Device and method for predicting pulse ablation region based on uncertainty parameter quantification

Abstract

This invention discloses a device and method for predicting pulse ablation regions based on uncertain parameter quantification, belonging to the field of pulse ablation technology. The device includes an acquisition module, an effective ablation boundary determination module, and an ablation region determination module. The acquisition module acquires the electrical parameters of the target object, which reflect the electrical characteristics of the target object's tissue and blood. The effective ablation boundary determination module determines the effective ablation boundary based on the electrical parameters and a pre-acquired ablation electric field distribution model. The ablation region determination module defines the region enclosed by the effective ablation boundary and the tissue surface as the ablation region.

Description

Technical Field

[0001] This invention relates to the field of pulse ablation technology, and in particular to a pulse ablation region prediction device and method based on uncertain parameter quantification. Background Technology

[0002] Pulsed ablation is a non-thermal ablation method that is effective in treating persistent atrial fibrillation and ventricular arrhythmias. It works by applying an ultrafast electric field to the target tissue, creating irreversible nanoscale pores in the tissue cells. This causes leakage of cell contents, disrupting cell stability and ultimately leading to cell death.

[0003] The efficacy of pulse ablation largely depends on the pulse parameters. Excessively high pulse doses can lead to over-ablation, while insufficient pulse doses may result in incomplete treatment and a high recurrence rate. To ensure appropriate pulse release, the ablation area needs to be determined, and the pulse parameters set accordingly. Therefore, accurately determining the ablation area is a crucial step in ensuring the effectiveness of pulse ablation.

[0004] In related technologies, the ablation area is determined based on the location of the tissue to be ablated, without taking into account the electrical characteristics of the patient's physiological tissues. This results in inaccurate determination of the ablation area and affects the pulse ablation effect. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art in determining the pulse ablation area for different patients, and to provide a pulse ablation area prediction device and method based on the quantification of uncertain parameters.

[0006] The present invention solves the above-mentioned technical problems through the following technical solution:

[0007] In a first aspect, the present invention provides a pulse ablation region prediction device based on uncertain parameter quantification, the device comprising:

[0008] An acquisition module is used to acquire electrical parameters of a target object, the electrical parameters reflecting the electrical properties of the target object's tissues and blood;

[0009] The effective ablation boundary determination module is used to determine the effective ablation boundary based on the electrical parameters and the pre-acquired ablation electric field distribution model.

[0010] The ablation area determination module is used to determine the area enclosed by the effective ablation boundary and the tissue surface as the ablation area.

[0011] Optionally, the effective ablation boundary determination module includes:

[0012] The first determining unit is used to determine the ablation depth based on the electrical parameters and the pre-acquired ablation electric field distribution model.

[0013] The second determining unit is used to determine the effective ablation boundary based on the ablation depth.

[0014] Optionally, the ablation electric field distribution model includes the following formula:

[0015]

[0016] Where E is the electric field strength, U is the ablation voltage, and x is the coordinate value in the tissue depth direction. These are the fitting coefficients.

[0017] This is a weighting coefficient for blood conductivity. It is a function of blood conductivity;

[0018] The weighting coefficients for the initial conductivity of the structure. It is a function of the initial conductivity of the tissue;

[0019] The weighting coefficients for the conductivity growth factor. It is a function of the conductivity growth factor;

[0020] This is the weighting coefficient for the electric field intensity corresponding to the center point of the transition region. It is a function of the electric field intensity at the center point of the transition region;

[0021] These are the weighting coefficients for the dynamic conductivity fitting coefficients. It is a function of the fitting coefficients for dynamic conductivity.

[0022] Optionally, in the ablation electric field distribution model,

[0023] , and / or

[0024] , and / or

[0025] , and / or

[0026] , and / or

[0027] ,

[0028] in, For blood conductivity, To organize the initial conductivity, As the conductivity growth factor,

[0029] The electric field strength is located at the center point of the transition region. These are the fitting coefficients for dynamic conductivity. ~ , ~ , ~ , ~ These are the pre-determined fitting coefficients.

[0030] Optionally, in the ablation electric field distribution model,

[0031]

[0032] Where i takes the values ​​1, 2, 3, 4, and 5;

[0033] The ablation depth variance is obtained based on the ablation electric field distribution model when any one of the electrical parameters is a preset variable value and the other parameters are preset fixed values.

[0034] in, This represents the variance of blood conductivity when the preset variation value is reached, and the variation range of blood conductivity is 0.1~1S / m;

[0035] This represents the variance corresponding to the preset variation value of the initial conductivity of the tissue, which ranges from 0.05 to 0.9 S / m.

[0036] This represents the variance corresponding to the conductivity growth factor being a preset value. The range of the conductivity growth factor is 1 to 6.

[0037] The variance of the electric field strength at the center point of the transition zone is the preset variation value. The variation range of the electric field strength at the center point of the transition zone is 200~1200V / cm.

[0038] This represents the variance corresponding to the dynamic conductivity fitting coefficient when it is the preset change value. The range of the conductivity fitting coefficient is 0.00001~0.00003.

[0039] The sum of the variances of ablation depth obtained when any of the electrical parameters are preset variation values.

[0040] Optionally, the second determining unit is specifically used to: determine the corresponding field strength contour lines according to the ablation depth, and use the corresponding field strength contour lines as the effective ablation boundary.

[0041] Optionally, the electrical parameters include blood conductivity; the acquisition module includes:

[0042] A first voltage application unit is used to apply a first test voltage to a first electrode placed in a target area, wherein the first electrode contacts the blood of the target object but does not contact the tissue of the target object;

[0043] The first acquisition unit is used to acquire the first test voltage and the first current through the first electrode;

[0044] The third determining unit is used to determine the blood conductivity based on the first voltage and the first current, as well as a pre-acquired first fitting function, wherein the first fitting function characterizes the relationship between blood conductivity and current and voltage.

[0045] Optionally, the electrical parameters further include the initial tissue conductivity; the acquisition module further includes:

[0046] The second voltage application unit is used to apply a second test voltage to a second electrode placed in the target area, wherein the second electrode contacts the blood and tissue of the target object;

[0047] The second acquisition unit is used to acquire the second test voltage and the second current through the second electrode;

[0048] The fourth determining unit is used to determine the initial conductivity of the tissue based on the second voltage, the second current, the blood conductivity, and a pre-acquired second fitting function, wherein the second fitting function characterizes the relationship between tissue conductivity and voltage, current, and blood conductivity.

[0049] Optionally, the electrical parameters further include dynamic conductivity parameters, which characterize the change in tissue conductivity with electric field strength during pulsed ablation; the acquisition module further includes:

[0050] The third acquisition unit is used to acquire the mapping relationship between the simulated current and the dynamic conductivity parameter;

[0051] The third voltage application unit is used to apply a pulsed ablation voltage to the third electrode placed in the target area, monitor the current through the third electrode, and use the stable output current as the third current;

[0052] The fifth determining unit is used to determine the simulated current that is closest to the third current from a plurality of pre-stored simulated currents;

[0053] The sixth determining unit is used to determine the dynamic conductivity parameter based on the closest simulated current and the mapping relationship.

[0054] Optionally, the dynamic conductivity parameter includes a conductivity growth factor and an electric field strength corresponding to the center point of the transition zone. The conductivity growth factor characterizes the change range of the tissue conductivity, and the electric field strength corresponding to the center point of the transition zone is the average of the electric field strength at the start time of the tissue conductivity change and the electric field strength at the end time of the tissue conductivity change.

[0055] The third acquisition unit includes:

[0056] The first acquisition subunit is used to acquire a function characterizing the relationship between the simulated current and the electric field intensity corresponding to the center point of the transition region under different conductivity growth factors;

[0057] The fitting sub-unit is used to fit the function to obtain the mapping relationship between the simulated current and the dynamic conductivity parameter.

[0058] Optionally, the fifth determining unit includes:

[0059] The second acquisition subunit is used to acquire the degree of agreement between the third current and the numerical values ​​of the pre-stored multiple simulated currents;

[0060] The first determining subunit is used to determine the simulated current corresponding to the minimum value among the numerical matching degrees as the closest simulated current.

[0061] Optionally, the electrical parameters include dynamic conductivity fitting coefficients; the acquisition module includes:

[0062] The fourth acquisition unit is used to acquire a first data group, the first data group including a plurality of dynamic conductivity fitting coefficients, the plurality of dynamic conductivity fitting coefficients being Gaussian distributed.

