0017] S130, generating optimal pulse sequence parameters to maximize the electroporation effect of tumor cells while minimizing the impact on healthy tissues.

[0018] As a preferred solution, in step S200, the closed-loop control system automatically adjusts the pulse sequence to achieve precise dose control by real-time monitoring of the electric field intensity and tissue response in the target area.

[0019] As a preferred solution, in step S300, multiple sensors and imaging technologies are integrated to monitor key parameters during the treatment process in real time, providing a basis for dose control.

[0020] As a preferred solution, in step S400, the information is integrated for comprehensive analysis to guide the adjustment of the treatment strategy, including changing pulse sequence parameters, electrode configuration or adding auxiliary treatment methods.

[0021] As a preferred solution, it includes:

[0022] The target recognition module is used to identify target areas in medical images and distinguish healthy tissues;

[0023] Electric field modeling module, used to establish electric field distribution model;

[0024] A dosage control module is used to form a closed-loop control system and dynamically adjust pulse parameters;

[0025] Monitoring and feedback module, used to monitor the treatment process in real time and provide feedback;

[0026] The strategy adjustment module is used to integrate information and guide the adjustment of treatment strategies.

[0027] As a preferred solution, the target recognition module uses deep learning and computer vision technology to perform accurate recognition.

[0028] As a preferred solution, the dose control module automatically adjusts the pulse sequence parameters by real-time monitoring the electric field intensity and tissue response of the target area.

[0029] As a preferred solution, the monitoring feedback module integrates multiple sensors and imaging technologies to monitor key parameters during the treatment process in real time.

[0030] As a preferred solution, the strategy adjustment module integrates information to guide the adjustment of personalized treatment strategies to improve treatment efficacy and safety.

[0031] The key innovative features of the present invention include:

[0032] 1. Use deep learning and computer vision technology to automatically identify tumor areas and distinguish healthy tissues to ensure accurate positioning of treatment.

[0033] 2. Establish a three-dimensional electric field distribution model, optimize the electric field distribution by combining parameters such as tissue conductivity, and predict the electric field effect.

[0034] 3. Design a closed-loop control system to automatically adjust the pulse sequence and achieve precise dose control by real-time monitoring of electric field strength and tissue response.

[0035] 4. Integrate multi-source data, use advanced data analysis and machine learning algorithms to evaluate treatment effects, predict complications, and optimize treatment strategies.

[0036] 5. Realize a dynamic feedback loop to intelligently adjust subsequent treatment plans based on treatment progress and individual patient differences, thereby improving treatment success rates and patient safety.

[0037] The present invention achieves precise control and personalized treatment of electroporation ablation by introducing advanced technologies and algorithms, solving the problems of inaccurate target identification, uneven electric field distribution, uncontrollable treatment process and difficulty in adjusting treatment strategies in traditional methods. The specific beneficial effects are as follows:

[0038] 1. Improved target recognition accuracy: Using deep learning and computer vision technology, it automatically identifies tumor areas in medical images and accurately distinguishes healthy tissues, ensuring that electroporation pulses act accurately on the target area, reducing damage to healthy tissues and improving the safety and effectiveness of treatment.

[0039] 2. Optimization of electric field distribution: Through a closed-loop system of electric field modeling and dose control, the electrode layout and pulse parameters are dynamically adjusted to ensure that the target area receives an accurate dose of electroporation pulses. At the same time, a multi-objective optimization algorithm is used to maintain protection of healthy tissues, thereby improving the accuracy and efficiency of treatment.

[0040] 3. Provides real-time monitoring and feedback: Integrated sensor technology and imaging technology can monitor key parameters in the treatment process in real time, such as electric field distribution, tissue temperature and degree of cell damage, providing a basis for dose control and ensuring the controllability and safety of the treatment process.

[0041] 4. Personalized treatment strategy is achieved: Through the comprehensive analysis module, the subsequent treatment plan is intelligently adjusted, including changing the pulse sequence parameters, electrode configuration or adding auxiliary treatment methods, which improves the treatment success rate and patient safety, while reducing the risk of complications.

[0042] 5. Promoted the intelligence of treatment decision-making: Through machine learning algorithms, learning from historical treatment cases, continuously optimizing feedback control algorithms and multi-objective optimization strategies, the accuracy and efficiency of future treatments were improved, forming a feedback loop of continuous improvement.

[0043] 6. Reduced uncertainty during treatment: Through real-time monitoring and feedback mechanisms, as well as strategies based on model predictive control (MPC), precise adjustment of electrode voltage output is ensured, avoiding dose deviations caused by individual differences and treatment progress, and ensuring consistency of treatment effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of a voltage control method and system for electroporation ablation based on a pulse sequence provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0047] Reference Figure 1, is a flow chart of a voltage control method and system for electroporation ablation based on a pulse sequence according to an embodiment of the present invention. The voltage control method and system for electroporation ablation based on a pulse sequence may at least include steps S100-S400:

[0048] S100, integrates target recognition and electric field modeling, and uses AI for intelligent identification and treatment planning.

