Temperature field reconstruction method and device for biological tissue

Abstract

The invention provides a temperature field reconstruction method and device for biological tissue. The method comprises the following steps: constructing a target biological tissue heat transfer finite element model under the influence of a heat source, calculating initial three-dimensional temperature field distribution based on initial parameters, acquiring real-time temperatures of multiple points near the target biological tissue by utilizing a temperature sensor array optical fiber, and performing inversion calculation on tissue heat transfer parameters based on the real-time temperatures to obtain a tissue heat transfer finite element model. And reconstructing three-dimensional temperature field distribution based on the updated heat transfer parameters. The temperature sensor array optical fiber can enter a human body along with the interventional needle and be close to a target tissue in an operation, can be correspondingly bent or twisted along with advancing of the interventional needle, and has good biocompatibility, so that real-time temperatures of multiple points near the target human tissue can be collected in the operation, and the temperature sensor array optical fiber can be used for detecting the temperature of the target human tissue. And accurate real-time three-dimensional temperature field distribution can be obtained based on a finite element model and inversion calculation, so that accurate real-time feedback information can be provided for doctors in an operation, and the safety and effectiveness of the operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature field reconstruction, and particularly relates to a method and device for reconstructing the temperature field of biological tissues. Background Art

[0002] In some minimally invasive interventional surgeries, it is necessary to monitor the temperature field of the target biological tissue and its vicinity. For example, in the minimally invasive interventional surgery for thermal ablation of tumors, ultrasonic waves or lasers are used to apply heat to the tumors in the body, causing the target cells to undergo irreversible necrosis due to high temperature, so as to achieve the treatment purpose. In such surgeries, it is necessary to ensure that the lesion location where heat is applied reaches a sufficient temperature to achieve the expected treatment effect, and it is necessary to avoid damaging other normal human tissues near the tumor as much as possible. Therefore, it is necessary to set the ablation heat dose (including ablation power, ablation time, etc.) before the surgery and plan the puncture path of the interventional needle. However, due to factors such as individual differences of patients, and the difficulty in keeping the position of the interventional needle and ablation power completely at the predetermined values, etc., it is possible that the heating temperature is insufficient during the actual surgery, resulting in incomplete ablation of the tumor, or the heating temperature is too high, causing damage to the surrounding normal tissue cells, or there is a deviation in the heating position, etc. Therefore, it is necessary to monitor the temperature of the lesion area and its vicinity to provide real-time feedback information to the surgeon, helping the surgeon to better complete the surgery and making the surgery safer and more reliable.

[0003] Currently, although there are some methods for reconstructing the three-dimensional temperature field of human tissues, however, there is still a lack of a real-time three-dimensional temperature field reconstruction method for human tissues that can be well used in minimally invasive surgeries in the prior art. The main reason is that to reconstruct the three-dimensional temperature field, it is necessary to collect the real-time temperature of the target human tissue or its vicinity for the inverse calculation of the three-dimensional temperature field. However, in the above minimally invasive interventional surgery, the lesion is in the human body, and the incision of the minimally invasive surgery is very small. It is difficult for conventional temperature sensors, etc. to be inserted and close to the lesion. Even if inserted, it is difficult to ensure its normal operation in the complex human body environment, and it may also cause damage to the patient's human tissues. Therefore, it is difficult to detect the real-time temperature of the lesion or its vicinity through devices such as conventional temperature sensors. Currently, for minimally invasive surgeries, the reconstruction of the temperature field is usually achieved through indirect measurement methods. In the indirect measurement method, imaging of the ablation site is achieved through magnetic resonance imaging, ultrasonic imaging, B-ultrasound, etc., and then combined with algorithms to achieve the reconstruction and prediction of the temperature field of the ablation site and its vicinity. Such indirect measurement methods have poor real-time performance, and affected by factors such as imaging quality, the accuracy of the reconstructed three-dimensional temperature field is not ideal enough. Summary of the Invention

[0004] The present invention is made to solve the above problems, and aims to provide a temperature field reconstruction method and device capable of being applicable to real-time reconstruction of the temperature field of a target biological tissue and its vicinity during minimally invasive interventional surgery. The present invention adopts the following technical solutions:

[0005] The present invention provides a temperature field reconstruction method for a biological tissue, which has the following technical features and includes the following steps: Step S1, construct a finite element model of heat transfer of the target biological tissue under the influence of the heat source according to the working parameters of the heat source and the composition of the target biological tissue; Step S2, calculate the initial three-dimensional temperature field distribution information of the target biological tissue based on the finite element model and initial parameters; Step S3, use a temperature sensor array optical fiber to obtain real-time temperature data of multiple points near the target biological tissue; Step S4, perform inverse calculation on the heat transfer parameters of the finite element model based on the working parameters of the heat source, the real-time temperature data of multiple points, and the temperatures of corresponding multiple points in the initial three-dimensional temperature field distribution, so as to update the heat transfer parameters; Step S5, reconstruct the three-dimensional temperature field distribution information of the target biological tissue and its vicinity based on the finite element model and the updated heat transfer parameters.

[0006] The temperature field reconstruction method for a biological tissue provided by the present invention may further have the following technical features, wherein the heat source is an ultrasonic transducer, and Step S2 includes the following sub-steps: Step S2-1, calculate the mean square sound pressure of ultrasonic waves based on the working parameters of the ultrasonic transducer; Step S2-2, calculate the initial average absorption coefficient of the target biological tissue for ultrasonic waves based on the mean square sound pressure and the initial parameters of the target biological tissue; Step S2-3, calculate the heat generated by the ultrasonic transducer at any point in the target biological tissue and its vicinity as the initial heat based on the initial average absorption coefficient and the mean square sound pressure of ultrasonic waves; Step S2-4, substitute the initial heat at any point into a predetermined bioheat transfer equation to calculate the local temperature of any point in the target biological tissue and its vicinity as the initial local temperature, so as to obtain the initial three-dimensional temperature field distribution information. In Step S5, calculate the average absorption coefficient of the target biological tissue for ultrasonic waves based on the mean square sound pressure and the updated heat transfer parameters, calculate the heat generated by the ultrasonic transducer at any point in the target biological tissue and its vicinity based on the average absorption coefficient and the mean square sound pressure, and then substitute the heat into the bioheat transfer equation to calculate the local temperature of any point in the target biological tissue and its vicinity, so as to obtain the three-dimensional temperature field distribution information.

[0007] The temperature field reconstruction method for biological tissues provided by the present invention may further have the following technical features: in step S1, the finite element model includes a human tissue model and an ultrasonic transducer model. The human tissue model is a two-dimensional axisymmetric model, which includes multiple tissue layers. The multiple tissue layers include a water layer, a skin layer, a fat layer, and a muscle layer. The initial parameters include the initial heat transfer parameters of each tissue layer. The initial heat transfer parameters include the initial specific heat capacity and the initial thermal conductivity. The working parameters of the ultrasonic transducer model include the opening radius, the curvature radius, the ultrasonic frequency, the ultrasonic power, the focal length, and the irradiation time.