[0063] Optionally, the electrical parameters further include an electric field strength threshold, which characterizes the electric field strength when irreversible electroporation occurs in tissue cells; the acquisition module includes:

[0064] The fifth acquisition unit is used to acquire a second data group, the second data group including a plurality of electric field strength thresholds, the plurality of electric field strength thresholds being Gaussian distributed.

[0065] Optionally, the device further includes:

[0066] The matching degree parameter acquisition module is used to acquire a matching degree parameter based on the ablation depth and the target ablation depth. The matching degree parameter represents the degree of matching between the pulse parameter corresponding to the ablation depth and the pulse parameter that achieves the target ablation depth.

[0067] The judgment module is used to determine whether to adjust the pulse ablation parameters based on the matching degree parameters.

[0068] Optionally, the matching degree parameter includes the mean value of the ablation depth; the judgment module is specifically used to: adjust the pulse ablation parameters in response to the difference between the mean value and the target ablation depth exceeding a preset threshold; or

[0069] The matching degree parameter includes the probability that the tissue at the target ablation depth will be ablated; the judgment module is specifically used to: adjust the pulse ablation parameter in response to the probability being less than 70%.

[0070] Secondly, the present invention provides a pulse ablation region prediction method based on uncertain parameter quantification, the method comprising:

[0071] Obtain the electrical parameters of the target object, wherein the electrical parameters reflect the electrical properties of the target object's tissues and blood;

[0072] The effective ablation boundary is determined based on the electrical parameters and the pre-acquired ablation electric field distribution model;

[0073] The area enclosed by the effective ablation boundary and the tissue surface is defined as the ablation zone.

[0074] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the pulse ablation region prediction method described in the second aspect above.

[0075] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the pulse ablation region prediction method described in the second aspect above.

[0076] The positive and progressive effects of this invention are as follows:

[0077] The pulse ablation region prediction device based on uncertain parameter quantification provided by this invention predicts the ablation range based on the electrical parameters of the target object, fully considering the differences in electrical parameters of different target objects, thereby improving the accuracy of pulse ablation region prediction and the applicability of the prediction device. Furthermore, the electrical parameters include uncertain parameters, and the electric field distribution model used in this prediction device quantifies the influence of uncertain electrical parameters on the electric field strength, optimizing the prediction accuracy of the pulse ablation region. Attached Figure Description

[0078] Figure 1 This is a schematic diagram of the structure of a pulse ablation device according to an exemplary embodiment;

[0079] Figure 2-1 This is a front view of the electrode and distal tube in a pulse ablation device according to an exemplary embodiment.

[0080] Figure 2-2 This is a side view illustrating a pulsed ablation device in conjunction with tissue, according to an exemplary embodiment;

[0081] Figure 3 A pulse waveform diagram illustrating an exemplary embodiment;

[0082] Figure 4 This is a block diagram illustrating a pulse ablation region prediction device based on uncertainty parameter quantization according to an exemplary embodiment;

[0083] Figure 5 This is a graph illustrating the relationship between the initial electrical conductivity of a tissue and the electric field strength, according to an exemplary embodiment.

[0084] Figure 6 This is a block diagram illustrating an effective ablation boundary determination module according to an exemplary embodiment;

[0085] Figure 7 This is a distribution diagram of the pulsed ablation electric field according to an exemplary embodiment;

[0086] Figures 8-1 to 8-6 This is a main effect diagram illustrating the degree of influence of different electrical parameters according to an exemplary embodiment;

[0087] Figure 9 This is a block diagram illustrating an acquisition module according to an exemplary embodiment;

[0088] Figure 10 This is a graph illustrating the relationship between blood conductivity and current according to an exemplary embodiment;

[0089] Figure 11 This is a block diagram illustrating an acquisition module according to another exemplary embodiment;

[0090] Figure 12 This is a block diagram illustrating an acquisition module according to another exemplary embodiment;

[0091] Figure 13 This is a graph illustrating the change in tissue conductivity with electric field strength, based on an exemplary example.

[0092] Figure 14 This is a block diagram illustrating a third acquisition unit according to an exemplary embodiment;

[0093] Figure 15 This is a graph illustrating the relationship between dynamic conductivity parameters and simulated current, based on an exemplary embodiment.

[0094] Figure 16 This is a block diagram illustrating the fifth determining unit according to an exemplary embodiment;

[0095] Figure 17 This is a block diagram illustrating an acquisition module according to another exemplary embodiment;

[0096] Figure 18 This is a dynamic conductivity fitting coefficient distribution diagram illustrated according to an exemplary embodiment;

[0097] Figure 19 This is a field strength distribution diagram along the depth direction of a tissue cross section, according to an exemplary embodiment.

[0098] Figure 20 This is an ablation depth distribution map illustrated according to an exemplary embodiment;

[0099] Figure 21 This is a block diagram of a pulse ablation region prediction device based on uncertainty parameter quantization, according to another exemplary embodiment.

[0100] Figure 22 This is a comparison diagram of the electric field intensity value at the target ablation depth and the electric field intensity threshold, according to an exemplary embodiment.

[0101] Figure 23 This is a flowchart illustrating a pulse ablation region prediction method based on uncertainty parameter quantification according to an exemplary embodiment;

[0102] Figure 24 This is a schematic diagram illustrating the structure of an electronic device according to an exemplary embodiment. Detailed Implementation

[0103] The present invention will be further illustrated by way of embodiments below, but the present invention is not limited to the scope of the embodiments described herein.

[0104] In a first aspect, embodiments of the present invention provide a pulse ablation region prediction device based on uncertain parameter quantification, which can be applied to pulse ablation equipment. The pulse ablation equipment includes a pulse ablation catheter and a pulse ablation generator.

[0105] Figure 1 This is a schematic diagram of the structure of a pulse ablation device according to an exemplary embodiment, as shown below. Figure 1 As shown, the pulse ablation catheter includes: a control handle 101, a main outer tube 102, an electrode 103, and electrode leads. The handle 101 is connected to the main outer tube 102, and an electrode socket is provided on the handle 101. The main outer tube 102 forms an inner cavity that communicates with the electrode socket. The electrode leads are disposed in the inner cavity, with one end connected to the electrode 103 and the other end connected to the electrode socket. The portion of the main outer tube 102 away from the handle 101 is the distal tube body 1021, which is used to house the electrode 103. Optionally, the electrode 103 is a ring electrode, sleeved on the distal tube body 1021. Multiple electrodes 103 are evenly distributed on the distal tube body 1021. The material of the ring electrode 103 can be platinum-iridium alloy or gold.

[0106] Optionally, the distal tube 1021 is a single-lumen tube, and the material of the distal tube 1021 is insulating and biocompatible, and the distal tube 1021 is flexible and deformable overall. For example, the material of the distal tube 1021 is PTFE, PU, ​​etc. In this way, the distal tube 1021 can carry the electrode 103 and extend into the target tissue to perform pulsed ablation.

[0107] When the pulse ablation catheter is in use, the distal tube body 1021 carrying the electrode 103 comes into contact with the tissue and blood. Figure 2-1 This is a front view of the electrode and distal tube in a pulse ablation device according to an exemplary embodiment. Figure 2-2 This is a side view illustrating a pulsed ablation device in conjunction with tissue, according to an exemplary embodiment. Figure 2-1 As shown, electrode 103 includes a first electrode 1031 and a second electrode 1032, which are sleeved on the distal tube body 1021. Figure 2-2 As shown, during pulse ablation, the outer tube 102 enters the tissue 301, and the distal tube 1021 and electrode 103 are in contact with the tissue 301. Furthermore, the distal tube 1021, electrode 103, and tissue 301 are all surrounded by blood.

[0108] In use, the electrode socket of this pulse ablation device is connected to the pulse ablation generator. The pulse signal output from the pulse ablation generator is then transmitted to electrode 103 via the electrode socket and electrode wires. Electrode 103 applies a pulsed electric field to the tissue of the target object, thereby achieving pulse ablation.

[0109] Figure 3 This is a pulse waveform diagram according to an exemplary embodiment. Optionally, a plurality of electrodes 103 are provided on the distal tube body, wherein adjacent electrodes have opposite polarities. As an example, electrodes numbered odd-numbered (e.g., the first, third, and fifth electrodes) are connected to the positive terminal of the pulse ablation device, and electrodes numbered even-numbered (e.g., the second, fourth, and sixth electrodes) are connected to the negative terminal of the pulse ablation device, with the negative terminal grounded. In this case, the pulse signal model output by the pulse ablation device is as follows: Figure 3 As shown, a pulsed ablation electric field is applied to the tissue via electrode 103.

