CN115300094A

CN115300094A - Pulse ablation region prediction device and method based on uncertain parameter quantification

Info

Publication number: CN115300094A
Application number: CN202210976462.1A
Authority: CN
Inventors: 王慧; 赵峰; 郭文娟; 赵乾成; 张维
Original assignee: Shanghai Shangyang Medical Technology Co ltd
Current assignee: Shanghai Shangyang Medical Technology Co ltd
Priority date: 2022-08-15
Filing date: 2022-08-15
Publication date: 2022-11-08

Abstract

The invention discloses a pulse ablation region prediction device and method based on uncertain parameter quantification, and belongs to the technical field of pulse ablation. The device comprises an acquisition module, an effective ablation boundary determination module and an ablation region determination module. The acquisition module is used for acquiring electrical parameters of a target object, and the electrical parameters reflect electrical characteristics of tissues and blood of the target object. And the effective ablation boundary determining module is used for determining an effective ablation boundary according to the electrical parameters and a pre-acquired ablation electric field distribution model. The ablation region determination module is used for determining an area enclosed by the effective ablation boundary and the tissue surface as an ablation region.

Description

Pulse ablation region prediction device and method based on uncertain parameter quantification

Technical Field

The invention relates to the technical field of pulse ablation, in particular to a pulse ablation region prediction device and method based on uncertain parameter quantification.

Background

Pulse ablation is a form of non-thermal ablation and is an effective method of treating persistent atrial fibrillation and ventricular arrhythmias. By applying an ultra-fast electric field to the target tissue, irreversible nanoscale pores are formed in the tissue cells, so that the cell contents leak to destroy the stability of the cells, thereby causing cell death.

The curative effect of the pulse ablation mainly depends on pulse parameters, excessive ablation can be caused by too high pulse dose, and incomplete treatment and relapse can be caused by too little pulse dose. To ensure proper pulse delivery, it is necessary to determine the ablation zone and set the pulse parameters according to the determined ablation zone. Therefore, the accurate determination of the ablation region is a key step for ensuring the pulse ablation effect.

In the related art, the ablation region is determined according to the position of the tissue to be ablated, and the electrical characteristics of the physiological tissue of a patient are not combined, so that the determined ablation region is inaccurate, and the pulse ablation effect is influenced.

Disclosure of Invention

The invention aims to overcome the defect that the pulse ablation area is determined inaccurately for different patients in the prior art, and provides a pulse ablation area prediction device and method based on uncertain parameter quantization.

The invention solves the technical problems through the following technical scheme:

in a first aspect, the present invention provides a pulse ablation region prediction device based on uncertain parameter quantization, the device comprising:

an acquisition module for acquiring electrical parameters of a target object, the electrical parameters reflecting electrical characteristics of tissue and blood of the target object;

the effective ablation boundary determining module is used for determining an effective ablation boundary according to the electrical parameters and a pre-acquired ablation electric field distribution model;

and the ablation region determining module is used for determining the region enclosed by the effective ablation boundary and the tissue surface as an ablation region.

Optionally, the effective ablation boundary determination module comprises:

the first determining unit is used for determining ablation depth according to the electrical parameters and a pre-acquired ablation electric field distribution model;

a second determination unit for determining the effective ablation boundary according to the ablation depth.

Optionally, the ablation electric field distribution model comprises the following formula:

wherein E is the electric field intensity, U is the ablation voltage, x is the coordinate value of the tissue depth direction, and gamma is the fitting coefficient,

ω ₁ is the weight coefficient of the blood conductivity, p ₁ As a function of the conductivity of the blood;

ω ₂ weight coefficient of initial conductivity of tissue, p ₂ As a function of the initial conductivity of the tissue;

ω ₃ weight coefficient, p, of conductivity increase factor ₃ As a function of the conductivity increase factor;

ω ₄ weighting factor, p, of the electric field strength corresponding to the centre point of the transition region ₄ As a function of the corresponding electric field strength with respect to the central point of the transition region;

ω ₅ weight coefficient, p, for the dynamic conductivity fitting coefficient ₅ As a function of the fitting coefficient for the dynamic conductivity.

Optionally, in the ablation electric field distribution model,

and/or

And/or

And/or

And/or

Wherein σ _b In order to be the electrical conductivity of the blood,

for initial tissue conductivity, A is a conductivity increasing factor, E _del Electric field intensity h corresponding to the central point of the transition region ₁ As a coefficient of dynamic conductivity fit, a ₁ ～a ₅ 、b ₁ ～b ₅ 、c ₁ ～c ₅ 、d ₁ ～d ₅ Respectively, are predetermined fitting coefficients.

Optionally, in the ablation electric field distribution model,

wherein, the value of i is 1, 2, 3, 4 and 5;

S _i when any one of the electrical parameters is a preset variation value and the other parameters are preset fixed values, obtaining an ablation depth variance according to the ablation electric field distribution model;

wherein S is ₁ Representing the corresponding variance when the blood conductivity is a preset change value, wherein the change range of the blood conductivity is 0.1-1S/m;

S ₂ representing the corresponding variance when the initial tissue conductivity is a preset change value, wherein the change range of the initial tissue conductivity is 0.05-0.9S/m;

S ₃ the variation range of the conductivity increase factor is that for the corresponding variance when the conductivity increase factor is a preset variation value1～6；

S ₄ The variance corresponding to the electric field intensity corresponding to the central point of the transition region is a preset variation value, and the variation range of the electric field intensity corresponding to the central point of the transition region is 200-1200V/cm;

S ₅ the variance is corresponding to the dynamic conductivity fitting coefficient when the dynamic conductivity fitting coefficient is a preset change value, and the change range of the conductivity fitting coefficient is 0.00001-0.00003;

∑s _i the sum of the ablation depth variances obtained when any one electrical parameter is a preset variation value is obtained.

Optionally, the second determining unit is specifically configured to: and determining a corresponding field intensity contour line according to the ablation depth, and taking the corresponding field intensity contour line as the effective ablation boundary.

Optionally, the electrical parameter comprises blood conductivity; the acquisition module includes:

a first voltage applying unit for applying a first test voltage to a first electrode placed at a target region, the first electrode contacting blood of the target object without contacting tissue of the target object;

the first acquisition unit is used for acquiring the first test voltage and a first current passing through the first electrode;

and the third determining unit is used for determining the blood conductivity according to the first voltage and the first current and a first fitting function which is acquired in advance, wherein the first fitting function represents the relationship between the blood conductivity and the current and the voltage.

Optionally, the electrical parameter further comprises tissue initial conductivity; the acquisition module further comprises:

a second voltage applying unit for applying a second test voltage to a second electrode placed on the target region, the second electrode contacting blood and tissue of the target object;

a second obtaining unit, configured to obtain the second test voltage and a second current passing through the second electrode;

a fourth determining unit, configured to determine the initial tissue conductivity according to the second voltage, the second current, and the blood conductivity, and a second fitting function obtained in advance, where the second fitting function represents a relationship between the tissue conductivity and the voltage, the current, and the blood conductivity.

Optionally, the electrical parameters further include a dynamic conductivity parameter characterizing a change in tissue conductivity with electric field strength during the pulse ablation; the acquisition module further comprises:

the third acquisition unit is used for acquiring the mapping relation between the simulation current and the dynamic conductivity parameter;

a third voltage applying unit for applying a pulse ablation voltage to a third electrode disposed at the target region, monitoring a current passing through the third electrode, and taking the stably output current as a third current;

a fifth determination unit configured to determine, among a plurality of simulated currents stored in advance, a simulated current closest to the third current;

a sixth determining unit, configured to determine the dynamic conductivity parameter according to the closest simulated current and the mapping relationship.