[0049] S200, combining electric field optimization and dose control to form a closed-loop control system and dynamically adjust pulse parameters.

[0050] S300 integrates treatment effect evaluation with sensor technology to provide real-time monitoring and feedback.

[0051] S400, integrates information from other modules, conducts comprehensive analysis, and guides the adjustment of treatment strategies.

[0052] The voltage control method and system for electroporation ablation based on a pulse sequence provided in an embodiment of the present invention are mainly used to solve the technical problem solved by the present invention, which is "how to accurately identify and apply pulse voltage to the tumor area during electroporation ablation treatment, while minimizing the impact on surrounding healthy tissues, to achieve personalized treatment and precise dose control."

[0053] The present invention discloses a method and system for controlling the voltage of electroporation ablation based on a pulse sequence, involving key technologies such as intelligent identification of target areas, electric field modeling, dose control, real-time monitoring and feedback. Tumors are accurately identified and treatment is planned through AI technology, the electric field distribution is optimized to ensure the precise action of electroporation pulses, closed-loop control dynamically adjusts pulse parameters, and sensor fusion technology provides treatment effect evaluation. The system integrates data analysis and machine learning, intelligently adjusts treatment strategies, realizes personalized treatment and complication prediction, and improves treatment effects and patient safety. The present invention is suitable for precise tumor treatment, ensuring efficacy while protecting healthy tissues.

[0054] S100, integrating target recognition and electric field modeling, using AI for intelligent recognition and treatment planning. Step S100 includes steps S110-S130:

[0055] S110: Use deep learning and computer vision technology to automatically identify target areas (such as tumors) in medical images and distinguish healthy tissues. This step involves image preprocessing, feature extraction, classification and positioning to ensure accurate identification of target areas. Specifically:

[0056] ① Data preprocessing and feature encoding: Let the medical image data be I, where I∈R H×W, H and W represent the height and width of the image, respectively. First, the image is preprocessed, including normalization and resizing, to optimize subsequent calculations. We introduce a preprocessing function Φ, whose input is the original image I and output is the preprocessed image I':

[0057] I′=Φ(I)

[0058] Next, we use a concept based on abstract algebra, feature encoding, to convert the preprocessed image I' into a feature vector F. Here, we define a feature extractor Ψ, which converts an image into a point in the feature space:

[0059] F = Ψ(I′)

[0060] ② The feature vector F contains the features of different regions in the image, including texture, shape, and color. We use the multivariate linear regression model M LR To predict the probability p of each pixel in the image belonging to the target area. Assuming F is a vector composed of n features, the multivariate regression model can be expressed as:

[0061]

[0062] Among them, β i is the weight coefficient of the model, F i is the i-th element in the eigenvector.

[0063] ③ In order to further improve the accuracy of target area recognition, we introduce differential equation modeling to simulate the boundary diffusion process of the tumor area. Let u(x,y,t) be the probability distribution of the tumor boundary at the spatial position (x,y) at time t, we can establish a model based on the diffusion equation:

[0064]

[0065] Where D is the diffusion coefficient, is the Laplace operator, and f(u) is a nonlinear function used to describe the interaction between the tumor boundary and healthy tissue. The initial condition is u(x,y,0)=p(x,y), that is, the result of multivariate regression analysis is used as the initial probability distribution.

[0066] ④Finally, by solving the above differential equation, we obtain the state of the probability distribution u(x, y, T) of the tumor region at the final time T. Then, we apply a threshold θ to determine the binary mask M of the target region:

[0067] M(x,y)=begin{cases}

[0068] 1&if u(x,y,T)>θ

[0069] 0&otherwise

[0070] end{cases}

[0071] In this way, the mask M will clearly distinguish the target area (such as a tumor) from healthy tissue. This process fully utilizes the data source and input quantity indicators in our patented invention to ensure that the electroporation pulse can accurately act on the target area without affecting the surrounding healthy tissue.

[0072] S120. Establish a three-dimensional electric field distribution model based on the location, shape and size of the target area, combined with biophysical parameters such as tissue conductivity. The model needs to consider factors such as electrode layout and pulse parameters to predict the distribution and effect of the electric field in the target area. Specifically:

[0073] ① Input data

[0074] G: Location, shape and size information of the target area.

[0075] σ g :σ g The electrical conductivity of the target area.

[0076] σ h :σ h The electrical conductivity of healthy tissue.

[0077] E: Electrode layout, including the location, orientation, and spacing of electrodes.

[0078] V: electrode voltage.

[0079] P: Pulse parameters, including frequency, intensity and duration.

[0080] ② Let's model the electric field distribution. Let the electric field strength be E and the electric potential be φ. The relationship between the electric field strength E and the electric potential φ is: The electric potential φ satisfies the equation:

[0081]

[0082] Here, σ(x) represents the conductivity at position x and Ω is the entire modeling area.