[0110] Based on the above-mentioned pulse ablation device, this embodiment of the invention provides a pulse ablation area prediction device. Figure 4 This is a block diagram illustrating a pulse ablation region prediction device based on uncertainty parameter quantization according to an exemplary embodiment, such as... Figure 4 As shown, the prediction device includes: an acquisition module 200, an effective ablation boundary determination module 300, and an ablation area determination module 400.

[0111] The acquisition module 200 is used to acquire the electrical parameters of the target object, which reflect the electrical characteristics of the target object's tissue and blood. The effective ablation boundary determination module 300 is used to determine the effective ablation boundary based on the electrical parameters and a pre-acquired ablation electric field distribution model. The ablation region determination module 400 is used to determine the region enclosed by the effective ablation boundary and the tissue surface as the ablation region.

[0112] In clinical treatment, the electrochemical properties of tissues and blood vary greatly among different target subjects, resulting in different electrical parameters. These differences in electrical parameters affect the distribution of the pulsed ablation electric field. In simulation calculations, the distribution of the pulsed ablation electric field within the tissue is calculated using the following formula:

[0113]

[0114]

[0115] Where E is the electric field strength, Let σ be the electrical potential, σ be the conductivity of each component, ε0 be the vacuum permittivity, and ε be the electrical potential. r Let be the relative permittivity of each component.

[0116] Calculations using the above formulas reveal that the main factors influencing the distribution of the pulsed ablation electric field within tissue include voltage, the tissue's electrical parameters, and the electrical parameters of the blood surrounding the tissue. By studying the effects of tissue and blood electrical parameters on the pulsed ablation electric field distribution, the following electrical parameters were determined:

[0117] Blood conductivity is a measure of the blood’s ability to conduct electrical current.

[0118] Tissue initial conductivity characterizes the ability of tissue to conduct current before ablation.

[0119] The dynamic conductivity parameter characterizes how tissue conductivity changes with electric field intensity during pulsed ablation. During pulsed ablation, the tissue cell structure continuously changes, resulting in a dynamic change in tissue conductivity. In one embodiment, the dynamic conductivity parameter includes a conductivity growth factor and the electric field intensity at the center point of the transition zone. The conductivity growth factor characterizes the magnitude of the change in tissue conductivity. The electric field intensity at the center point of the transition zone is the average of the electric field intensity at the start and end of the tissue conductivity change.

[0120] The dynamic conductivity fitting coefficient characterizes the trend of tissue conductivity variation with electric field strength.

[0121] The electric field strength threshold characterizes the electric field strength when irreversible electroporation occurs in tissue cells.

[0122] For electrical parameters other than the electric field strength threshold, the relationship between these electrical parameters and the electric field strength was analyzed using a single-variable method. Changes in any parameter will affect the distribution of the electric field strength. Specifically, changes in any parameter affect the electric field strength on the tissue surface and the rate of decay of the electric field strength in the depth direction. Figure 5 This is a graph illustrating the relationship between the initial electrical conductivity of tissue and the electric field strength, according to an exemplary embodiment. Taking the initial electrical conductivity of tissue as an example... Figure 5 As shown, with other electrical parameters remaining constant, the electric field strength is greatest at the electrode surface and decreases exponentially along the tissue depth direction. That is, changes in the initial tissue conductivity affect the distribution of the pulsed ablation electric field strength.

[0123] The pulse ablation prediction device provided in this embodiment of the invention uses an acquisition module 200 to determine the electrical parameters of the target object, and then predicts the ablation area based on the electrical parameters. It fully considers the differences caused by the physiological characteristics of different target objects, so that the device can predict the ablation area for different target objects, improve the accuracy of the prediction results, and effectively solve the defects in related technologies.

[0124] Based on the electrical parameters acquired by the acquisition module 200, the effective ablation boundary determination module 300 determines the effective ablation boundary according to the pre-constructed ablation electric field distribution model. The effective ablation boundary is the dividing line where the pulsed ablation electric field can exert its ablation effect. By determining the effective ablation boundary, the ablation region determination module 400 can predict the range of the pulsed ablation region.

[0125] Figure 6 This is a block diagram illustrating an effective ablation boundary determination module according to an exemplary embodiment. Figure 6 As shown, the effective ablation boundary determination module 300 includes a first determination unit 310 and a second determination unit 320.

[0126] The first determining unit 310 is used to determine the ablation depth based on the electrical parameters acquired by the acquisition module and the pre-acquired ablation electric field distribution model. The ablation depth characterizes the depth of tissue that can be effectively ablated.

[0127] The second determining unit 320 is used to determine the effective ablation boundary based on the ablation depth. Optionally, the second determining unit 320 is specifically used to determine the corresponding field strength contour lines based on the ablation depth, and use the corresponding field strength contour lines as the effective ablation boundary.

[0128] Figure 7 This is a distribution diagram of the pulsed ablation electric field according to an exemplary embodiment. (Combined with...) Figure 7 This will be used to explain the purpose of the effective ablation boundary determination module 300 and the ablation area determination module 400.

[0129] Reference Figure 7 The distribution of electric field strength contour lines from 400V / cm to 800V / cm shows that the pulse ablation electric field strength contour lines are distributed in a concentric circle structure, and the electric field strength gradually decreases along the tissue depth direction 702. In the effective ablation boundary determination module 300, the second determination unit 320 determines the effective ablation boundary by extending the ablation depth along the tissue depth direction 702 from the surface 701 of the distal tube 1021, thus defining the field strength contour lines as the effective ablation boundary. In determining the ablation region, the ablation region determination module 400 defines the area enclosed by the effective ablation boundary and the tissue surface (i.e., the surface 701 of the distal tube) as the ablation region.

[0130] The ablation electric field distribution model used by the first determining unit 310 takes into account the influence of the electrical parameters acquired by the acquisition module 200 on the electric field distribution. In one embodiment, the following ablation electric field distribution model is constructed by combining the electrical parameters and the weights of different electrical parameters:

[0131]

[0132] Where E is the electric field strength, in V / cm or V / m; U is the ablation voltage, in V; and x is the coordinate value in the tissue depth direction, in cm or m. The pre-defined fitting coefficients are in cm. - ¹ or m - ¹; This is a weighting coefficient for blood conductivity. It is a function of blood conductivity; The weighting coefficients for the initial conductivity of the structure. It is a function of the initial conductivity of the tissue; The weighting coefficients for the conductivity growth factor. It is a function of the conductivity growth factor; This is the weighting coefficient for the electric field intensity corresponding to the center point of the transition region. It is a function of the electric field intensity at the center point of the transition region; These are the weighting coefficients for the dynamic conductivity fitting coefficients. It is a function of the fitting coefficients for dynamic conductivity. All are dimensionless coefficients. The unit is cm - ¹ or m - ¹.

[0133] Optionally, in the above ablation electric field distribution model, , and / or , and / or , and / or , and / or .

[0134] in, Blood conductivity, measured in S / m or S / cm; The initial conductivity of the tissue is expressed in S / m or S / cm. This is the conductivity growth factor, which has no unit. This represents the electric field strength at the center point of the transition region, expressed in V / cm or V / m. These are the fitting coefficients for dynamic conductivity, and are dimensionless. ~ , ~ , ~ , ~ These are the pre-determined fitting coefficients; ~ and ~ The units are all cm - ¹ or m - ¹, ~ and ~ The units are all m / s or cm / s. Based on the above formula, ~ The value varies with the electrical parameters of the tissue, reflecting individual differences among different target subjects. Therefore, the ablation electric field distribution model has good applicability to different individuals.

[0135] Based on the above ablation electric field distribution model, the ablation depth is specifically calculated using the following formula:

[0136]

[0137] in, For the i-th ablation depth, Let be the i-th electric field strength threshold, where i is a positive integer greater than or equal to 0. In this embodiment of the invention, the values ​​of the electric field strength threshold and the dynamic conductivity fitting coefficient are arrays. By substituting different electric field strength thresholds and dynamic conductivity fitting coefficients, a set of ablation depths is obtained, where i is used to distinguish different ablation depths.

[0138] Furthermore, based on the mean and variance of a set of ablation depths obtained, the overall distribution of the predicted ablation depth can be determined when the electrical parameters are uncertain, thus achieving pulse ablation region prediction. Accordingly, the pulse ablation region prediction device provided in this embodiment of the invention also incorporates uncertain electrical parameters into its consideration, quantifying the influence of uncertain electrical parameters on the electric field strength. Moreover, electrical parameters conforming to a Gaussian distribution can reflect the differences between different target objects as a whole, improving the prediction accuracy of the pulse ablation prediction device for different target objects.