Optionally, the dynamic conductivity parameter includes a conductivity increase factor and an electric field intensity corresponding to a central point of the transition region, the conductivity increase factor represents a change amplitude of the tissue conductivity, and the electric field intensity corresponding to the central point of the transition region is a mean value of an electric field intensity corresponding to a starting time of the change of the tissue conductivity and an electric field intensity corresponding to an ending time of the change of the tissue conductivity;

the third acquisition unit includes:

the first acquisition subunit is used for acquiring a function representing the electric field intensity relationship between the simulation current and the central point of the transition region under different conductivity growth factors;

and the fitting subunit is used for fitting the function to obtain a mapping relation between the simulation current and the dynamic conductivity parameter.

Optionally, the fifth determining unit includes:

a second obtaining subunit, configured to obtain a degree of coincidence between the third current and a plurality of prestored values of the simulated currents;

and the first determining subunit is used for determining the simulated current corresponding to the minimum value in the numerical matching degrees as the closest simulated current.

Optionally, the electrical parameter comprises a dynamic conductivity fitting coefficient; the acquisition module includes:

and the fourth acquisition unit is used for acquiring a first data set, wherein the first data set comprises a plurality of dynamic conductivity fitting coefficients, and the plurality of dynamic conductivity fitting coefficients are in Gaussian distribution.

Optionally, the electrical parameters further comprise an electric field intensity threshold value, the electric field intensity threshold value being indicative of an electric field intensity at which irreversible electroporation of the tissue cells occurs; the acquisition module comprises:

and the fifth acquisition unit is used for acquiring a second data group, wherein the second data group comprises a plurality of electric field strength thresholds, and the electric field strength thresholds are in Gaussian distribution.

Optionally, the apparatus further comprises:

the matching degree parameter acquisition module is used for acquiring a matching degree parameter according to the ablation depth and the target ablation depth, and the matching degree parameter represents the matching degree of the pulse parameter corresponding to the ablation depth and the pulse parameter realizing the target ablation depth;

and the judging module is used for judging whether to adjust the pulse ablation parameters according to the matching degree parameters.

Optionally, the degree of match parameter comprises a mean of the ablation depths; the judgment module is specifically configured to: adjusting the pulse ablation parameters in response to the difference between the mean value and the target ablation depth exceeding a preset threshold; or alternatively

The degree of match parameter comprises a probability that tissue at the target ablation depth is ablated; the judgment module is specifically configured to: adjusting the pulse ablation parameter in response to the probability being less than 70%.

In a second aspect, the present invention provides a method for predicting a pulse ablation region based on uncertain parameter quantization, the method comprising:

acquiring electrical parameters of a target object, wherein the electrical parameters reflect electrical characteristics of tissue and blood of the target object;

determining an effective ablation boundary according to the electrical parameters and a pre-acquired ablation electric field distribution model;

determining an area enclosed by the effective ablation boundary and the tissue surface as an ablation area.

In a third aspect, the present invention provides an electronic device, comprising 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 according to the second aspect.

In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the pulse ablation zone prediction method of the second aspect described above.

The positive progress effects of the invention are as follows:

the pulse ablation region prediction device based on uncertain parameter quantification provided by the invention carries out ablation range prediction based on the electrical parameters of the target object, fully considers the difference of the electrical parameters of different target objects, and improves the pulse ablation region prediction accuracy and the application range of the prediction device. And the electric parameters comprise uncertain parameters, and an electric field distribution model adopted by the prediction device quantitatively analyzes the influence of the uncertain electric parameters on the electric field intensity, so that the prediction accuracy of the pulse ablation region is optimized.

Drawings

FIG. 1 is a schematic structural diagram of a pulse ablation device according to an exemplary embodiment;

figure 2-1 is an elevation view of an electrode and a distal tube of a pulse ablation device according to one exemplary embodiment,

fig. 2-2 is a side view of a pulse ablation device shown in cooperation with tissue according to an exemplary embodiment;

FIG. 3 is a diagram illustrating a pulse waveform according to an exemplary embodiment;

FIG. 4 is a block diagram illustrating a pulse ablation zone prediction apparatus based on uncertain parameter quantification according to an exemplary embodiment;

FIG. 5 is a graph illustrating the variation of initial tissue conductivity with electric field strength according to an exemplary embodiment;

FIG. 6 is a block diagram illustrating an effective ablation boundary determination module according to an exemplary embodiment;

FIG. 7 is a graph illustrating a profile of a pulsed ablation electric field according to an exemplary embodiment;

8-1-8-6 are main effect graphs illustrating the degree of influence of different electrical parameters according to an exemplary embodiment;

FIG. 9 is a block diagram illustrating an acquisition module in accordance with an exemplary embodiment;

FIG. 10 is a graph illustrating blood conductivity versus current in accordance with an exemplary embodiment;

FIG. 11 is a block diagram illustrating an acquisition module in accordance with another exemplary embodiment;

FIG. 12 is a block diagram illustrating an acquisition module in accordance with another exemplary embodiment;

FIG. 13 is a graph illustrating tissue conductivity as a function of electric field strength according to an exemplary embodiment;

FIG. 14 is a block diagram of a third acquisition unit shown in accordance with an exemplary embodiment;

FIG. 15 is a graph illustrating a dynamic conductivity parameter versus a simulated current in accordance with an exemplary embodiment;

FIG. 16 is a block diagram of a fifth determination unit shown in accordance with an exemplary embodiment;

FIG. 17 is a block diagram of an acquisition module shown in accordance with another exemplary embodiment;

FIG. 18 is a graph of a dynamic conductivity fit coefficient distribution shown in accordance with an exemplary embodiment;

FIG. 19 is a field strength distribution plot for a depth direction of a cross-section of tissue shown in accordance with an exemplary embodiment;

fig. 20 is an ablation depth profile shown in accordance with an exemplary embodiment;

fig. 21 is a block diagram illustrating a pulse ablation zone prediction apparatus based on uncertain parameter quantification according to another exemplary embodiment;

FIG. 22 is a graph illustrating electric field intensity values versus electric field intensity thresholds at a target ablation depth according to an exemplary embodiment;

FIG. 23 is a flowchart illustrating a method of pulsed ablation region prediction based on uncertain parameter quantization in accordance with an exemplary embodiment;

fig. 24 is a schematic structural diagram of an electronic device according to an exemplary embodiment.

Detailed Description

The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.

In a first aspect, the embodiments of the present invention provide a pulse ablation region prediction device based on uncertain parameter quantization, which may be applied to a pulse ablation apparatus. The pulse ablation device includes a pulse ablation catheter and a pulse ablation generation device.

Fig. 1 is a schematic structural diagram illustrating a pulse ablation device according to an exemplary embodiment, as shown in fig. 1, a pulse ablation catheter includes: a control handle 101, a main body outer tube 102, an electrode 103 and an electrode lead. The handle 101 is connected with the main body outer tube 102, an electrode socket is arranged on the handle 101, an inner cavity is formed in the main body outer tube 102, and the inner cavity is communicated with the electrode socket. The electrode lead is disposed in the lumen with one end connected to the electrode 103 and the other end connected to the electrode receptacle. The part of the main body outer tube 102 away from the handle 101 is a distal tube 1021, and the distal tube 1021 is used for arranging the electrode 103. Optionally, the electrode 103 is a ring-shaped electrode disposed on the distal tube 1021. A plurality of electrodes 103 are equally spaced on the distal tube 1021. The material of the ring electrode 103 can be platinum-iridium alloy and gold.

Optionally, the distal tube 1021 is a single lumen tube, the material of the distal tube 1021 is insulative and biocompatible, and the distal tube 1021 is flexible and deformable as a whole. For example, the material of the distal tube 1021 is PTFE, PU, or the like. In this manner, the distal tube 1021 can carry the electrode 103 into the target subject tissue, performing a pulse ablation.