[0083] At the electrode location, the potential satisfies the Dirichlet boundary condition:

[0084] φ(x)=V for x∈Γ e

[0085] At the boundary Γ b On the surface, the electric potential satisfies the Neumann boundary condition:

[0086]

[0087] Where n is the boundary Γ b The external normal vector of .

[0088] ③Use the finite element method to solve the above partial differential equation and obtain the numerical solution of the electric potential φ(x), thereby obtaining the electric field intensity E(x). In order to optimize the electric field distribution, we define an objective function J, which comprehensively considers the expected distribution of the electric field intensity in the target area and the minimization of the electric field intensity in the healthy tissue area:

[0089]

[0090] Among them, E d is the desired electric field intensity distribution in the target area, and λ is the Lagrange multiplier used to balance the importance of the two objectives.

[0091] ④ By solving the above optimization problem, we can adjust the electrode layout E and pulse parameter P to optimize the electric field distribution. Using optimization algorithms such as gradient descent or Newton's method, we iteratively update E and P until the objective function J reaches the minimum.

[0092] ⑤ Output the optimized electric field distribution model, including the distribution diagram of the electric field intensity E(x) and the prediction of the electric field effect. Perform numerical simulation verification to ensure that the electroporation pulse acts accurately on the target area while protecting the surrounding healthy tissue from damage.

[0093] Where: G: geometric information of the target area, σ g is the conductivity of the target area, σ h is the conductivity of healthy tissue, E is the electrode layout, V is the electrode voltage, P is the pulse parameter, φ is the potential, Ω is the modeling area, Γ e is the electrode position, Γ b is the boundary, n is the normal vector outside the boundary, J is the objective function, E d is the desired electric field intensity distribution, and λ is the Lagrange multiplier.

[0094] S130. Based on the electric field model and target recognition results, the intelligent algorithm will generate the optimal pulse sequence parameters (such as frequency, intensity, and duration) to maximize the electroporation effect of tumor cells while minimizing the impact on surrounding healthy tissues. The planning process needs to consider multi-objective optimization issues to ensure the safety and effectiveness of treatment. Specifically:

[0095] ① Data input and preprocessing. First, we define the input data set, which includes:

[0096] X: spatial coordinate information of the tumor area; C: electrode configuration matrix; S: tissue state matrix, including conductivity, resistance and dielectric constant, etc.; D: preliminary parameters of the pulse sequence, including frequency, intensity and duration; H: healthy tissue protection threshold matrix.

[0097] ② Establish a nonlinear model of electric field distribution. Assuming that the boundary between the tumor area and healthy tissue can be represented on the manifold, we use the Laplace-Beltrami operator ΔM on the Riemann manifold to describe the change of the electric potential V, where M represents the composite manifold composed of the tumor and healthy tissue:

[0098]

[0099] λ(x) is the eigenvalue at position x, which is related to the tissue state S; F(x) is the external electric field source introduced by the electrode configuration C.

[0100] ③ In order to maximize the electroporation effect of tumor cells and minimize the impact on healthy tissues, we constructed an objective function J based on functional analysis, which combines the tumor electroporation efficiency Φ and the healthy tissue protection coefficient Ψ:

[0101]

[0102] Among them, Ω t and Ω h represent the tumor area and the healthy tissue area respectively; α is the weighting factor that adjusts the balance between the two objectives.

[0103] ④ We use the variational method to solve the extreme point of the above objective function J and find the optimal pulse sequence parameter D * and electrode configuration C * :

[0104]

[0105] ⑤ Apply algorithms to check whether the iterative process of the model converges to ensure the stability of the treatment plan.

[0106] If the convergence condition is not met, fine-tune α or reinitialize D and C and repeat steps ③ and ④.

[0107] ⑥Once the model converges, output the optimal pulse sequence parameters D * and electrode configuration C * , as well as the corresponding electric field distribution map and treatment effect evaluation report.

[0108] S200, combining electric field optimization and dose control to form a closed-loop control system and dynamically adjust pulse parameters.

[0109] Step S200 includes steps S210-S220:

[0110] S210: During the treatment process, the voltage output of the electrode is dynamically adjusted according to the real-time feedback information to optimize the electric field distribution and ensure that the target area receives an accurate dose. Specifically:

[0111] ① Real-time monitoring and data collection. Electric field strength monitoring: Use a sensor network to measure the electric field strength E(x, t) in the tumor area and its surrounding healthy tissue in real time, where x represents the spatial position and t represents time. Tissue state feedback: Collect the tissue state matrix S(t), including the changes in conductivity, resistance, and dielectric constant over time. These parameters affect the distribution and penetration depth of the electric field. Dose distribution evaluation: Calculate the dose distribution D(x, t) in the tumor area based on the current electric field distribution and tissue state.