[0008] The temperature field reconstruction method for biological tissues provided by the present invention may further have the following technical features: the initial average absorption coefficient and the average absorption coefficient are calculated by the following formula:

[0009]

[0010] In the formula, α n is the absorption coefficient of human tissue under the nth harmonic, ε n is the ratio of the harmonic amplitude to the fundamental wave amplitude, P rms,n and P rms,1 are the mean square values of the sound pressure under the nth harmonic and the sound pressure at the fundamental frequency respectively. α avg and P rms are the average absorption coefficient of human tissue and the mean square value of the sound pressure respectively. The initial heat and the heat are calculated by the following formula:

[0011]

[0012] In the formula, t i and t f are the start time and the end time of the integration. The bioheat transfer equation is the simplified Pennes bioheat transfer equation:

[0013]

[0014] In the formula, Q is the heat, T t is the three-dimensional temperature field distribution function of human tissue, k is the thermal conductivity of human tissue, and pc is the volume specific heat of human tissue.

[0015] The temperature field reconstruction method for biological tissues provided by the present invention may further have the following technical features: the finite element model further includes boundary conditions. The boundary conditions include: the sound pressure boundary condition on the surface of the ultrasonic transducer, which is expressed as:

[0016] p t =p 0 sin(wt)

[0017] where p 0 is the initial sound pressure amplitude on the surface of the ultrasonic transducer, t is time, and w is the ultrasonic frequency; and the boundary conditions of the human tissue are expressed as:

[0018]

[0019] The method for reconstructing the temperature field of biological tissue provided by the present invention may further have the following technical characteristics, wherein the initial specific heat capacity of the water layer is 4200 J·kg -1 ·°C -1 , the initial thermal conductivity is 0.6 W·m -1 ·°C -1 , the initial specific heat capacity of the skin layer is 3500 J·kg -1 ·°C -1 , the initial thermal conductivity is 0.28 W·m -1 ·°C -1 , the initial specific heat capacity of the fat layer is 3800 J·kg -1 ·°C -1 , the initial thermal conductivity is 0.24 W·m -1 ·°C -1 , the initial specific heat capacity of the muscle is 3700 J·kg -1 ·°C -1 , the initial thermal conductivity is 0.51 W·m -1 ·°C -1 , and the initial parameters further include the density of each tissue layer, the sound velocity of the ultrasonic wave propagating in the tissue layer, the nonlinear coefficient, the attenuation coefficient of the ultrasonic wave propagating in the tissue layer, the bulk viscosity, and the dynamic viscosity.

[0020] The present invention provides a method for reconstructing the temperature field of biological tissue, which has the following technical characteristics. It includes the following steps: Step S1', training a neural network model with training data to obtain the trained neural network model as the temperature field prediction model, wherein the training data includes temperature data of multiple points, the working parameters of the heat source, and the corresponding three-dimensional temperature field distribution information; Step S2', using a temperature sensor array optical fiber to obtain real-time temperature data of multiple points near the target biological tissue; Step S3', inputting the real-time temperature data of multiple points and the current working parameters of the heat source into the temperature field prediction model, and the model outputs the three-dimensional temperature field distribution information of the target biological tissue and its vicinity.

[0021] The temperature field reconstruction method for biological tissues provided by the present invention is used in minimally invasive interventional surgery and may also have the following technical features. The temperature sensor array optical fiber includes: an optical fiber part, which includes the optical fiber body and a plurality of temperature sensing points, and the temperature sensing points are fiber Bragg gratings formed in the core of the optical fiber body; and a packaging part, which is used to package the optical fiber part and fix the temperature sensor array optical fiber to the interventional needle used for the minimally invasive interventional surgery.

[0022] The temperature field reconstruction method for biological tissues provided by the present invention is used in minimally invasive interventional surgery and may also have the following technical features. The temperature sensor array optical fiber further includes an interface part, which is connected to one end of the optical fiber part and is used to connect the optical fiber part to a fiber grating demodulator. Both ends of the optical fiber part are non-sensing areas, and the middle section is a sensing area. A plurality of the temperature sensing points are distributed in the sensing area. The packaging part includes: an end protective layer, which is coated on the non-sensing area connected to the interface part; an optical fiber protective layer, which is at least coated on the sensing area; a first fixing part, which is an adhesive part formed by an adhesive and is formed between the end protective layer and the interventional needle; and a second fixing part, which includes a plurality of fixing pieces and is used to fix the optical fiber protective layer to the interventional needle.

[0023] The present invention provides a temperature field reconstruction device for biological tissues, which has the following technical features. It includes: a finite element model storage module, which stores a finite element model, and the finite element model is a heat transfer model of the target biological tissue under the influence of the heat source constructed according to the working parameters of the heat source and the composition of the target biological tissue; an initial temperature field construction module, which is used to calculate the initial three-dimensional temperature field distribution information of the target biological tissue based on the finite element model and the initial parameters; a temperature data receiving module, which is used to obtain the real-time temperature data of multiple points near the target biological tissue through the temperature sensor array optical fiber; a heat transfer parameter inversion calculation module, which is used to inversely calculate the heat transfer parameters of the finite element model based on the working parameters of the heat source, the real-time temperature data of multiple points, and the temperatures of the corresponding multiple points in the initial three-dimensional temperature field distribution, so as to update the heat transfer parameters; and a temperature field reconstruction module, which is used to reconstruct the three-dimensional temperature field distribution information of the target biological tissue and its vicinity based on the finite element model and the updated heat transfer parameters.

[0024] Functions and effects of the invention

[0025] The method and device for reconstructing the temperature field of biological tissue provided by the present invention. The method includes steps of constructing a finite element model of heat transfer of the target biological tissue under the influence of a heat source, calculating the initial three-dimensional temperature field distribution based on initial parameters, obtaining the real-time temperatures of multiple points near the target biological tissue by using a temperature sensor array optical fiber, inversely calculating the tissue heat transfer parameters based on the real-time temperatures, and reconstructing the three-dimensional temperature field distribution of the target biological tissue and its vicinity based on the updated heat transfer parameters. Among them, due to the use of the temperature sensor array optical fiber, in minimally invasive interventional surgery, this optical fiber can enter the human body along with the interventional needle and approach the target human tissue. The optical fiber can bend or twist correspondingly as the interventional needle advances, and the optical fiber has good biocompatibility and will not cause damage to the human body. Therefore, the temperatures of multiple points near the target human tissue can be collected during minimally invasive interventional surgery. Furthermore, an accurate real-time three-dimensional temperature field distribution can be obtained based on the finite element model and inverse calculation, so as to provide accurate real-time feedback information for doctors during minimally invasive interventional surgery, assist doctors to perform the surgery better, ensure that the lesion area is heated to a predetermined temperature, and can timely detect problems such as too high local temperature, improving the safety and effectiveness of the surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flowchart of the method for reconstructing the temperature field of biological tissue in Embodiment 1 of the present invention;

[0027] Figure 2 is a schematic structural diagram of the temperature sensor array optical fiber and the interventional needle in Embodiment 1 of the present invention;

[0028] Figure 3 is a schematic structural diagram of the temperature sensor array optical fiber in Embodiment 1 of the present invention;

[0029] Figure 4 is a cross-sectional schematic diagram of a partial structure of the temperature sensor array optical fiber in Embodiment 1 of the present invention;

[0030] Figure 5 is a schematic diagram of the position distribution of multiple temperature sensing points in the temperature sensor array optical fiber in Embodiment 1 of the present invention;

[0031] Figure 6 is a spectrum position information diagram of each temperature sensing point in the temperature sensor array optical fiber in Embodiment 1 of the present invention;

[0032] Figure 7 is a schematic diagram of the usage state of the interventional surgical instrument in Embodiment 1 of the present invention;

[0033] Figure 8 is a logic block diagram of the device for reconstructing the temperature field of biological tissue in Embodiment 1 of the present invention;

[0034] Figure 9 It is an example of the finite element model of human tissue under the action of ultrasound in the first embodiment of the present invention Figure 1 ;

[0035] Figure 10 It is an example of the finite element model of human tissue under the action of ultrasound in the first embodiment of the present invention Figure 2 ;

[0036] Figure 11 It is the flowchart of step S2 in the first embodiment of the present invention;

[0037] Figure 12 It is an example diagram of the three-dimensional temperature field distribution in the first embodiment of the present invention;

[0038] Figure 13 It is the flowchart of the temperature field reconstruction method of biological tissue in the second embodiment of the present invention;

[0039] Figure 14 It is the logic block diagram of the temperature field reconstruction device of biological tissue in the second embodiment of the present invention.