When the pulse ablation catheter is in use, the distal body 1021 carries the electrodes 103 in contact with the tissue and blood. Fig. 2-1 is an elevation view of an electrode and a distal tube of a pulse ablation device according to an exemplary embodiment, and fig. 2-2 is a side view of a pulse ablation device in cooperation with tissue according to an exemplary embodiment. As shown in fig. 2-1, the electrode 103 includes a first electrode 1031 and a second electrode 1032, and the first electrode 1031 and the second electrode 1032 are disposed on the distal tube 1021. As shown in fig. 2-2, during the pulse ablation, the outer tube 102 of the body enters the interior of the tissue 301 and the distal tube 1021 and electrodes 103 are brought into contact with the tissue 301. Also, the distal tube 1021, the electrode 103, and the tissue 301 are all enclosed in blood.

When the pulse ablation equipment is used, the electrode socket is connected with the pulse ablation generation equipment. Accordingly, the pulse signal output from the pulse ablation generating device is transmitted to the electrode 103 through the electrode socket and the electrode lead. The electrodes 103 apply a pulsed electric field to the tissue of the target object, effecting pulsed ablation.

Fig. 3 is a diagram illustrating a pulse waveform according to an exemplary embodiment. Optionally, a plurality of electrodes 103 are disposed on the distal shaft, wherein adjacent electrodes are of opposite polarity. As an example, the odd-numbered electrodes (e.g., the first electrode, the third electrode, and the fifth electrode) are connected to the positive electrode of the pulse ablation generating device, the even-numbered electrodes (e.g., the second electrode, the fourth electrode, and the sixth electrode) are connected to the negative electrode of the pulse ablation generating device, and the negative electrode is grounded. At this time, the pulse signal output by the pulse ablation device is modeled as shown in fig. 3. Accordingly, a pulsed ablation electric field is applied to the tissue through the electrodes 103.

Based on the pulse ablation device, the embodiment of the invention provides a pulse ablation region prediction device. Fig. 4 is a block diagram illustrating a pulse ablation region prediction apparatus based on uncertain parameter quantization according to an exemplary embodiment, as shown in fig. 4, the prediction apparatus comprising: an acquisition module 200, an effective ablation boundary determination module 300, and an ablation zone determination module 400.

The obtaining module 200 is configured to obtain an electrical parameter of the target object, wherein the electrical parameter reflects electrical characteristics of tissue and blood of the target object. The effective ablation boundary determination module 300 is configured to determine an effective ablation boundary according to the electrical parameters and the pre-acquired ablation electric field distribution model. The ablation zone determination module 400 is configured to determine an area enclosed by the effective ablation boundary and the tissue surface as an ablation zone.

In clinical treatments, the electrochemical properties of tissue and blood vary widely from target subject to target subject, resulting in different electrical parameters of different target subjects. The difference in the electrical parameters of the target object affects the distribution of the pulsed ablation electric field. In simulation calculation, the distribution of the pulsed ablation electric field inside the tissue is solved by the following formula:

wherein E is the electric field intensity,

is the potential, σ is the conductivity of the constituents, ε ₀ Is a vacuum dielectric constant of ∈ _r Is the relative dielectric constant of each constituent.

Through the calculation of the above formula, the main factors influencing the distribution of the pulse ablation electric field inside the tissue include the voltage, the electrical parameters of the tissue, and the electrical parameters of the blood covering the whole tissue. The following electrical parameters were determined by studying the effect of the tissue and blood electrical parameters on the pulsed ablation electric field distribution:

blood conductivity, which characterizes the ability of blood to conduct current.

Tissue initial conductivity, characterizing the ability of the tissue to conduct electrical current without ablation.

And the dynamic conductivity parameter is used for representing the condition that the tissue conductivity changes along with the electric field intensity in the pulse ablation process. During the pulse ablation, the tissue cell structure is constantly changed, which also makes the tissue conductivity in a dynamically changing state. In one embodiment, the dynamic conductivity parameter includes a conductivity growth factor and an electric field strength corresponding to a center point of the transition region. The conductivity increase factor characterizes the magnitude of the change in tissue conductivity. The electric field intensity corresponding to the central point of the transition region is the mean value of the electric field intensity corresponding to the starting moment of the tissue conductivity change and the electric field intensity corresponding to the ending moment of the tissue conductivity change.

And the dynamic conductivity fitting coefficient represents the trend of the change of the tissue conductivity along with the change of the electric field intensity.

And the electric field intensity threshold value is used for representing the electric field intensity when the tissue cells are subjected to irreversible electroporation.

And analyzing the relation between the electrical parameters and the electric field intensity by using a single variable method for the electrical parameters except the electric field intensity threshold value. Wherein a change in either parameter affects the distribution of the electric field strength. Specifically, a change in either parameter affects the electric field strength at the tissue surface and the rate of decay of the electric field strength in the depth direction. FIG. 5 is a graph illustrating the variation of initial tissue conductivity with electric field strength, according to an exemplary embodiment. Taking the initial conductivity of the tissue as an example, as shown in fig. 5, under the condition that other electrical parameters are not changed, the electric field intensity at the surface of the electrode is the largest, and the electric field intensity is exponentially attenuated along the depth direction of the tissue. That is, the initial conductivity change of the tissue affects the intensity distribution of the pulsed ablation electric field.

According to the pulse ablation prediction device provided by the embodiment of the invention, the acquisition module 200 is adopted to determine the electrical parameters of the target object, and then the ablation region is predicted based on the electrical parameters, so that the difference caused by the physiological characteristics of different target objects is fully considered, the device predicts the ablation region for different target objects, the accuracy of the prediction result is improved, and the defects in the related technology are effectively overcome.

Based on the electrical parameters acquired by the acquisition module 200, the effective ablation boundary determination module 300 determines an effective ablation boundary according to a pre-constructed ablation electric field distribution model. The effective ablation boundary is the dividing line where the pulsed ablation electrical field can function as an ablation. By determining the effective ablation boundary, the ablation zone determination module 400 is enabled to predict the extent of the pulsatile ablation zone.

Fig. 6 is a block diagram illustrating an effective ablation boundary determination module according to an exemplary embodiment. As shown in fig. 6, the effective ablation boundary determining module 300 includes a first determining unit 310 and a second determining unit 320.

The first determining unit 310 is configured to determine an ablation depth according to the electrical parameter acquired by the acquiring module and a pre-acquired ablation electric field distribution model. The ablation depth characterizes the depth in the tissue that can be effectively ablated.

The second determination unit 320 is configured to determine an effective ablation boundary based on the ablation depth. Optionally, the second determination unit 320 is specifically configured to determine a corresponding field strength contour according to the ablation depth, and to use the corresponding field strength contour as the effective ablation boundary.

Fig. 7 is a graph illustrating a profile of a pulsed ablation electric field according to an exemplary embodiment. The use of the effective ablation boundary determination module 300 and the ablation region determination module 400 is explained in connection with fig. 7.

Referring to the distribution of the electric field intensity contours of 400V/cm to 800V/cm in FIG. 7, the contours of the pulse ablation electric field intensity contours are distributed in a concentric-circle-like structure, and the electric field intensity gradually attenuates along the tissue depth direction 702. The second determination unit 320 of the effective ablation boundary determination module 300, when determining an effective ablation boundary, extends the surface 701 of the distal tube 1021 in the tissue depth direction 702 by the ablation depth distance, and thus determines the field strength contour as an effective ablation boundary. The ablation region determination module 400 determines the region enclosed by the effective ablation boundary and the tissue surface (i.e., the surface 701 of the distal tube) as the ablation region when determining the ablation region.

