A method, apparatus, device, medium and product for predicting radio frequency heating of a medical implant in magnetic resonance

Abstract

The application discloses a method, device, equipment, medium and product for predicting radio frequency heating of a medical implant in magnetic resonance, and relates to the field of medical instruments and equipment. The device comprises an electromagnetic field model, a phantom model, a plurality of to-be-tested medical implant models with different configuration parameters, and a host computer module. By simulating a magnetic resonance scanning environment, combining the physical characteristics of the phantom model and the to-be-tested medical implant, and coupling the electromagnetic field to the temperature field by using the host computer module and performing data processing, the temperature prediction of the radio frequency heating effect of the medical implant in the magnetic resonance examination is realized. The application considers that the implant is in different parts of the medium, and the temperature of the radio frequency heating effect of the medical implant in the magnetic resonance examination is predicted, so that the radio frequency heating safety evaluation can be performed in advance, potential safety risks can be effectively avoided, and the detection efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of medical instruments and equipment, and in particular to a method, apparatus, equipment, medium and product for predicting radio frequency heating of medical implants in magnetic resonance imaging. Background Art

[0002] With the continuous advancement of medical technology, magnetic resonance imaging (MRI), as a non-invasive, high-resolution medical imaging technology, plays an increasingly important role in clinical diagnosis.

[0003] However, patients with medical implants may face certain risks when undergoing MRI examinations, the most significant of which is radiofrequency heating. Radiofrequency heating refers to the fact that during an MRI scan, the radiofrequency pulses in the medical implant may absorb energy and convert it into heat, causing the temperature of the surrounding tissue to rise, potentially causing harm to the patient.

[0004] To address the issue of radiofrequency heating of medical implants in MRI, research has primarily focused on evaluating the effects of radiofrequency heating on implants through experimental measurements and numerical simulations. However, these methods often require actual testing in MRI equipment, which is not only time-consuming and labor-intensive, but may not fully cover all possible implant types and scanning conditions. Therefore, developing a method that can quickly and accurately predict radiofrequency heating of medical implants in MRI is crucial. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, equipment, medium and product for predicting radiofrequency heating of medical implants in magnetic resonance imaging, which can conduct radiofrequency heating safety assessment in advance, effectively avoid potential safety risks, and improve detection efficiency.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a device for predicting radiofrequency heating of a medical implant in magnetic resonance imaging, comprising:

[0008] An electromagnetic field model, a phantom model, multiple medical implant models to be tested, and a host computer module; wherein each medical implant model to be tested has different configuration parameters; the configuration parameters include the length, trajectory, or direction of the medical implant to be tested relative to the phantom model;

[0009] The electromagnetic field model is used to simulate the electromagnetic field in magnetic resonance scanning; the phantom model is used to simulate local human tissue; and the medical implant model to be tested is used to simulate a medical implant implanted in a human body.

[0010] The phantom model is located inside the electromagnetic field model; the medical implant model to be tested is located at a preset implantation site inside the phantom model;

[0011] The host computer module is respectively connected to the electromagnetic field model, the phantom model and the medical implant model to be tested, and is used to drive the electromagnetic field model to generate an electromagnetic field. It is also used to couple the electromagnetic field to the temperature field based on the electromagnetic field data generated by the received electromagnetic field model, combined with the physical properties of the phantom model and the medical implant model to be tested, and obtain the temperature prediction results of each medical implant model to be tested under the action of the electromagnetic field model and the phantom model.

[0012] In a second aspect, the present application provides a method for predicting radiofrequency heating of a medical implant in magnetic resonance imaging, comprising:

[0013] Building an electromagnetic field model, a phantom model, and multiple medical implant models to be tested; wherein each medical implant model to be tested has different configuration parameters;

[0014] Adjusting the dielectric parameters of the phantom model according to the preset implantation site; the dielectric parameters include the length, height, thickness, conductivity, dielectric constant, and specific heat capacity of the phantom model;

[0015] In the electromagnetic field model, the temperature field of the phantom model under the influence of the current implant model to be tested is constructed. The temperature distribution of each medical implant model to be tested under the action of the electromagnetic field and the interaction with the phantom model is calculated using an electromagnetic-thermal coupling algorithm. The temperature of the current medical implant model to be tested and the phantom model are predicted using a finite element analysis method to obtain a temperature prediction result.

[0016] The next medical implant model to be tested is used as the current medical implant model to be tested, and the process returns to the step of "constructing the temperature field of the phantom model under the influence of the current implant model to be tested in the electromagnetic field model, calculating the temperature distribution of each medical implant model to be tested that interacts with the phantom model under the action of the electromagnetic field using an electromagnetic-thermal coupling algorithm, and predicting the temperatures of the current medical implant model to be tested and the phantom model using a finite element analysis method to obtain temperature prediction results" until an end condition is met; the end condition includes that temperature prediction has been completed for all medical implant models to be tested.

