CN113506633A

CN113506633A - Method and device for predicting ablation voltage value

Info

Publication number: CN113506633A
Application number: CN202110754586.0A
Authority: CN
Inventors: 罗中宝; 王海峰; 诸敏
Original assignee: Shanghai Remedicine Co ltd
Current assignee: Shanghai Remedicine Co ltd
Priority date: 2021-07-01
Filing date: 2021-07-01
Publication date: 2021-10-15

Abstract

The invention relates to a method and a device for predicting an ablation voltage value. An exemplary method for predicting ablation voltage values includes: obtaining corresponding electric field strength values based on the plurality of preset ablation voltage values and the plurality of preset ablation parameter values to construct a database; generating a calculation model based on the database, wherein the calculation model is used for predicting an ablation voltage value through an electric field intensity value and an ablation parameter value; acquiring an ablation parameter value and an electric field intensity ablation threshold value of an ablation region; and predicting an ablation voltage value for the ablation region using the computational model based on the acquired ablation parameter value and the electric field strength ablation threshold. Compared with the prior art, the method and the device for predicting the ablation voltage value can rapidly and effectively predict the ablation voltage value required for ablating the region based on the ablation region, and the prediction value has higher precision.

Description

Method and device for predicting ablation voltage value

Technical Field

The invention relates to the technical field of medical instruments, in particular to a method and a device for predicting an ablation voltage value.

Background

Research has shown that cancer is one of the major diseases that endanger human health, and the therapeutic method of ablating cancer cells by a pulsed electric field is applied to the treatment of cancer because of its tissue selectivity.

For focal ablation regions (also referred to as ablation regions, focal regions, target regions, etc.), there are two approaches to current conventional ablation needles: a needle distribution scheme adopting a surrounding center type is mainly suitable for larger lesions, and particularly comprises the following steps: arranging one electrode needle at the center of the lesion, and arranging a plurality of electrode needles (for example, 3-4 electrode needles) around the electrode needle, wherein the plurality of peripheral electrode needles are arranged near the edge of the lesion; the other scheme adopts a filling type needle distribution scheme which is mainly suitable for small focuses or strip focuses, and generally, electrode needles are uniformly distributed in the focuses in a manner of clinging to the focuses.

After the needle arrangement is completed, the number of ablation needle groups (for example, two needles are in one group) and the corresponding ablation voltage of each group are determined according to the ablation requirements, such as the size of a lesion, so that the ablation region generated by the voltage used by the electrode needle can cover the lesion region as much as possible.

In the existing scheme, an ablation region generated by an electrode needle group under a specific voltage is sequentially calculated in an enumeration manner, and whether the ablation region can cover a focus is determined. For example, enumerating specific voltage values, e.g., selectable voltages as a plurality of discrete values 1400V, 1450V, 1500V, 1550V, 1600V, etc., and calculating ablation regions generated thereby one by one. However, determining the ablation effect of various needle set protocols in an enumerated manner is time consuming and laborious, and is not sufficiently accurate. Therefore, a solution capable of quickly determining the value of the ablation voltage is needed.

Disclosure of Invention

In view of the above problems in the prior art, a first aspect of the present disclosure proposes a method for predicting an ablation voltage value, comprising:

obtaining corresponding electric field strength values based on the plurality of preset ablation voltage values and the plurality of preset ablation parameter values to construct a database;

generating a calculation model based on the database, wherein the calculation model is used for predicting an ablation voltage value through an electric field strength value and an ablation parameter value;

acquiring an ablation parameter value and an electric field intensity ablation threshold value of an ablation region; and

predicting an ablation voltage value for the ablation region using the computational model based on the obtained ablation parameter value and an electric field strength ablation threshold.

In some embodiments, the ablation parameter values include at least: an electrode needle spacing value, a conductivity ratio value and an ablation boundary value;

wherein acquiring an ablation boundary value of the ablation region includes: determining the ablation boundary value based on a profile of the ablation region.

In certain embodiments, building the database comprises:

establishing an electric field simulation model based on a constraint equation, and applying the preset ablation voltage value, the preset electrode needle spacing value, the preset conductivity ratio value and the preset ablation boundary value to the electric field simulation model to generate a corresponding electric field strength value;

and adding the preset ablation voltage value, the electrode needle spacing value, the conductivity ratio value, the ablation boundary value and the corresponding electric field intensity value into the database as a group of data.

In certain embodiments, generating the computational model comprises:

for each set of data, selecting an electrode needle spacing value and an ablation boundary value as a first parameter element in a first parameter set, and selecting an electric field intensity value and a conductivity ratio value as a second parameter element in a second parameter set;

for each of the first parameter elements, performing the following:

obtaining a plurality of second parameter elements and a plurality of ablation voltage values corresponding to the first parameter elements from the database; and

generating a first fit function that predicts the ablation voltage values by two values of the second parameter elements based on the plurality of second parameter elements and a plurality of ablation voltage values, the first fit function having a set of first coefficients;

generating a second fitting function that predicts each coefficient in the first coefficient group by two values of the first parameter elements based on a plurality of the first parameter elements and a plurality of sets of the first coefficient groups corresponding thereto, the second fitting function having a second coefficient group;

generating the computational model based on the first and second coefficient sets.

In certain embodiments, generating the first fitting function comprises:

and determining the order of the electric field strength value and the order of the conductivity ratio value in the first fitting function based on an enumeration manner.

In certain embodiments, generating the second fitting function comprises:

for two values of the first parameter element, respectively determining a third fitting order and a fourth fitting order of polynomial fitting of each coefficient of the first coefficient group by each of the two values;

generating the second fitting function based on the third fitting order and the fourth fitting order.