[0112] ② Deviation calculation: Compare the current dose distribution D(x, t) with the target dose distribution D target (x), calculate the deviation ΔD(x,t) = D(x,t)-D target (x) Electric field distribution assessment: Evaluate the effectiveness and safety of electric field distribution through real-time electric field strength data and tissue status feedback, and identify whether it deviates from the ideal state.

[0113] ③ Dynamic dose control strategy. Feedback control algorithm: A strategy based on model predictive control (MPC) is adopted, combined with feedforward control and feedback control, to adjust the electrode voltage output V(t) in real time to correct the deviation ΔD(x, t). Multi-objective optimization: While adjusting the voltage output, the healthy tissue protection threshold matrix H is taken into account, and a multi-objective optimization algorithm is used to ensure that the dose in the tumor area is increased while avoiding damage to healthy tissue.

[0114] ④ Dynamic adjustment of electrode voltage. Voltage adjustment formula: Based on the deviation ΔD(x, t) and the tissue state matrix S(t), the electrode voltage output V(t) is dynamically adjusted. Assume k v is the voltage adjustment coefficient, ΔV(t) is the voltage increment, then:

[0115]

[0116] in It represents the sensitivity of electric field strength to voltage. The integral term reflects the comprehensive response of the electric field strength in the entire tumor area to voltage changes.

[0117] Safety threshold check: After each adjustment, check whether the new electrode voltage output V(t)+ΔV(t) exceeds the healthy tissue protection threshold H. If exceeded, limit the voltage adjustment range to ensure safety.

[0118] ⑤ Implementation and monitoring. Execution adjustment: Update the electrode voltage output V(t) based on the calculated voltage increment ΔV(t). Continuous monitoring: Continuously monitor the electric field distribution, dose distribution and tissue status throughout the treatment process to ensure treatment effectiveness and safety.

[0119] ⑥ Result feedback and system learning. Result evaluation: After the treatment, evaluate the consistency between the final dose distribution and the target distribution, and record the deviation and adjustment process. System learning: Use machine learning algorithms to learn from historical treatment cases, continuously optimize feedback control algorithms and multi-objective optimization strategies, and improve the accuracy and efficiency of future treatments.

[0120] S220, closed-loop control system automatically adjusts pulse sequence to achieve precise dose control by real-time monitoring of electric field strength and tissue response in the target area. This includes fine-tuning the frequency, intensity and duration of pulses based on real-time data to accommodate individual differences and treatment progress. Specifically:

[0121] ① Real-time monitoring of the electric field intensity E(x,t) of the target area through the sensor array, where x represents the spatial position and t represents the time. Tissue response monitoring: Collecting real-time response data of the target area and surrounding tissues, including but not limited to temperature changes, electrical impedance changes, and metabolic activity indicators, which reflect the immediate response of the tissue to the electroporation pulse.

[0122] Electrophysiological parameter update: Update tissue conductivity σ(x, t) and dielectric constant ε(x, t), which change with time and tissue state.

[0123] ②Data processing and analysis. Electric field distribution reconstruction: Use the real-time monitored electric field strength data and tissue electrophysiological parameters to reconstruct the three-dimensional electric field distribution E3D(x,t) of the target area. Tissue response analysis: Analyze tissue response data, evaluate the impact of the pulse sequence on the target area and surrounding tissues, and identify possible over- or under-dose areas.

[0124] ③ Model prediction and dose assessment. Dose distribution prediction: Based on the reconstructed electric field distribution E3D(x, t) and tissue response analysis results, the dose distribution D(x, t) of the target area is predicted. Dose deviation identification: Compare the predicted dose distribution D(x, t) with the ideal dose distribution D ideal x, calculate the dose deviation ΔD(x,t) = D(x,t)-D ideal x.

[0125] ④ Pulse sequence parameter adjustment. Parameter adjustment algorithm: Design a parameter adjustment algorithm to dynamically adjust the pulse sequence parameters according to the dose deviation ΔD(x,t), including frequency f, intensity I and duration t pulse . Adaptation to individual differences: The algorithm needs to take into account individual differences, including but not limited to tissue type, thickness and metabolic rate, to ensure that the adjusted pulse sequence parameters are suitable for the current patient condition. Adaptation to treatment progress: According to the treatment stage and tissue response, the pulse sequence parameters are gradually adjusted to adapt to the treatment progress and optimize the overall treatment effect.

[0126] ⑤ Closed-loop control and feedback. Dose control closed loop: The dose control function forms a closed-loop control system to monitor the dose deviation ΔD(x,t) in real time and adjust the pulse sequence parameters according to the deviation until the deviation is reduced to an acceptable range. Safety threshold monitoring: During the entire treatment process, the pulse sequence parameters are monitored to ensure that they do not exceed the safety threshold to avoid damage to surrounding healthy tissues.