[0040] Reference numerals:

[0041] Temperature field reconstruction system 100; Temperature sensor array optical fiber 10; Optical fiber part 11; Non-sensing area 11A; Sensing area 11B; Optical fiber body 111; Temperature sensing point 112; Interface part 12; Encapsulation part 13; End protective layer 131; Optical fiber protective layer 132; Sensing point protection part 133; First fixing part 134; Second fixing part 135; Temperature isolation part 136; Optical fiber grating demodulator 20; Intervention needle 30; Temperature field reconstruction device 40; Temperature data receiving module 401; Heat source parameter acquisition module 402; Finite element model storage module 403; Initial parameter setting module 404; Initial temperature field construction module 405; Heat transfer parameter inversion calculation module 406; Temperature field reconstruction module 407; Visualization generation module 408; Reconstruction device side communication module 409; Reconstruction device side control module 410; Prediction model storage module 411. Detailed implementation manners

[0042] In order to make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the temperature field reconstruction method and device of biological tissue of the present invention will be specifically described below in conjunction with embodiments and drawings.

[0043] <Embodiment 1>

[0044] This embodiment provides a method for reconstructing the temperature field of biological tissue, which is used to reconstruct the three-dimensional temperature field of biological tissue and its vicinity. In this embodiment, the method is used in minimally invasive interventional surgeries with corresponding needs to reconstruct the three-dimensional temperature field distribution of the human tissue under the action of a heat source based on the temperatures of multiple sensed points near the target human tissue. The minimally invasive interventional surgery is, for example, a minimally invasive interventional surgery for ablating tumors by ultrasound.

[0045] Figure 1 It is a flowchart of the temperature field reconstruction method in this embodiment.

[0046] As Figure 1 shown, the temperature field reconstruction method of this embodiment includes the following steps:

[0047] Step S1, construct a finite element model of heat transfer of the target biological tissue under the influence of the heat source according to the heat source and the composition of the target biological tissue.

[0048] Step S2, calculate the initial three-dimensional temperature field distribution of the target biological tissue based on the finite element model and the initial parameters.

[0049] Step S3, use the temperature sensor array optical fiber to obtain the real-time temperature data of multiple points near the target biological tissue.

[0050] Step S3A, obtain the current working parameters of the heat source.

[0051] Step S4, perform inverse calculation on the heat transfer parameters of the finite element model based on the current working parameters of the heat source, the real-time temperature data of multiple points, and the temperatures of the corresponding multiple points in the initial three-dimensional temperature field distribution, so as to update the heat transfer parameters.

[0052] Step S5, reconstruct the three-dimensional temperature field distribution information of the target biological tissue and its vicinity based on the finite element model and the updated heat transfer parameters.

[0053] Step S6, generate corresponding visualization information based on the three-dimensional temperature field distribution information.

[0054] In the above steps, the temperature sensor array optical fiber is used to obtain the real-time temperature near the target human tissue. To facilitate the description of the above steps, an exemplary structure of such a temperature sensor array optical fiber will be shown below.

[0055] Figure 2 It is a schematic structural diagram of the temperature sensor array optical fiber and the interventional needle in this embodiment, Figure 3 and is a schematic structural diagram of the temperature sensor array optical fiber in this embodiment.

[0056] As Figure 2 and Figure 3As shown, the temperature sensor array optical fiber 10 includes an optical fiber portion 11, an interface portion 12, and a packaging portion 13. In this embodiment, the temperature sensor array optical fiber 10 is fixed to the interventional needle 30. During minimally invasive interventional surgery, the optical fiber portion 11 enters the human body with the interventional needle 30 to sense the temperature in the human body.

[0057] Among them, the optical fiber part 11 includes an optical fiber body 111 and a plurality of independent temperature sensing points 112 arranged in the optical fiber body 111, which can measure the temperature of multiple points at the same time. Each temperature sensing point 112 is a fiber Bragg grating (FBG) directly engraved into the core of the optical fiber body 111 by writing technology. In this embodiment, the grating length of each temperature sensing point 112 is 1mm to 3.5mm, preferably 1mm, and multiple temperature sensing points 112 are arranged at equal intervals. Each temperature sensing point 112 can detect a small temperature change of 0.1K in less than 200ms, thereby realizing fast response temperature perception.

[0058] The fiber Bragg grating has high sensitivity and high resolution, and can accurately measure small temperature changes, providing a reliable data source for three-dimensional temperature field inversion calculation. When the temperature changes, the Bragg wavelength of the fiber Bragg grating will change accordingly, so the corresponding temperature change can be demodulated according to the wavelength change of each temperature sensing point 112.

[0059] One end of the interface portion 12 is connected to one end of the optical fiber portion 11 , and the other end of the interface portion 12 is connected to the fiber Bragg grating demodulator 20 .

[0060] Figure 4 Schematic diagram of the cross-section of the optical fiber structure of the temperature sensor array in this embodiment.

[0061] like Figure 3 and Figure 4 As shown, the packaging portion 13 includes an end protection layer 131 , an optical fiber protection layer 132 , a plurality of sensing point protection portions 133 , a first fixing portion 134 , a second fixing portion 135 , and a temperature isolation portion 136 .

[0062] There are no temperature sensing points formed at the two ends of the optical fiber part 11, and the two ends are non-sensing areas 11A. A plurality of temperature sensing points are distributed in the middle section of the optical fiber part 11, which is the sensing area 11B. The end protection layer 131 is coated on the outer surface of a non-sensing area 11A of the optical fiber part 11 connecting the interface part 12. In this embodiment, the end protection layer 131 is a glass fiber layer that can withstand a high temperature of 155°C, and the outer diameter of the end protection layer 131 is 1.6 mm.

[0063] The optical fiber protective layer 132 is coated on the outer surfaces of the sensing region 11B and another non-sensing region 11A of the optical fiber portion 11, playing a protective role to prevent it from being affected by high temperature, chemical corrosion and / or mechanical damage. In this embodiment, the optical fiber protective layer 132 is a polyimide layer, which has excellent high-temperature resistance, biocompatibility and transparency, can withstand a high temperature of 350 °C, and the outer diameter at the optical fiber protective layer 132 is 0.15 mm. In this embodiment, since the temperature sensor array optical fiber 10 needs to enter the human body along with the intervention needle 30, bending and twisting will occur during the operation of the intervention needle 30. Therefore, the optical fiber portion 11 and the optical fiber protective layer 132 also need to have a certain flexibility, and the optical fiber itself and the polyimide material also have good flexibility. Optionally, the optical fiber protective layer 132 includes multiple laminated polyimide films.