In some embodiments, the first and second light sources, wherein,

the first fitting function is a polynomial fitting function, and the expression of the polynomial fitting function is as follows:

wherein U represents an ablation voltage value, E represents an electric field intensity value, σ_rAs a conductivity ratio value, { a_iI is 1 to 5, and represents the first coefficient group,

for each coefficient a in the first coefficient group_iThe second fitting function is a polynomial fitting function, and the expression of the polynomial fitting function is as follows:

wherein D represents the electrode needle spacing value, P represents the ablation boundary value,

representing the second set of coefficients and,

the expression of the calculation model is as follows:

in some embodiments, the ablation parameter value for the ablation region comprises a conductivity ratio value for the ablation region, and obtaining the conductivity ratio value for the ablation region comprises:

obtaining a plurality of electric field strength values and a plurality of corresponding conductivity values;

determining a function parameter of a conductivity function based on the plurality of electric field strength values and the plurality of conductivity values;

determining the conductivity ratio value based on the function parameter.

In some embodiments, determining an ablation boundary value for the ablation region based on the profile of the ablation region further comprises:

determining an ablation boundary required to cover at least a portion of a contour of the ablation region based on a coverage rule;

determining the ablation boundary value based on the ablation boundary.

In some embodiments, the ablation boundary values include at least a first boundary value determined in a first direction and a second boundary value determined in a second direction different from the first direction.

In some embodiments, the ablation boundary is represented by a cassini curve, and wherein,

determining the ablation boundary value based on the ablation boundary comprises: determining the first boundary value and the second boundary value based on the intersection of the Casini curve with its two axes of symmetry.

In some embodiments, predicting an ablation voltage value for the ablation region using the computational model comprises:

generating a first ablation voltage value using the computational model based on an electric field strength ablation threshold, an electrode needle spacing value, a conductivity ratio value, and the first boundary value;

generating a second ablation voltage value using the computational model based on the electric field strength ablation threshold, the electrode needle spacing value, the conductivity ratio value, and the second boundary value;

generating the ablation voltage value based on the first ablation voltage value and the second ablation voltage value.

A second aspect of the present disclosure proposes an apparatus for predicting an ablation voltage value, comprising:

a construction unit configured to obtain corresponding electric field strength values based on a plurality of preset ablation voltage values and a plurality of preset ablation parameter values to construct a database;

a model generation unit configured to generate a calculation model based on the database, wherein the calculation model is used for predicting an ablation voltage value through an electric field intensity value and an ablation parameter value;

an acquisition unit configured to acquire an ablation parameter value of an ablation region and an electric field intensity ablation threshold; and

a prediction unit configured to predict an ablation voltage value for the ablation region using the calculation model based on the acquired ablation parameter value and an electric field intensity ablation threshold value.

wherein the obtaining unit is further configured to: determining the ablation boundary value based on a profile of the ablation region.

In certain embodiments, the building unit is further configured to:

In some embodiments, the model generation unit is further configured to:

for each of the first parameter elements, performing the following:

In some embodiments, the model generation unit is further configured to:

and determining a first fitting order of the electric field intensity value and a second fitting order of the conductivity ratio value in the first fitting function based on an enumeration manner.

In some embodiments, the model generation unit is further configured to:

for two values of the first parameter element, respectively determining a third fitting order and a fourth fitting order of polynomial fitting of the ablation voltage value by each of the two values;

generating the first fitting function based on the first fitting order and the second fitting order.

In some embodiments, the model generation unit is further configured to:

In some embodiments, the first and second light sources, wherein,

representing the second set of coefficients and,

the expression of the calculation model is as follows:

in some embodiments, the ablation parameter value of the ablation region comprises a conductivity ratio value of the ablation region, and the obtaining unit is further configured to:

determining the conductivity ratio value based on the function parameter.

In some embodiments, the obtaining unit is further configured to:

determining the ablation boundary value based on the ablation boundary.

the acquisition unit is further configured to: determining the first boundary value and the second boundary value based on the intersection of the Casini curve with its two axes of symmetry.

In certain embodiments, the prediction unit is further configured to:

generating a second ablation voltage value using the computational model based on the electric field strength ablation threshold, the electrode needle spacing value, the conductivity ratio value, and the second boundary value; and

A third aspect of the present disclosure proposes an apparatus for predicting an ablation voltage value, comprising:

a prediction unit configured to predict an ablation voltage value for the ablation region using a calculation model based on the acquired ablation parameter value and an electric field strength ablation threshold value, wherein the calculation model is generated from a plurality of preset ablation voltage values and a plurality of preset ablation parameter values, and the calculation model defines the ablation voltage value as a function of the electric field strength value and the ablation parameter value.

In certain embodiments, the computational model is generated by:

obtaining corresponding electric field strength values based on the preset ablation voltage values and the preset ablation parameter values to construct a database; and

generating the computational model based on the database.

The invention has the beneficial effects that: the disclosed method and apparatus for predicting ablation voltage values are capable of quickly and efficiently predicting ablation voltage values required to ablate an area based on the area, with greater accuracy, than prior approaches that are time consuming and labor intensive, and less accurate.

Drawings

The features, advantages and other aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description in conjunction with the accompanying drawings, in which several embodiments of the present disclosure are shown by way of illustration and not limitation, wherein:

fig. 1 is a schematic diagram of an exemplary apparatus for predicting ablation voltage values in accordance with an embodiment of the present disclosure;

fig. 2 is a schematic diagram of an exemplary method for predicting ablation voltage values in accordance with an embodiment of the present disclosure;

fig. 3 shows a schematic diagram of another exemplary apparatus for predicting ablation voltage values according to an embodiment of the present disclosure;

fig. 4 shows a schematic diagram of an exemplary ablation zone and ablation boundary in accordance with an embodiment of the present disclosure;

FIG. 5 shows a schematic diagram of an ablation boundary according to an exemplary coverage rule;

FIG. 6 shows a schematic diagram of an ablation boundary according to an exemplary coverage rule;

FIG. 7 shows a schematic diagram of an ablation boundary according to an exemplary coverage rule;

FIG. 8 shows a schematic diagram of an ablation boundary according to an exemplary coverage rule;

FIG. 9 shows an exemplary conductivity-electric field strength graph according to an embodiment of the present disclosure;

FIG. 10 shows a voltage-electric field strength-conductivity ratio plot according to an embodiment of the present disclosure;

FIG. 11 illustrates an exemplary coefficient-pin spacing value relationship diagram in a first coefficient group according to an embodiment of the disclosure;

FIG. 12 illustrates an exemplary coefficient-ablation boundary value relationship in a first coefficient set according to an embodiment of the disclosure;

fig. 13 shows an exemplary coefficient-needle spacing-ablation boundary value relationship in the first coefficient group.