[0139] In this embodiment of the invention, different electrical parameters have varying effects on the pulse ablation electric field distribution. Therefore, it is necessary to determine the weight of each electrical parameter in the ablation electric field distribution model. Optionally, a single-variable approach can be used to qualitatively analyze the influence of each electrical parameter on the pulse ablation electric field distribution. Any electrical parameter is used as a preset variable value, while other electrical parameters are preset fixed values. The electrical parameters selected as preset variable values ​​are chosen from low to high, and the ablation depth is determined according to the aforementioned pulse ablation model.

[0140] Substitute the obtained ablation depth into the following formula to calculate the influence of the electrical parameters, which are preset variation values, on the ablation depth:

[0141]

[0142] Where, deep represents the obtained ablation depth; SN represents the degree of influence of the electrical parameter, which is a preset change value, on the ablation depth. The larger the value of SN, the greater the influence of the change in the electrical parameter corresponding to that SN value on the ablation depth.

[0143] Figures 8-1 to 8-6 This is a main effect diagram illustrating the degree of influence of different electrical parameters according to an exemplary embodiment. For example... Figures 8-1 to 8-6 As shown, all electrical parameters have a certain impact on the ablation depth. Among them, the conductivity growth factor and the electric field strength corresponding to the center point of the transition zone have a more significant impact on the ablation depth, while the blood conductivity and the initial tissue conductivity have a weaker impact on the ablation depth.

[0144] Based on the above analysis, it is evident that different electrical parameters have varying degrees of influence on the pulsed electric field distribution. In this embodiment of the invention, the weighting coefficients of different electrical parameters are determined using variance analysis as a quantitative method to reflect the influence of different electrical parameters on the electric field strength. Optionally, in the above ablation electric field distribution model, the weighting coefficients are determined according to the following formula:

[0145]

[0146] The value of i is 1, 2, 3, 4, or 5. This refers to the ablation depth variance obtained from the ablation electric field distribution model when any one of the electrical parameters is a preset variable value and the remaining parameters are preset fixed values. Specifically, This represents the variance corresponding to the preset change value of blood conductivity. This represents the variance corresponding to the initial conductivity of the tissue when it changes by a preset value. This represents the variance when the conductivity growth factor is set to a preset value. This represents the variance of the electric field strength at the center point of the transition zone when the preset variation value is reached. This represents the variance corresponding to the dynamic conductivity fitting coefficients when they are set to preset variation values. The sum of the variances of ablation depth obtained when any of the electrical parameters are preset variation values.

[0147] Specifically, the electric field strength is determined to be a threshold value (e.g., 400 V / cm), the blood conductivity ranges from 0.1 to 1.0 S / m, the initial tissue conductivity ranges from 0.05 to 0.9 S / m, the conductivity growth factor ranges from 1 to 6, the electric field strength at the center point of the transition zone ranges from 200 to 1200 V / cm, and the dynamic conductivity fitting coefficient ranges from 0.00001 to 0.0003. The weighting coefficients obtained based on these values ​​are shown in Table 1.

[0148] Table 1 Correspondence between electrical parameters and weighting coefficients

[0149] Electrical parameters blood conductivity initial conductivity of tissue conductivity growth factor Electric field strength at the center point of the transition region Dynamic conductivity fitting coefficient Weighting coefficient 21.86% 18.32% 26.69% 17.46% 18.91%

[0150] In summary, the pulse ablation region prediction device provided in this embodiment of the invention predicts the ablation range based on the electrical parameters of the target object, fully considering the differences in electrical parameters of different target objects, thereby improving the accuracy of pulse ablation region prediction and the applicability of the prediction device. Furthermore, the electric field strength threshold and dynamic conductivity fitting coefficient among the electrical parameters are uncertain parameters. The electric field distribution model used in this prediction device quantifies the influence of uncertain electrical parameters on the electric field strength, fully considering the differences of different target objects and optimizing the prediction accuracy of the pulse ablation region.

[0151] Based on the overall working principle of the pulse ablation region prediction device provided in the embodiments of the present invention, the following details the implementation method of the acquisition module 200 acquiring electrical parameters.

[0152] First, obtain blood conductivity.

[0153] In this embodiment of the invention, blood conductivity is obtained using a pulse ablation catheter before pulse ablation. Before pulse ablation begins and before the ablation catheter contacts the tissue, the electrode of the ablation catheter is placed in a blood pool, so that the target patient's blood surrounds the electrode. A voltage is applied to the electrode, and the voltage and current on the electrode are monitored to obtain the blood conductivity, as follows.

[0154] Figure 9 This is a block diagram illustrating an acquisition module according to an exemplary embodiment. When electrical parameters include blood conductivity, such as... Figure 9 As shown, the acquisition module 200 includes: a first voltage application unit 210, a first acquisition unit 220, and a third determination unit 230.

[0155] The first voltage application unit 210 is used to apply a first test voltage to a first electrode placed in the target area. The first electrode contacts the blood of the target object but not the tissue of the target object. The first test voltage is 10~100V (e.g., 20V, 40V, 60V, 80V, etc.). During the test, a pair of first electrodes or multiple pairs of first electrodes can be used for discharge.

[0156] The first acquisition unit 220 is used to acquire the first test voltage and the first current passing through the first electrode. The third determination unit 230 is used to determine the blood conductivity based on the first voltage and the first current, and a pre-acquired first fitting function, wherein the first fitting function characterizes the relationship between blood conductivity and current and voltage.

[0157] Figure 10 This is a graph illustrating the relationship between blood conductivity and current, according to an exemplary embodiment. Figure 10As shown, there is a direct proportional relationship between current and blood conductivity. By fitting the data of blood conductivity and current using a function, the expression of the first fitting function is obtained:

[0158]

[0159] Where U1 is the voltage, I is the current calculated in the first simulation, σb is the blood conductivity, and k1 is the fitting coefficient. Based on the above, the specific formula for calculating the blood conductivity is as follows:

[0160]

[0161] Among them, I b The first current is obtained by the first acquisition unit 220.

[0162] Second, obtain the initial conductivity of the tissue.

[0163] Initial tissue conductivity refers to the conductivity of tissue before electroporation occurs due to the ablation electric field. In this embodiment of the invention, the initial tissue conductivity is obtained using a pulsed ablation catheter. Before starting pulsed ablation, the electrodes of the ablation catheter simultaneously contact the tissue and blood. At this time, a voltage is applied to the electrodes, and the voltage and current on the electrodes are monitored. The initial tissue conductivity is obtained based on the monitored voltage and current, as follows.

[0164] Figure 11 This is a block diagram illustrating an acquisition module according to another exemplary embodiment. The acquisition module 200 also includes a second voltage application unit 240, a second acquisition unit 250, and a fourth determination unit 260.

[0165] The second voltage application unit 240 applies a second test voltage to a second electrode placed in the target area, the second electrode contacting the blood and tissue of the target object. The second test voltage is between 10 and 100V, a voltage condition insufficient to cause electroporation in the tissue, ensuring accurate acquisition of the initial tissue conductivity. The second acquisition unit 250 acquires the second test voltage and a second current passing through the second electrode. The fourth determination unit 260 determines the initial tissue conductivity based on the second voltage, the second current, the blood conductivity, and a pre-acquired second fitting function, the second fitting function characterizing the relationship between tissue conductivity and voltage, current, and blood conductivity.

[0166] During testing, the second electrode simultaneously contacts both blood and tissue. In this case, both the blood conductivity and the initial tissue conductivity affect the value of the second current. A database of the relationship between simulated current and voltage is established through simulation. The simulated current is obtained by surface integration of the normal current density on the electrode surface. By fitting the data from the simulation-established database, the expression for the second fitting function is obtained:

[0167]

[0168] Where U2 is the voltage value, Is is the current value calculated in the simulation, σb is the blood conductivity, σt is the initial tissue conductivity, and k2 and k3 are fitting coefficients.

[0169] The second current acquired by the second acquisition unit 250 is the target value of the simulation calculation current. Substituting the value of the second current into the second fitting function mentioned above, the formula for calculating the initial conductivity of the tissue is obtained:

[0170]

[0171] Among them, I tissue This is the second current.

[0172] Third, obtain dynamic conductivity parameters.