[0017] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for predicting radiofrequency heating of a medical implant in magnetic resonance as described above.

[0018] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for predicting radiofrequency heating of a medical implant in magnetic resonance imaging.

[0019] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for predicting radiofrequency heating of a medical implant in magnetic resonance.

[0020] According to the specific embodiments provided in this application, this application has the following technical effects:

[0021] The present application provides a method, device, equipment, medium and product for predicting radiofrequency heating of medical implants in magnetic resonance imaging. By constructing an electromagnetic field model, a phantom model and multiple medical implant models to be tested with different configuration parameters, the problem of the lack of accurate simulation of the radiofrequency heating effect of medical implants with different configurations in a complex electromagnetic field environment in traditional prediction methods is solved, and accurate simulation of the electromagnetic field environment in magnetic resonance scanning and diversified coverage of medical implant configurations are achieved. The electromagnetic field model is driven by the host computer module to generate an electromagnetic field, and the electromagnetic field is coupled to the temperature field for temperature prediction based on the physical properties of the phantom model and the medical implant model to be tested. This solves the problem of how to effectively convert electromagnetic field data into temperature field data, and then accurately predict the radiofrequency heating effect of medical implants in magnetic resonance imaging. This combined step achieves accurate quantitative prediction of the radiofrequency heating effect of medical implants, providing a scientific basis for safety assessment.

[0022] In summary, the prediction device provided by this application not only improves prediction accuracy but also significantly enhances detection efficiency, providing strong technical support for the safe use of medical implants and the safe implementation of magnetic resonance imaging (MRI) examinations, effectively avoiding potential safety risks. Furthermore, the entire prediction process is performed using computer simulation, reducing the cost of physical experiments, accelerating the progress of implant research, and helping doctors quickly assess the safety of medical implants and protect patients from burns. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic diagram of a device for predicting radiofrequency heating of a medical implant in magnetic resonance imaging according to one embodiment of the present application;

[0025] Figure 2 A schematic flow chart of a method for predicting radiofrequency heating of a medical implant in magnetic resonance imaging provided by one embodiment of the present application;

[0026] Figure 3 A schematic diagram of a birdcage coil provided in one embodiment of the present application;

[0027] Figure 4 A schematic diagram of a phantom model provided in one embodiment of the present application;

[0028] Figure 5 A schematic diagram of a cochlear implant model with different track types and an electrode length of 22.5 mm provided in another embodiment of the present application; wherein, Figure 5 (a) Schematic diagram of the cochlear implant model showing the first trajectory; Figure 5 (b) Schematic diagram of the cochlear implant model showing the second trajectory; Figure 5 (c) Schematic diagram of the cochlear implant model showing the third trajectory;

[0029] Figure 6 A perspective view of a cochlear implant model provided by another embodiment of the present application on a phantom model;

[0030] Figure 7 A network independence verification diagram provided in an embodiment of the present application;

[0031] Figure 8 This is a temperature diagram provided in an embodiment of the present application, wherein: Figure 8 (a) shows the spatial distribution of temperature rise of the cochlear implant model. Figure 8 (b) Schematic diagram showing the cochlear implant temperature prediction results for different trajectories;

[0032] Figure 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] First, some technical terms involved in the embodiments of this application are introduced.

[0034] Magnetic resonance imaging (MRI) is an important imaging modality for clinical disease diagnosis. It offers advantages such as being noninvasive, radiation-free, able to visualize the heart and blood vessels without contrast agents, free of bone artifacts, high soft tissue resolution, and the ability to image from multiple angles and multiple parameters. Globally, more than 80 million MRI scans are performed annually. Due to the specific nature of MRI technology, and with the continuous increase in MRI magnetic field strength and the emergence of new MRI technologies, the biological effects and safety issues of MRI cannot be ignored. The high-power radiofrequency coils in MRI systems can cause electromagnetic resonance in conductive implants, leading to radiofrequency-induced heating and potentially irreversible tissue damage (i.e., burns). In particular, for patients with implants deep within the brain, radiofrequency heating is considered the greatest risk in MRI.

[0035] Implantable medical devices (IMDs) are semi-permanently implanted in the human body. IMDs can be categorized as either passive or active. Passive implants compensate for physical deficiencies in internal organs (e.g., hip implants). Meanwhile, active implants restore certain functions of body parts or relieve pain through electrical stimulation. Unlike some decorative accessories worn on the body, it is nearly impossible to detect an IMD in a patient during an MRI scan. Time-varying magnetic pulses generate eddy currents in the metal plates of the implant, which can heat the plates through Joule heating or heat tissue in contact with the implant by flowing into the tissue. In particular, the structure of lead wires, such as those in pacemakers, deep brain stimulators, and cochlear implants, acts like an antenna, significantly absorbing external radiofrequency energy and increasing its heat. The current generated along the entire length of the lead wire flows along a path and accumulates at the tip of the lead wire. Because the excess energy generated by the unwanted current is transferred to adjacent tissue, the risk of radiofrequency heating is significantly increased, leading to lead detachment and permanent tissue damage.