[0064] A plurality of sensing point protection portions 133 are respectively coated on the outer surfaces of the optical fiber portion 11 or the optical fiber protective layer 132 at each temperature sensing point 112, for protecting the temperature sensing point 112. In this embodiment, the sensing point protection portion 133 is a polyimide-based composite material coating formed on the outer surface of the optical fiber portion 11, and the optical fiber protective layer 132 is further coated outside the coating. The coating formed by this material has excellent high-temperature resistance, biocompatibility and transparency, and can effectively prevent the fiber Bragg grating sensing point from being damaged mechanically and / or chemically. In an alternative solution, the sensing point protection portion 122 can also be a tiny outer shell made of polyimide material, sleeved on the outer periphery of the optical fiber protective layer 132. In the case of using a tiny outer shell, the thickness of the outer shell needs to be made as thin as possible to avoid the outer optical fiber protective layer 132 being lifted too high by it, resulting in gaps on both sides of the outer shell.

[0065] The first fixing portion 134 is used to fix the non-sensing region 11B of the temperature sensor array optical fiber 10 to the intervention needle 30. In this embodiment, the first fixing portion 134 is an adhesive portion formed by an adhesive, that is, the outer surface of the end protective layer 131 is bonded to the outer surface of the intervention needle 30 by using an adhesive. The selection of the adhesive needs to consider factors such as its compatibility with the optical fiber and the intervention needle materials, as well as the bonding strength and high-temperature resistance. The thickness and distribution of the adhesive also need to be optimized to ensure that the temperature sensor array optical fiber 10 will not fall off or shift from the intervention needle 30 during the intervention operation; since the adhesive is used to bond the non-sensing region 11B, relatively speaking, it is acceptable. In this embodiment, the material of the adhesive is a biodegradable adhesive, its thickness is less than 1 mm, the adhesive is arranged at the non-sensing region 11B, and no adhesive is arranged within a range of 2 mm at both ends of the non-sensing region 11B. This adhesive can withstand a high temperature of 200 °C, so while effectively bonding the end protective layer 131 of polyimide and the metal intervention needle, it has good biocompatibility and can adapt to the temperature in the thermal ablation minimally invasive surgery.

[0066] A plurality of temperature isolation portions 136 are respectively arranged between the outer surface of the optical fiber protection layer 132 at each temperature sensing point 112 and the outer surface of the intervention needle 30, and are used to isolate each temperature sensing point 112 from the surface of the intervention needle 30, so as to avoid temperature measurement errors caused by direct contact. The temperature isolation portion 136 is a gluing portion formed by a non-thermal conductive adhesive. Similarly, the selection of the non-thermal conductive adhesive needs to consider factors such as its thermal conductivity, bonding strength, and biocompatibility. In this embodiment, the non-thermal conductive adhesive is a porous material, the thermal conductivity of this material is below 0.1 W / (m·K), and it has good bonding strength and biocompatibility. The thickness of the non-thermal conductive adhesive is greater than 1 mm. The non-thermal conductive adhesive is arranged at the sensing area 11A and extends 2 mm outside both ends of the sensing area 11A.

[0067] A plurality of second fixing portions 135 are used to fix the optical fiber protection layer 132 and the intervention needle 30. In this embodiment, the second fixing portion 135 is a cylindrical high-thermal conductive metal sheet, such as a copper sheet or a copper alloy sheet. The axial length of each high-thermal conductive metal sheet is about 5 mm. After forming the temperature isolation portion 136 (gluing portion) between the optical fiber protection layer 132 and the intervention needle 30 at each temperature sensing point 112, the second fixing portion 135 (high-thermal conductive metal sheet) covers the optical fiber protection layer 132, the gluing portion and the outside of the intervention needle 30 at the temperature sensing point 112 to be fixed, further fixes them, and can also play a further protective role for the temperature sensing point 112, without affecting the perception of temperature by each temperature sensing point 112.

[0068] In this embodiment, according to the needs and characteristics of minimally invasive interventional surgery, the number and position distribution of the temperature sensing points 112 in the temperature sensor array optical fiber 10 are precisely designed.

[0069] Figure 5 It is a schematic diagram of the position distribution of a plurality of temperature sensing points in the temperature sensor array optical fiber in this embodiment.

[0070] As Figure 5 shown, in this embodiment, considering the size, shape, and temperature distribution characteristics of the surgical area in minimally invasive interventional surgery for thermal ablation of tumor tissues, the number of temperature sensing points 112 is set to 30, and the position distribution of the 30 temperature sensing points 112 in the optical fiber portion 11 is precisely designed.

[0071] Among them, the length of the non-sensing area 11A of the optical fiber portion 11 (the distance from one end of the optical fiber portion 11 to point A in the figure) is denoted as X in ; in the sensing area 11B, the distance between the first temperature sensing point 112 and the proximal end of the sensing area 11B is denoted as X 1 and the interval distance between the (n - 1)-th temperature sensing point 112 and the n-th temperature sensing point 112 is denoted as Xn , where n > 1; Denote the distance between the 30th temperature sensing point 112 (the one closest to the outer end of the sensing area 11B) and the outer end of the sensing area 11B as X out , X 1 > 20 mm, X out > 1 mm; Optionally, X 1 < 100 mm and X n < 50 mm; Optionally, X 1 > 100 mm and X n > 50 mm. In this embodiment, X in = 100 mm to 300 mm, X out = 10 mm, X 1 = 110 mm, X n = 60 mm.

[0072] Figure 6 is the spectral position information diagram of each temperature sensing point in the fiber of the temperature sensor array in this embodiment.

[0073] As Figure 6 shown, in this embodiment, through the setting and control of the demodulator control module 24 of the fiber Bragg grating demodulator 20, specific spectral position information is assigned to each temperature sensing point 112. Each temperature sensing point 112 corresponds to a spectral peak in the figure in sequence. Within a predetermined temperature change range, the spectral peak offset of each temperature sensing point 112 is significantly smaller than the difference between adjacent two spectral peaks. Therefore, multiple temperature sensing points 112 can perform temperature sensing independently without interference.

[0074] Figure 7 is a schematic diagram of the usage state of the interventional surgical instrument in this embodiment.

[0075] As Figure 7 shown, during use, connect the interface portion 12 of the temperature sensor array fiber 10 to the fiber Bragg grating demodulator 20. The fiber Bragg grating demodulator 20 can provide a light source (laser excitation) for the fiber, and acquire the reflected light of multiple temperature sensing points 112, and obtain the corresponding real-time temperature data after demodulating the reflected light. The fiber Bragg grating demodulator 20 is further connected to the temperature field reconstruction device 40. The temperature field reconstruction device 40 acquires the real-time temperature data from the fiber Bragg grating demodulator 20, and performs three-dimensional temperature field reconstruction of the human tissue based on this. The temperature field reconstruction device 40 can be, for example, a computer or a remote server with corresponding calculation programs, and it can be further connected or communicatively connected to a display device arranged in the operating room, so as to display the reconstructed three-dimensional temperature field distribution information on the display device, thereby being able to provide temperature field reference information for doctors during the operation and facilitating doctors to observe the action effects of the ultrasonic transducer on the lesion site and the surrounding normal tissues.

[0076] Figure 8 It is a logic block diagram of the temperature field reconstruction device for biological tissues in this embodiment.

[0077] As Figure 8 shown, the temperature field reconstruction device 40 includes a temperature data receiving module 401, a heat source parameter acquisition module 402, a finite element model storage module 403, an initial parameter setting module 404, an initial temperature field construction module 405, a heat transfer parameter inversion calculation module 406, a temperature field reconstruction module 407, a visualization generation module 408, a reconstruction device side communication module 409, and a reconstruction device side control module 410.