[0127] ⑥ Result feedback and iterative optimization. Result evaluation and recording: Record the pulse sequence parameters and dose distribution during each dose control process to evaluate the treatment effect and safety. Iterative optimization: Use historical data and machine learning algorithms to continuously optimize the dose control algorithm and improve the accuracy and efficiency of dose control.

[0128] Through the above process, the dose control function realizes precise dose control based on real-time data, ensuring that the electroporation pulse acts accurately on the target area while adapting to individual differences and treatment progress, reflecting the innovation and practice of the patent of this invention in precise dose control and prediction.

[0129] S300, integrating treatment effect evaluation with sensor technology to provide real-time monitoring and feedback. Step S300 includes steps S310-S320:

[0130] S310 integrates multiple sensors and imaging technologies (such as ultrasound, MRI, CT) to monitor key parameters such as electric field distribution, tissue temperature, and cell damage during treatment in real time, providing a basis for dose control. Specifically:

[0131] ① Data input and acquisition. Electric field strength data: The electric field strength E(x, t) is collected in real time through an electric field sensor array arranged around the target area, where x represents the spatial position and t represents the time. Tissue temperature data: The temperature sensor is used to monitor the temperature changes of the target area and surrounding tissues, and the temperature data T(x, t) is collected. Cell damage index: The degree of cell damage is monitored through biosensors or markers, and the cell damage index C(x, t) is collected. Imaging data: Integrate ultrasound, MRI, CT and other imaging technologies to obtain the structural and functional changes of the target area and surrounding tissues in real time, and collect imaging data I(x, t).

[0132] ②Data processing and analysis. Electric field distribution reconstruction: Based on the electric field strength data E(x, t), the three-dimensional electric field distribution of the target area is reconstructed in real time using the inverse problem solving method. Temperature distribution analysis: Combine the temperature data T(x, t) and the tissue heat conduction model to analyze the temperature distribution and identify possible thermal damage areas. Cell damage assessment: Based on the cell damage index C(x, t) and imaging data I(x, t), the degree of cell damage and treatment effect are assessed.

[0133] ③Real-time feedback and basis for dose control. Dose control feedback: Compare the key parameters monitored in real time (electric field distribution, tissue temperature, degree of cell damage) with the treatment goals and safety thresholds to generate real-time feedback signals. Basis for parameter adjustment: Based on the real-time feedback signal, the dose control function is provided with a basis for adjusting the pulse sequence parameters (such as frequency, intensity, duration) to optimize the treatment effect and safety.

[0134] ④Data fusion and intelligent analysis. Multimodal data fusion: Data fusion algorithms are used to integrate data from different sensors and imaging technologies to improve the accuracy and reliability of monitoring results. Intelligent analysis model: Machine learning or deep learning models are used to perform intelligent analysis on fused data to predict treatment progress and possible complications, provide early warnings, and guide dosage control.

[0135] ⑤ Result output and recording. Monitoring result display: Real-time display of dynamic changes in key parameters such as electric field distribution, tissue temperature, and degree of cell damage, so that clinicians can intuitively understand the treatment process. Historical data recording: Save all monitoring data and analysis results for subsequent efficacy evaluation, research analysis, and iterative optimization of dose control algorithms.

[0136] S320. Compare monitoring data with expected effects and generate feedback signals for dynamic adjustment of treatment strategies. This includes identifying under- or over-treatment and any unexpected tissue reactions so that corrective measures can be taken in a timely manner. Specifically:

[0137] ① Data input and expected effect setting. Electric field strength and distribution: Collect real-time electric field strength data from the sensor array and compare it with the preset ideal electric field distribution to check whether sufficient coverage and uniformity are achieved. Tissue temperature data: Use temperature sensors to monitor temperature changes in the target area to ensure that the temperature remains within the effective treatment range without causing thermal damage. Cell damage indicators: Collect cell damage degree data through biomarkers or imaging technology and compare it with the target damage level defined in the treatment plan. Imaging feedback: Integrate ultrasound, MRI or CT imaging technology to obtain real-time images of tissue structure changes and evaluate changes in the treatment area.

[0138] ② Data processing and analysis. Real-time comparative analysis: Compare the above monitoring data with the pre-set treatment goals and safety thresholds in real time to identify deviations in the treatment process. Abnormal detection: Use statistical methods or machine learning models to identify insufficient, excessive or unexpected tissue reactions, such as excessive local temperature and uneven electric field distribution. Trend prediction: Based on historical data and current status, predict the development trend of treatment effects and determine whether they meet expectations.

[0139] ③ Feedback signal generation and strategy adjustment. Feedback signal generation: When the monitoring data deviates from the expected effect, feedback signals are generated, including suggestions for adjustment of treatment parameters, such as fine-tuning of electric field strength, pulse frequency or duration. Strategy adjustment instructions: Feedback signals are converted into specific strategy adjustment instructions, such as increasing or decreasing the intensity of electroporation pulses, extending or shortening the treatment cycle, etc. Immediate intervention: For detected emergencies, such as potential risk of thermal injury, warnings are immediately issued and corrective measures are proposed to avoid unnecessary tissue damage.