The ablation electric field distribution model adopted by the first determining unit 310 takes into account the influence of the electrical parameters acquired by the acquiring module 200 on the electric field distribution. In one embodiment, the following ablation electric field distribution model is constructed in combination with the electrical parameters and the weights of the different electrical parameters:

wherein E is the electric field intensity, U is the ablation voltage, x is the coordinate value of the tissue depth direction, gamma is the preset fitting coefficient, and omega is ₁ Is the weight coefficient of the blood conductivity, p ₁ As a function of the conductivity of the blood; omega ₂ Weight coefficient of initial conductivity of tissue, p ₂ As a function of the initial conductivity of the tissue; omega ₃ Weight coefficient, p, of conductivity increase factor ₃ As a function of the conductivity increase factor; omega ₄ Weighting factor, p, of the electric field strength corresponding to the centre point of the transition region ₄ As a function of the corresponding electric field strength with respect to the central point of the transition region; omega ₅ Weight coefficient, p, for the dynamic conductivity fitting coefficient ₅ As a function of the fitting coefficient for the dynamic conductivity.

Optionally, in the ablation electric field distribution model described above,

and/or the presence of a gas in the gas,

and/or the presence of a gas in the gas,

and/or the presence of a gas in the gas,

and/or the presence of a gas in the gas,

wherein σ _b In order to be the electrical conductivity of the blood,

for initial tissue conductivity, A is a conductivity increasing factor, E _del Electric field intensity, h, corresponding to the center point of the transition zone ₁ As a coefficient of dynamic conductivity fit, a ₁ ～a ₅ 、b ₁ ～b ₅ 、c ₁ ～c ₅ 、d ₁ ～d ₅ Respectively, are predetermined fitting coefficients. Based on the above formula, p ₁ ～p ₅ The value of (A) is changed along with the change of the electrical parameters of the tissues, and the individual difference of different target objects is reflected. Accordingly, the ablation electric field distribution model has good applicability to different individuals.

Based on the ablation electric field distribution model, the ablation depth is specifically solved by the following formula:

wherein deep _i For the (i) th ablation depth,

i is a positive integer greater than or equal to 0. In the embodiment of the invention, values of the electric field intensity threshold and the dynamic conductivity fitting coefficient are taken as an array, a group of ablation depths are obtained by substituting different electric field intensity thresholds and dynamic conductivity fitting coefficients, and i is used for distinguishing different ablation depths.

And according to the obtained mean value and variance of the group of ablation depths, the overall distribution condition of the predicted ablation depths when the electrical parameters have uncertainty can be determined, and pulse ablation region prediction is realized. Therefore, the pulse ablation region prediction device provided by the embodiment of the invention also takes the uncertain electrical parameters into consideration, and quantitatively analyzes the influence of the uncertain electrical parameters on the electric field intensity. Moreover, the electrical parameters conforming to Gaussian distribution can reflect the difference of different target objects on the whole, and the prediction accuracy of the pulse ablation prediction device on different target objects is improved.

In embodiments of the present invention, the different electrical parameters have different effects on the pulsed ablation electric field distribution. Therefore, the weights of the different electrical parameters in the ablation electric field distribution model need to be determined one by one. Optionally, the influence of each electrical parameter on the distribution of the pulsed ablation electric field is qualitatively analyzed in a single variable manner. And taking any one electrical parameter as a preset change value, and taking other electrical parameters as preset fixed values. And selecting the preset variation value of the electrical parameter from low to high, and determining the ablation depth according to the pulse ablation model.

Substituting the acquired ablation depth into the following formula, and calculating the influence degree of the electrical parameter as a preset change value on the ablation depth:

wherein deep is the acquired ablation depth; SN is the degree of influence of the electrical parameter as a preset variation value on the ablation depth. The larger the value of SN, the larger the effect of the change in the electrical parameter corresponding to the SN value on the ablation depth.

Fig. 8-1 through 8-6 are diagrams illustrating the main effects of different degrees of influence of electrical parameters according to an exemplary embodiment. As shown in fig. 8-1 to 8-6, the electrical parameters all have some influence on ablation depth. The electric field intensity corresponding to the conductivity growth factor and the central point of the transition region has a remarkable influence on the ablation depth, and the influence of the blood conductivity and the initial tissue conductivity on the ablation depth is weaker.

Based on the above analysis, different electrical parameters have different degrees of influence on the pulse electric field distribution. In the embodiment of the invention, the weight coefficient of different electrical parameters is quantitatively determined by adopting an analysis of variance method so as to reflect the influence of different electrical parameters on the electric field intensity. Optionally, in the ablation electric field distribution model described above, the weight coefficient is determined according to the following formula:

wherein, the value of i is 1, 2, 3, 4 and 5.S _i When any one of the electrical parameters is a preset variation value and the other parameters are preset fixed values, the ablation depth variance is obtained according to an ablation electric field distribution model. Specifically, S ₁ Representing the corresponding variance, S, of the blood conductivity at a preset variation value ₂ Representing the corresponding variance, S, of the initial conductivity of the tissue at a predetermined change value ₃ Is the corresponding variance when the conductivity increase factor is a preset change value, S ₄ The variance corresponding to the electric field intensity corresponding to the central point of the transition region is a preset variation value S ₅ And fitting the corresponding variance when the coefficient is the preset change value for the dynamic conductivity. Sigma s _i The sum of the ablation depth variances obtained when any one electrical parameter is a preset variation value is obtained.

Specifically, the electric field strength is determined as an electric field strength threshold (for example, 400V/cm), the variation range of the blood conductivity is 0.1 to 1.0S/m, the variation range of the initial tissue conductivity is 0.05 to 0.9S/m, the variation range of the conductivity increase factor is 1 to 6, the variation range of the electric field strength corresponding to the central point of the transition region is 200 to 1200V/cm, and the variation range of the dynamic conductivity fitting coefficient is 0.00001 to 0.0003. The weight coefficient obtained according to the above values is shown in table 1.

TABLE 1 correspondence table of electrical parameters and weighting coefficients

In conclusion, the pulse ablation region prediction device provided by the embodiment of the invention predicts the ablation range based on the electrical parameters of the target object, fully considers the difference of the electrical parameters of different target objects, and improves the pulse ablation region prediction accuracy and the application range of the prediction device. And the electric field intensity threshold and the dynamic conductivity fitting coefficient in the electrical parameters are uncertain parameters, an electric field distribution model adopted by the prediction device quantitatively analyzes the influence of the uncertain electrical parameters on the electric field intensity, the difference of different target objects is fully considered, and the prediction accuracy of the pulse ablation region is optimized.

The following describes in detail the implementation of the obtaining module 200 for obtaining the electrical parameters, in conjunction with the overall working principle of the pulse ablation region prediction apparatus provided in the embodiment of the present invention.

First, obtaining blood conductivity

In an embodiment of the invention, the blood conductivity is obtained by a pulse ablation catheter test before pulse ablation. Before initiating pulse ablation, when the ablation catheter has not contacted the tissue, the electrode of the ablation catheter is placed in a blood pool such that blood of the target object surrounds the electrode. A voltage is applied to the electrodes and the voltage and current on the electrodes are monitored to obtain the blood conductivity in the following manner.

FIG. 9 is a block diagram illustrating an acquisition module according to an example embodiment. When the electrical parameter includes blood conductivity, as shown in fig. 9, the obtaining module 200 includes: a first voltage applying unit 210, a first acquiring unit 220, and a third determining unit 230.

The first voltage applying unit 210 is used to apply a first test voltage to the first electrode placed at the target region. The first electrode contacts blood of the target object and does not contact tissue of the target object. The first test voltage is 10 to 100V (e.g., 20V, 40V, 60V, 80V, etc.), and during the test, one pair of first electrodes may be used for discharging, or a plurality of pairs of first electrodes may be used for discharging.

The first obtaining unit 220 is used for obtaining the first test voltage and the first current passing through the first electrode. The third determination unit 230 is configured to determine the blood conductivity according to the first voltage and the first current, and a first fitting function obtained in advance, wherein the first fitting function represents the relationship between the blood conductivity and the current and the voltage.