Detailed Description

Various exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagrams may represent a module, a program segment, or a portion of code, which may include at least one executable instruction for implementing the logical function specified in the respective embodiment. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

As used herein, the terms "include," "include," and similar terms are to be construed as open-ended terms, i.e., "including/including but not limited to," meaning that additional content can be included as well. The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment," and so on.

As mentioned above, the existing solutions are time-consuming and labor-consuming to determine the ablation effect by enumerating specific ablation voltages, and the accuracy is not high enough, and based on this, the embodiments of the present disclosure provide an apparatus and a method for rapidly predicting the value of the ablation voltage.

In order that the objects, technical solutions and advantages of the present invention will be more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings. Those skilled in the art will appreciate that the present invention is not limited to the drawings and the following examples.

Fig. 1 illustrates an example apparatus 100 for predicting ablation voltage values in accordance with an embodiment of the present disclosure. The apparatus 100 comprises: a construction unit 110, a model generation unit 120, an acquisition unit 130, and a prediction unit 140. The apparatus 100 and the various units included therein may implement

step

210 and 240 of the exemplary method 200 for predicting ablation voltage values of FIG. 2. Specifically, step 210 may be implemented by the construction unit 110, step 220 may be implemented by the model generation unit 120, step 230 may be implemented by the acquisition unit 130, and step 240 may be implemented by the prediction unit 140.

As shown in fig. 1 and 2, at step 210, corresponding electric field intensity values E are obtained by the construction unit 110 based on a plurality of preset ablation voltage values and a plurality of preset ablation parameter values to construct a database. The ablation voltage value may be denoted by U, which represents the pulse voltage of the electrical pulse. Ablation parameters are one or more parameters that relate to forming a particular ablation range. For example, the constructed database may store data through entries consisting of preset ablation voltage values, preset ablation parameter values and obtained corresponding electric field strength values or otherwise, in order to retrieve data from the database for further processing.

In step 220, the method 200 comprises generating, by the model generation unit 120, a calculation model based on the database, wherein the calculation model is used for predicting the ablation voltage value by the electric field strength value, the ablation parameter value. In this step 220, a calculation model for predicting the ablation voltage value by the electric field strength value and the ablation parameter value is generated using the database, for example, the generated calculation model may represent the voltage U and the four parameters D, E_th，σ_rP, such as U ═ f (D, P, E)_th,σ_r)。

In step 230, ablation parameter values of the ablation region and an electric field strength ablation threshold are acquired by the acquisition unit 130. The electric field intensity ablation threshold can be defined as E_thIt represents a threshold value of an electric field intensity applied to the biological tissue to cause cell death of the biological tissue. For a particular cell type, a threshold electric field strength of, for example, 500V/cm is required to ablate the cell (note: this value can be adjusted according to ablation requirements). .

In step 240, an ablation voltage value for the ablation region is predicted by the prediction unit 140 using a calculation model based on the acquired ablation parameter value and the electric field intensity ablation threshold value.

Compared with the time-consuming and labor-consuming prior art scheme of determining the ablation effect by enumerating the ablation voltage, the device 100 shown in fig. 1 and the method 200 shown in fig. 2 can quickly and effectively predict the value of the ablation voltage required for ablating the region based on the ablation region, and the predicted value has higher precision.

In some embodiments, the preset ablation parameter values may include at least: one or more of an electrode needle spacing value, a conductivity ratio value, and an ablation boundary value. The electrode needle spacing value may be denoted by D, which is the distance between two candidate needle layout points according to the ablation needle set, e.g. the distance between the coordinates of two needle layout points. Conductivity ratio value(conductivity ratio) may be expressed as σ_rShows that the tissue conductivity σ of the cells of the biological tissue is fully electroporated_maxWith initial conductivity σ₀The ratio of (a) to (b). The ablation boundary value may be denoted by P, which represents a boundary parameter that achieves a particular ablation range, e.g., when the closed boundary of the ablation range is represented by a particular curve, the boundary parameter may take on a curve parameter that characterizes the particular curve. It will be appreciated that the ablation parameter value may also be other ablation-related parameter values, including, but not limited to, pulse width of the ablation pulse, number of pulses, etc., for example.

In certain embodiments, step 210 may further comprise: an electric field simulation model is established by the construction unit 110 based on a constraint equation, and a preset ablation voltage value U, an electrode needle distance value D and a conductivity ratio value sigma are used_rAnd the ablation boundary value P is applied to the electric field simulation model to generate a corresponding electric field intensity value E, and preset ablation voltage value U, electrode needle distance value D and conductivity ratio value sigma are used_rThe ablation boundary values and the corresponding electric field strength values E are added as a set of data to the database. For example, the electric field intensity value corresponding to the ablation voltage value and the ablation parameter value can be obtained by numerical calculation through an electric field simulation model established by a constraint equation.

About constraint equations

The constraint equations may be used to build an electric field simulation model to obtain electric field strength values corresponding to ablation parameter values.