[0036] The related research and standards are usually performed by a transfer function method mainly relying on physical experiment tests or a combination of physical experiments and numerical simulations to detect the radio frequency induced heating of implants in a magnetic resonance environment. The physical experiment test method mainly places the implant in a human tissue phantom material represented by an ASTM (American Society for Testing and Materials) phantom, and places the entire system in an MRI environment to monitor specific parameters such as the specific absorption rate (SAR) or temperature rise. In order to determine the heating effect in the worst case, a large number of physical tests need to be performed to test all size, placement position and posture combinations of the implant, which is not feasible in practical applications. In addition, the cost of physical experiments is high and the phantom cannot completely simulate the actual situation of the human body. The transfer function method is based on numerical simulation and physical measurement method, and the incident field inside the human body obtained by numerical simulation is applied to the transfer function to calculate the radio frequency induced heat. The accuracy of this method is highly dependent on the accuracy of the model and the transfer function, and physical experiments are still needed, which still has the problems of high cost and too late testing in the implant design cycle.

[0037] Therefore, the embodiments of the present application provide a method, device, equipment, medium and product for predicting radio frequency induced heating of a medical implant in a magnetic resonance environment, which can consider the difference of different implant implanted media, quickly predict the radio frequency induced heating of the implant in the magnetic resonance environment according to the implant, has a guiding effect on implant design and development, and is also important for improving the safety of magnetic resonance examination.

[0038] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0039] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail in combination with the drawings and specific embodiments.

[0040] In one exemplary embodiment, as shown in Figure 1 A prediction device for radio frequency induced heating of a medical implant in a magnetic resonance environment is provided, comprising:

[0041] An electromagnetic field model, a phantom model, a plurality of to-be-tested medical implant models and a host computer module; wherein the configuration parameters of each to-be-tested medical implant model are different; the configuration parameters include the length, trajectory or relative direction of the to-be-tested medical implant to the phantom model.

[0042] The electromagnetic field model is used to simulate the electromagnetic field in magnetic resonance scanning; the phantom model is used to simulate local tissue of the human body; and the medical implant model to be tested is used to simulate a medical implant implanted in the human body.

[0043] The phantom model is located inside the electromagnetic field model; the medical implant model to be tested is located at a preset implantation position inside the phantom model.

[0044] The host computer module is respectively connected to the electromagnetic field model, the phantom model and the medical implant model to be tested, and is used to drive the electromagnetic field model to generate an electromagnetic field. It is also used to couple the electromagnetic field to the temperature field based on the electromagnetic field data generated by the received electromagnetic field model, combined with the physical properties of the phantom model and the medical implant model to be tested, and obtain the temperature prediction results of each medical implant model to be tested under the action of the electromagnetic field model and the phantom model.

[0045] As an optional implementation, the electromagnetic field model specifically includes:

[0046] An air sphere, a radio frequency shielding layer and a birdcage coil; the birdcage coil is located inside the radio frequency shielding layer; the air sphere is located between the birdcage coil and the radio frequency shielding layer; the air sphere, the radio frequency shielding layer and the birdcage coil have the same central axis.

[0047] The air sphere is used to simulate the air space between the birdcage coil and the radio frequency shielding layer.

[0048] The radio frequency shielding layer is a cylindrical structure and is used for electromagnetic isolation.

[0049] The birdcage coil is a cylindrical structure and is used to generate an electromagnetic field.

[0050] Wherein, in implementing this embodiment, the birdcage coil specifically includes:

[0051] Two bottom rings with the same structure are located at the upper end and the lower end; the bottom rings are metal rings.

[0052] The two bottom rings are connected via a plurality of cage feet located in the vertical direction; the cage feet are cylindrical channels made of metal material.

[0053] Each of the cage legs is evenly provided with capacitors, and the capacitance value of each section of the capacitor is the same.

[0054] Two lumped ports with a preset constant potential difference are respectively displaced onto any two of the cage feet.

[0055] Wherein, in the implementation of the embodiment, the diameter of the birdcage coil is 800 mm, and the height is 700 mm; the diameter of the radio frequency shielding layer is 1000 mm, and the height is 800 mm; the diameter of the air sphere is greater than or equal to twice the diameter of the birdcage coil to ensure that the propagation of the radio frequency field and the boundary effect can be correctly processed in the simulation. Specifically, the basic structure is composed of 8 pairs of cage legs in the longitudinal direction of the cylinder and the bottom ring at both ends; the model contains 2 lumped ports, a constant potential difference is set between the two lumped ports as a voltage source excitation, and the phase difference is kept at π / 2 to provide orthogonal excitation for the birdcage coil; a total of 22 capacitors with the same capacitance value are arranged on the 8 cage legs to determine the resonant frequency of the birdcage coil and the uniformity of the generated field, so as to ensure that the birdcage coil can generate a relatively uniform radio frequency field at a specific frequency in the magnetic resonance environment. The absorbing boundary is arranged at the periphery of the electromagnetic field model, specifically, it is arranged outside the air sphere, the radio frequency shielding layer and the birdcage coil. This absorbing boundary simulates the electromagnetic environment of an open space, so that the electromagnetic wave can be effectively absorbed when it reaches the boundary, thereby avoiding the influence of unrealistic reflection on the simulation results.