FIG. 10 is a graph illustrating blood conductivity versus current, according to an exemplary embodiment. As shown in fig. 10, there is a direct relationship between the current and the blood conductivity, and the data relationship between the blood conductivity and the current is functionally fitted to obtain an expression of a first fitting function:

I＝k ₁ ·U ₁ ·σ _b

where U1 is the voltage, I is the current calculated by the first simulation, σ b is the blood conductivity, and k1 is the fitting coefficient. Based on the above, the solving formula of the blood conductivity is specifically

Wherein, I _b Is the first current acquired by the first acquisition unit 220.

Second, obtaining initial tissue conductivity

The initial electrical conductivity of the tissue refers to the electrical conductivity of the tissue before electroporation occurs without the influence of the ablating electric field. In an embodiment of the invention, acquiring tissue with a pulsed ablation catheter shows the electrical conductivity. Prior to initiating pulse ablation, the electrodes of the ablation catheter are simultaneously contacted with tissue and blood, whereupon a voltage is applied to the electrodes, the voltage and current on the electrodes are monitored, and the initial conductivity of the tissue is obtained based on the monitored voltage and current, as follows.

FIG. 11 is a block diagram illustrating an acquisition module according to another example embodiment. The acquisition module 200 further includes a second voltage applying unit 240, a second acquisition unit 250, and a fourth determination unit 260.

The second voltage applying unit 240 is used for applying a second test voltage to a second electrode placed on the target region, the second electrode contacting blood and tissue of the target object. The second test voltage is 10-100V, and the voltage condition is not enough to cause the electroporation of the tissue so as to ensure that the accurate initial conductivity of the tissue is obtained. The second obtaining unit 250 is configured to obtain the second test voltage and a second current passing through the second electrode. The fourth determining unit 260 is configured to determine the initial tissue conductivity according to the second voltage, the second current, and the blood conductivity, and a second fitting function obtained in advance, where the second fitting function represents a relationship between the tissue conductivity and the voltage, the current, and the blood conductivity.

During the test, the second electrode is in contact with both blood and tissue, where both blood conductivity and tissue initial conductivity affect the value of the second current. And establishing a database of the corresponding relation between the simulation calculation current and the simulation calculation voltage through simulation. The surface area of the normal current density on the surface of the electrode is divided to obtain the simulation calculation current I _s =: [ integral ] n integral multiple of n.J. Fitting the database data established by simulation to obtain an expression of a second fitting function:

Is＝U ₂ ·(k ₂ ·σ _b +k ₃ ·σ _{t_ori} )

wherein, U ₂ Is the voltage value, is the current value calculated by simulation, σ b the blood conductivity, σ t the initial tissue conductivity, k ₂ 、k ₃ Are fitting coefficients.

The second current obtained by the second obtaining unit 250 is the target value of the simulation calculation current, and the numerical value of the second current is substituted into the second fitting function to obtain the tissue initial conductivity solving formula:

wherein, I _tissue Is the second current.

Thirdly, acquiring dynamic conductivity parameters

As the pulse ablation progresses, irreversible perforation of the tissue occurs, resulting in a change in the electrical conductivity of the tissue. Furthermore, the application of pulsed energy to tissue causes a change in tissue temperature, which in turn causes a change in tissue conductivity. In other words, during pulse ablation, the tissue conductivity is in a dynamically changing state. In the invention, dynamic conductivity parameters are introduced to reflect the influence of the pulse ablation process on tissues, and the dynamic conductivity parameters represent the condition that the tissue conductivity changes along with the electric field intensity in the pulse ablation process.

Fig. 12 is a block diagram illustrating an acquisition module according to another exemplary embodiment, as shown in fig. 12, 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.

The third obtaining unit 270 is configured to obtain a mapping relationship between the simulated current and the dynamic conductivity parameter.

In one embodiment, the dynamic conductivity parameter includes a conductivity growth factor and an electric field strength corresponding to a center point of the transition region. Fig. 13 is a graph illustrating tissue conductivity as a function of electric field strength according to an exemplary embodiment. With reference to FIG. 13, the conductivity increase factor characterizes the amplitude of the change in tissue conductivity, and is solved using the formula

Wherein σ _{t_ori} As the initial value of the change in tissue conductivity, σ _max The conductivity values at which electroporation had occurred for all tissues were obtained.

Electric field intensity (E) corresponding to the center point of the transition zone _del ) Corresponding electric field intensity (E) for the start of a change in tissue conductivity ₁ ) Electric field intensity (E) corresponding to the time at which the change in tissue conductivity ends ₂ ) Is specifically solved by the formula of

In one embodiment, the electric field strength corresponding to the conductivity increase factor and the central point of the transition region is used as a parameter set, and the mapping relationship between the simulation current and the dynamic conductivity parameter specifically includes a one-to-one correspondence relationship between the simulation current and the parameter set. Fig. 14 is a block diagram illustrating a third acquisition unit according to an exemplary embodiment, and as shown in fig. 14, the third acquisition unit 270 includes a first acquisition sub-unit 271 and a fitting sub-unit 272. The first obtaining subunit 271 is configured to obtain a function representing a relationship between the simulated current and an electric field strength corresponding to a central point of the transition region under different conductivity increase factors. The fitting subunit 272 is configured to fit the function obtained by the first obtaining subunit 271, so as to obtain a mapping relationship between the simulation current and the dynamic conductivity parameter.

The electric field intensity corresponding to the conductivity growth factor and the central point of the transition region affects the tissue conductivity, and the change of the tissue conductivity directly affects the simulation current. Therefore, the conductivity growth factor and the electric field intensity corresponding to the central point of the transition region can be related to the simulated current through the conductivity.

During pulse ablation, tissue conductivity changes due to tissue perforation and tissue temperature, with the degree of tissue perforation being primarily dependent on the electric field strength. Thus, a dynamic model of tissue conductivity was constructed as follows:

wherein σ _{t_ori} For the initial conductivity of the tissue, E is the electric field intensity, edel is the electric field intensity corresponding to the central point of the transition region, A is the conductivity growth factor, h ₁ Is the dynamic conductivity fitting coefficient, T is the temperature, T ₀ As the initial temperature, α is the coefficient of influence of temperature increase on the electrical conductivity. Among the above parameters, h ₁ And alpha is the empirical value, and the value of alpha is usually 0.01 to 0.03; t and T ₀ Can be obtained by model calculation.

And the current density can be determined according to the conductivity and the electric field intensity, and then the simulation current is determined according to the current density. In this way, the corresponding relation between the electric field intensity and the simulation current corresponding to the central points of the different conductivity growth factors and the transition region is determined through simulation.

FIG. 15 is a graph illustrating a dynamic conductivity parameter versus simulated current in accordance with an exemplary embodiment. As shown in fig. 15, in the case of the electric field intensity corresponding to the center point of the same transition region, the simulated current increases as the conductivity increase factor increases; under the condition of the same conductivity increase factor, the simulation current is reduced along with the increase of the electric field intensity corresponding to the central point of the transition region.

Fitting the corresponding relation between the electric field intensity corresponding to different conductivity growth factors and the central point of the transition region and the simulation current to obtain the functional relation between the simulation current and the initial tissue conductivity and the blood conductivity:

wherein, I _c To simulate the current, σ _b Is the blood conductivity, σ _{t_ori} For initial conductivity of the tissue, U is the voltage, E _del The electric field intensity corresponding to the central point of the transition region, A is the conductivity increase factor, k ₂ ～k ₆ As fitting coefficient, k ₂ ～k ₆ Fitting may be done based on historical data.

Based on the functional relationship, the one-to-one corresponding relationship between the simulation current and the parameter group consisting of the conductivity increase factor and the electric field intensity corresponding to the central point of the transition region can be obtained. Namely, the mapping relation between the simulation current and the dynamic conductivity parameter is obtained.

