Method and device for temperature measurement by means of magnetic resonance in conjunction with the alternating direction implicit temperature calculation method

Abstract

The application relates to the technical field of magnetic resonance imaging, in particular to a synergic magnetic resonance temperature measurement method and device of an alternating direction implicit temperature calculation method, wherein the method comprises the following steps: collecting a magnetic resonance image of human tissue; based on the magnetic resonance image, a preset alternating direction implicit method is used to obtain a temperature prediction value, and a preset water-fat mixed temperature measurement method is used to obtain a temperature measurement value; and based on an alternating direction multiplier method, a global temperature field result is obtained by combining the temperature prediction value and the temperature measurement value. Therefore, the problems in the prior art that the calculation amount of the Kalman filter synergic promotion algorithm is large, the Kalman filter can only process temperature synergic promotion in a two-dimensional case, has limitations, and the efficiency and accuracy are low, and the temperature synergic promotion demand cannot be met are solved.

Description

Technical Field

[0001] This application relates to the field of magnetic resonance imaging technology, and in particular to a collaborative magnetic resonance temperature measurement method and apparatus based on an alternating direction implicit temperature calculation method. Background Technology

[0002] In related technologies, a Kalman filter is used to combine predicted and measured values ​​based on noise analysis. A biological heat transfer model is used as the predicted value, and magnetic resonance temperature measurement is used as the measured value. The predicted and measured values ​​are linearly combined using a weighted matrix with the minimum mean square error to obtain a denoised image and suppress artifacts caused by phase disorder. This results in a synergistic improvement of finite element temperature calculation results and magnetic resonance temperature measurement results.

[0003] However, due to the large computational load of the Kalman filter collaborative enhancement algorithm, the Kalman filter can only handle temperature collaborative enhancement in two-dimensional cases, which has limitations and low efficiency and accuracy, and cannot meet the requirements of temperature collaborative enhancement, so it is urgent to solve this problem. Summary of the Invention

[0004] This application is based on the inventor's understanding and insights into the following issues:

[0005] Human organs and tissues are complex and have many tissue interfaces. The phase information at the interfaces in magnetic resonance images is chaotic, which can introduce large temperature measurement errors. In response to this error source, some studies have used Kalman filters to synergistically improve the finite element temperature calculation results and magnetic resonance temperature measurement results.

[0006] Kalman filters can combine predicted and measured values ​​based on noise analysis. This method uses a biological heat transfer model as the predicted value and magnetic resonance thermometry as the measured value. It uses a weighted matrix with the minimum mean square error to linearly combine the predicted and measured values ​​to obtain a denoised image and suppress artifacts caused by phase disorder. However, the finite element temperature calculation results are computationally intensive, especially under Kalman filtering, which can only slowly process temperature synergy improvement in two-dimensional cases (it is difficult to achieve near real-time requirements), and therefore cannot process temperature synergy improvement in three-dimensional cases.

[0007] This application provides a collaborative magnetic resonance temperature measurement method and apparatus based on an alternating direction implicit temperature calculation method, in order to solve the problems in related technologies, such as the large computational load of the Kalman filter collaborative enhancement algorithm, which limits the Kalman filter to only handle temperature collaborative enhancement in two-dimensional cases, resulting in low efficiency and accuracy, and failing to meet the requirements of temperature collaborative enhancement.

[0008] The first aspect of this application provides a collaborative magnetic resonance thermometry method based on an alternating direction implicit temperature calculation method, comprising the following steps: acquiring magnetic resonance images of human tissue; obtaining a temperature prediction value using a preset alternating direction implicit method based on the magnetic resonance images, while simultaneously obtaining a temperature measurement value using a preset water-lipid mixture thermometry method; and obtaining a global temperature field result by combining the temperature prediction value and the temperature measurement value based on the alternating direction multiplier method.

[0009] Optionally, in one embodiment of this application, obtaining the temperature prediction value using a preset alternating direction implicit method includes: when performing multidimensional heat conduction calculation, calculating the first temperature conduction in each coordinate axis direction within a preset time step using a preset tridiagonal matrix implicit algorithm, and calculating the second temperature conduction in other directions using a preset explicit processing algorithm; obtaining the temperature prediction value after a single preset time step based on the first temperature conduction and the second temperature conduction, and obtaining the temperature prediction value after a preset time step after multiple iterations.

[0010] Optionally, in one embodiment of this application, obtaining the global temperature field result based on the alternating direction multiplier method, combining the predicted temperature value and the measured temperature value, includes: using the sum of the L2 norm of the difference between the final temperature distribution and the predicted temperature value, the L2 norm of the difference between the final temperature distribution and the measured temperature value, and the L1 norm of the discrete cosine change of the final temperature distribution as the loss function of the iterative equation, performing iterative calculation based on the alternating direction multiplier method until a preset iteration stopping condition is met, stopping the iteration, and using the iterated final temperature distribution as the global temperature field result.