[0173] As pulsed ablation proceeds, irreversible perforation occurs in the tissue, leading to changes in its electrical conductivity. Furthermore, the pulse energy acting on the tissue causes temperature changes, which in turn alter the tissue's conductivity. In other words, the tissue conductivity is in a dynamic state during pulsed ablation. This invention introduces a dynamic conductivity parameter to reflect the impact of pulsed ablation on the tissue; this parameter characterizes how the tissue conductivity changes with the electric field strength during pulsed ablation.

[0174] Figure 12 This is a block diagram illustrating an acquisition module according to another exemplary embodiment, such as... Figure 12 As shown, the acquisition module 200 further includes: a third acquisition unit 270, a third voltage application unit 280, a fifth determination unit 290, and a sixth determination unit 211.

[0175] The third acquisition unit 270 is used to acquire the mapping relationship between the simulation current and the dynamic conductivity parameter.

[0176] In one embodiment, the dynamic conductivity parameter includes the conductivity growth factor and the electric field strength corresponding to the center point of the transition region. Figure 13 This is a graph illustrating the change in tissue conductivity with electric field strength, based on an exemplary example. Figure 13 The conductivity growth factor characterizes the magnitude of change in tissue conductivity, and has

[0177] The formula for solving the volume is as follows ,in, The initial value for the change in tissue conductivity. The conductivity value when the entire tissue undergoes electroporation.

[0178] The electric field strength (E) at the center point of the transition region del The electric field strength (E1) at the start of the tissue conductivity change and the electric field strength (E2) at the end of the tissue conductivity change are the average values.

[0179] The specific solution formula is Edel .

[0180] In one embodiment, the conductivity growth factor and the electric field strength corresponding to the center point of the transition region are used as a parameter set, and the mapping relationship between the simulated current and the dynamic conductivity parameters specifically includes a one-to-one correspondence between the simulated current and the parameter set. Figure 14 This is a block diagram illustrating a third acquisition unit according to an exemplary embodiment, such as... Figure 14 As shown, the third acquisition unit 270 includes a first acquisition subunit 271 and a fitting subunit 272. The first acquisition subunit 271 is used to acquire functions characterizing the relationship between the simulated current and the electric field intensity corresponding to the center point of the transition region under different conductivity growth factors. The fitting subunit 272 is used to fit the function acquired by the first acquisition subunit 271 to obtain the mapping relationship between the simulated current and the dynamic conductivity parameters.

[0181] The conductivity growth factor and the electric field strength at the center point of the transition zone both affect tissue conductivity, and changes in tissue conductivity directly affect the simulated current. Therefore, conductivity can be used to correlate the conductivity growth factor, the electric field strength at the center point of the transition zone, and the simulated current.

[0182] During pulsed ablation, tissue conductivity changes due to tissue perforation and tissue temperature, with the degree of perforation primarily depending on the electric field strength. Therefore, a dynamic model of tissue conductivity is constructed as follows:

[0183]

[0184] in, Let E be the initial conductivity of the structure, E be the electric field strength, Edel be the electric field strength at the center point of the transition region, A be the conductivity growth factor, h1 be the dynamic conductivity fitting coefficient, T be the temperature, T0 be the initial temperature, and α be the influence coefficient of temperature increase on conductivity. Among these parameters, h1 and α are taken as empirical values, with α typically ranging from 0.01 to 0.03; T and T0 can be obtained through model calculation.

[0185] Furthermore, the current density can be determined based on the conductivity and electric field strength, and then the simulated current can be determined based on the current density. In this way, the relationship between the electric field strength and the simulated current corresponding to different conductivity growth factors and the center point of the transition region can be determined through simulation.

[0186] Figure 15 This is a graph illustrating the relationship between dynamic conductivity parameters and simulated current, based on an exemplary embodiment. Figure 15 As shown, under the same electric field strength at the center point of the transition region, the simulated current increases with the increase of the conductivity growth factor; under the same conductivity growth factor, the simulated current decreases with the increase of the electric field strength at the center point of the transition region.

[0187] By fitting the relationship between the electric field strength and the simulated current corresponding to different conductivity growth factors and the center point of the transition zone, the functional relationship between the simulated current and the initial conductivity of the tissue and the conductivity of blood is obtained:

[0188]

[0189] Among them, I c To simulate current, For blood conductivity, The initial conductivity of the tissue is given by U, where U is the voltage and E is the voltage. del Let be the electric field intensity at the center point of the transition zone, A be the conductivity growth factor, and k2~k6 be the fitting coefficients, which can be obtained by fitting based on historical data.

[0190] Based on the above functional relationship, a one-to-one correspondence can be obtained between the simulated current and the parameter set consisting of the conductivity growth factor and the electric field intensity corresponding to the center point of the transition region. That is, the mapping relationship between the simulated current and the dynamic conductivity parameter can be obtained.

[0191] Continue to refer to Figure 12 The third voltage application unit 280 in the acquisition module 200 is used to apply a pulse ablation voltage to the third electrode placed in the target area, monitor the current through the third electrode, and take the stable output current as the third current.

[0192] The pulsed ablation voltage applied by the third voltage application unit 280 is 500V~2000V. As the voltage application time increases, the conductivity growth factor increases, and the electric field strength at the center point of the transition region decreases. Furthermore, the pulsed ablation voltage signal released by the third voltage application unit 280 consists of millisecond-level pulse trains, each containing multiple bipolar pulses. Therefore, the third voltage application unit 280 acquires the voltage and current data of each pulsed ablation voltage signal in real time and uses the final stable current value as the third current.

[0193] The fifth determining unit 290 is used to determine the simulated current that is closest to the third current among a plurality of pre-stored simulated currents.

[0194] Figure 16 This is a block diagram illustrating the fifth determining unit according to an exemplary embodiment. Figure 16As shown, the fifth determining unit 290 includes a second obtaining subunit 291 and a first determining subunit 292.

[0195] The second acquisition subunit 291 is used to acquire the degree of numerical agreement between the third current and a plurality of pre-stored simulated currents. The first determination subunit 292 is used to determine the simulated current corresponding to the minimum value among the numerical agreement values ​​as the closest simulated current.

[0196] Optionally, the third acquisition unit 270 pre-stores multiple simulated currents. The second acquisition subunit 291 determines the degree of agreement between the third current and the simulated current using the following formula:

[0197]

[0198] Where SS represents the degree of agreement, I w For the third current, I c This represents the simulated current. The smaller the value of SS, the better the match between the third current and the simulated current.

[0199] Continue to refer to Figure 12 In the acquisition module 200, the sixth determining unit 211 is used to determine the dynamic conductivity parameter based on the closest simulated current and the mapping relationship pre-stored by the third acquisition unit 270.

[0200] Fourth, obtain the dynamic conductivity fitting coefficients.

[0201] Figure 17 This is a block diagram illustrating an acquisition module according to another exemplary embodiment. Figure 17 As shown, the acquisition module 200 includes a fourth acquisition unit 212, which is used to acquire a first data set. The first data set includes multiple dynamic conductivity fitting coefficients, which are Gaussian distributed.

[0202] Due to individual differences, the dynamic conductivity fitting coefficient is an uncertain parameter. In this embodiment of the invention, a dynamic conductivity parameter conforming to a Gaussian distribution is selected to reflect the physiological characteristics of most patients, thereby quantifying the uncertain parameter through an electric field distribution model.

[0203] Figure 18 This is a dynamic conductivity fitting coefficient distribution diagram illustrated according to an exemplary embodiment. For example... Figure 18 As shown, the dynamic conductivity fitting coefficients satisfy a Gaussian distribution. .in, The mean of the fitting coefficients for dynamic conductivity. This represents the variance of the dynamic conductivity fitting coefficients. In this embodiment of the invention, , Take 0.000045.

[0204] Fifth, obtain the electric field strength threshold.

[0205] See also Figure 17 The acquisition module 200 further includes a fifth acquisition unit 213, which is used to acquire a second data group. The second data group includes multiple electric field intensity thresholds, which are Gaussian distributed.

[0206] The electric field strength threshold characterizes the electric field strength when irreversible electroporation occurs in tissue cells. Figure 19 This is a field strength distribution diagram along the depth direction of a tissue cross-section, illustrated according to an exemplary embodiment. For example... Figure 19 As shown, the intensity of the ablation electric field gradually decreases along the tissue depth direction. The field strength at depth x is E(x). When the electric field value E(x) decays to the electric field strength threshold, the corresponding depth x is the ablation depth. In other words, in a pulsed ablation electric field, tissue ablation cannot be achieved in the portion where the electric field strength is less than the electric field strength threshold, but it can be achieved in the portion where the electric field strength is greater than or equal to the electric field strength threshold.