With continued reference to fig. 12, the third voltage applying unit 280 in the acquiring module 200 is used to apply a pulsed ablation voltage to a third electrode placed in the target area, monitor the current passing through the third electrode, and take the stably outputted current as the third current.

The pulse ablation voltage applied by the third voltage applying unit 280 is 500V to 2000V. Along with the increase of the voltage action time, the conductivity growth factor is increased, and the electric field intensity corresponding to the central point of the transition region is reduced. Furthermore, the pulsed ablation voltage signal delivered by the third voltage application unit 280 includes pulse trains of millisecond order, each pulse train including a plurality of bipolar pulses. Therefore, the third voltage applying unit 280 collects the voltage and current data of the ablation voltage signal of each pulse in real time, and takes the final stable current value as the third current.

The fifth determining unit 290 is configured to determine a simulated current closest to the third current among a plurality of simulated currents stored in advance.

Fig. 16 is a block diagram illustrating a fifth determination unit according to an example embodiment. As shown in fig. 16, the fifth determining unit 290 includes a second obtaining subunit 291 and a first determining subunit 292.

The second obtaining subunit 291 is configured to obtain a degree of coincidence between the third current and a plurality of values of the simulation currents stored in advance. The first determining subunit 292 is configured to determine the simulated current corresponding to the minimum value of the numerical matching degrees as the closest simulated current.

Alternatively, the third obtaining unit 270 stores a plurality of simulation currents in advance. The second obtaining subunit 291 determines the matching degree between the third current and the simulated current by the following formula:

wherein SS is the degree of anastomosis, I _w Is a third current, I _c To simulate a current. A smaller value of SS indicates a higher degree of coincidence between the third current and the simulated current.

With continued reference to fig. 12, in the obtaining module 200, the sixth determining unit 211 is configured to determine the dynamic conductivity parameter according to the closest simulated current and a mapping relationship pre-stored by the third obtaining unit 270.

Fourthly, obtaining the fitting coefficient of the dynamic conductivity

Fig. 17 is a block diagram illustrating an acquisition module according to another example embodiment. As shown in fig. 17, the acquiring module 200 includes a fourth acquiring unit 212, and the fourth acquiring unit 212 is configured to acquire the first data group. The first data set includes a plurality of dynamic conductivity fitting coefficients, which are gaussian distributed.

The dynamic conductivity fit coefficient is an uncertain parameter due to the differences of different individuals. In the embodiment of the invention, the dynamic conductivity parameters conforming to Gaussian distribution are selected to reflect the physiological characteristics of most patients, so that the uncertain parameters are quantized through an electric field distribution model.

FIG. 18 is a graph of a dynamic conductivity fit coefficient distribution, shown in accordance with an exemplary embodiment. As shown in FIG. 18, the dynamic conductivity fitting coefficient satisfies the Gaussian distribution

Wherein,

is the average of the fit coefficients for the dynamic conductivity,

the variance of the coefficient was fitted to the dynamic conductivity. In the embodiment of the present invention, the first and second substrates,

taking out the mixture of 0.0001 percent,

0.000045 was taken.

Fifthly, acquiring electric field intensity threshold

With continued reference to fig. 17, the acquiring module 200 further comprises a fifth acquiring unit 213, wherein the fifth acquiring unit 213 is configured to acquire the second data group. The second data group comprises a plurality of electric field intensity thresholds which are in Gaussian distribution.

The electric field intensity threshold value represents the electric field intensity when irreversible electroporation of the tissue cells occurs. Fig. 19 is a field strength distribution plot for a depth direction of a cross-section of tissue shown in accordance with an exemplary embodiment. As shown in fig. 19, the intensity of the ablation electric field gradually decreases in the depth direction of the tissue. The field strength at depth value x is E (x), and when the electric field value E (x) decays to the electric field strength threshold value, its corresponding depth value x is the ablation depth. In other words, in a pulsed ablation electric field, tissue ablation cannot be achieved in portions where the electric field strength is less than the electric field strength threshold, and tissue ablation can be achieved in portions where the electric field strength is greater than or equal to the electric field strength threshold.

Aiming at the difference of tissue cells of different individuals, the threshold value of the electric field intensity for generating irreversible electroporation on the tissue also has difference. In the embodiment of the invention, the electric field intensity threshold is used as an uncertain parameter, and the electric field intensity threshold conforming to Gaussian distribution is selected so as to reflect the physiological characteristics of most patients, thereby realizing the quantification of the uncertain parameter through an electric field distribution model.

Alternatively, the electric field strength threshold satisfies a gaussian distribution

Wherein,

is the average of the threshold values of the electric field strength,

the variance of the electric field strength threshold value is, in the embodiment of the present invention,

taking out the weight of the product of 100,

the following relationship is satisfied:

wherein, T _H The discharge duration of the high level of the pulse signal can be directly obtained according to the signal output by the pulse ablation generating equipment; k is a radical of ₇ 、k ₈ 、k ₉ As a fitting coefficient, fitting determination is made based on the historical data.

In summary, the electrical parameters obtained by the pulse ablation prediction apparatus provided by the embodiment of the present invention fully take into account the differences of different individuals. Therefore, the accuracy of the pulse ablation region predicted according to the electrical parameters is better, and the application range is wider.

In one embodiment, the ablation depth obtained from the pulsed ablation electric field distribution model is a set of data (hereinafter referred to as an ablation depth set) because the electric field strength threshold and the dynamic conductivity fitting coefficient are in an array conforming to a gaussian distribution. Fig. 20 is an ablation depth profile, as shown in fig. 20, with a certain regularity of the predicted ablation depths, according to an exemplary embodiment. In such a case, it is necessary to evaluate whether or not the predicted acquired ablation depth is effective.

Optionally, the pulse ablation region prediction device provided by the embodiment of the invention is further configured to determine and adjust the pulse ablation parameters according to the ablation depth. Fig. 21 is a block diagram illustrating a pulse ablation zone prediction apparatus based on uncertain parameter quantification according to another exemplary embodiment. As shown in fig. 21, the apparatus further includes: a matching degree parameter obtaining module 500 and a judging module 600.

The matching degree parameter obtaining module 500 is configured to obtain a matching degree parameter according to the ablation depth and the target ablation depth, where the matching degree parameter represents a matching degree between a pulse parameter corresponding to the ablation depth and a pulse parameter for achieving the target ablation depth. The judging module 600 is configured to judge whether to adjust a pulse ablation parameter according to the matching degree parameter.

In one embodiment, the degree of match parameter comprises a mean value of ablation depths. At this time, the determining module 600 adjusts the pulse ablation parameter in response to the difference between the mean value and the target ablation depth exceeding a preset threshold. Specifically, when the difference between the mean value of the ablation depths and the target ablation depth exceeds a preset threshold value and the mean value of the ablation depths is smaller than the target ablation depth, the duration of the high level of the pulse ablation is increased, and/or the voltage value of the pulse ablation signal is increased. And when the difference value of the mean value of the ablation depth and the target ablation depth exceeds a preset threshold value and the mean value of the ablation depth is greater than the target ablation depth, shortening the time length of the pulse ablation high level and/or reducing the voltage value of the pulse ablation signal.