In a preferred embodiment, the constraint equations may include one or more of an electric field equation, a conductivity equation, and a boundary condition. For example, the electric field equation may be:

wherein J represents a current density,

representing the current density vector, sigma the conductivity, E the electric field strength of the electric pulse,

represents the vector of the electric field strength of the electric pulse,

denotes the divergence of the current density vector, u denotes the potential of the electrical pulse,

the gradient of u is indicated. Alternatively, other conditions or functions may be defined. For example, the conductivity function may be: sigma (E) ═ sigma₀+(σ_max-σ₀)·exp[-A·exp(-B·E)]，σ_maxAnd σ₀As previously defined, A and B are coefficients that determine the location and rate of growth of the tissue conductivity curve. Alternatively, other conductivity functions may be defined. For example, the boundary condition equation may be:

u₁＝U，u ₂0, wherein,

representing the normal unit vector of each point on the boundary, the boundary surface is a curved surface, the normal vector of each point on the boundary surface is perpendicular to the plane of the point, J represents the current density, u represents the current density₁And u₂The potentials on the two electrode needles are respectively shown, U represents the pulse voltage of the electric pulse, and the boundary condition is used for representing that the current does not flow out of the boundary surface of the ablation parameter.

For example, the electric field simulation model may be established by simulation software (e.g., COMSOL simulation software, etc.) in combination with constraint equations (e.g., selecting or setting an electric field equation, a conductivity function, and boundary conditions), and preset ablation voltage values U, electrode needle spacing values D, conductivity ratio values σ_rAnd substituting the ablation boundary value P into the electric field simulation model, and calculating to obtain a corresponding electric field intensity value E when a specific value is taken, so as to generate a database.

In certain embodiments, step 220 may further comprise performing the following by the model generation unit 120:

for each set of data (D, P, E)_th,σ_rU), selecting the electrode needle spacing value and the ablation boundary value as one first parameter element in a first parameter set (e.g., (D)_j,P_j) And selecting the electric field strength value and the conductivity ratio value as one of the second parameter elements (e.g., (E)) in the second parameter set_th,σ_r))；

For each first parameter element (e.g., (D)_j，P_j) J 1, 2. -) performs the following operations:

a plurality of second parameter elements corresponding to the first parameter elements are retrieved from the database (e.g.,

and multiple ablation voltage values (e.g., U)_iI ═ 1,2, ·); and

based on the plurality of second parameter elements (e.g.,

) And multiple ablation voltage values (e.g., U)_i1, 2..) generating a first fitting function g (e.g., U) that predicts the ablation voltage value from two values of the second parameter element_i＝g_D,P(E_th,σ_r) Having a first set of coefficients (e.g., { a })_k},k＝1,2,...)；

Based on a plurality of first parameter elements (e.g., (D)_j,P_j) J 1, 2..) and a plurality of first coefficient sets (e.g., { a } corresponding thereto_k1, 2), each coefficient (e.g., a) in the first coefficient set is generated to be predicted by two values in the first parameter element_kK 1, 2. -) of a second fitting function h (e.g., a_k＝h_k(D, P)), the second fit function having a second set of coefficients (e.g.,

)；

based on a first coefficient set (e.g., { a })_k}) and a second coefficient set (e.g.,

) And generating a calculation model.

In certain embodiments, step 230 may further include determining, by model generation unit 120, a first fitting order of electric field strength values E to conductivity ratio values σ in the first fitting function based on enumeration_rAnd (3) a second fitting order.

Determining polynomial fitting order

For example, the first fitting order of the electric field strength values E to the conductivity ratio value σ in the first fitting function may be determined by enumeration by any of the following criteria_rSecond fitting order of (a):

criterion 1, in the case of not more than a certain order, calculating the error (e.g., absolute value difference, mean square error, etc.) between the actual value and the polynomial fit value of the different first and second fitting orders, taking the order of the smallest error as the determined polynomial order;

criterion 2, in the case of not more than a certain order, sets an error threshold, calculates the error (e.g., absolute value difference, mean square error, etc.) between the actual value and the polynomial fit value of the different first and second fitting orders, takes the minimum order falling within the error threshold as the determined polynomial order,

criterion 3, under the condition that the order is not more than a specific order, calculating a correlation coefficient between the actual value and a polynomial fitting value with different first fitting order and second fitting order, and taking the order of the maximum correlation coefficient as the determined polynomial order;

and 4, setting a correlation threshold value under the condition that the correlation is not more than a specific order, calculating a correlation coefficient between the actual value and a polynomial fitting value with different first fitting order and second fitting order, and taking the minimum order falling within the correlation threshold value as the determined polynomial order.

The above criteria 1-4 are merely illustrative and not limiting, and the fitting order may be determined based on enumeration by other criteria. In other examples, other criteria may be designed according to fit accuracy and computational complexity. For example, a value between two orders determined by any two of the above criteria 1-4 may be taken as a polynomial order to balance fitting accuracy and computational complexity.

In certain embodiments, step 220 may further comprise generating, by the model generation unit 120, the second fitting function by performing the following operations:

for two values in the first parameter element (e.g., D)_jAnd P_j) A third fitting order for a polynomial fit to each coefficient in the first coefficient set by each of the two values is determined separately (e.g., by coefficient a)_k-third fitting order of polynomial fit determined by pin spacing (D) curve) and fourth fitting order (e.g. by coefficient a)_k-the ablation boundary value (P) curve determines a fourth fitting order of the polynomial fit;

a first fitting function is generated based on the third fitting order and the fourth fitting order.

Determining polynomial fitting order

For example, the order of the polynomial fit may be determined by any of the following criteria:

criterion 1, in the case of not more than a certain order, calculating the error (e.g., absolute value difference, mean square error, etc.) between the actual value and the polynomial fitting value of a different order, taking the order of the smallest error as the determined polynomial order;

criterion 2, in the case of no more than a certain order, sets an error threshold, calculates the error (e.g., absolute value difference, mean square error, etc.) between the actual value and the polynomial fit value of a different order, takes the minimum order falling within the error threshold as the determined polynomial order,

criterion 3, under the condition that the order is not more than a specific order, calculating a correlation coefficient between an actual value and a polynomial fitting value with different orders, and taking the order of the maximum correlation coefficient as the determined polynomial order;

and 4, setting a correlation threshold value under the condition that the correlation is not more than a specific order, calculating a correlation coefficient between the actual value and the polynomial fitting values of different orders, and taking the minimum order falling in the correlation threshold value as the determined polynomial order.