[0140] ④ Dynamically adjust treatment strategies. Real-time adjustment: According to feedback signals and strategy adjustment instructions, dynamically adjust treatment parameters to ensure that the treatment process is always in the best state. Adaptive control: The system has self-learning capabilities and can adaptively adjust treatment plans based on individual differences of patients and changes in the treatment process. Efficacy optimization: Through a continuous feedback loop, continuously optimize dosage control to achieve the most ideal treatment effect while minimizing side effects.

[0141] ⑤Result recording and analysis. Feedback loop recording: Each feedback signal generated and the resulting strategy adjustment are recorded as the basis for subsequent data analysis and treatment strategy improvement. Long-term efficacy evaluation: Collect and analyze feedback data to evaluate the long-term effect and safety of treatment and provide reference for future treatment planning.

[0142] S400, integrating information from other modules, conducting comprehensive analysis, and guiding the adjustment of treatment strategies. Step S400 includes steps S410-S430:

[0143] S410: Collect and analyze all data from intelligent identification and planning, dynamic dose control and multimodal monitoring modules, including target area information, treatment parameters, real-time feedback, etc. Specifically:

[0144] ① Data collection. Target area information: The specific location, shape, size and relationship with surrounding important structures of the tumor or other lesions are obtained from the intelligent recognition and planning module. These data are derived from high-precision imaging technologies such as MRI, CT or PET scans. Treatment parameters: Collect electroporation treatment parameters set by the dynamic dose control module, including but not limited to electric field strength, pulse width, pulse interval, total number of pulses, etc. These parameters are carefully designed based on the characteristics of the target area and the expected degree of cell damage. Real-time feedback: Receive real-time monitoring data from the multimodal monitoring module, including electric field distribution, tissue temperature, cell damage indicators, and imaging feedback, etc., to verify the treatment effect and promptly detect potential problems.

[0145] ②Data analysis and integration. Cross-validation: Cross-validate the collected data to ensure the consistency and accuracy of the information. For example, by comparing imaging data at different time points, confirm whether the changes in the treatment area are in line with expectations. Pattern recognition: Use machine learning algorithms to analyze data patterns and identify factors that may affect the treatment effect, such as tumor heterogeneity, hemodynamic changes, etc. Deviation detection: Compare the actual monitoring data with the expected values ​​in the treatment plan to identify deviations or anomalies that may occur during the treatment process, such as uneven electric field distribution or tissue reactions beyond expectations.

[0146] ③ Comprehensive processing and decision-making data fusion: Data from all sources are integrated and processed to form a comprehensive view that fully reflects the current status of treatment. Intelligent decision-making: Based on comprehensive data, the intelligent decision-making system can automatically identify the best treatment path, including whether it is necessary to adjust treatment parameters, change dosage, or replan the treatment area. Predictive analysis: Use historical data and current treatment status to predict possible future treatment effects and help doctors make forward-looking treatment decisions.

[0147] ④ Output guidance. Optimization suggestions: Send optimized treatment parameters and strategies to the treatment execution unit to ensure that subsequent treatment operations are more accurate and effective. Alarms and warnings: If a signal that may have an adverse effect on the patient is detected, the system will immediately issue an alarm to prompt the medical team to take necessary intervention measures. Report generation: Provide the medical team with a detailed analysis report, including treatment progress, effect evaluation and potential risks, so that doctors can review and adjust the treatment plan.

[0148] S420: Based on the collected data, use advanced data analysis and machine learning algorithms to evaluate treatment effects, predict possible complications, and optimize treatment strategies. Specifically:

[0149] ① Data input and preprocessing. Target area information: including the precise location, shape, volume of the tumor or lesion, and the boundary with the surrounding healthy tissue. This information comes from high-resolution imaging data, such as MRI, CT or PET scan results. Treatment parameters: details of the electroporation pulse, such as pulse frequency, duration, intensity, etc. These are key factors to ensure that electroporation acts accurately on the target area. Real-time monitoring data: feedback from the multimodal monitoring system, including electric field distribution, tissue temperature, cell damage indicators, etc., used to monitor the progress and effect of treatment. Patient physiological data: including hemodynamic parameters, heart rate, blood pressure, etc., used to assess the patient's overall health status in order to predict possible complications.

[0150] ②Data analysis and machine learning applications. Efficacy evaluation model: Use machine learning algorithms, such as random forests, neural networks, or support vector machines, to analyze imaging changes before and after treatment, quantify the degree of tumor shrinkage, cell apoptosis rate, etc., to evaluate the treatment effect. Complication prediction model: Based on historical data sets, train the model to identify which treatment parameters or patient characteristics may lead to specific complications, such as cardiac rhythm disorders, skin burns, etc. Dose optimization model: Use algorithms such as reinforcement learning to adjust electroporation pulse parameters based on real-time feedback to minimize the impact on healthy tissue while maximizing the treatment effect.

