CN112562859A - Intelligent simulation model training system and training method for tumor thermal ablation operation - Google Patents
Intelligent simulation model training system and training method for tumor thermal ablation operation Download PDFInfo
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Abstract
The application discloses an intelligent simulation model training system and method for tumor thermal ablation surgery. The intelligent simulation model training system for the tumor thermal ablation operation comprises: the image module is used for acquiring a medical image of a tumor lesion of a patient; a three-dimensional modeling module for processing the medical image to obtain a three-dimensional model of the internal organ tissue of the human body; and the simulation system is used for acquiring the three-dimensional model and the heat source model required by the operation, simulating the heat source insertion and heating process of the actual operation according to the three-dimensional model and the heat source model, and acquiring the ablation range and the temperature field distribution. According to the intelligent simulation model training system and method for the tumor thermal ablation operation, the medical images are collected and the three-dimensional model is built, so that the modeling speed is high, the size and the position of the model are accurate, and the intelligent simulation model training system and method are more suitable for preoperative simulation in practice.
Description
Technical Field
The application relates to the technical field of medical simulation models, in particular to a training system and a training method for an intelligent simulation model of a tumor thermal ablation operation.
Background
At present, in the preoperative surgical planning stage of the microwave ablation operation, doctors mainly imagine the internal structure of a human body according to medical scanning images and design a surgical scheme according to clinical experience. In view of the individual operations in the world, computer modeling has been adopted to complete the preoperative simulation, and the preoperative simulation modeling technology of the microwave ablation operation has also received much attention.
The microwave ablation operation modeling is researched by a computer in China, but the simulation model focuses on temperature field calculation in the simulation operation, and little attention is paid to how to obtain a three-dimensional human body structure necessary for the simulation model. On the contrary, most simulation models only provide the algorithm of the temperature field in a general way, and adopt a large amount of simplified human body modeling, which is far away from the actual situation. The simulation models are applied to clinical operation, firstly, three-dimensional modeling in a human body is solved, and the modeling accuracy needs to be ensured, so that the simulation models are helpful to the actual operation.
Disclosure of Invention
The application provides an intelligent simulation model training system and method for tumor thermal ablation surgery.
The intelligent simulation model training system for the tumor thermal ablation operation comprises: the image module is used for acquiring a medical image of a tumor lesion of a patient; a three-dimensional modeling module for processing the medical image to obtain a three-dimensional model of the internal organ tissue of the human body; and the simulation system is used for acquiring the three-dimensional model and a heat source model required by the operation, simulating the heat source inserting and heating process of the actual operation according to the three-dimensional model and the heat source model and acquiring the ablation range and the temperature field distribution.
In certain embodiments, the medical image is a magnetic resonance image.
In some embodiments, the three-dimensional modeling module is configured to extract human body structural information in the medical image by using a neural network to obtain a three-dimensional structure and position of a human body internal organ tissue, and to build the three-dimensional model according to the three-dimensional structure and position of the human body internal organ tissue.
In some embodiments, the simulation system is configured to set the thermal conductivity and blood perfusion rate of the three-dimensional model, and to design the path and final position of the heat source for insertion into the three-dimensional model, and to set heat source heating parameters.
In some embodiments, the simulation system is configured to use a finite element analysis method for the post-heating temperature calculation to obtain the ablation volume and the temperature field distribution.
In some embodiments, the simulation system is configured to grid the three-dimensional model, divide the three-dimensional model into a plurality of discrete units, derive a time-varying temperature value for each of the discrete units based on the initial temperature parameter of each of the discrete units and the heating parameter of the heat source, and obtain a time-varying temperature field distribution of the entire three-dimensional model.
In some embodiments, the simulation system is configured to calculate an effective ablation range based on a preset temperature threshold effective to destroy the tumor, the temperature threshold being a boundary in the temperature field distribution of the three-dimensional model.