[0207] Due to the differences in tissue cells among different individuals, the electric field strength threshold for irreversible electroporation also varies. In this embodiment of the invention, the electric field strength threshold is used as an uncertain parameter, and an electric field strength threshold conforming to a Gaussian distribution is selected to reflect the physiological characteristics of most patients. Furthermore, the uncertain parameter is quantified through an electric field distribution model.

[0208] Optionally, the electric field intensity threshold follows a Gaussian distribution. .in, The mean value of the electric field strength threshold. The variance of the electric field strength threshold is given in this embodiment of the invention. Take 100, The following relationship must be satisfied:

[0209]

[0210] in, The discharge duration of the high-level pulse signal can be directly obtained from the signal output by the pulse ablation generator. , , The fitting coefficients are determined based on historical data.

[0211] In summary, the electrical parameters acquired by the pulse ablation prediction device provided in this embodiment of the invention fully take into account the differences between individuals. Therefore, the accuracy of the pulse ablation area predicted based on these electrical parameters is better, and its applicability is wider.

[0212] In one embodiment, since the electric field strength threshold and dynamic conductivity fitting coefficients are arrays that conform to a Gaussian distribution, the ablation depth obtained according to the pulse ablation electric field distribution model is a set of data (hereinafter referred to as the ablation depth set). Figure 20 This is an ablation depth distribution map illustrated according to an exemplary embodiment, such as... Figure 20 As shown, the predicted ablation depths exhibit a certain distribution pattern. In such cases, it is necessary to evaluate the effectiveness of the predicted ablation depths.

[0213] Optionally, the pulse ablation region prediction device provided in this embodiment of the invention is also used to determine and adjust the pulse ablation parameters based on the ablation depth. Figure 21 This is a block diagram illustrating a pulse ablation region prediction device based on uncertainty parameter quantization, according to another exemplary embodiment. Figure 21 As shown, the device also includes a matching degree parameter acquisition module 500 and a judgment module 600.

[0214] The matching degree parameter acquisition module 500 is used to acquire a matching degree parameter based on the ablation depth and the target ablation depth. The matching degree parameter characterizes the degree of matching between the pulse parameter corresponding to the ablation depth and the pulse parameter that achieves the target ablation depth. The judgment module 600 is used to determine whether to adjust the pulse ablation parameters based on the matching degree parameter.

[0215] In one embodiment, the matching degree parameter includes the average ablation depth. In this case, the judgment module 600 adjusts the pulse ablation parameters in response to the difference between the average and the target ablation depth exceeding a preset threshold. Specifically, when the difference between the average and the target ablation depth exceeds the preset threshold, and the average ablation depth is less than the target ablation depth, the duration of the high-level pulse ablation is increased, and / or the voltage value of the pulse ablation signal is increased. When the difference between the average and the target ablation depth exceeds the preset threshold, and the average ablation depth is greater than the target ablation depth, the duration of the high-level pulse ablation is shortened, and / or the voltage value of the pulse ablation signal is decreased.

[0216] The mean pulse ablation depth is just one example of the matching degree parameter, which can also include the probability that tissue at the target ablation depth is effectively ablated. Regarding the probability that tissue at the target ablation depth is effectively ablated, it should be noted that:

[0217] In this embodiment of the invention, the dynamic conductivity fitting coefficients are a first data set that satisfies a Gaussian distribution. Based on the first data set and the blood conductivity, initial tissue conductivity, conductivity growth factor, and electric field intensity corresponding to the center point of the transition zone determined according to the above scheme, an electric field distribution model is determined. Then, based on the electric field distribution model, a corresponding set of electric field intensities can be determined according to the target depth. This set of electric field intensities is the calculated electric field intensity value at the target ablation depth. Furthermore, this embodiment of the invention also selects a second data set containing multiple electric field intensity thresholds. Based on the calculated electric field intensity value at the target ablation depth and the second data set, the probability that the tissue at the target ablation depth is effectively ablated can be obtained. Figure 22 This is a graph showing the comparison between the electric field intensity value at the target ablation depth and the electric field intensity threshold, according to an exemplary embodiment. Combined with... Figure 22 The probability that tissue at the target ablation depth is effectively ablated is specifically defined as follows:

[0218]

[0219] in, denoted as denoted as , where is the probability that the tissue at the target depth is effectively ablated, 'n' is the total number of calculated electric field strength thresholds, and 'm' is the number of tissue electric field strength values ​​at the target depth that are greater than the electric field strength thresholds in the second data set.

[0220] When the matching degree parameter includes the probability that tissue at the target ablation depth will be effectively ablated, the judgment module 600 adjusts the pulse ablation parameters in response to the probability being less than 70%. Here, 70% is merely an exemplary threshold value and can be set according to user needs.

[0221] The matching degree parameter can also include the standard deviation of the predicted ablation depth. In this case, the specific method for the judgment module 600 to make the judgment is not limited; the judgment conditions can be set by the user according to their needs.

[0222] In summary, the pulse ablation region prediction device provided in this embodiment of the invention predicts the ablation range based on the electrical parameters of the target object, fully considering the differences in electrical parameters of different target objects, thereby improving the accuracy of pulse ablation region prediction and the applicability of the prediction device. Furthermore, among the electrical parameters, the electric field strength threshold, the dynamic conductivity fitting coefficient, and the second dynamic conductivity fitting coefficient are uncertain parameters. The electric field distribution model used in this prediction device quantifies the influence of uncertain electrical parameters on the electric field strength, optimizing the prediction accuracy of the pulse ablation region.

[0223] Secondly, embodiments of the present invention also provide a pulse ablation region prediction method based on uncertain parameter quantification, which is applied to pulse ablation devices. Figure 23This is a flowchart illustrating a pulse ablation region prediction method based on uncertain parameter quantization according to an exemplary embodiment, such as... Figure 23 As shown, the pulse ablation area prediction method includes:

[0224] Step S701: Obtain the electrical parameters of the target object, wherein the electrical parameters reflect the electrical characteristics of the tissue and blood of the target object.

[0225] Step S702: Determine the effective ablation boundary based on the electrical parameters and the pre-acquired ablation electric field distribution model.

[0226] Step S703: The area enclosed by the effective ablation boundary and the tissue surface is defined as the ablation area.

[0227] In one embodiment, determining the effective ablation boundary based on electrical parameters and a pre-acquired ablation electric field distribution model includes:

[0228] The ablation depth is determined based on electrical parameters and a pre-obtained ablation electric field distribution model.

[0229] The effective ablation boundary is determined based on the ablation depth.

[0230] In one embodiment, the ablation electric field distribution model includes the following formula:

[0231]

[0232] Where E is the electric field strength, U is the ablation voltage, and x is the coordinate value in the tissue depth direction. These are the fitting coefficients.

[0233] This is a weighting coefficient for blood conductivity. It is a function of blood conductivity;

[0234] The weighting coefficients for the initial conductivity of the structure. It is a function of the initial conductivity of the tissue;

[0235] The weighting coefficients for the conductivity growth factor. It is a function of the conductivity growth factor;

[0236] This is the weighting coefficient for the electric field intensity corresponding to the center point of the transition region. It is a function of the electric field intensity at the center point of the transition region;

[0237] These are the weighting coefficients for the dynamic conductivity fitting coefficients. It is a function of the fitting coefficients for dynamic conductivity.

[0238] In one embodiment, in the ablation electric field distribution model

[0239] , and / or

[0240] , and / or

[0241] , and / or

[0242] , and / or

[0243] ,

[0244] in, For blood conductivity, To organize the initial conductivity, As the conductivity growth factor, The electric field strength is located at the center point of the transition region. These are the fitting coefficients for dynamic conductivity. ~ , ~ , ~ , ~ These are predetermined fitting coefficients, which can be determined based on historical data.

[0245] In one embodiment, in the ablation electric field distribution model

[0246]

[0247] Where i takes the values ​​1, 2, 3, 4, and 5;

[0248] The ablation depth variance is obtained from the ablation electric field distribution model when any one of the electrical parameters is a preset variable value and the other parameters are preset fixed values.