[0056] Further, the number of cage legs of the birdcage coil can be expanded. The more the cage legs, the better the symmetry of the coil, and the closer the current to the ideal cosine distribution, so that the uniformity and the magnetic field strength of the magnetic field generated in the coil are better. Preferably, for the 1.5T and 3T MRI commonly used in current medical examinations, the number of cage legs can be expanded to 16, which can balance between uniformity and complexity.

[0057] As an optional embodiment, the phantom model specifically comprises: an internal model and an external model; the internal model comprises a saline gel; and the external model comprises an insulating non-magnetic non-metallic material. Specifically, the container of the external model and all parts thereof are made of an insulating non-magnetic non-metallic material to prevent electromagnetic induction from generating heat in a magnetic resonance environment and affecting the results. The internal model is a saline gel, and the required saline gel can be prepared by using sodium chloride (NaCl), polyacrylic acid (PAA) and deionized water. The electrical and thermal properties of the saline gel will simulate the actual human tissue conditions, and the electrical conductivity is required to be 0.47±10% S / m, the dielectric constant is required to be 80±20, the specific heat capacity is required to be about 4150 J / (kg·℃), and the viscosity is required to be large enough to prevent mass transfer or convection. The phantom model is shown in Figure 3 As shown, the width is 420 mm, the height is 650 mm, and the thickness is 90 mm.

[0058] Further, for the special case of a larger instrument, the thickness of the phantom model can be increased.

[0059] Furthermore, the phantom model can be added with a head-like part, with the preferred size being 150 mm in width and 270 mm in height, and the thickness being consistent with the main part. The assembly relationship is that the central axis of the head is aligned with the main part for splicing.

[0060] Furthermore, the electrical and thermal parameters of the phantom model can be adjusted to simulate local tissue condition parameters according to the implantation site of the medical implant to be tested.

[0061] Based on the same inventive concept, embodiments of the present application also provide a method for predicting radiofrequency heating of a medical implant in magnetic resonance imaging, which is applied to the aforementioned device for predicting radiofrequency heating of a medical implant in magnetic resonance imaging. The solution provided by this method is similar to the solution described in the aforementioned device. Therefore, the specific limitations of one or more embodiments of the method for predicting radiofrequency heating of a medical implant in magnetic resonance imaging provided below can be found in the above-mentioned limitations of the device for predicting radiofrequency heating of a medical implant in magnetic resonance imaging, and are not further elaborated here.

[0062] In an exemplary embodiment, Figure 2 As shown, a method for predicting radiofrequency heating of a medical implant in magnetic resonance imaging is provided, comprising:

[0063] Step 101: Build an electromagnetic field model, a phantom model, and multiple medical implant models to be tested. Each medical implant model has different configuration parameters. Specifically, a 3D CAD model is created, including a birdcage coil, a phantom model based on ASTM standards, and multiple medical implant models to be tested. The RF module in COMSOL Multiphysics software is used to simulate the excitation and tuning of the birdcage coil used to generate the RF field, completing electromagnetic field simulation modeling and obtaining an electromagnetic field model. Configuration parameters are determined based on the potential and common scenarios of the specific medical implant to be tested.

[0064] Step 102 : adjusting the dielectric parameters of the phantom model according to the preset implantation site; the dielectric parameters include the length, height, thickness, conductivity, dielectric constant, and specific heat capacity of the phantom model.

[0065] Step 103 constructs the temperature field of the phantom model under the influence of the current implant model under test within the electromagnetic field model. Using an electromagnetic-thermal coupling algorithm, the temperature distribution of each medical implant model under test, influenced by the interaction with the phantom model, is calculated. Finite element analysis is then used to predict the temperatures of the current medical implant model under test and the phantom model, yielding temperature prediction results. Specifically, the birdcage coil, the phantom model placed within, and the current medical implant model under test are appropriately meshed, and then finite element simulation is performed. Meshing can be performed by performing a mesh independence analysis on the model and selecting the appropriate number of meshes near the inflection points to reduce computational cost while maintaining accurate modeling.

[0066] The electromagnetic field was coupled to the temperature field using the electromagnetic thermal interface of the COMSOL Multiphysics software. The heat transfer module was used to construct the temperature field of the phantom model in the MRI birdcage coil under the influence of the current medical implant to be tested. Finite element simulation was performed on the temperature rise distribution of the current medical implant to be tested and the phantom model.