The intelligent simulation model training method for the tumor thermal ablation operation comprises the following steps: acquiring a medical image of a tumor lesion of a patient; processing the medical image to obtain a three-dimensional model of internal organ tissue of the human body; and acquiring the three-dimensional model and a heat source model required by the operation, simulating the heat source insertion and heating process of the actual operation according to the three-dimensional model and the heat source model, and acquiring the ablation range and the temperature field distribution.
In some embodiments, the method for training an intelligent simulation model for tumor thermal ablation surgery processes the medical image to obtain a three-dimensional model of internal organ tissue of a human body, and the training comprises: extracting human body structure information in the medical image by using a neural network to obtain the three-dimensional structure and position of internal organ tissues of the human body; and establishing the three-dimensional model according to the three-dimensional structure and the position of the internal organ tissue of the human body.
In some embodiments, the training method comprises: setting the thermal conductivity and the blood perfusion rate of the three-dimensional model, designing a path and a final position of a heat source inserted into the three-dimensional model, and setting heating parameters of the heat source.
In some embodiments, the training method comprises: and calculating the heated temperature by adopting a finite element analysis method to obtain the ablation range and the temperature field distribution.
In some embodiments, the training method comprises: gridding the three-dimensional model into a plurality of discrete units; and deducing the temperature value of each discrete unit changing along with the time according to the initial temperature parameter of each discrete unit and the heating parameter of the heat source, and acquiring the temperature field distribution of the whole three-dimensional model changing along with the time.
In some embodiments, the training method comprises: and calculating an effective ablation range by taking the temperature threshold as a boundary in the temperature field distribution of the three-dimensional model according to a preset temperature threshold for effectively eliminating the tumor.
According to the intelligent simulation model training system and method for the tumor thermal ablation operation, the medical images are collected and the three-dimensional model is built, so that the modeling speed is high, the size and the position of the model are accurate, and the intelligent simulation model training system and method can be suitable for preoperative simulation in practice.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a block diagram of an intelligent simulation model training system for tumor thermal ablation surgery according to an embodiment of the present application;
fig. 2 to 7 are schematic flow charts of the intelligent simulation model training method for tumor thermal ablation surgery according to the embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
Referring to fig. 1, an intelligent simulation model training system 100 for tumor thermal ablation surgery according to an embodiment of the present disclosure includes an image module 10, a three-dimensional modeling module 20, and a simulation system 30. The image module 10 is used for acquiring medical images of tumor lesion positions of patients, the three-dimensional modeling module 20 is used for processing the medical images to acquire a three-dimensional model of internal organs and tissues of human bodies, the simulation system 30 is used for acquiring the three-dimensional model and a heat source model required by the operation, simulating the insertion heat source and heating process of the actual operation according to the three-dimensional model and the heat source model, and acquiring the ablation range and the temperature field distribution.
Specifically, in one embodiment, the tumor thermal ablation procedure intelligent simulation model training system 100 may be applied to pre-operative simulation of a vertebral tumor microwave ablation procedure. For convenience of explanation, the following embodiments are all explained by taking preoperative simulation of the microwave ablation operation of the vertebral tumor as an embodiment. It is understood that the intelligent simulation model training system 100 for tumor thermal ablation surgery of the present application can also be applied to preoperative simulation of other organs including, but not limited to, liver, kidney, spleen, etc., and the adopted preoperative surgical method is not limited to microwave ablation surgery, and can also include other surgical methods, such as thermotherapy surgical method, radio frequency ablation surgical method, laser ablation surgical method, etc.
The heat source model comprises a microwave probe heat source model, a radio frequency probe heat source model and a laser optical fiber heat source model. For example: if the preoperative surgical method is a microwave ablation operation, the heat source model comprises a microwave probe heat source model; if the preoperative surgical method is a radio frequency ablation surgical method, the heat source model comprises a radio frequency probe heat source model; if the preoperative surgical method is a laser ablation surgical method, the heat source model comprises a laser optical fiber heat source model. Specifically, the microwave probe heat source model and the radio frequency probe heat source model adopt a probe as a carrier to guide power supply electromagnetic waves into a human body for action. The difference is that the microwave probe heat source model adopts a microwave probe, and a microwave generator is adopted in the microwave probe to emit microwaves to the periphery to form an ablation range. The radio frequency probe is used in the heat source model, and the radio frequency probe adopts high-wavelength radio frequency current in the exposed area of the probe tip and forms a current loop in human tissue for ablation. The laser fiber heat source model adopts pure quartz fiber to transmit laser, and the ablation range of the laser fiber is the most accurate. For convenience of explanation, in the following embodiments, the microwave probe heat source model is explained as an embodiment of the heat source model, and the present invention is not limited to the embodiment.