The above criteria 1-4 are merely illustrative and not limiting, and the fitting order may be determined by other criteria. In other examples, other criteria may be designed according to fit accuracy and computational complexity. For example, a value between two orders determined by any two of the above criteria 1-4 may be taken as a polynomial order to balance fitting accuracy and computational complexity.

In some embodiments, in generating the computational model, the first fitting function is a polynomial fitting function expressed as:

wherein U represents an ablation voltage value, E represents an electric field intensity value, σ_rAs a conductivity ratio value, { a_iI is 1 to 5, and represents a first coefficient group,

for each coefficient a in the first coefficient group_iThe second fitting function is a polynomial fitting function, and the expression is as follows:

representing the second set of coefficients and,

the expression of the finally obtained calculation model is as follows:

in certain embodiments, step 230 may further comprise: the ablation parameter values of the ablation region comprise conductivity ratio values of the ablation region, and the obtaining of the conductivity ratio values of the ablation region by the obtaining unit 130 comprises: obtaining a plurality of electric field strength values and a plurality of corresponding conductivity values; determining a function parameter of a conductivity fitting function based on the plurality of electric field strength values and the plurality of conductivity values; the conductivity ratio value is determined based on a function parameter.

For example, the conductivity function may be defined as follows:

σ_r(E)＝σ₀+(σ_max-σ₀)·exp[-A·exp(-B·E)]

the corresponding conductivity σ, which can be measured from the test data points, i.e. at a specific electric field strength E_r(E) Performing a function fitting based on the above conductivity equation, the fitting function comprising four function parameters, wherein the tissue conductivity σ for which the biological tissue cells are fully electroporated occurs_maxσ when E tends to be plus infinity_r(E) Upper limit value of, initial conductivity σ₀σ when E tends to be minus infinity_r(E) A and B are coefficients that determine the location and rate of growth of the tissue conductivity curve.

It is understood that the above method for obtaining the conductivity ratio value of the ablation region is only exemplary, and other disclosed methods may be used to obtain the conductivity ratio value of the ablation region, which will not be described in detail herein.

In certain embodiments, step 230 may further comprise: the ablation boundary value of the ablation region is determined by the acquisition unit 130 based on the profile of the ablation region. For example, the outline of the ablation region may be obtained by acquiring an ablation region image in which the outline has been outlined from a database or a processing device (e.g., a computer, a video device, etc.), or an ablation region image in which the outline has not been outlined may be acquired from a database or a processing device (e.g., a computer, a video device, etc.), and the outline of the ablation region is extracted by performing recognition processing on the image, thereby determining an ablation boundary value of the ablation region based on the outline.

In certain embodiments, step 230 may further include performing, by acquisition unit 130: determining an ablation boundary required to cover at least a portion of the contour of the ablation region based on the coverage rules; determining the ablation boundary value based on an ablation boundary.

Coverage rules

Based on the needle set location consisting of the two needles and the outline or shape of the ablation region (e.g., focal zone), the extent of the ablation boundary needed to cover at least a portion of the outline of the ablation region (e.g., the local focal zone) is determined. For the range covered by the ablation boundary, the integrity of the coverage needs to be taken into account. The connecting line of the cloth needle points of the two needles is taken as a first direction, and the perpendicular bisector of the connecting line of the cloth needle points of the two needles is taken as a second direction. For example, the first direction may be a horizontal direction and the second direction may be a vertical direction. Alternatively, the first direction may be any direction (e.g., at an angle to the horizontal), and the second direction may be a different direction from the first direction (e.g., a direction perpendicular to the first direction, etc.).

For example, the ablation boundary may be determined based on the following coverage rules:

rule 1: maximum coverage is achieved on both sides in the first direction, i.e. so that the ablation boundary is able to cover the points on both sides of the contour of the ablation area which are furthest from the needle placement point.

Rule 2: minimal coverage is achieved on both sides in the first direction, i.e. so that the ablation boundary covers only the points of the contour of the ablation region which are furthest from the needle deployment point on a single side.

Rule 3: medium coverage, i.e. between maximum and minimum coverage, is achieved on both sides in the first direction.

Rule 4: maximum coverage is achieved on both sides in the second direction, i.e. so that the ablation boundary is able to cover the points on both sides of the contour of the ablation area which are furthest from the needle placement point.

Rule 5: minimal coverage is achieved on both sides in the second direction, i.e. so that the ablation boundary covers only the points of the contour of the ablation region which are furthest from the needle deployment point on a single side.

Rule 6: medium coverage, i.e. between maximum and minimum coverage, is achieved on both sides in the second direction.

In rules 1-6 above, maximum coverage may determine a larger ablation boundary to maximize ablation needs on both sides in a given direction, while minimum coverage may determine a smaller ablation boundary to prevent over-ablation or ablation of normal tissue. The above rules 1-6 are merely illustrative and not limiting, and ablation boundaries and ablation boundary values may also be determined based on other coverage rules.

In some embodiments, the ablation boundary values may include at least a first boundary value determined in a first direction and a second boundary value determined in a second direction different from the first direction. For example, the first direction may be a direction of a line of clothing points, and the first boundary value may be a boundary distance determined according to the coverage rule at which an intersection of the ablation boundary with the line of clothing points is away from a center between the two clothing points. For example, the second direction may be the direction of a perpendicular bisector of the line of clothing points, and the second boundary value may be a boundary distance, determined according to the coverage rule, of an intersection of the ablation boundary with the perpendicular bisector of the line of clothing points from the center between the two clothing points.