[0249] in, This represents the variance of blood conductivity when the preset variation value is reached, and the variation range of blood conductivity is 0.1~1S / m; This represents the variance corresponding to the preset variation value of the initial conductivity of the tissue, which ranges from 0.05 to 0.9 S / m. This represents the variance corresponding to the conductivity growth factor being a preset value. The range of the conductivity growth factor is 1 to 6. The variance of the electric field strength at the center point of the transition zone is the preset variation value. The variation range of the electric field strength at the center point of the transition zone is 200~1200V / cm. This represents the variance corresponding to the dynamic conductivity fitting coefficient when it is the preset change value. The range of the conductivity fitting coefficient is 0.00001~0.00003.

[0250] This is the sum of the variances of ablation depth obtained when any electrical parameter is a preset variation value.

[0251] In one embodiment, determining the effective ablation boundary based on the ablation depth includes: determining the corresponding field strength contour lines based on the ablation depth, and using the corresponding field strength contour lines as the effective ablation boundary.

[0252] In one embodiment, the electrical parameters include blood conductivity; obtaining the electrical parameters of the target object includes:

[0253] A first test voltage is applied to a first electrode placed in the target area. The first electrode contacts the blood of the target object but does not contact the tissue of the target object.

[0254] Obtain the first test voltage and the first current through the first electrode;

[0255] The blood conductivity is determined based on the first voltage and the first current, as well as a pre-acquired first fitting function, which characterizes the relationship between the blood conductivity and the current and voltage.

[0256] In one embodiment, the electrical parameters further include the initial conductivity of the tissue; obtaining the electrical parameters of the target object further includes:

[0257] A second test voltage is applied to a second electrode placed in the target area, and the second electrode contacts the blood and tissue of the target object;

[0258] Obtain the second test voltage and the second current through the second electrode;

[0259] The initial tissue conductivity is determined based on the second voltage, the second current, and the blood conductivity, as well as a pre-acquired second fitting function. The second fitting function characterizes the relationship between tissue conductivity and voltage, current, and blood conductivity.

[0260] In one embodiment, the electrical parameters further include a dynamic conductivity parameter, which characterizes the change in tissue conductivity with electric field strength during pulsed ablation; obtaining the electrical parameters of the target object further includes:

[0261] Obtain the mapping relationship between simulated current and dynamic conductivity parameters;

[0262] A pulsed ablation voltage is applied to the third electrode placed in the target area, the current passing through the third electrode is monitored, and the stable output current is taken as the third current.

[0263] Among the pre-stored multiple simulated currents, determine the simulated current that is closest to the third current;

[0264] The dynamic conductivity parameters are determined based on the closest simulated current and mapping relationship.

[0265] In one embodiment, the dynamic conductivity parameter includes a conductivity growth factor and an electric field strength corresponding to the center point of the transition zone. The conductivity growth factor characterizes the magnitude of the change in tissue conductivity, and the electric field strength corresponding to the center point of the transition zone is the average of the electric field strength at the start of the tissue conductivity change and the electric field strength at the end of the tissue conductivity change.

[0266] Obtaining the mapping relationship between simulated current and dynamic conductivity parameters includes:

[0267] Obtain the function of simulated current with respect to the electric field intensity at the center point of the transition region under different conductivity growth factors;

[0268] By fitting the function, a mapping relationship is obtained.

[0269] In one embodiment, determining the simulated current that is closest to the third current includes:

[0270] The degree of agreement between the third current and the values ​​of multiple pre-stored simulated currents is obtained;

[0271] The simulated current corresponding to the minimum value among the numerical matches is determined as the closest simulated current.

[0272] In one embodiment, the electrical parameters include dynamic conductivity fitting coefficients; obtaining the electrical parameters of the target object includes: obtaining a first data set, the first data set including multiple dynamic conductivity fitting coefficients, the multiple dynamic conductivity fitting coefficients being Gaussian distributed.

[0273] In one embodiment, the electrical parameters further include an electric field strength threshold, which characterizes the electric field strength when irreversible electroporation occurs in tissue cells; obtaining the electrical parameters of the target object includes: obtaining a second data set, which includes multiple electric field strength thresholds, and the multiple electric field strength thresholds are Gaussian distributed.

[0274] In one embodiment, the pulse ablation region prediction method further includes: obtaining a matching degree parameter based on the ablation depth and the target ablation depth, wherein the matching degree parameter characterizes the degree of matching between the pulse parameter corresponding to the ablation depth and the pulse parameter for achieving the target ablation depth; and determining whether to adjust the pulse ablation parameters based on the matching degree parameter.

[0275] In one embodiment, the matching degree parameter includes the average ablation depth. In this case, determining whether to adjust the pulse ablation parameters based on the matching degree parameter includes: adjusting the pulse ablation parameters in response to the difference between the average and the target ablation depth exceeding a preset threshold. Specifically, when the difference between the average ablation depth and the target ablation depth exceeds the preset threshold, and the average ablation depth is less than the target ablation depth, the duration of the high-level pulse ablation is increased, and / or the voltage value of the pulse ablation signal is increased. When the difference between the average ablation depth and the target ablation depth exceeds the preset threshold, and the average ablation depth is greater than the target ablation depth, the duration of the high-level pulse ablation is shortened, and / or the voltage value of the pulse ablation signal is decreased.

[0276] In one embodiment, the matching degree parameter includes the probability that tissue at the target ablation depth is effectively ablated. In this case, the judgment module 600 adjusts the pulse ablation parameters in response to a probability less than 70%. Here, 70% is merely an exemplary threshold value and can be set according to user needs.

[0277] In summary, the pulse ablation region prediction device based on uncertain parameter quantification provided in this embodiment of the invention predicts the ablation range based on the electrical parameters of the target object, fully considering the differences in electrical parameters of different target objects, thereby improving the accuracy of pulse ablation region prediction and the applicability of the prediction device. Furthermore, the electrical parameters include uncertain parameters, and the influence of uncertain electrical parameters on the electric field strength is quantified and analyzed through an electric field distribution model, optimizing the prediction accuracy of the pulse ablation region.

[0278] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the pulse ablation region prediction method provided in the second aspect.

[0279] Figure 24 This is a schematic diagram illustrating the structure of an electronic device according to an exemplary embodiment. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the pulse ablation region prediction method provided in the second aspect. Figure 24 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0280] like Figure 24 As shown, the electronic device 30 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0281] Bus 33 includes a data bus, an address bus, and a control bus.

[0282] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0283] The memory 321 may also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0284] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32, such as the pulse ablation region prediction method provided in the second aspect of the present invention.

[0285] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, the model-generated electronic device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. Figure 24 As shown, network adapter 36 communicates with other modules of the model-generated electronic device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated electronic device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0286] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0287] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the pulse ablation region prediction method provided in the second aspect above. The readable storage medium may specifically include, but is not limited to, portable disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0288] In a possible implementation, the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, causes the terminal device to perform the steps in the pulse ablation region prediction method provided in the second aspect above.

[0289] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0290] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the embodiments of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the embodiments of the present invention, but all such changes and modifications fall within the scope of protection of the embodiments of the present invention.

Claims

1. A pulse ablation region prediction device based on uncertain parameter quantification, characterized in that, The device includes: An acquisition module is used to acquire electrical parameters of a target object, the electrical parameters reflecting the electrical properties of the target object's tissues and blood; The effective ablation boundary determination module is used to determine the effective ablation boundary based on the electrical parameters and the pre-acquired ablation electric field distribution model. The ablation area determination module is used to determine the area enclosed by the effective ablation boundary and the tissue surface as the ablation area; The effective ablation boundary determination module includes: The first determining unit is used to determine the ablation depth based on the electrical parameters and the pre-acquired ablation electric field distribution model. The second determining unit is used to determine the effective ablation boundary based on the ablation depth; The ablation electric field distribution model includes the following formulas: ， Where E is the electric field strength, in V / cm or V / m; U is the ablation voltage, in V; and x is the coordinate value in the tissue depth direction, in cm or m. These are the fitting coefficients, in cm. -1 or m -1 ; This is a weighting coefficient for blood conductivity. It is a function of blood conductivity; The weighting coefficients for the initial conductivity of the structure. It is a function of the initial conductivity of the tissue; The weighting coefficients for the conductivity growth factor. It is a function of the conductivity growth factor; This is the weighting coefficient for the electric field intensity corresponding to the center point of the transition region. It is a function of the electric field intensity at the center point of the transition region; These are the weighting coefficients for the dynamic conductivity fitting coefficients. It is a function of the fitting coefficients for dynamic conductivity; All are dimensionless coefficients. The unit is cm -1 or m -1 ; In the ablation electric field distribution model, ， Where i takes the values ​​1, 2, 3, 4, and 5; The ablation depth variance is obtained based on the ablation electric field distribution model when any one of the electrical parameters is a preset variable value and the other parameters are preset fixed values. in, This represents the variance of blood conductivity when the preset variation value is reached, and the variation range of blood conductivity is 0.1~1S / m; This represents the variance corresponding to the preset variation value of the initial conductivity of the tissue, which ranges from 0.05 to 0.9 S / m. This represents the variance corresponding to the conductivity growth factor being a preset value. The range of the conductivity growth factor is 1 to 6. The variance of the electric field strength at the center point of the transition zone is the preset variation value. The variation range of the electric field strength at the center point of the transition zone is 200~1200V / cm. This represents the variance corresponding to the dynamic conductivity fitting coefficient when it is the preset change value. The range of the conductivity fitting coefficient is 0.00001~0.00003. The sum of the variances of ablation depth obtained when any of the electrical parameters are preset variation values.