Where the mean of the pulse ablation depths is only one example of a degree of match parameter, the degree of match parameter may also include a probability that tissue at the target ablation depth is effectively ablated. What needs to be accounted for with respect to the probability that tissue at the target ablation depth is effectively ablated is:

in the embodiment of the invention, the dynamic conductivity fitting coefficient is a first data group which satisfies Gaussian distribution, an electric field distribution model is determined based on the first data group and the blood conductivity, the tissue initial conductivity, the conductivity growth factor and the electric field intensity corresponding to the central point of the transition region determined according to the scheme, and then a corresponding group of electric field intensities can be determined according to the target depth through the electric field distribution model, wherein the group of electric field intensities are electric field intensity values at the target ablation depth obtained through calculation. In addition, the embodiment of the invention also selects a second data group containing a plurality of electric field intensity thresholds. And obtaining the probability that the tissue at the target ablation depth is effectively ablated according to the electric field intensity value at the target ablation depth and the second data group. Figure 22 is a graph illustrating electric field intensity values versus electric field intensity thresholds at a target ablation depth according to an exemplary embodiment. In connection with fig. 22, the probability that tissue at the target ablation depth is effectively ablated specifically takes the following definition:

wherein p (x) 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 electric field strength values of the tissue at the target depth that are greater than the electric field strength thresholds in the second data set.

When the match degree parameter includes a probability that tissue at the target ablation depth is effectively ablated, the determination module 600 adjusts the impulse ablation parameter in response to the probability being less than 70%. Wherein, 70% is only an exemplary critical value, and can be set by users according to the use requirements.

The degree of match parameter may also include a standard deviation of the predicted ablation depth. At this time, the specific method for the determination by the determining module 600 is not limited, and the determining conditions may be set according to the user's usage requirements.

In summary, the pulse ablation region prediction device provided by the embodiment of the invention predicts the ablation range based on the electrical parameters of the target object, fully considers the difference of the electrical parameters of different target objects, and improves the pulse ablation region prediction accuracy and the application range of the prediction device. In addition, the electric field intensity threshold, the dynamic conductivity fitting coefficient and the second dynamic conductivity fitting coefficient in the electrical parameters are uncertain parameters, and the electric field distribution model adopted by the prediction device quantitatively analyzes the influence of the uncertain electrical parameters on the electric field intensity, so that the prediction accuracy of the pulse ablation region is optimized.

In a second aspect, the embodiment of the invention further provides a pulse ablation region prediction method based on uncertain parameter quantization, which is applied to pulse ablation equipment. Fig. 23 is a flowchart illustrating a method of impulsive ablation region prediction based on uncertain parameter quantization, according to an exemplary embodiment, as shown in fig. 23, the method of impulsive ablation region prediction comprising:

step S701, obtaining an electrical parameter of a target object, where the electrical parameter reflects electrical characteristics of tissue and blood of the target object.

Step S702, determining an effective ablation boundary according to the electrical parameters and a pre-acquired ablation electric field distribution model.

Step S703, determining an area enclosed by the effective ablation boundary and the tissue surface as an ablation area.

In one embodiment, determining an effective ablation boundary from the electrical parameters and a pre-acquired ablation electrical field distribution model comprises:

determining ablation depth according to the electrical parameters and a pre-acquired ablation electric field distribution model;

an effective ablation boundary is determined from the ablation depth.

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

ω ₄ weighting factor, p, of the electric field intensity corresponding to the center point of the transition region ₄ As a function of the corresponding electric field strength with respect to the central point of the transition region;

In one embodiment, in the ablation electric field distribution model,

and/or

And/or

And/or

And/or

Wherein σ _b Is the conductivity of blood, σ _{t_ori} For initial tissue conductivity, A is a conductivity increasing factor, E _del Electric field intensity, h, corresponding to the center point of the transition zone ₁ As a coefficient of dynamic conductivity fit, a ₁ ～a ₅ 、b ₁ ～b ₅ 、c ₁ ～c ₅ 、d ₁ ～d ₅ Respectively, predetermined fitting coefficients, which can be determined by fitting according to historical data.

In one embodiment, in the ablation electric field distribution model,

wherein, the value of i is 1, 2, 3, 4 and 5;

S _i when any one of the electrical parameters is a preset variation value and the other parameters are preset fixed values, obtaining an ablation depth variance according to an ablation electric field distribution model;

wherein S is ₁ Representing the corresponding variance when the blood conductivity is a preset change value, wherein the change range of the blood conductivity is 0.1-1S/m; s. the ₂ Representing the corresponding variance when the initial tissue conductivity is a preset change value, wherein the change range of the initial tissue conductivity is 0.05-0.9S/m; s ₃ The variance is corresponding to the change value when the conductivity increase factor is a preset change value, and the change range of the conductivity increase factor is 1-6; s. the ₄ The variance corresponding to the electric field intensity corresponding to the central point of the transition area is a preset variation value, and the variation range of the electric field intensity corresponding to the central point of the transition area is 200-1200V/cm; s ₅ The variance is corresponding to the dynamic conductivity fitting coefficient when the dynamic conductivity fitting coefficient is a preset change value, and the change range of the conductivity fitting coefficient is 0.00001-0.00003;

∑s _i the sum of the ablation depth variances obtained when any electrical parameter is a preset variation value.

In one embodiment, determining the effective ablation boundary as a function of the ablation depth comprises: and determining a corresponding field intensity contour line according to the ablation depth, and taking the corresponding field intensity contour line as an effective ablation boundary.

In one embodiment, the electrical parameter comprises blood conductivity; acquiring electrical parameters of a target object, comprising:

applying a first test voltage to a first electrode disposed in the target area, the first electrode contacting blood of the target object and not contacting tissue of the target object;

acquiring a first test voltage and a first current passing through a first electrode;

and determining the blood conductivity according to the first voltage and the first current and a first fitting function acquired in advance, wherein the first fitting function represents the relationship between the blood conductivity and the current and the voltage.

In one embodiment, the electrical parameter further comprises tissue initial conductivity; acquiring the electrical parameter of the target object further comprises:

applying a second test voltage to a second electrode disposed at the target area, the second electrode contacting blood and tissue of the target subject;

acquiring a second test voltage and a second current passing through the second electrode;

and determining the initial conductivity of the tissue according to the second voltage, the second current and the blood conductivity and a second fitting function which is obtained in advance, wherein the second fitting function represents the relationship between the tissue conductivity and the voltage, the current and the blood conductivity.

In one embodiment, the electrical parameters further comprise a dynamic conductivity parameter characterizing the change in tissue conductivity with electric field strength during the pulse ablation process; acquiring electrical parameters of a target object, further comprising:

acquiring a mapping relation between the simulation current and the dynamic conductivity parameter;

applying a pulsed ablation voltage to a third electrode disposed in the target region, monitoring a current passing through the third electrode, and taking the stably-output current as a third current;

determining a simulation current closest to the third current among a plurality of simulation currents stored in advance;

and determining the dynamic conductivity parameter according to the closest simulation current and the mapping relation.

In one embodiment, the dynamic conductivity parameter includes a conductivity increase factor and an electric field intensity corresponding to a central point of the transition region, the conductivity increase factor represents a change amplitude of the tissue conductivity, and the electric field intensity corresponding to the central point of the transition region is an average value of the electric field intensity corresponding to a starting time of the tissue conductivity change and the electric field intensity corresponding to an ending time of the tissue conductivity change;

obtaining a mapping relationship between the simulated current and the dynamic conductivity parameter comprises:

acquiring a function of the electric field intensity corresponding to the central point of the transition region of the simulation current under different conductivity growth factors;

and fitting the function to obtain a mapping relation.

In one embodiment, determining the simulated current that is closest to the third current comprises:

acquiring the coincidence degree of the third current and a plurality of prestored simulation currents;

and determining the simulation current corresponding to the minimum value in the numerical matching degree as the closest simulation current.

In one embodiment, the electrical parameter comprises a dynamic conductivity fitting coefficient; acquiring the electrical parameter of the target object comprises: and acquiring a first data set, wherein the first data set comprises a plurality of dynamic conductivity fitting coefficients which are in Gaussian distribution.

In one embodiment, the electrical parameter further comprises an electric field strength threshold, the electric field strength threshold being indicative of an electric field strength at which irreversible electroporation of the tissue cells occurs; acquiring the electrical parameters of the target object comprises: and acquiring a second data group, wherein the second data group comprises a plurality of electric field intensity thresholds, and the electric field intensity thresholds are in Gaussian distribution.