[0078] Among them, the temperature data receiving module 401 is used to receive real-time temperature data corresponding to each temperature sensing point 112 from the fiber Bragg grating demodulator 20. The real-time temperature data includes, for example, a channel number, a timestamp, and a temperature value.

[0079] The heat source parameter acquisition module 402 is used to acquire the current working parameters of the heat source.

[0080] The finite element model storage module 403 is used to store a pre-constructed finite element model of heat transfer of the target biological tissue under the influence of the heat source.

[0081] The initial parameter setting module 404 is used to set the initial parameters of the finite element model.

[0082] The initial temperature field construction module 405 is used to construct the initial three-dimensional temperature field distribution of the target human tissue according to the stored finite element model and the set initial parameters.

[0083] The heat transfer parameter inversion calculation module 406 is used to perform inversion calculation on the heat transfer parameters of the finite element model according to the real-time temperature data at multiple points and the temperature data at the corresponding points in the initial three-dimensional temperature field distribution, so as to update the heat transfer parameters.

[0084] The temperature field reconstruction module 407 is used to reconstruct the three-dimensional temperature field distribution of the target biological tissue according to the finite element model and the updated heat transfer parameters.

[0085] The visualization generation module 408 is used to generate corresponding visualization information according to the reconstructed three-dimensional temperature field distribution.

[0086] The reconstruction device side communication module 409 is used to communicate with other devices. For example, it sends the visualization information to a display device for display, or sends the reconstructed three-dimensional temperature field distribution information to other computing devices with visualization generation functions for visualization generation and display, or sends the reconstructed three-dimensional temperature field distribution information and / or visualization information to a storage device for storage as historical data.

[0087] The reconstruction device-side control module 410 is used to control the operation of each of the above function modules.

[0088] The more specific algorithms for each module will be described in detail in the corresponding steps below.

[0089] The method of this embodiment will be described in detail below in conjunction with the above structure.

[0090] Step S1: According to the heat source and the composition of the target biological tissue, construct a finite element model of the heat transfer of the target biological tissue under the influence of the heat source, and set the initial parameters of the heat source and the initial parameters of the target biological tissue.

[0091] Among them, the constructed model is stored in the above model storage module 403.

[0092] Figure 9 is an example of the finite element model of human tissue under ultrasonic action in this embodiment Figure 1 , Figure 10 is an example of the finite element model of human tissue under ultrasonic action in this embodiment Figure 2 .

[0093] As Figure 9 and Figure 10 shown, for the minimally invasive surgery involved in this embodiment, the heat source is an ultrasonic transducer, and the target biological tissue is the human tissue (surgical target area tissue) involved in the minimally invasive surgery. Therefore, in step S1, a finite element model of the three-dimensional temperature field of human tissue under ultrasonic action is constructed.

[0094] Among them, the finite element model 9 includes a human tissue model 91 and an ultrasonic transducer model 92.

[0095] The human tissue model 91 is a two-dimensional axisymmetric model, which includes four tissue layers stacked in sequence, namely a liquid layer (water) 911, a skin layer 912, a fat layer 913, and a muscle layer 914. Each layer has a predetermined thickness and width.

[0096] In this embodiment, the width of the human tissue model 91 is 10 mm, where the thickness of the water layer 911 is 3 mm, the thickness of the skin layer 912 is 1.6 mm, the thickness of the fat layer 913 is 3.4 mm, and the thickness of the muscle layer 914 is 4 mm. The following Table 1 shows the material properties of each layer, that is, the predetermined initial parameters of each layer.

[0097] Table 1 Material properties of each layer of the human tissue model

[0098]

[0099]

[0100] The ultrasonic transducer model 92 is arranged on one side of the water layer 911 and is spherical. Its initial parameters (operating parameters) include working parameters such as the opening radius, curvature radius, and ultrasonic frequency, the relative position of the ultrasonic transducer with respect to the human tissue, and the boundary conditions for the propagation of ultrasonic waves in the space of the human tissue model. Based on these working parameters, relative position, and boundary conditions, the position of the focal region 921 of the ultrasonic transducer in the human tissue can be calculated, and the influence of the change in the working parameters of the ultrasonic transducer on the three-dimensional temperature field of the human tissue model 91 can be obtained.

[0101] In this embodiment, the working parameters of the ultrasonic transducer include fixed parameters and adjustable parameters. The fixed parameters include: the ultrasonic frequency is 7f / MHz, the curvature radius is 12r / mm, and the opening diameter is 20d / mm. The adjustable parameters include power, focal length, irradiation time, etc.

[0102] In order to simulate the propagation of ultrasonic waves in a two-dimensional axisymmetric space, two sets of boundary conditions need to be set, and the boundary conditions are defined as follows:

[0103] One is the sound pressure boundary condition on the surface of the ultrasonic transducer. The sound pressure on the surface of the ultrasonic transducer is set as a sine function, as shown in the following formula:

[0104] p t =p 0 sin(wt)

[0105] In the formula, p 0 is the initial sound pressure amplitude on the surface of the ultrasonic transducer, t is the time, and w is the ultrasonic frequency.

[0106] The second is the human tissue boundary condition, which is a plane wave radiation boundary condition, and it is assumed that the reflection coefficient and transmission coefficient are zero to prevent the reflection of sound waves, as shown in the following formula:

[0107]

[0108] In this embodiment, the finite element simulation software COMSOL's pressure acoustics transient module and bioheat transfer module are used for simulation to construct the above finite element model 9.

[0109] Step S2, calculate the initial three-dimensional temperature field distribution of the target biological tissue based on the finite element model and the initial parameters.

[0110] Among them, the initial parameters are set through the above initial parameter setting module 404.

[0111] Figure 11 is the flowchart of step S2 in this embodiment.

[0112] As Figure 11 shown, step S2 specifically includes the following sub-steps:

[0113] Step S2-1: Calculate the mean square sound pressure of the ultrasonic wave based on the working parameters of the ultrasonic transducer. The calculation method in the existing technology can be adopted.

[0114] Step S2-2: Calculate the average absorption coefficient of the target biological tissue for the ultrasonic wave based on the mean square sound pressure of the ultrasonic transducer and the parameters of the target biological tissue.

[0115] Specifically, the absorption coefficient of the human tissue for the ultrasonic wave is calculated according to the following formula:

[0116] α = α 0 (f / f 0 ) b

[0117] In the formula, f 0 is the fundamental frequency, α 0 is the absorption coefficient of the human tissue at the fundamental frequency, f is the frequency at the higher harmonic, and b is the human tissue-dependent constant.

[0118] Since the absorption coefficient of the human tissue varies with the frequency of the ultrasonic transducer and higher harmonics will appear in the non-linear wave, the average absorption coefficient of the human tissue can be calculated by the following formula:

[0119]

[0120] In the formula, α n is the tissue absorption coefficient at the nth harmonic, ε n is the ratio of the harmonic amplitude to the fundamental wave amplitude, and this value can be calculated from the focal spectrum curve, P rms,n and P rms,1 are the mean square values of the sound pressure at the nth harmonic and the fundamental frequency respectively, α avg and P rms are the average absorption coefficient of the human tissue and the mean square value of the sound pressure respectively.

[0121] Step S2-3: Calculate the heat generated by the ultrasonic transducer at any point in and near the target biological tissue as the initial heat based on the average absorption coefficient of the target biological tissue for the ultrasonic wave and the mean square sound pressure of the ultrasonic wave.

[0122] Specifically, the heat generated by the ultrasonic transducer can be expressed as:

[0123]

[0124] In the formula, t i and t f are the start time and end time of the integration.