[0151] ③ Result interpretation and strategy adjustment. Intelligent decision-making: Based on the output of the comprehensive analysis model, the intelligent system can recommend adjustments to the treatment strategy, such as changing the pulse sequence, increasing or decreasing the pulse energy, etc., to achieve a more ideal treatment result. Personalized treatment plan: Based on the unique situation of each patient, the system can generate a customized treatment plan to ensure the accuracy of dose control while reducing the risk of complications. Dynamic feedback loop: The treatment decision and optimization center continuously collects new data from the treatment process and updates the analysis model to form a closed-loop system that continuously learns and improves itself.

[0152] ④ Output and reporting. Treatment effect report: Provided to doctors and patients, showing treatment progress, efficacy evaluation and possible risk points. Strategy optimization suggestions: Send optimized treatment parameters to the treatment execution unit to ensure more accurate follow-up operations. Long-term prediction and monitoring: Provide patients with long-term follow-up suggestions, including possible follow-up treatment or monitoring indicators.

[0153] Through the above process, the comprehensive analysis function ensures that the treatment decision and optimization center can efficiently use all available data, improve the personalization and safety of treatment through intelligent means, and provide strong support for clinicians to cope with the challenges brought by precise dose control and prediction. This mechanism achieves the maximum effectiveness of electroporation ablation treatment while protecting healthy tissue.

[0154] S430, intelligently adjust the subsequent treatment plan based on the comprehensive analysis results, including changing the pulse sequence parameters, electrode configuration or adding auxiliary treatment methods to improve the treatment success rate and patient safety. Specifically:

[0155] ① Receive data from sensors during treatment, including electrode configuration status, pulse sequence parameters (frequency, amplitude, duration), tissue responses (such as conductivity changes, thermal effects), and patient physiological responses (such as heart rate, blood pressure). Evaluate treatment effectiveness: Use the machine learning model discussed previously to analyze the impact of the current treatment on the target area, including the degree of tumor reduction, cell apoptosis rate, etc., while monitoring potential damage to healthy tissue. Complication prediction: Based on historical data and patient-specific conditions, predict the risk of possible complications, such as local inflammatory reactions, skin burns, or other organ dysfunction.

[0156] ② Based on the gap between the current treatment parameters and the expected treatment goals, the intelligent system evaluates the effectiveness of the existing strategy. Scheme optimization: Use technologies such as reinforcement learning or genetic algorithms to simulate the effects of different combinations of treatment parameters and find the most likely solution to improve treatment success and patient safety. Integration of auxiliary means: Consider whether to introduce chemical drugs, photodynamic therapy or other auxiliary treatments to enhance the effect of electroporation or reduce side effects.

[0157] ③ Pulse sequence parameter adjustment: Based on intelligent decision-making, fine-tune the pulse frequency, intensity and duration to ensure that the electroporation pulse can act on the target area more accurately while avoiding damage to surrounding healthy tissues. Electrode configuration optimization: Recalculate the optimal placement and direction of the electrode to optimize the electric field distribution and improve electroporation efficiency.

[0158] ④ The intelligent system generates an updated treatment plan, including new pulse sequence parameters, electrode layout, and any additional adjuvant therapy recommendations. Execution and supervision: The adjusted treatment plan is transmitted to the treatment execution unit, the modified treatment procedure is implemented, and the treatment progress and patient response are continuously monitored in real time.

[0159] ⑤ Feedback loop and continuous optimization. After treatment, evaluate the treatment effect and patient condition again, and collect data as input for future intelligent decision-making. Learning and iteration: The intelligent system learns from each treatment, gradually improving its predictive ability and strategy adjustment algorithm, forming a feedback loop of continuous improvement.

[0160] The key innovative features of the present invention include:

[0161] 1. Use deep learning and computer vision technology to automatically identify tumor areas and distinguish healthy tissues to ensure accurate positioning of treatment.

[0162] 2. Establish a three-dimensional electric field distribution model, optimize the electric field distribution by combining parameters such as tissue conductivity, and predict the electric field effect.

[0163] 3. Design a closed-loop control system to automatically adjust the pulse sequence and achieve precise dose control by real-time monitoring of electric field strength and tissue response.

[0164] 4. Integrate multi-source data, use advanced data analysis and machine learning algorithms to evaluate treatment effects, predict complications, and optimize treatment strategies.

[0165] 5. Realize a dynamic feedback loop to intelligently adjust subsequent treatment plans based on treatment progress and individual patient differences, thereby improving treatment success rates and patient safety.