In some embodiments, the ablation boundary is represented by a cassini curve, and wherein step 240 may further comprise: the first boundary value and the second boundary value are determined on the basis of the intersection of the Cassini (Cassini) curve with its two axes of symmetry.

About the Cacini curve

Assuming that the cassini curve is symmetric about the x-axis and the y-axis, the center is located at the origin, and the two focal points are located on the x-axis, as (-c,0) and (c,0), respectively, the equation for the cassini curve is:

where (x, y) is the coordinates of a point on the cassini curve whose shape depends on the shape parameters c, a. If a/c>1, the Casini curve is a closed curve, the intersection points of the curve with the x axis are (-M,0) and (M,0), the intersection points of the curve with the y axis are (0, N) and (0, -N), wherein,

similarly, in the case where two mutually perpendicular straight lines of the cassini curve with respect to the other directions are symmetry axes, M and N can be similarly obtained.

In the case where the ablation boundary is a cassini curve, the first boundary value may be M and the second boundary value may be N.

In certain embodiments, step 240 may further comprise:

generating a first ablation voltage value using a computational model based on the electric field intensity ablation threshold, the electrode needle spacing value, the conductivity ratio value, and the first boundary value;

generating a second ablation voltage value by using a calculation model based on the electric field intensity ablation threshold value, the electrode needle spacing value, the conductivity ratio value and a second boundary value;

an ablation voltage value is generated based on the first ablation voltage value and the second ablation voltage value.

As previously mentioned, the first ablation voltage value generated by the first boundary value and the second ablation voltage value generated by the second boundary value represent applicable pulse voltages for the ablation degree in the first and second directions, respectively. For example, the maximum of the first ablation voltage value or the second ablation voltage value may be taken as the predicted ablation voltage value to maximally satisfy the ablation requirements on both sides in a given direction. For example, the minimum of the first ablation voltage value or the second ablation voltage value may be taken as the predicted ablation voltage value to prevent over-ablation or ablation of normal tissue. For example, an intermediate value (e.g., an average value, a weighted value, etc.) between the first ablation voltage value or the second ablation voltage value may be taken as the predicted ablation voltage value to create a balance between meeting ablation requirements and preventing excessive ablation.

Fig. 3 shows a schematic diagram of another exemplary apparatus 300 for predicting ablation voltage values in accordance with an embodiment of the present disclosure.

The apparatus 300 comprises: an acquisition unit 310 and a prediction unit 320. The obtaining unit 310 is similar to the obtaining unit 130 of the apparatus 100 as described above with respect to fig. 1, and is configured to obtain ablation parameter values of the ablation region and an electric field strength ablation threshold, which are not described in detail for the sake of brevity. The prediction unit 320 is similar to the prediction unit 140 of the apparatus 100 as described above with respect to fig. 1, and is configured to predict an ablation voltage value for the ablation region using a calculation model based on the acquired ablation parameter value and the electric field strength ablation threshold value, which will not be described in detail for the sake of brevity. Unlike the apparatus 100 of fig. 1, the apparatus 300 may not include the model generation unit 120, but may be preconfigured with the computational model, e.g., the computational model may be stored in the apparatus 300 or in a device associated with the apparatus 300 (e.g., in a repository or database, or in a storage unit, etc.), such that the apparatus 300 may retrieve the computational model. The computational model is generated from a plurality of preset ablation voltage values and a plurality of preset ablation parameter values, and the ablation voltage values are defined as a function of an electric field strength ablation threshold and the ablation parameter values.

Similarly, the computational model may be generated by the model generation unit 120 of the apparatus 100 of fig. 1 or by performing step 220 of fig. 2. For example, in some embodiments, the computational model is generated by: obtaining corresponding electric field strength values based on the plurality of preset ablation voltage values and the plurality of preset ablation parameter values to construct a database; and generating a computational model based on the database. For the sake of brevity, the generation process of the calculation model is not described in detail, and reference may be made to the foregoing detailed description of the generation of the calculation model.

To more clearly illustrate the principles of the present disclosure, examples of specific implementations of the apparatus 100, the method 200, or the apparatus 300 described above are given below.

Fig. 4 shows a schematic diagram of an exemplary ablation zone and ablation boundary.

As shown in fig. 4, the gray area is a lesion area (or ablation area), the electrode needles can be distributed in the lesion area for ablation, and the boundary range of the ablation area required for covering the local lesion area is determined according to the position of the needle group consisting of the current two needles and the outline or shape of the lesion area. As previously mentioned, the ablation boundary may be represented by a cassini curve. Without loss of generality, assuming that the cassini curve is symmetric about the x-axis and the y-axis, the intersection points of the curve with the x-axis are (-M,0) and (M,0), and the intersection points of the curve with the y-axis are (0, N) and (0, -N), the ablation boundary values may include a first boundary value M in the first direction and a second boundary value N in the second direction. For ablation boundaries to cover the lesion area, coverage integrity should be taken into account and the area should not be too large. In the example of fig. 4, the ablation boundary coincides well with the contour of the lesion area. However, in practical cases, there may be misalignment due to irregularity of the lesion area and the contour. Thus, an ablation boundary required to cover at least a part of the contour of the lesion area may be determined, e.g. based on the coverage rules, and an ablation boundary value may be determined based on the ablation boundary.

Fig. 5-8 respectively show schematic views of an ablation boundary according to various coverage rules. To meet the ablation needs in different directions, different ablation boundaries may need to be formed. For example, for a cassini curve, the value of M determines the ablation range in the x-axis direction, the value of N determines the ablation range in the y-axis direction, and two cassini curve equations with different parameters may need to be formed to satisfy the ablation requirements in the x-axis direction and the ablation requirements in the y-axis direction.