[0125] Step S2-4: Substitute the initial heat of any point into the bioheat transfer equation to calculate the local temperature of any point in the target biological tissue and its vicinity as the initial local temperature, thereby obtaining the initial three-dimensional temperature field distribution.

[0126] Specifically, in order to accurately predict the change of the three-dimensional temperature field of the target tissue, the currently most widely used three-dimensional temperature field model for describing biological tissue under the action of ultrasound - the Pennes bioheat transfer equation - is introduced:

[0127]

[0128] In the formula, pc is the volumetric specific heat of human tissue, k is the thermal conductivity of human tissue, T t and T b are the three-dimensional temperature field distribution functions of human tissue and blood flow respectively, w b and c b are the blood perfusion rate and the specific heat capacity of blood respectively, and Q is the heat generated by the external heat source.

[0129] The above bioheat transfer equation can be simplified to the following formula:

[0130]

[0131] Substitute the initial heat calculated in step S2-3 into the simplified bioheat transfer equation, and the initial three-dimensional temperature field distribution function T t .

[0132] Step S3: Use the temperature sensor array optical fiber to obtain the real-time temperature data of multiple points near the target biological tissue.

[0133] That is, during the minimally invasive surgery, the above temperature sensor array optical fiber 10 enters the human body along with the intervention needle and approaches the target tissue, so as to sense the temperature at multiple points near the target tissue, and obtain the demodulated temperature data from the fiber Bragg grating demodulator 20 through the above temperature data receiving module 401.

[0134] Step S3A: Obtain the current working parameters of the heat source.

[0135] Among them, the current working parameters of the heat source are obtained through the above heat source parameter acquisition module 402. For example, the doctor can input the working parameters used by the ultrasonic transducer to the system, or the working parameters of the ultrasonic transducer can be obtained from the corresponding ultrasonic transducer parameter identification device, or multiple groups of working parameters can be stored in advance for the doctor to select, and so on.

[0136] Optionally, when other working parameters of the heat source remain unchanged during the operation and only the irradiation time increases, the device can time the irradiation time, so this method may not have step S3A.

[0137] Step S4: Based on the current working parameters of the heat source, the real-time temperature data at multiple points, and the temperatures at the corresponding multiple points in the initial three-dimensional temperature field distribution, perform an inverse calculation on the heat transfer parameters of the finite element model, thereby updating the heat transfer parameters.

[0138] That is, the heat transfer parameter inverse calculation module 406 corrects and calibrates some key heat transfer parameters that are difficult to measure in the human tissue heat transfer model under ultrasonic action through inverse calculation, such as heat transfer parameters such as the anisotropic thermal conductivity and specific heat capacity of human tissues.

[0139] Optionally, the local temperature at the corresponding position can be recalculated based on the updated heat transfer parameters, and the local temperature is compared with the measured real-time temperature at the corresponding position. When the difference between the two is large, the inverse calculation is repeated, thereby iteratively optimizing the heat transfer parameters.

[0140] Step S5: Reconstruct the three-dimensional temperature field distribution information of the target biological tissue and its vicinity based on the finite element model and the updated heat transfer parameters.

[0141] Among them, the specific calculation method is basically the same as that in Step S2. The difference is only that in Step S6, the temperature field reconstruction module 407 uses the heat transfer parameters optimized through inverse calculation to calculate the three-dimensional temperature field distribution, so it will not be repeated here.

[0142] Step S6: Generate corresponding visualization information based on the three-dimensional temperature field distribution information.

[0143] For example, the visualization generation module 408 performs color coding on the three-dimensional temperature field distribution information, thereby generating corresponding visualization information. The visualization information can be displayed to the surgeon in real time through the display device in the operating room during minimally invasive surgery, thereby providing real-time feedback to the doctor.

[0144] The above has specifically described the method and device of this embodiment in combination with their applications in minimally invasive interventional surgery for thermal ablation of tumors. However, in fact, the applications of this method and device are not limited to this, and it can also be similarly used in other types of surgeries, such as cardiac interventional surgery, obstetrics and gynecology surgery, etc.

[0145] In addition, the finite element modeling method and the initial three-dimensional temperature field distribution calculation method in the above temperature field reconstruction method can also be used for surgical effect simulation and deduction before surgery, which will be exemplarily described below.

[0146] Figure 12 It is an example diagram of the simulated three-dimensional temperature field distribution in this embodiment.

[0147] As Figure 12As shown in the figure, (a) and (b) in the figure respectively show the schematic temperature distribution of the x-z plane at different times when the input power of the ultrasonic transducer is 10 W and the heating time (irradiation time) is 10 ms. It can be seen from the figure that during the heating process, the temperature at the focus continuously rises, and at the same time, the heat continuously diffuses to the surrounding tissues, the temperature of the surrounding tissues rises accordingly, the focal area continuously expands, and an elliptical focal spot is formed on the axial section. The focal temperature reaches the maximum value of 82 °C at 10 ms. Therefore, simulation can be carried out before the operation to observe the working parameters of the ultrasonic transducer when the lesion area can reach the target temperature and the surrounding normal tissues are basically not affected, so as to correspondingly select the most appropriate working parameters of the ultrasonic transducer.

[0148] In addition, when ultrasonic waves pass through multiple layers of tissues, due to the difference in acoustic impedance, part of the acoustic energy will be reflected back, forming a reflection point in front of the focus. It is found through simulation by the above method that when the tissue thickness is different, the position where the reflection point is formed is also different. Therefore, the surgical effect can also be simulated before the operation by the above method, and the appropriate working parameters of the ultrasonic transducer can be set to avoid the formation of reflection points at unexpected positions in human tissues.

[0149] As described above, before the operation, the surgical effect can also be simulated and deduced by the above method, and the working parameters of the ultrasonic transducer can be optimized according to the simulation and deduction results to improve the safety and effectiveness of the surgical treatment.

[0150] Function and effect of Embodiment 1

[0151] According to the method and device for reconstructing the temperature field of biological tissues provided in this embodiment, the method includes the steps of constructing a finite element model of heat transfer of the target biological tissue under the influence of a heat source, calculating the initial three-dimensional temperature field distribution based on initial parameters, obtaining the real-time temperatures of multiple points near the target biological tissue by using a temperature sensor array optical fiber, inversely calculating the tissue heat transfer parameters based on the real-time temperatures, and reconstructing the three-dimensional temperature field distribution of the target biological tissue and its vicinity based on the updated heat transfer parameters. Among them, due to the use of a temperature sensor array optical fiber, in minimally invasive interventional surgery, the optical fiber can enter the human body along with the interventional needle and approach the target human tissue. The optical fiber can bend or twist accordingly as the interventional needle advances, and the optical fiber has good biocompatibility and will not cause damage to the human body. Therefore, the temperatures of multiple points near the target human tissue can be collected during minimally invasive interventional surgery. Furthermore, an accurate real-time three-dimensional temperature field distribution can be obtained based on the finite element model and inverse calculation, so as to provide accurate real-time feedback information for doctors during minimally invasive interventional surgery, help doctors perform the operation better, ensure that the lesion area is heated to a predetermined temperature, and can timely detect problems such as too high local temperature, improving the safety and effectiveness of the operation.

[0152] In the embodiment, the fiber core of the temperature sensor array optical fiber has a plurality of directly written fiber Bragg gratings (FBGs), forming a plurality of temperature sensing points. Therefore, the temperature of multiple points can be monitored simultaneously through a row of multiple temperature sensing points, and the corresponding real-time temperature data can be obtained by demodulating with an optical fiber grating demodulator. The fiber Bragg grating has high sensitivity and high resolution, and can accurately measure tiny temperature changes, providing a reliable data source for three-dimensional temperature field reconstruction.