[0166] The present invention "A voltage control method and system for electroporation ablation based on pulse sequence" realizes precise control and personalized treatment of electroporation ablation by introducing advanced technology and algorithms, and solves the problems of inaccurate target identification, uneven electric field distribution, uncontrollable treatment process and difficulty in adjusting treatment strategy in traditional methods. The specific beneficial effects are as follows:

[0167] 1. Improved target recognition accuracy: Using deep learning and computer vision technology, it automatically identifies tumor areas in medical images and accurately distinguishes healthy tissues, ensuring that electroporation pulses act accurately on the target area, reducing damage to healthy tissues and improving the safety and effectiveness of treatment.

[0168] 2. Optimization of electric field distribution: Through a closed-loop system of electric field modeling and dose control, the electrode layout and pulse parameters are dynamically adjusted to ensure that the target area receives an accurate dose of electroporation pulses. At the same time, a multi-objective optimization algorithm is used to maintain protection of healthy tissues, thereby improving the accuracy and efficiency of treatment.

[0169] 3. Provides real-time monitoring and feedback: Integrated sensor technology and imaging technology can monitor key parameters in the treatment process in real time, such as electric field distribution, tissue temperature and degree of cell damage, providing a basis for dose control and ensuring the controllability and safety of the treatment process.

[0170] 4. Personalized treatment strategy is achieved: Through the comprehensive analysis module, the subsequent treatment plan is intelligently adjusted, including changing the pulse sequence parameters, electrode configuration or adding auxiliary treatment methods, which improves the treatment success rate and patient safety, while reducing the risk of complications.

[0171] 5. Promoted the intelligence of treatment decision-making: Through machine learning algorithms, learning from historical treatment cases, continuously optimizing feedback control algorithms and multi-objective optimization strategies, the accuracy and efficiency of future treatments were improved, forming a feedback loop of continuous improvement.

[0172] 6. Reduced uncertainty during treatment: Through real-time monitoring and feedback mechanisms, as well as strategies based on model predictive control (MPC), precise adjustment of electrode voltage output is ensured, avoiding dose deviations caused by individual differences and treatment progress, and ensuring consistency of treatment effects.

[0173] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" 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 application. Moreover, the described specific features, structures, materials or characteristics may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0174] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0175] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0176] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0177] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0178] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0179] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A voltage control method for electroporation ablation based on a pulse sequence, characterized in that: The following steps are involved: S100, integrating target recognition and electric field modeling, uses AI technology to automatically identify target areas in medical images, such as tumors, and distinguish healthy tissues to establish a three-dimensional electric field distribution model; S200, combining electric field optimization and dose control to form a closed-loop control system, dynamically adjusting pulse parameters to optimize electric field distribution and ensure that the target area receives an accurate dose; S300, integrates treatment effect evaluation with sensor technology to monitor electric field strength, tissue status and dose distribution in real time, and provide feedback for dose control; S400 integrates information from other modules, conducts comprehensive analysis, guides the adjustment of treatment strategies, and achieves personalized treatment.

2. The method according to claim 1, characterized in that Step S100 includes: S110, use deep learning and computer vision technology to identify the target area and ensure accurate identification of the target area; S120, establishing an electric field distribution model according to the position, shape and size of the target area in combination with parameters such as tissue conductivity; S130, generating optimal pulse sequence parameters to maximize the electroporation effect of tumor cells while minimizing the impact on healthy tissues.

3. The method according to claim 1, characterized in that In step S200, the closed-loop control system automatically adjusts the pulse sequence to achieve precise dose control by real-time monitoring of the electric field strength and tissue response in the target area.

4. The method according to claim 1, characterized in that: In step S300, a variety of sensors and imaging technologies are integrated to monitor key parameters during the treatment process in real time, providing a basis for dose control.

5. The method according to claim 1, characterized in that In step S400, the information is integrated for comprehensive analysis to guide the adjustment of the treatment strategy, including changing pulse sequence parameters, electrode configuration or adding auxiliary treatment methods.

6. An implementation device of a voltage control system for electroporation ablation based on a pulse sequence, characterized in that: include: The target recognition module is used to identify target areas in medical images and distinguish healthy tissues; Electric field modeling module, used to establish electric field distribution model; A dosage control module is used to form a closed-loop control system and dynamically adjust pulse parameters; Monitoring and feedback module, used to monitor the treatment process in real time and provide feedback; The strategy adjustment module is used to integrate information and guide the adjustment of treatment strategies.

7. The system according to claim 6, characterized in that The target recognition module uses deep learning and computer vision technology to perform accurate recognition.

8. The system according to claim 6, characterized in that The dose control module automatically adjusts the pulse sequence parameters by real-time monitoring of the electric field intensity and tissue response in the target area.

9. The system according to claim 6, characterized in that The monitoring feedback module integrates multiple sensors and imaging technologies to monitor key parameters during the treatment process in real time.

10. The system according to claim 6, characterized in that The strategy adjustment module integrates information and guides the adjustment of personalized treatment strategies to improve treatment efficacy and safety.

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