2. The apparatus according to claim 1, characterized in that, In the ablation electric field distribution model, , and / or , and / or , and / or , and / or ， in, Blood conductivity, measured in S / m or S / cm; The initial conductivity of the tissue is expressed in S / m or S / cm. This is the conductivity growth factor, which has no unit. This represents the electric field strength at the center point of the transition region, expressed in V / cm or V / m. These are the fitting coefficients for dynamic conductivity, and are dimensionless. ~ , ~ , ~ , ~ These are the pre-determined fitting coefficients. ~ and ~ The units are all cm -1 or m -1 , ~ and ~ The units are all m / s or cm / s.

3. The apparatus according to claim 1, characterized in that, The second determining unit is specifically used to: determine the corresponding field strength contour lines according to the ablation depth, and use the corresponding field strength contour lines as the effective ablation boundary.

4. The apparatus according to claim 1, characterized in that, The electrical parameters include blood conductivity; the acquisition module includes: A first voltage application unit is used to apply a first test voltage to a first electrode placed in a target area, wherein the first electrode contacts the blood of the target object but does not contact the tissue of the target object; The first acquisition unit is used to acquire the first test voltage and the first current through the first electrode; The third determining unit is used to determine the blood conductivity based on the first voltage and the first current, as well as a pre-acquired first fitting function, wherein the first fitting function characterizes the relationship between blood conductivity and current and voltage.

5. The apparatus according to claim 4, characterized in that, The electrical parameters also include the initial tissue conductivity; the acquisition module further includes: The second voltage application unit is used to apply a second test voltage to a second electrode placed in the target area, wherein the second electrode contacts the blood and tissue of the target object; The second acquisition unit is used to acquire the second test voltage and the second current through the second electrode; The fourth determining unit is used to determine the initial conductivity of the tissue based on the second voltage, the second current, the blood conductivity, and a pre-acquired second fitting function, wherein the second fitting function characterizes the relationship between tissue conductivity and voltage, current, and blood conductivity.

6. The apparatus according to claim 5, characterized in that, The electrical parameters also include dynamic conductivity parameters, which characterize the change in tissue conductivity with electric field strength during pulsed ablation; the acquisition module further includes: The third acquisition unit is used to acquire the mapping relationship between the simulated current and the dynamic conductivity parameter; The third voltage application unit is used to apply a pulsed ablation voltage to the third electrode placed in the target area, monitor the current through the third electrode, and use the stable output current as the third current; The fifth determining unit is used to determine the simulated current that is closest to the third current from a plurality of pre-stored simulated currents; The sixth determining unit is used to determine the dynamic conductivity parameter based on the closest simulated current and the mapping relationship.

7. The apparatus according to claim 6, characterized in that, The dynamic conductivity parameters include a conductivity growth factor and an electric field strength corresponding to the center point of the transition zone. The conductivity growth factor characterizes the magnitude of change in the tissue conductivity, and the electric field strength corresponding to the center point of the transition zone is the average of the electric field strength at the start of the tissue conductivity change and the electric field strength at the end of the tissue conductivity change. The third acquisition unit includes: The first acquisition subunit is used to acquire a function characterizing the relationship between the simulated current and the electric field intensity corresponding to the center point of the transition region under different conductivity growth factors; The fitting sub-unit is used to fit the function to obtain the mapping relationship between the simulated current and the dynamic conductivity parameter.

8. The apparatus according to claim 7, characterized in that, The fifth determining unit includes: The second acquisition subunit is used to acquire the degree of agreement between the third current and the numerical values ​​of the pre-stored multiple simulated currents; The first determining subunit is used to determine the simulated current corresponding to the minimum value among the numerical matching degrees as the closest simulated current.

9. The apparatus according to claim 1, characterized in that, The electrical parameters include dynamic conductivity fitting coefficients; the acquisition module includes: The fourth acquisition unit is used to acquire a first data group, the first data group including a plurality of dynamic conductivity fitting coefficients, the plurality of dynamic conductivity fitting coefficients being Gaussian distributed.

10. The apparatus according to claim 1, characterized in that, The electrical parameters also include an electric field strength threshold, which characterizes the electric field strength when irreversible electroporation occurs in tissue cells; the acquisition module includes: The fifth acquisition unit is used to acquire a second data group, the second data group including a plurality of electric field strength thresholds, the plurality of electric field strength thresholds being Gaussian distributed.

11. The apparatus according to claim 1, characterized in that, The device further includes: The matching degree parameter acquisition module is used to acquire a matching degree parameter based on the ablation depth and the target ablation depth. The matching degree parameter represents the degree of matching between the pulse parameter corresponding to the ablation depth and the pulse parameter that achieves the target ablation depth. The judgment module is used to determine whether to adjust the pulse ablation parameters based on the matching degree parameters.

12. The apparatus according to claim 11, characterized in that, The matching degree parameter includes the average value of the ablation depth; the judgment module is specifically used for: In response to the difference between the mean and the target ablation depth exceeding a preset threshold, the pulse ablation parameters are adjusted; or The matching degree parameter includes the probability that the tissue at the target ablation depth will be ablated; the judgment module is specifically used to: adjust the pulse ablation parameter in response to the probability being less than 70%.

13. A pulse ablation region prediction method based on uncertain parameter quantification, characterized in that, The method includes: Obtain the electrical parameters of the target object, wherein the electrical parameters reflect the electrical properties of the target object's tissues and blood; The effective ablation boundary is determined based on the electrical parameters and the pre-acquired ablation electric field distribution model; The area enclosed by the effective ablation boundary and the tissue surface is defined as the ablation area; specifically, this includes the following steps: The ablation depth is determined based on the electrical parameters and the pre-acquired ablation electric field distribution model. The effective ablation boundary is determined based on the ablation depth; The ablation electric field distribution model includes the following formulas: ， Where E is the electric field strength, in V / cm or V / m; U is the ablation voltage, in V; and x is the coordinate value in the tissue depth direction, in cm or m. These are the fitting coefficients, in cm. -1 or m -1 ; This is a weighting coefficient for blood conductivity. It is a function of blood conductivity; The weighting coefficients for the initial conductivity of the structure. It is a function of the initial conductivity of the tissue; The weighting coefficients for the conductivity growth factor. It is a function of the conductivity growth factor; This is the weighting coefficient for the electric field intensity corresponding to the center point of the transition region. It is a function of the electric field intensity at the center point of the transition region; These are the weighting coefficients for the dynamic conductivity fitting coefficients. It is a function of the fitting coefficients for dynamic conductivity; All are dimensionless coefficients. The unit is cm -1 or m -1 ; In the ablation electric field distribution model, ， Where i takes the values ​​1, 2, 3, 4, and 5; The ablation depth variance is obtained based on the ablation electric field distribution model when any one of the electrical parameters is a preset variable value and the other parameters are preset fixed values. in, This represents the variance of blood conductivity when the preset variation value is reached, and the variation range of blood conductivity is 0.1~1S / m; This represents the variance corresponding to the preset variation value of the initial conductivity of the tissue, which ranges from 0.05 to 0.9 S / m. This represents the variance corresponding to the conductivity growth factor being a preset value. The range of the conductivity growth factor is 1 to 6. The variance of the electric field strength at the center point of the transition zone is the preset variation value. The variation range of the electric field strength at the center point of the transition zone is 200~1200V / cm. This represents the variance corresponding to the dynamic conductivity fitting coefficient when it is the preset change value. The range of the conductivity fitting coefficient is 0.00001~0.00003. The sum of the variances of ablation depth obtained when any of the electrical parameters are preset variation values.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes a computer program, it implements the pulse ablation region prediction method of claim 13.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the pulse ablation region prediction method of claim 13.

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