[0153] Furthermore, the temperature sensor array optical fiber also has a packaging part, which is fixed to the intervention needle. The non-sensing area of the optical fiber is fixed to the intervention needle through an adhesive, and the sensing area of the optical fiber is fixed to the intervention needle through a plurality of highly thermally conductive metal sheets. In addition, a temperature isolation part formed by a non-thermally conductive adhesive is provided between the outer layer of the sensing area and the intervention needle. Therefore, the bonding strength between the two can be ensured, so that the temperature sensor array optical fiber will not fall off from the intervention needle or generate displacement relative to the intervention needle during the operation. Moreover, using highly thermally conductive metal sheets can basically not affect the temperature perception of each temperature sensing point. The temperature isolation part can also avoid the influence of heat conduction from the intervention needle on the temperature sensing points. Therefore, it can ensure that accurate real-time temperature data can be stably and reliably measured during minimally invasive surgery, and then a more accurate three-dimensional temperature field distribution can be obtained through inversion calculation.

[0154] Furthermore, the non-sensing area of the optical fiber is coated with a glass fiber layer, making it have a certain high temperature resistance and sufficient strength. The sensing area of the optical fiber is coated with a polyimide layer, enabling the sensing area to withstand higher temperatures and having good biocompatibility, which can avoid affecting human tissues. Moreover, the optical fiber itself and the polyimide layer also have a certain toughness, can be bent or twisted along with the insertion of the intervention needle, and do not affect the optical signal conduction. Therefore, it can well meet the requirements of thermal ablation minimally invasive intervention surgery.

[0155] Furthermore, a sensing point protection part, which is a polyimide coating, is also coated on the optical fiber at each temperature sensing point. Therefore, it can play a better protective role for the sensitive temperature sensing points, and since it is a relatively thin coating, it will not affect other packaging structures.

[0156] Furthermore, according to the characteristics and requirements of thermal ablation minimally invasive surgery, the sizes and position distributions of multiple temperature sensing points are precisely designed. Therefore, based on the real-time temperature data measured by multiple temperature sensing points, the temperature field distribution covering the entire surgical area of concern can be reconstructed, and the temperature accuracy and others meet the surgical requirements. In addition, corresponding spectral position information is assigned to each temperature sensing point. Therefore, the temperature perception of multiple temperature sensing points can be non-interfering, and the sensing results of multiple temperature sensing points can be processed more conveniently simultaneously.

[0157] Further, since the finite element model can be pre-constructed and stored in the temperature field reconstruction device according to the structure and parameters of the ultrasonic transducer and the target human tissue, only real-time inversion calculation and visualization generation are required during actual use. Therefore, the computational load is relatively small, high real-time temperature field information can be provided, and it is beneficial to reduce the equipment cost.

[0158] Further, since the acoustic pressure boundary condition on the surface of the ultrasonic transducer and the boundary condition of the human tissue are set, and the heat transfer parameters such as the thermal conductivity and specific heat capacity of each layer of human tissue that are anisotropic and difficult to accurately set are corrected and optimized through inversion calculation, high-precision three-dimensional temperature field distribution information can be reconstructed, and more accurate and valuable feedback information can be provided for doctors during minimally invasive interventional surgery.

[0159] Further, since the simplified Pennes bioheat transfer equation is used to calculate the local temperature in the temperature field reconstruction, the computational amount can be reduced while ensuring accuracy, and the real-time performance of the three-dimensional temperature field reconstruction can be improved.

[0160] <Example Two>

[0161] This embodiment provides a temperature field reconstruction method and device. In this embodiment, the same reference numerals are given to the same constituent elements as in Example One, and the corresponding descriptions are omitted.

[0162] Figure 13 is a flowchart of the temperature field reconstruction method for biological tissues in this embodiment, Figure 14 is a logic block diagram of the temperature field reconstruction device for biological tissues in this embodiment.

[0163] As Figure 13 and Figure 14 shown, compared with Example One, the difference is that in the method of this embodiment, the way of temperature field reconstruction is different, and correspondingly, the composition of the temperature field reconstruction device 40' is different.

[0164] The temperature field reconstruction method of this embodiment includes the following steps:

[0165] Step S1', training the neural network model with training data to obtain a trained neural network model as the temperature field prediction model.

[0166] Among them, the training data includes the temperatures of multiple points measured near the target biological tissue, the working parameters of the heat source (ultrasonic transducer) when detecting the temperature, and the corresponding reconstructed three-dimensional temperature field distribution information. For example, such training data can be obtained through the method of Example One. Let the neural network model learn the relationship between the measured multi-point temperatures, the working parameters of the heat source, and the three-dimensional temperature field distribution information, so as to obtain the temperature field prediction model.

[0167] Step S2’, obtaining real-time temperature data of multiple points near the target biological tissue by using the temperature sensor array optical fiber.

[0168] Step S2A’, obtaining the current working parameters of the heat source.

[0169] Step S3’, inputting the real-time temperature data of multiple points and the current working parameters of the heat source into the temperature field prediction model, and the model outputs the predicted three-dimensional temperature field distribution information of the target biological tissue and its vicinity.

[0170] Step S4’, generating corresponding visualization information based on the three-dimensional temperature field distribution information.

[0171] Corresponding to the above method, the temperature field reconstruction device 40’ in this embodiment does not have the above-mentioned finite element model storage module 403, initial parameter setting module 404, initial temperature field construction module 405, and heat transfer parameter inversion calculation module 406, but has a prediction model storage module 411 that stores the above-mentioned trained temperature field prediction model; the temperature field reconstruction module 407 inputs the real-time temperature data of multiple points received by the temperature data receiving module 401 and the current working parameters of the heat source obtained by the heat source parameter acquisition module 402 into the temperature field prediction model, so as to obtain the predicted three-dimensional temperature field distribution information.

[0172] In this embodiment, other structures and method steps are the same as those in Embodiment 1, so they will not be repeated here.

[0173] Functions and effects of Embodiment 2

[0174] According to the temperature field reconstruction method and device provided in this embodiment, on the basis of the functions and effects of Embodiment 1, since a trained neural network model is used to predict the three-dimensional temperature field distribution of human tissues according to the input temperature data, the processing speed is faster and the calculation amount is smaller compared with the inversion calculation. Therefore, the three-dimensional temperature field distribution can be generated more quickly and efficiently during actual use, which is also beneficial to further reducing the equipment cost. And since the training data of the neural network model are the temperature data collected from real human tissues, the working parameters of the actually used ultrasonic transducer, and the corresponding accurate three-dimensional temperature field distribution obtained by the inversion calculation method of Embodiment 1, the trained neural network model can output a very accurate three-dimensional temperature field distribution.

[0175] The above embodiments are only used to illustrate the specific implementation manners of the present invention, and the present invention is not limited to the description scope of the above embodiments. Those skilled in the art should understand that the present invention is not restricted by the above embodiments. What is described in the above embodiments and the specification only explains the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A method for reconstructing the temperature field of biological tissue, characterized in that: The following steps are involved: Step S1, constructing a finite element model of heat transfer of the target biological tissue under the influence of the heat source according to the composition of the heat source and the target biological tissue; Step S2, calculating the initial three-dimensional temperature field distribution information of the target biological tissue based on the finite element model and initial parameters; Step S3, using a temperature sensor array optical fiber to obtain real-time temperature data of multiple points near the target biological tissue; Step S4, based on the working parameters of the heat source, the real-time temperature data of the multiple points and the temperatures of the corresponding multiple points in the initial three-dimensional temperature field distribution, inversely calculate the heat transfer parameters in the initial parameters, so as to update the heat transfer parameters; Step S5, reconstructing the three-dimensional temperature field distribution information of the target biological tissue and its vicinity based on the finite element model and the updated heat transfer parameters.