[0067] The electromagnetic field is coupled to the temperature field. Specifically, the steady-state electromagnetic solution of the electromagnetic field model provides a heat source for the transient thermal problem. Specifically, the SAR value is used as the heat source for the temperature rise effect. The finite element method is used to calculate the heat conduction equation to obtain the temperature distribution in the phantom model. Furthermore, the SAR value needs to be normalized. This means that the average SAR value in the phantom model is normalized to 2 W / kg. This scaling factor is then multiplied by the original simulated temperature distribution to obtain a temperature distribution that meets ASTM measurement standards.

[0068] Step 104 : The next medical implant model to be tested is used as the current medical implant model to be tested, and the process returns to step 103 until an end condition is met; the end condition includes that temperature prediction has been completed for all medical implant models to be tested.

[0069] By implementing steps 101 to 104 above, this application can not only accurately predict the radiofrequency heating of medical implants in magnetic resonance imaging, but also analyze the maximum temperature rise of the implant to be tested, thereby determining the safety of magnetic resonance imaging for patients wearing the implant, and ensuring that radiofrequency heating will not cause damage to surrounding tissues in actual applications. At the same time, this method provides strong support for design optimization by simulating medical implants with different configuration parameters, reducing the risk of radiofrequency heating and making medical implants more in line with clinical needs. Medical implant designers can predict the safety of radiofrequency heating of medical implants before product production and obtain guidance. Medical staff can also quickly conduct safety assessments before patients enter the magnetic resonance imaging instrument and take appropriate preventive measures accordingly.

[0070] This application reduces the influence of contact thermal resistance from the formula by correcting the method of deep temperature measurement. At the same time, a special heat flux measurement method is designed for the special path of the probe to expand the measurement range.

[0071] In another exemplary embodiment of the present application, an electromagnetic field model is constructed, which then includes: performing an excitation simulation and a tuning simulation on the birdcage coil; the excitation simulation includes: setting a default potential difference between two lumped ports as a voltage source excitation; the default voltage difference is fixed to 200V; applying a perfect conductor boundary condition to the birdcage coil and the radio frequency shielding layer to obtain the birdcage coil after the excitation simulation. After applying the perfect electrical conductor boundary (PEC) condition on the surface of the birdcage coil, the electromagnetic field of the birdcage coil will be reflected and concentrated on its surface, which helps to generate a stronger local electromagnetic field, while the radio frequency shielding layer effectively reflects the external radio frequency field through the perfect conductor condition, which can prevent the field propagation within the shield, thereby ensuring the function of the shielding layer, and applying a scattering boundary condition on the boundary to simulate open space, ensuring that the electromagnetic wave simulates the natural scattering behavior of the external environment at the boundary, and avoiding unrealistic reflections returning to the calculation domain.

[0072] The tuning simulation includes: setting the initial capacitance values ​​of all capacitors in the birdcage coil after the excitation simulation to a reference capacitance value; applying the voltage source excitation to the birdcage coil after the excitation simulation to obtain a resonant frequency; if the resonant frequency is not equal to the preset resonant frequency, adjusting the reference capacitance value, and returning to the step of "setting the initial capacitance values ​​of all capacitors in the birdcage coil after the excitation simulation to the reference capacitance value" until the resonant frequency is equal to the preset resonant frequency, thereby obtaining the birdcage coil after the tuning simulation. Specifically, the capacitance value is adjusted so that the resonant frequency is aligned with the desired operating frequency. The reference capacitance value is first calculated using Birdcage Builder software, and this reference value is set as the initial capacitance value of all capacitors. Then, the excitation is applied to check whether the appropriate resonant frequency is reached. If not, the capacitance value is adjusted, and the above steps are repeated until the resonant frequency is consistent with the desired operating frequency.

[0073] Furthermore, the capacitance of the birdcage coil can be parametrically scanned around the above-mentioned adjusted capacitance value, and the uniformity of the magnetic field can be quantified by determining the standard deviation of the electric field inside the birdcage coil to find the optimal magnetic field of the surrounding air at the required operating frequency.

[0074] In another exemplary embodiment of the present application, a method for predicting radiofrequency heating of medical implants in different media in magnetic resonance imaging can be divided into five steps, S1 to S5:

[0075] Step S1: Referring to ASTM F2182-02a and YY / T 0987.4-2016 standards, a birdcage coil model, a phantom model, and a medical implant model to be tested that is consistent with the actual design are established.

[0076] The birdcage coil model structure design in this embodiment can be found in Figure 3 As shown, the overall shape is cylindrical, with a diameter of 800 mm and a height of 700 mm; the basic structure is composed of 8 paired cage legs in the longitudinal direction of the cylinder and bottom rings at both ends; the model contains the first lumped port 1 and the second lumped port 2, two lumped ports, a constant potential difference is set between the two lumped ports as voltage source excitation, and the phase difference is maintained at π / 2 to provide orthogonal excitation for the coil; 22 capacitors with the same capacitance value are set on the cage legs to determine the resonant frequency of the coil and the uniformity of the generated field, ensuring that the birdcage coil model can generate a relatively uniform RF field at a specific frequency in the magnetic resonance environment.