The microwave ablation operation for the vertebral body tumor is a microwave ablation operation, and at present, the microwave ablation operation is designed in an operation stage before an operation, and a doctor mainly designs an operation scheme according to clinical experience by imagining an internal structure of a human body according to medical scanning images. In view of the individual operations in the world, computer modeling has been adopted to complete the preoperative simulation, and the preoperative simulation modeling technology of the microwave ablation operation has also received much attention.
The microwave ablation operation modeling is researched by a computer in China, but the simulation model focuses on temperature field calculation in the simulation operation, and little attention is paid to how to obtain a three-dimensional human body structure necessary for the simulation model. On the contrary, most simulation models only provide the algorithm of the temperature field in a general way, and adopt a large amount of simplified human body modeling, which is far away from the actual situation. The simulation models are applied to clinical operation, firstly, three-dimensional modeling in a human body is solved, and the modeling accuracy needs to be ensured, so that the simulation models are helpful to the actual operation.
The intelligent simulation model training system 100 for the tumor thermal ablation operation in the embodiment of the application has the advantages that the medical images are collected and the three-dimensional model is built, so that the modeling speed is high, the size and the position of the model are accurate, and the system can be suitable for preoperative simulation in practice.
The intelligent simulation model training system 100 for tumor thermal ablation surgery is a virtual surgery simulation system, and the intelligent simulation model training system 100 for tumor thermal ablation surgery can help doctors to reasonably make surgery schemes, and has very important significance in selecting an optimal surgery path, reducing surgery injuries, reducing damage to adjacent tissues, improving tumor positioning accuracy, performing complex surgery, improving surgery success rate and the like. The intelligent simulation model training system 100 for tumor thermal ablation surgery can provide a training environment with a sense of reality and an immersion sense for an operator, so that the surgery can be performed in a virtual environment without serious accidents, and the cooperation capability of doctors can be improved.
The imaging module 10 may include hardware and/or programming software, etc. necessary to acquire medical images. In some embodiments, the Imaging module 10 may be configured to acquire a Magnetic Resonance image, and the Magnetic Resonance image may be obtained by a Magnetic Resonance Imaging (MRI) technique, in which a substance containing specific nuclei with non-zero spins in a Magnetic field is excited by using a radio frequency pulse with a specific frequency to generate a nuclear Magnetic Resonance phenomenon, and then signals are acquired by using an induction coil, and the acquired signals are processed according to a certain mathematical method to establish an Imaging method of a digital image. The essence of the magnetic resonance imaging technology is that the magnetic resonance effect of hydrogen atomic nuclei in a human body is utilized, a static magnetic field and a radio frequency magnetic field are adopted to image human tissues, and a clear image with high contrast can be obtained without using electron ion radiation or contrast agents in the imaging process. The magnetic resonance imaging technology has the advantages of high resolution capability, good imaging effect and no radiation damage. In one example, the intelligent simulation model training system 100 for tumor thermal ablation surgery may be a training system for vertebral tumor microwave ablation surgery, and the image module 10 of the training system 100 for tumor thermal ablation surgery may be used to acquire magnetic resonance images of a tumor lesion of a patient. In other embodiments, the medical image may also be other medical images, not limited to a magnetic resonance image.
In one embodiment, the simulation system may include a computer, wherein the three-dimensional model and the heat source model are imported into the computer, and the computer is used to simulate the insertion of the heat source and the heating process for the actual surgery.