In one embodiment, the pulse ablation zone prediction method further comprises: obtaining a matching degree parameter according to the ablation depth and a target ablation depth, wherein the matching degree parameter represents the matching degree of a pulse parameter corresponding to the ablation depth and a pulse parameter for realizing the target ablation depth; and judging whether to adjust the pulse ablation parameters according to the matching degree parameters.

In one embodiment, the degree of match parameter comprises a mean value of ablation depths. At this time, judging whether to adjust the pulse ablation parameter according to the matching degree parameter includes: and adjusting the pulse ablation parameters in response to the difference between the mean value and the target ablation depth exceeding a preset threshold value. Specifically, when the difference value between the mean value of the ablation depths and the target ablation depth exceeds a preset threshold value and the mean value of the ablation depths is smaller than the target ablation depth, the duration of the high level of the pulse ablation is increased and/or the voltage value of the pulse ablation signal is increased. And when the difference value of the mean value of the ablation depth and the target ablation depth exceeds a preset threshold value and the mean value of the ablation depth is greater than the target ablation depth, shortening the time length of the high level of the pulse ablation and/or reducing the voltage value of the pulse ablation signal.

In one embodiment, the match degree parameter includes a probability that tissue at the target ablation depth is effectively ablated. At this point, the decision module 600 adjusts the impulse ablation parameters in response to the probability being less than 70%. Wherein, 70% is only an exemplary critical value, and can be set according to the user's requirement.

In summary, the pulse ablation region prediction device based on the uncertain parameter quantification provided by the embodiment of the present invention performs ablation range prediction based on the electrical parameters of the target object, fully considers the difference of the electrical parameters of different target objects, and improves the pulse ablation region prediction accuracy and the application range of the prediction device. And the electric parameters comprise uncertain parameters, the influence of the uncertain electric parameters on the electric field intensity is quantitatively analyzed through an electric field distribution model, and the prediction accuracy of the pulse ablation region is optimized.

In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor, when executing the computer program, implements the pulse ablation region prediction method provided by the second aspect.

Fig. 24 is a schematic structural diagram of an electronic device according to an exemplary embodiment. The electronic device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the method for predicting a pulsating ablation zone as provided by the second aspect. The electronic device 30 shown in fig. 24 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.

As shown in fig. 24, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 that couples various system components including the memory 32 and the processor 31.

The bus 33 includes a data bus, an address bus, and a control bus.

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.

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

The processor 31 executes a computer program stored in the memory 32 to perform various functional applications and data processing, such as a pulse ablation zone prediction method provided by the second aspect of the embodiment of the present invention.

The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, the model-generating electronic device 30 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 36. As shown in FIG. 24, network adapter 36 communicates with the other modules of model-generated electronic device 30 via bus 33. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating 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, etc.

It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.

In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the pulse ablation region prediction method provided in the second aspect. More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.

In a possible embodiment, the present invention may also be embodied in the form of a program product comprising program code means for causing a terminal device to carry out the steps of a method for pulse ablation zone prediction as provided for by the above-mentioned second aspect when the program product is run on the terminal device.

Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.

While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the embodiments of the invention is defined by the appended claims. Various changes or modifications to these embodiments can be made by those skilled in the art without departing from the principle and spirit of the embodiments of the present invention, and these changes and modifications all fall into the scope of the embodiments of the present invention.

Claims

1. A pulse ablation zone prediction device based on uncertain parameter quantification, the device comprising:

2. The apparatus of claim 1, wherein the effective ablation boundary determination module comprises:

3. The apparatus of claim 2, wherein the ablation electric field distribution model comprises the following formula:

ω ₃ weight coefficient, p, being a conductivity increase factor ₃ As a function of the conductivity increase factor;

ω ₄ weighting factor, p, of the electric field strength corresponding to the centre point of the transition region ₄ As a function of the corresponding electric field strength with respect to the center point of the transition zone;

ω ₅ weight coefficient, p, for the dynamic conductivity fitting coefficient ₅ To relate to dynamic conductanceA function of the rate fitting coefficients.

4. The apparatus of claim 3, wherein, in the ablation electric field distribution model,

and/or

And/or

And/or

And/or

Wherein σ _b In order to be the electrical conductivity of the blood,

for initial tissue conductivity, A is a conductivity increasing factor, E _del Electric field intensity, h, corresponding to the center point of the transition zone ₁ As a coefficient of dynamic conductivity fit, a ₁ ～a ₅ 、b ₁ ～b ₅ 、c ₁ ～c ₅ 、d ₁ ～d ₅ Respectively, are predetermined fitting coefficients.

5. The apparatus of claim 3, wherein, in the ablation electric field distribution model,

wherein, the value of i is 1, 2, 3, 4 and 5;

S ₂ representing the corresponding variance when the initial conductivity of the tissue is a preset change value, wherein the change range of the initial conductivity of the tissue is 0.05-0.9S/m;

S ₃ the variance is corresponding to the change value when the conductivity increase factor is a preset change value, and the change range of the conductivity increase factor is 1-6;

6. The apparatus according to claim 2, wherein the second determining unit is specifically configured to: and determining a corresponding field intensity contour line according to the ablation depth, and taking the corresponding field intensity contour line as the effective ablation boundary.

7. The device of claim 1, wherein the electrical parameter comprises blood conductivity; the acquisition module comprises:

8. The device of claim 7, wherein the electrical parameter further comprises tissue initial conductivity; the acquisition module further comprises:

the second acquisition unit is used for acquiring the second test voltage and a second current passing through the second electrode;

and the fourth determining unit is used for determining the initial tissue conductivity according to the second voltage, the second current and the blood conductivity and a second fitting function which is acquired in advance, wherein the second fitting function represents the relationship between the tissue conductivity and the voltage, the current and the blood conductivity.

9. The device of claim 8, wherein the electrical parameters further comprise a dynamic conductivity parameter characterizing a change in tissue conductivity with electric field strength during pulse ablation; the acquisition module further comprises:

a third voltage applying unit for applying a pulsed ablation voltage to a third electrode disposed in the target region, monitoring a current passing through the third electrode, and taking a stably output current as a third current;

10. The apparatus of claim 9, wherein the dynamic conductivity parameter comprises a conductivity increase factor and an electric field intensity corresponding to a center point of the transition region, wherein the conductivity increase factor represents a variation range of the tissue conductivity, and the electric field intensity corresponding to the center point of the transition region is a mean value of the electric field intensity corresponding to a start time of the tissue conductivity variation and the electric field intensity corresponding to an end time of the tissue conductivity variation;

the third acquisition unit includes:

the first acquisition subunit is used for acquiring a function representing the electric field intensity relationship between the simulated current and the central point of the transition region under different conductivity growth factors;

11. The apparatus according to claim 10, wherein the fifth determining unit comprises:

12. The apparatus of claim 1, wherein the electrical parameter comprises a dynamic conductivity fitting coefficient; the acquisition module includes:

13. The apparatus of claim 1, wherein the electrical parameter further comprises an electric field strength threshold indicative of an electric field strength at which irreversible electroporation of the tissue cells occurs; the acquisition module comprises:

14. The apparatus of claim 2, further comprising:

15. The apparatus of claim 14, wherein the degree of match parameter comprises a mean of the ablation depths; the judgment module is specifically configured to:

adjusting the pulse ablation parameters in response to the difference between the mean value and the target ablation depth exceeding a preset threshold; or

16. A method for predicting a pulse ablation region based on uncertain parameter quantization, the method comprising:

acquiring electrical parameters of a target object, wherein the electrical parameters reflect electrical characteristics of tissues and blood of the target object;

17. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the pulse ablation region prediction method of claim 16 when executing the computer program.

18. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the pulse ablation zone prediction method according to claim 16.