[0077] The phantom model in this embodiment is made of extruded polystyrene foam board, which meets the requirements of non-magnetic and non-metallic insulation materials and has good thermal insulation properties. It can keep heat concentrated in the phantom to improve prediction accuracy. The internal model is saline gel, whose electrical and thermal properties will simulate the real human tissue. The phantom model can be found in Figure 4 As shown, it is 420 mm wide, 650 mm high and 90 mm thick.

[0078] The medical implant to be tested in this embodiment is a cochlear implant model, see Figure 5 , including intra-cochlear electrode array, wire, extra-cochlear electrode, electronic shell and gold coil antenna. The intra-cochlear electrode array is modeled as 24 cylindrical electrodes with radii of 0.4 mm, 0.5 mm and 0.6 mm. As the radius decreases near the tip of the wire, in order to meet the needs of selecting electrodes of different lengths according to the patient's cochlear anatomy and case changes, the electrode length can be selected as 17.5 mm, 22.0 mm and 25.5 mm, and two extra-cochlear electrodes are set at the same time. Among them, when the electrode length is 25.5 mm, the cochlear implant models with three different trajectories are as follows Figure 5 (a), (b) and (c) are shown. The perspective view of the position and trajectory of the cochlear implant in the phantom model is shown in Figure 6 As shown, it is located at the interior center of the phantom model, 45 mm from the top and bottom of the gel, aligned with the aperture direction, and 20 mm from the side wall because of the relatively high and uniform electric field there.

[0079] Step S2: setting the material properties of the birdcage coil, the phantom model and the medical implant model to be tested in step S1, and changing the phantom model properties according to the tissue properties of the implantation site of the medical implant.

[0080] In this embodiment, a cochlear implant is made in the human body. The main components of the cochlea are the cochlear duct and the internal and external lymph. The configuration of the cochlear environment is simulated. The conductivity is 0.32S / m, the dielectric constant is 57.75, the specific heat capacity is 3226J / (kg·℃), and the thermal conductivity is 0.46W / (m 2 ·k).

[0081] Step S3: Use the RF module of COMSOL Multiphysics software to perform excitation and tuning simulation on the birdcage coil used to generate the RF field, complete the electromagnetic field simulation modeling, and obtain the electromagnetic field model.

[0082] In this example, a default potential difference of 200 V is set between the two lumped ports as a voltage source for orthogonal excitation. Perfect electric conductor (PEC) conditions are applied to the coil surface and the RF shielding layer, and scattering boundary conditions are applied to the boundaries to simulate open space. This ensures that the natural scattering behavior of electromagnetic waves at the boundaries simulates the external environment and avoids unrealistic reflections returning to the computational domain.

[0083] In this embodiment, the test environment is a 1.5T magnetic resonance environment, that is, the birdcage coil operates at a frequency of 64 MHz. The birdcage coil is tuned by adjusting the capacitance value so that the resonant frequency is aligned with 64 MHz. Birdcage Builder software is used to first calculate a reference capacitance value, which is set as the initial capacitance value of all capacitors. A parametric sweep is then performed on the coil capacitance around the adjusted capacitance value to align the resonant frequency with 64 MHz. The standard deviation of the electric field inside the birdcage coil is determined to quantify the uniformity of the magnetic field, thereby finding the optimal magnetic field of the surrounding air. The nearest capacitance value that achieves the optimal magnetic field is approximately 12.3 pF.

[0084] In this embodiment, a non-uniform grid is implemented in the simulation. The grid independence verification of the electrode lengths of 17.5 mm, 22.0 mm, and 25.5 mm is shown in Figure 7 As shown, the number of grids near the inflection point is selected as the final simulation grid to reduce the computational cost and maintain accurate modeling.

[0085] Step S4: Use the electromagnetic thermal interface of the multiphysics module of COMSOL Multiphysics software to couple the electromagnetic field to the temperature field. Use the heat transfer module to construct the temperature field of the phantom model in the MRI birdcage coil under the influence of the medical implant model to be tested. Perform finite element simulation on the temperature rise distribution of the medical implant to be tested and the phantom model.

[0086] In this embodiment, the steady-state electromagnetic solution obtained in step S3 is used to provide a heat source for the transient thermal problem, that is, the SAR value is used as the heat source for the temperature rise effect. The simulation time is 15 minutes. After the simulation is completed, the temperature distribution in the phantom model is obtained. It is necessary to perform SAR value normalization processing, that is, the average SAR value in the phantom model is normalized to 2W / kg, and the proportionality coefficient is multiplied by the temperature distribution obtained by the original simulation to obtain a temperature distribution that meets the ASTM measurement standard. In this embodiment, the whole-body average SAR value in the phantom model measured in step S3 is 0.789W / kg. For a magnetic resonance system operating in normal mode, the whole-body average SAR value is limited to 2W / kg, so the proportionality coefficient of 2.53 should be used. Applied to temperature distribution, the RF temperature rise is linearly related to the SAR value, so geometric scaling can be performed directly.