In some embodiments, the three-dimensional modeling module 20 may be configured to extract the human body structure information in the medical image by using a neural network to obtain the three-dimensional structure and position of the internal organ tissue of the human body, and build a three-dimensional model according to the three-dimensional structure and position of the internal organ tissue of the human body. Thus, the establishment of the three-dimensional model is realized.
Specifically, the Neural network may be an Artificial Neural Network (ANNS), which relates to a plurality of fields such as Artificial intelligence, neuroscience, thinking science, and computer science. The artificial neural network has a self-learning function, an association storage function and the capability of searching an optimized solution at a high speed. The artificial neural network can exert the high-speed computing capability of the computer, and can quickly extract and process required information. In one example, the three-dimensional modeling module 20 may extract human body structure information in the magnetic resonance image using an artificial neural network, the human body structure information may include structure information of internal organ tissues of the human body, and the three-dimensional modeling module 20 may obtain a three-dimensional structure and a position of the internal organ tissues of the human body according to the human body structure information and build a three-dimensional model according to the three-dimensional structure and the position of the internal organ tissues of the human body. In one embodiment, the three-dimensional modeling module may include a computer in which a three-dimensional model of internal organ tissue of the human body is created. This process achieves the transformation from medical images to computer three-dimensional models, replacing the human eye with a computer for recognizing medical images.
In one example, the three-dimensional model may be built based on three-dimensional reconstruction techniques. The three-dimensional reconstruction technology has a unique three-dimensional reproduction visual angle, medical examination results which are difficult to understand in a plane become popular and easy to understand, holographic three-dimensional display of focus can be achieved, and the results are more visual and clear.
In some embodiments, the simulation system 30 is used to set the thermal conductivity and blood perfusion rate of the three-dimensional model, and to design the path and final position of the heat source for insertion into the three-dimensional model, and to set the heat source heating parameters. Thus, through parameter setting, the simulation process can be more fit to the actual situation of the patient.
Specifically, the simulation system 30 sets the thermal conductivity and the blood perfusion rate of the three-dimensional model so that the three-dimensional model is more real and accurate, and the simulation training is closer to the real clinical operation. Meanwhile, personalized parameters of a patient can be considered, and the physical characteristics of human organs are emphasized on the basis of using the three-dimensional model. The simulation system 30 may also design the path and final position of the heat source to insert into the three-dimensional model and set the heat source heating parameters. In one example, the simulation system 30 may design a path for inserting a heat source into the three-dimensional model, and may utilize the heat source to act as a path, and after performing path planning based on the medical image, the user may select and control the movement of the heat source to adjust the planned path and determine the final position.
In some embodiments, the simulation system 30 is configured to employ finite element analysis methods for post-heating temperature calculations to obtain ablation range and temperature field distributions. In this way, an ablation range and a temperature field distribution can be obtained.
Specifically, the finite element analysis method is used for solving a complex problem after replacing the complex problem with a simpler problem, and the finite element analysis method can consider a solution domain as a proper or simpler approximate solution to each unit which is formed by a plurality of small interconnected sub-domains called finite elements, and then deduces the total satisfied condition for solving the domain, thereby obtaining the solution of the problem. It is worth mentioning that the numerical value obtained by the finite element analysis method is not an accurate value but an approximate value, and the calculation precision of the approximate value obtained by the finite element analysis method is very high. Therefore, the simulation system 30 can acquire the ablation range and the temperature field distribution by using a finite element analysis method for the heated temperature calculation, and the finite element analysis method can acquire the ablation range and the temperature field distribution more quickly and accurately.
In some embodiments, simulation system 30 is configured to grid the three-dimensional model, divide the three-dimensional model into a plurality of discrete units, derive a time-varying temperature value for each discrete unit based on the initial temperature parameter for each discrete unit and the heating parameter for the heat source, and obtain a time-varying temperature field distribution for the entire three-dimensional model. The implementation mode can efficiently and quickly obtain the temperature field distribution of the whole three-dimensional model which is actually consistent with the time change through a large amount of calculation.