As shown in FIG. 5, the contour of the lesion area is spaced M distances from the center of the needle distribution point on both sides of the first direction (e.g., x-axis direction), respectively₁And M₂When the ablation boundary is considered both right and left sides, M can be taken as a larger value max (M)₁,M₂) Or a smaller value min (M)₁,M₂) Or between the two.

As shown in fig. 6, for considering only one side of the outline covering the lesion area, M may be taken as the distance (e.g., horizontal distance) from the center of the needle placement point to the outline.

As shown in FIG. 7, the contour of the lesion area is spaced from the center of the needle distribution point by N distances on both sides in the second direction (e.g., y-axis direction)₁And N₂When the upper and lower sides of the ablation boundary are considered simultaneously, N can be taken as a larger value max (N)₁,N₂) Or a smaller value min (N)₁,N₂) Or between the two.

As shown in fig. 8, for considering only one side of the outline covering the lesion area, N may be taken as the distance (e.g., vertical distance) from the center of the needle placement point to the outline.

As already mentioned, the conductivity function σ (E) · σ (E) can be based on the respective conductivity σ (E) measured at a particular electric field strength E from test data points, i.e. the respective conductivity σ (E) obtained at that particular electric field strength E₀+(σ_max-σ₀)·exp[-A·exp(-B·E)]And (6) performing function fitting. FIG. 9 illustrates an exemplary conductivity-electric field relationship graph, where the dots represent test data points, the curves represent fit lines, and the dashed lines represent σ_r(E) Upper and lower asymptotic values of (c). Tissue conductivity σ for complete electroporation of biological tissue cells_maxThe upper limit of σ (E) when E tends to be plus infinity, the initial conductivity σ₀The lower limit of σ (E) when E tends to be negative infinity, and A and B are coefficients that determine the location and rate of growth of the tissue conductivity curve. From fig. 9, the conductivity ratio value σ can be determined_r＝σ_max/σ₀. First, a corresponding electric field intensity value is obtained based on a plurality of preset ablation voltage values and a plurality of preset ablation parameter values to construct a database.

In this example, the preset ablation voltage value U ranges from 500-; the preset value range of the needle spacing value D is 4-50mm, wherein one value is taken at every 1mm interval; preset conductivity ratio value sigma_rHas a value range of (1.1, 3)]Taking a value every 0.1; the preset ablation boundary values P (first boundary value M and second boundary value N), for example, range from 3 to 26mm for each 1mm interval.

For example, simulation software (for example, COMSOL) is used for establishing a model of the electric field simulation of the electrode needle,the model at least comprises model parameters U, D, sigma_rM, for preset U, D, sigma_rAnd taking different values of M and substituting the values into the electric field simulation model, and calculating to obtain the corresponding field intensity E when the value is specifically taken, wherein E is the electric field intensity at the coordinate M, so that simulation data are generated to form a database.

Then, using the database, ablation parameters D, E are determined_th,σ_rM, and the ablation voltage value U to generate a computational model. To generate the computational model, computational complexity may be reduced by dividing ablation parameters into different sets. Two parameters can be selected from the four parameters, the values of the two parameters are fixed, and the relation between the remaining two parameters and the voltage U is investigated. Due to the needle spacing D, conductivity ratio σ_rThe ablation boundary value M is a known value, and the electric field intensity value is a simulated value, so that D and sigma are required_rAnd selecting two parameters from the three parameters M to be fixed. For example, D, M may be selected as the fixed parameter to determine E_th、σ_rThe relation to the voltage U.

Is formed by m groups

Corresponding voltage U collected by value_iData constituting a set of three-dimensional spatial points

From these points, a surface fit may be performed, for example, to generate a first fit function U-g_D,M(E_th,σ_r)。

Fig. 10 shows an exemplary voltage-electric field strength-conductivity ratio graph, with dots being simulated data points taken from the database and a grid surface being a fitted polynomial surface. In the example of fig. 10, D is 16mm and M is 18 mm.

As previously mentioned, the first fitting order of the electric field strength values E in the first fitting function to the conductivity ratio value σ may be obtained by enumeration based on various criteria_rAnd (3) a second fitting order. For example, in the example of FIG. 10, it is determined that 1 is employed via an attempt to employ polynomial fits of different orders2 nd order (E)_thIs the highest power of 1, σ_rOf highest power of 2) polynomial surface pairs

The data were fitted. At this time, the order of the fitting function is low, the fitting error is minimal, and the computational complexity is low. In the example of fig. 10, the expression of the first fitting function U is:

wherein U represents an ablation voltage value, E represents an electric field intensity value, σ_rAs a conductivity ratio value, { a_iAnd i is 1-5, and represents the first coefficient group.

To obtain the complete f_M(D,E_th,σ_rM) fitting equation, which can be based on the specific E obtained above_th,σ_rWhen taking values

Analyzing each fitting coefficient a in the first coefficient group_iAnd E_th,σ_rThe relationship (2) of (c). Taking n groups

In combination, each group (D)_j,M_j) All have a corresponding set of fitting coefficients a_i(i ═ 1,2, …, 5). 5 fitting coefficients were examined, with the first fitting coefficient a₁For example, for data points

Collecting, analyzing M and taking fixed value, D and fitting coefficient a₁And D is a fixed value, M and the fitting coefficient a₁The change curve therebetween.

Fig. 11 shows an exemplary coefficient a in the first coefficient group₁-graph of pin pitch values, where the dots are simulated data points and the dashed lines are fitted lines. In the example of FIG. 11, the firstA boundary value M is fixed, for example, M is 16 mm. The fitting order may be determined based on various criteria as previously described, for example, a quadratic function fit may be employed, i.e. for the coefficient a₁And a needle spacing value D, the fitting order of D being of second order.

Fig. 12 shows an exemplary coefficient a in the first coefficient group₁Ablation boundary value relationship maps, where the dots are simulated data points and the dashed lines are fitted lines. In the example of fig. 12, the needle spacing value D takes a fixed value, e.g., D ═ 16 mm. The fitting order may be determined based on various criteria as previously described, e.g. a cubic function fit may be taken, i.e. for the coefficient a₁And a first boundary value M, the fitting order of M being third order.