[0087] Step S5: Changing the configuration parameters of the medical implant to be tested, and using the electromagnetic field model constructed in the above steps to conduct a set of radio frequency temperature rise simulation experiments, obtain the maximum temperature rise of the medical implant to be tested and the electromagnetic compatibility safety assessment results, as well as design guidance.

[0088] In this embodiment, based on common cochlear implant situations, in order to meet the needs of selecting different length electrodes according to the patient's cochlear anatomical structure and case variations, the electrode length can be selected as 17.5 mm, 22.0 mm or 25.5 mm. Also, because cochlear implants often involve electrode bending, this embodiment is discussed in detail in the following. Figure 5 Several typical simplified cochlear implant bending trajectories shown in (a), (b) and (c) are predicted using the electromagnetic field model constructed in the above steps. The spatial distribution of temperature rise in each part of the cochlear implant model is shown in Figure 2. Figure 8 As shown in (a), the closer the color is to red, the higher the temperature is; the maximum temperature rise estimation results under different cochlear implant bending trajectories are as follows: Figure 8 (b) shows that when the electrode length is 25.5 mm, the corresponding Figure 5 (a), (b) and (c) show the maximum temperature rise of the cochlear implant model with three different trajectories, specifically marked as Lead1, Lead2 and Lead3.

[0089] This application also provides an application scenario that utilizes the aforementioned method for predicting radiofrequency heating of medical implants during magnetic resonance imaging. Specifically, the method for predicting radiofrequency heating of medical implants during magnetic resonance imaging provided in this embodiment can be applied in a medical device safety assessment scenario. This scenario primarily includes a design phase, a testing and verification phase, and a safety assessment phase prior to clinical application. Beginning with the design phase, a medical implant that meets the requirements is designed based on clinical needs and the patient's anatomical characteristics. The completed medical implant enters the testing and verification phase, where the manufactured implant undergoes radiofrequency heating testing and verification. By comparing the prediction method with actual measurement results, the accuracy of the prediction method can be verified, and the implant parameters can be adjusted to ensure compliance with safety standards. During the safety assessment phase prior to clinical application, doctors or medical technicians use the prediction method of this application to predict and assess radiofrequency heating of medical implants in patients undergoing magnetic resonance imaging. Based on the evaluation results, doctors can determine whether the implant will cause damage to surrounding tissue during the magnetic resonance imaging process, thereby making reasonable medical decisions and ensuring patient safety. The method for predicting radiofrequency heating of medical implants in magnetic resonance imaging provided in this embodiment belongs to the test and verification link in the safety assessment of medical devices and the safety assessment link before clinical application. Specifically, this method predicts and evaluates the radiofrequency heating effect of medical implants by simulating the radiofrequency field in the magnetic resonance environment, providing a scientific basis and technical support for the safety of medical devices. In practical applications, this method can be widely used in the safety assessment of various medical implants such as pacemakers, deep brain stimulators, stents, and prosthetic implants. Through the application of this method, accurate prediction of radiofrequency heating of medical implants in a magnetic resonance environment can be achieved, providing an important reference for the design, production and clinical application of medical devices, thereby ensuring patient safety and medical quality.

[0090] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 9As shown in the figure. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the prediction data of the radio frequency heating of the medical implant in the magnetic resonance. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor to realize a prediction method of radio frequency heating of a medical implant in magnetic resonance.

[0091] Those skilled in the art can understand that, Figure 9 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement. In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the steps in each of the above method embodiments.

[0092] In one exemplary embodiment, a computer readable storage medium is provided, storing a computer program, which is executed by a processor to realize the steps in each of the above method embodiments.

[0093] In one exemplary embodiment, a computer program product is provided, including a computer program, which is executed by a processor to realize the steps in each of the above method embodiments.

[0094] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0095] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0096] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0097] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A device for predicting radiofrequency heating of medical implants in magnetic resonance imaging, characterized in that: The device for predicting radiofrequency heating of a medical implant in magnetic resonance imaging comprises: an electromagnetic field model, a phantom model, a plurality of medical implant models to be tested, and a host computer module; wherein each medical implant model to be tested has different configuration parameters; the configuration parameters include the length, trajectory, or direction of the medical implant to be tested relative to the phantom model; The electromagnetic field model is used to simulate the electromagnetic field in magnetic resonance imaging; the phantom model is used to simulate local human tissue; the medical implant model to be tested is used to simulate a medical implant implanted in the human body; the electromagnetic field model specifically includes: an air sphere, a radio frequency shielding layer, and a birdcage coil; the birdcage coil is located inside the radio frequency shielding layer; the air sphere is located between the birdcage coil and the radio frequency shielding layer; the air sphere, radio frequency shielding layer, and birdcage coil have the same central axis; the air sphere is used to simulate the air domain between the birdcage coil and the radio frequency shielding layer; the radio frequency shielding layer is a cylindrical structure for electromagnetic isolation; the birdcage coil is a cylindrical structure for generating an electromagnetic field; The phantom model is located inside the electromagnetic field model; the medical implant model to be tested is located at a preset implantation site inside the phantom model; The host computer module is respectively connected to the electromagnetic field model, the phantom model and the medical implant model to be tested, and is used to drive the electromagnetic field model to generate an electromagnetic field. It is also used to couple the electromagnetic field to the temperature field based on the electromagnetic field data generated by the received electromagnetic field model, combined with the physical properties of the phantom model and the medical implant model to be tested, and obtain the temperature prediction results of each medical implant model to be tested under the action of the electromagnetic field model and the phantom model.