Specifically, the initial temperature parameter of each discrete unit and the heating parameter of the heat source can be substituted into the biological heat transfer equation, numerical calculation is completed by using a computer, and the temperature value of each discrete unit changing along with time is deduced.
In some embodiments, the intelligent simulation model training system 100 for tumor thermal ablation surgery may be an intelligent simulation model training system for centrum tumor microwave ablation surgery, the simulation system 30 may set a preset temperature threshold value for effectively eliminating a tumor, and the simulation system 30 is configured to calculate an effective ablation range in the temperature field distribution of the three-dimensional model with the temperature threshold value as a boundary according to the preset temperature threshold value for effectively eliminating the tumor. In one example, tumors can be destroyed using thermal ablation in thermotherapy, which can rapidly heat tumor tissue at a high temperature of 50-80 ℃ to induce rapid coagulation denaturation of proteins in tumor cells, thereby causing necrosis of the tumor cells. For example: the temperature threshold value for effectively eliminating the tumor is determined to be 50 ℃, the simulation system 30 can set the preset temperature threshold value for effectively eliminating the tumor to be 50 ℃, and the simulation system 30 is used for calculating an effective ablation range by taking the temperature threshold value as a boundary in the temperature field distribution of the three-dimensional model according to the preset temperature threshold value for effectively eliminating the tumor, and inactivating the tumor cells in the effective ablation range. Meanwhile, doctors can try their own surgical schemes continuously by means of the intelligent simulation model training system 100 for tumor thermal ablation surgery, and evaluate and refer to the results of the simulation surgery to improve the success rate of the surgery.
The application discloses a training method for an intelligent simulation model of a tumor thermal ablation operation, which can be realized by a training system 100 for the intelligent simulation model of the tumor thermal ablation operation. Referring to fig. 2, the training method of the intelligent simulation model for tumor thermal ablation surgery includes:
01: acquiring a medical image of a tumor lesion of a patient;
02: processing the medical image to obtain a three-dimensional model of the internal organ tissue of the human body;
03: and acquiring a three-dimensional model and a heat source model required by the operation, simulating the heat source insertion and heating process of the actual operation according to the three-dimensional model and the heat source model, and acquiring the ablation range and the temperature field distribution.
In some embodiments, referring to fig. 3, step 02 includes:
022: extracting human body structure information in the medical image by using a neural network to obtain the three-dimensional structure and position of internal organ tissues of the human body;
024: and establishing a three-dimensional model according to the three-dimensional structure and position of the internal organ tissue of the human body.
In some embodiments, referring to fig. 4, the method for training the intelligent simulation model of tumor thermal ablation surgery includes:
031: setting the heat conductivity and the blood perfusion rate of the three-dimensional model, designing a path and a final position of a heat source inserted into the three-dimensional model, and setting heating parameters of the heat source.
In some embodiments, referring to fig. 5, step 03 includes:
033: and (4) calculating the heated temperature, and acquiring the ablation range and the temperature field distribution by adopting a finite element analysis method.
In some embodiments, referring to fig. 6, step 033 includes:
035: gridding the three-dimensional model, and dividing the three-dimensional model into a plurality of discrete units;
037: and deducing the temperature value of each discrete unit along with the time according to the initial temperature parameter of each discrete unit and the heating parameter of the heat source, and acquiring the temperature field distribution of the whole three-dimensional model along with the time.
In some embodiments, referring to fig. 7, step 03 includes:
039: and calculating an effective ablation range by taking the temperature threshold as a boundary in the temperature field distribution of the three-dimensional model according to a preset temperature threshold for effectively eliminating the tumor.
It should be noted that the above explanation of the implementation and beneficial effects of the training system 100 for the intelligent simulation model for tumor thermal ablation surgery is also applicable to the training method for the intelligent simulation model for tumor thermal ablation surgery of the implementation, and is not detailed here to avoid redundancy.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (13)
1. An intelligent simulation model training system for tumor thermal ablation surgery is characterized by comprising:
the image module is used for acquiring a medical image of a tumor lesion of a patient;
a three-dimensional modeling module for processing the medical image to obtain a three-dimensional model of the internal organ tissue of the human body;
and the simulation system is used for acquiring the three-dimensional model and a heat source model required by the operation, simulating the heat source inserting and heating process of the actual operation according to the three-dimensional model and the heat source model and acquiring the ablation range and the temperature field distribution.