2. The method for reconstructing the temperature field of biological tissue according to claim 1, Features: in, The heat source is an ultrasonic transducer, Step S2 includes the following sub-steps: Step S2-1, calculating the mean square sound pressure of the ultrasonic wave based on the working parameters of the ultrasonic transducer; Step S2-2, calculating an initial average absorption coefficient of the target biological tissue to ultrasound based on the mean square sound pressure and the initial parameters of the target biological tissue; Step S2-3, calculating the heat generated by the ultrasonic transducer at any point in the target biological tissue and its vicinity as the initial heat based on the initial average absorption coefficient and the mean square sound pressure of the ultrasonic wave; Step S2-4, substituting the initial heat of any point into a predetermined biological heat transfer equation, calculating the local temperature of the target biological tissue and any point nearby as the initial local temperature, thereby obtaining the initial three-dimensional temperature field distribution information, In step S5, the average absorption coefficient of the target biological tissue to ultrasound is calculated based on the mean square sound pressure and the updated heat transfer parameters, and the heat generated by the ultrasonic transducer at any point in the target biological tissue and its vicinity is calculated based on the average absorption coefficient and the mean square sound pressure. The heat is then substituted into the biological heat transfer equation to calculate the local temperature of the target biological tissue and any point in its vicinity, thereby obtaining the three-dimensional temperature field distribution information.

3. The method for reconstructing the temperature field of biological tissue according to claim 2, characterized in that: in, In step S1, the finite element model includes a human tissue model and an ultrasonic transducer model. The human body tissue model is a two-dimensional axisymmetric model, which includes multiple tissue layers, including a water layer, a skin layer, a fat layer, and a muscle layer. The initial parameters include initial heat transfer parameters of each tissue layer, and the initial heat transfer parameters include initial specific heat capacity and initial thermal conductivity. The initial parameters also include working parameters of the ultrasonic transducer, and the working parameters include opening radius, curvature radius, ultrasonic frequency, ultrasonic power, focal length, and irradiation time.

4. The method for reconstructing the temperature field of biological tissue according to claim 3, characterized in that: in, The initial average absorption coefficient and the average absorption coefficient are calculated by the following formula: In the formula, α n is the absorption coefficient of human tissue under the nth harmonic, ε n is the ratio of harmonic amplitude to fundamental amplitude, P rms,n and P rms,1 are the mean square value of sound pressure at the nth harmonic and the mean square value of sound pressure at the fundamental frequency, α avg and P rms are the average absorption coefficient of human tissue and the mean square value of sound pressure, The initial heat and the heat are calculated by the following formula: Where, t i and t f are the start and end time of the integration, The bioheat transfer equation is a simplified Pennes bioheat transfer equation: In the formula, Q is the heat, T t is the three-dimensional temperature field distribution function of human tissue, k is the thermal conductivity of human tissue, and pc is the volume specific heat of human tissue.

5. The method for reconstructing the temperature field of biological tissue according to claim 3, characterized in that: in, The finite element model also includes boundary conditions, which include: The acoustic pressure boundary condition on the ultrasonic transducer surface is expressed as: p t =p0sin(wt) Where p0 is the initial sound pressure amplitude on the surface of the ultrasonic transducer, t is the time, and w is the ultrasonic frequency; and The boundary condition of human tissue is expressed as:

6. The method for reconstructing the temperature field of biological tissue according to claim 3, characterized in that: in, The initial specific heat capacity of the water layer is 4200 J·kg -1 ℃ -1 , the initial thermal conductivity is 0.6 W·m -1 ℃ -1 , The initial specific heat capacity of the skin layer is 3500 J·kg -1 ℃ -1 , the initial thermal conductivity is 0.28W·m -1 ℃ -1 , The initial specific heat capacity of the fat layer is 3800 J·kg -1 ℃ -1 , the initial thermal conductivity is 0.24W·m -1 ℃ -1 , The initial specific heat capacity of the muscle is 3700 J·kg -1 ℃ -1 , the initial thermal conductivity is 0.51 W·m -1 ℃ -1 , The initial parameters also include the density of each tissue layer, the speed of sound propagating in the tissue layer, the nonlinear coefficient, the attenuation coefficient of the ultrasound propagating in the tissue layer, the bulk viscosity and the dynamic viscosity.

7. A method for reconstructing the temperature field of biological tissue, characterized in that: The following steps are involved: Step S1', training the neural network model using training data to obtain the trained neural network model as a temperature field prediction model, wherein the training data includes temperature data of multiple points, working parameters of the heat source, and corresponding three-dimensional temperature field distribution information; Step S2', using the temperature sensor array optical fiber to obtain real-time temperature data of multiple points near the target biological tissue; Step S3', inputting the real-time temperature data of multiple points and the current working parameters of the heat source into the temperature field prediction model, and the model outputs the three-dimensional temperature field distribution information of the target biological tissue and its vicinity.

8. The method for reconstructing the temperature field of biological tissue according to any one of claims 1 to 7, used in minimally invasive interventional surgery. Features: in, The temperature sensor array optical fiber comprises: An optical fiber part, comprising the optical fiber body and a plurality of the temperature sensing points, wherein the temperature sensing points are optical fiber Bragg gratings formed in the core of the optical fiber body; as well as The packaging part is used to package the optical fiber part and fix the temperature sensor array optical fiber to the intervention needle used for the minimally invasive interventional surgery.

9. The method for reconstructing the temperature field of biological tissue according to claim 8, Features: The temperature sensor array optical fiber further comprises an interface portion connected to one end of the optical fiber portion and used to connect the optical fiber portion to a fiber Bragg grating demodulator. The two ends of the optical fiber portion are non-sensing areas, the middle section is a sensing area, and a plurality of temperature sensing points are distributed in the sensing area. The packaging unit includes: an end protection layer, covering the non-sensing area connected to the interface portion; An optical fiber protective layer, covering at least the sensing area; A first fixing portion, which is an adhesive portion formed of an adhesive, formed between the end protection layer and the intervention needle; and The second fixing part includes a plurality of fixing members for fixing the optical fiber protective layer and the intervention needle.

10. A temperature field reconstruction device for biological tissue, characterized in that: include: A finite element model storage module for storing a finite element model, wherein the finite element model is a heat transfer model of the target biological tissue under the influence of the heat source, which is constructed according to the working parameters of the heat source and the composition of the target biological tissue; An initial temperature field construction module, used to calculate the initial three-dimensional temperature field distribution information of the target biological tissue based on the finite element model and initial parameters; A temperature data receiving module, used to obtain real-time temperature data of multiple points near the target biological tissue through a temperature sensor array optical fiber; a heat transfer parameter inversion calculation module, for performing inversion calculation on the heat transfer parameters of the finite element model based on the working parameters of the heat source, the real-time temperature data of the multiple points, and the temperatures of the multiple points corresponding to the initial three-dimensional temperature field distribution, so as to update the heat transfer parameters; as well as The temperature field reconstruction module is used to reconstruct the three-dimensional temperature field distribution information of the target biological tissue and its vicinity based on the finite element model and the updated heat transfer parameters.

Images