FIG. 13 shows an exemplary coefficient a₁-needle spacing-ablation boundary value map. As can be seen from FIGS. 11 and 12, a polynomial curve pair (D, M, a) of order 2-3 (the highest power of D is 2, and the highest power of M is 3) can be used₁) And fitting the data to generate a second fitting function. For the coefficient a₁The expression of the second fitting function is:

similarly, the coefficients ai (i ═ 2, …,5) in the remaining first coefficient set can be fitted using a similar formula, and the second fitting function is expressed as:

the voltage U and the four parameters D and E can be obtained by two times of surface fitting and combining the obtained fitting coefficients_th，σ_rAnd between MI.e. generating a computational model:

in a similar manner, a calculation model f for the second boundary value N can be generated_N(D，N,E_th,σ_r)。

According to the fitting formula f_M(D,M,E_th,σ_r) And f_N(D,N,E_th,σ_r) Respectively calculating voltage values U corresponding to the ablation boundary values M and N_MAnd U_N. Usually, the two voltages may not be the same, and in a preferred embodiment, the larger one of the two voltages is used as the actual voltage, i.e., U-max (U ═ max)_M,U_N) It is ensured that the ablation requirements of the resulting ablation zone region in both directions (e.g., x-axis and y-axis directions) are met. In another preferred embodiment, the smaller of the two is used as the actual voltage, i.e., U ═ min (U ═ min)_M,U_N) To prevent over-ablation or ablation of normal tissue. In other embodiments, U may be taken as U_MAnd U_NIntermediate values in between.

The devices and methods disclosed herein are capable of predicting the ablation voltage values needed for an ablation region quickly and efficiently, with greater accuracy, than the prior art.

In general, the various example embodiments of this disclosure may be implemented in hardware or special purpose circuits, software, firmware, logic or any combination thereof. Certain aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While aspects of embodiments of the disclosure have been illustrated or described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that the blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.

Alternatively, the above-described method can be implemented by a computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for carrying out various aspects of the present disclosure. The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, a punch card or an in-groove protrusion structure such as those having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

It should be noted that although in the above detailed description several means or sub-means of the device are mentioned, this division is only exemplary and not mandatory. Indeed, the features and functions of two or more of the devices described above may be embodied in one device in accordance with embodiments of the present disclosure. Conversely, the features and functions of one apparatus described above may be further divided into embodiments by a plurality of apparatuses.

While embodiments of the present disclosure have been described with reference to several particular embodiments, it should be understood that embodiments of the present disclosure are not limited to the particular embodiments disclosed. The embodiments of the disclosure are intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

Claims

1. A method for predicting ablation voltage values, comprising:

2. The method of claim 1, wherein the ablation parameter values include at least: an electrode needle spacing value, a conductivity ratio value and an ablation boundary value;

3. The method of claim 2, wherein building the database comprises:

4. The method of claim 3, wherein generating the computational model comprises:

for each of the first parameter elements, performing the following:

5. The method of claim 4, wherein generating the first fitting function comprises:

6. The method of claim 4, wherein generating the second fitting function comprises:

7. The method of claim 4, wherein,

representing the second set of coefficients and,

the expression of the calculation model is as follows:

8. the method of claim 1, wherein the ablation parameter value for the ablation region comprises a conductivity ratio value for the ablation region, and obtaining the conductivity ratio value for the ablation region comprises:

determining the conductivity ratio value based on the function parameter.

9. The method of claim 2, wherein determining an ablation boundary value for the ablation region based on the profile of the ablation region further comprises:

determining the ablation boundary value based on the ablation boundary.

10. The method of claim 9, wherein the ablation boundary values include at least a first boundary value determined in a first direction and a second boundary value determined in a second direction different from the first direction.

11. The method of claim 10, wherein the ablation boundary is represented by a Cassini curve, and wherein,

12. The method of claim 10, wherein predicting an ablation voltage value for the ablation region using the computational model comprises:

13. An apparatus for predicting ablation voltage values, comprising:

14. The apparatus of claim 13, wherein the ablation parameter values comprise at least: an electrode needle spacing value, a conductivity ratio value and an ablation boundary value;

15. The apparatus of claim 14, wherein the construction unit is further configured to:

16. The apparatus of claim 15, wherein the model generation unit is further configured to:

for each of the first parameter elements, performing the following:

17. The apparatus of claim 16, wherein the model generation unit is further configured to:

18. The apparatus of claim 16, wherein the model generation unit is further configured to:

19. The apparatus of claim 16, wherein,

representing the second set of coefficients and,

the expression of the calculation model is as follows:

20. the apparatus of claim 13, wherein the ablation parameter value of the ablation region comprises a conductivity ratio value of the ablation region, and the obtaining unit is further configured to:

determining the conductivity ratio value based on the function parameter.

21. The apparatus of claim 14, wherein the obtaining unit is further configured to:

determining the ablation boundary value based on the ablation boundary.

22. The apparatus of claim 21, wherein the ablation boundary values comprise at least a first boundary value determined in a first direction and a second boundary value determined in a second direction different from the first direction.

23. The apparatus of claim 22, wherein the ablation boundary is represented by a Cassini curve, and wherein,

24. The apparatus of claim 22, wherein the prediction unit is further configured to:

25. An apparatus for predicting ablation voltage values, comprising:

a prediction unit configured to predict an ablation voltage value for the ablation region using a calculation model based on the acquired ablation parameter value and an electric field strength ablation threshold value, wherein the calculation model is generated from a plurality of preset ablation voltage values and a plurality of preset ablation parameter values, and defines the ablation voltage value as a function of the electric field strength value and the ablation parameter value.

26. The apparatus of claim 25, wherein the computational model is generated by:

generating the computational model based on the database.