2. The intelligent simulation model training system for tumor thermal ablation surgery according to claim 1, wherein the medical image is a magnetic resonance image.
3. The intelligent simulation model training system for tumor thermal ablation surgery according to claim 1, wherein the three-dimensional modeling module is used for extracting human body structure information in the medical image by using a neural network to obtain the three-dimensional structure and position of the human body internal organ tissue, and establishing the three-dimensional model according to the three-dimensional structure and position of the human body internal organ tissue.
4. The intelligent simulation model training system for tumor thermal ablation surgery according to claim 1, wherein the simulation system is used for setting the thermal conductivity and blood perfusion rate of the three-dimensional model, designing the path and final position of the heat source inserted into the three-dimensional model and setting the heating parameters of the heat source.
5. The intelligent simulation model training system for tumor thermal ablation surgery according to claim 1, wherein the simulation system is used for acquiring the ablation range and the temperature field distribution by using a finite element analysis method for the heated temperature calculation.
6. The intelligent simulation model training system for tumor thermal ablation surgery according to claim 5, wherein the simulation system is used for gridding the three-dimensional model, dividing the three-dimensional model into a plurality of discrete units, deriving a temperature value of each discrete unit changing with time according to an initial temperature parameter of each discrete unit and a heating parameter of the heat source, and acquiring a temperature field distribution of the whole three-dimensional model changing with time.
7. The intelligent simulation model training system for tumor thermal ablation surgery according to claim 1, wherein the simulation system is configured to calculate an effective ablation range based on a preset temperature threshold for effectively eliminating a tumor, and the temperature threshold is used as a boundary in the temperature field distribution of the three-dimensional model.
8. An intelligent simulation model training method for tumor thermal ablation surgery is characterized by comprising the following steps:
acquiring a medical image of a tumor lesion of a patient;
processing the medical image to obtain a three-dimensional model of internal organ tissue of the human body;
and acquiring the three-dimensional model and a heat source model required by the operation, simulating the heat source insertion and heating process of the actual operation according to the three-dimensional model and the heat source model, and acquiring the ablation range and the temperature field distribution.
9. The intelligent simulation model training method for tumor thermal ablation surgery according to claim 8, wherein the processing of the medical image to obtain a three-dimensional model of internal organ tissues of a human body comprises:
extracting human body structure information in the medical image by using a neural network to obtain the three-dimensional structure and position of internal organ tissues of the human body;
and establishing the three-dimensional model according to the three-dimensional structure and the position of the internal organ tissue of the human body.
10. The intelligent simulation model training method for tumor thermal ablation surgery according to claim 8, characterized by comprising the following steps:
setting the thermal conductivity and the blood perfusion rate of the three-dimensional model, designing a path and a final position of a heat source inserted into the three-dimensional model, and setting heating parameters of the heat source.
11. The intelligent simulation model training method for tumor thermal ablation surgery according to claim 8, characterized by comprising the following steps:
and calculating the heated temperature by adopting a finite element analysis method to obtain the ablation range and the temperature field distribution.
12. The intelligent simulation model training method for tumor thermal ablation surgery according to claim 11, characterized by comprising the following steps:
gridding the three-dimensional model into a plurality of discrete units;
and deducing the temperature value of each discrete unit changing along with the time according to the initial temperature parameter of each discrete unit and the heating parameter of the heat source, and acquiring the temperature field distribution of the whole three-dimensional model changing along with the time.
13. The intelligent simulation model training method for tumor thermal ablation surgery according to claim 8, characterized by comprising the following steps:
and calculating an effective ablation range by taking the temperature threshold as a boundary in the temperature field distribution of the three-dimensional model according to a preset temperature threshold for effectively eliminating the tumor.
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