2. The device for predicting radiofrequency heating of medical implants in magnetic resonance imaging according to claim 1, characterized in that: The birdcage coil specifically comprises: Two bottom rings with the same structure located at the upper and lower ends; the bottom rings are metal rings; The two bottom rings are connected by a plurality of cage feet located in the vertical direction; the cage feet are cylindrical channels made of metal material; Each of the cage legs is evenly provided with capacitors, and the capacitance value of each section of the capacitor is the same; Two lumped ports with a preset constant potential difference are respectively displaced onto any two of the cage feet.

3. The device for predicting radiofrequency heating of medical implants in magnetic resonance imaging according to claim 1, characterized in that: The diameter of the birdcage coil is 800 mm and the height is 700 mm; the diameter of the radio frequency shielding layer is 1000 mm and the height is 800 mm; the diameter of the air sphere is greater than or equal to twice the diameter of the birdcage coil.

4. The device for predicting radiofrequency heating of medical implants in magnetic resonance imaging according to claim 1, wherein: The phantom model specifically includes: an internal model and an external model; the internal model includes saline gel; and the external model includes insulating non-magnetic non-metallic material.

5. A method for predicting radiofrequency heating of medical implants in magnetic resonance imaging, characterized in that: The method for predicting radiofrequency heating of a medical implant in magnetic resonance imaging is applied to the device for predicting radiofrequency heating of a medical implant in magnetic resonance imaging according to any one of claims 1 to 4, and the method for predicting radiofrequency heating of a medical implant in magnetic resonance imaging comprises: Building an electromagnetic field model, a phantom model, and multiple medical implant models to be tested; wherein each medical implant model to be tested has different configuration parameters; Adjusting the dielectric parameters of the phantom model according to the preset implantation site; the dielectric parameters include the length, height, thickness, conductivity, dielectric constant, and specific heat capacity of the phantom model; In the electromagnetic field model, the temperature field of the phantom model under the influence of the current implant model to be tested is constructed. The temperature distribution of each medical implant model to be tested under the action of the electromagnetic field and the interaction with the phantom model is calculated using an electromagnetic-thermal coupling algorithm. The temperature of the current medical implant model to be tested and the phantom model are predicted using a finite element analysis method to obtain a temperature prediction result. The next medical implant model to be tested is used as the current medical implant model to be tested, and the process returns to the step of "constructing the temperature field of the phantom model under the influence of the current implant model to be tested in the electromagnetic field model, calculating the temperature distribution of each medical implant model to be tested that interacts with the phantom model under the action of the electromagnetic field using an electromagnetic-thermal coupling algorithm, and predicting the temperatures of the current medical implant model to be tested and the phantom model using a finite element analysis method to obtain temperature prediction results" until an end condition is met; the end condition includes completing temperature prediction for all medical implant models to be tested.

6. The method for predicting radiofrequency heating of medical implants in magnetic resonance imaging according to claim 5, wherein: Build an electromagnetic field model, which includes: excitation simulation and tuning simulation of the birdcage coil; The excitation simulation includes: setting a default potential difference between two lumped ports as a voltage source excitation; the default potential difference is fixed to 200V; Apply perfect conductor boundary conditions to the birdcage coil and RF shielding layer to obtain the birdcage coil after excitation simulation; The tuning simulation includes: setting the initial capacitance values ​​of all capacitors in the birdcage coil after the excitation simulation to reference capacitance values; Applying the voltage source excitation to the birdcage coil after the excitation simulation to obtain the resonant frequency; If the resonant frequency is not equal to the preset resonant frequency, adjust the reference capacitance value and return to the step "Set the initial capacitance values ​​of all capacitors in the birdcage coil after the excitation simulation to the reference capacitance value" until the resonant frequency is equal to the preset resonant frequency, thereby obtaining the birdcage coil after the tuning simulation.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting radio frequency heating of a medical implant in magnetic resonance according to any one of claims 5 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting radio frequency heating of a medical implant in magnetic resonance according to any one of claims 5 to 6 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting radio frequency heating of a medical implant in magnetic resonance according to any one of claims 5 to 6 is implemented.

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