[0011] Optionally, in one embodiment of this application, the preset iteration stopping condition is that the calculated value of the loss function is less than a preset threshold.

[0012] A second aspect of this application provides a collaborative magnetic resonance thermometry device based on an alternating direction implicit temperature calculation method, comprising: an acquisition module for acquiring magnetic resonance images of human tissue; a first calculation module for obtaining a temperature prediction value based on the magnetic resonance images using a preset alternating direction implicit method, and simultaneously obtaining a temperature measurement value using a preset water-lipid mixture thermometry method; and a second calculation module for obtaining a global temperature field result based on an alternating direction multiplier method, combining the temperature prediction value and the temperature measurement value.

[0013] Optionally, in one embodiment of this application, the first calculation module includes: a first calculation unit, configured to calculate the first temperature conduction in each coordinate axis direction within a preset time step using a preset tridiagonal matrix implicit algorithm, and calculate the second temperature conduction in other directions using a preset explicit processing algorithm; and an acquisition unit, configured to acquire the temperature prediction value after a single preset time step based on the first temperature conduction and the second temperature conduction, and obtain the temperature prediction value after a preset time step after multiple iterations.

[0014] Optionally, in one embodiment of this application, the second calculation module includes: a second calculation unit, used to use the sum of the L2 norm of the difference between the final temperature distribution and the predicted temperature value, the L2 norm of the difference between the final temperature distribution and the measured temperature value, and the L1 norm of the discrete cosine change of the final temperature distribution as the loss function of the iterative equation, and to perform iterative calculation based on the alternating direction multiplier method until a preset iteration stopping condition is met, at which point the iteration is stopped, and the final temperature distribution after iteration is used as the global temperature field result.

[0015] Optionally, in one embodiment of this application, the preset iteration stopping condition is that the calculated value of the loss function is less than a preset threshold.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a cooperative magnetic resonance temperature measurement method based on the alternating direction implicit temperature calculation method described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described co-magnetic resonance temperature measurement method using the alternating direction implicit temperature calculation method.

[0018] This application embodiment can acquire magnetic resonance images of human tissue and obtain predicted temperature values ​​using a preset alternating direction implicit method, while simultaneously obtaining measured temperature values ​​using a preset water-lipid mixture thermometry method. Based on the alternating direction multiplier method, it combines the predicted and measured temperature values ​​to obtain a global temperature field result, effectively reducing temperature measurement errors and improving the accuracy of the measurement results. Furthermore, it can synergistically enhance the temperature distribution in three-dimensional cases, improving the applicability of temperature synergistic enhancement. Therefore, it solves the problems in related technologies where the computational load of the Kalman filter synergistic enhancement algorithm is too large, resulting in Kalman filtering only being able to handle two-dimensional temperature synergistic enhancement, which has limitations, low efficiency and accuracy, and cannot meet the needs of temperature synergistic enhancement.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0021] Figure 1 This is a flowchart of a cooperative magnetic resonance thermometry method based on an alternating direction implicit temperature calculation method provided in an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of the structure of a cooperative magnetic resonance temperature measuring device based on the alternating direction implicit temperature calculation method according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0024] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0025] The following description, with reference to the accompanying drawings, describes a cooperative magnetic resonance thermometry method and apparatus based on an alternating direction implicit temperature calculation method according to embodiments of this application. Addressing the limitations of the Kalman filter cooperative enhancement algorithm mentioned in the background section, which is computationally intensive and can only handle two-dimensional temperature enhancement, resulting in low efficiency and accuracy and failing to meet the requirements for temperature enhancement, this application provides a cooperative magnetic resonance thermometry method based on an alternating direction implicit temperature calculation method. In this method, magnetic resonance images of human tissue can be acquired, and while obtaining a temperature prediction value using a preset alternating direction implicit method, a temperature measurement value can be obtained using a preset water-lipid mixture thermometry method. Based on the alternating direction multiplier method, the global temperature field result is obtained by combining the temperature prediction value and the temperature measurement value, thereby effectively reducing temperature measurement errors, improving the accuracy of the measurement results, and enabling cooperative enhancement of temperature distribution in three-dimensional cases, thus improving the applicability of temperature enhancement. Therefore, this solves the problems in the related technologies where the Kalman filter cooperative enhancement algorithm is computationally intensive, limiting it to two-dimensional temperature enhancement, resulting in low efficiency and accuracy and failing to meet the requirements for temperature enhancement.

[0026] Specifically, Figure 1This is a schematic flowchart of a cooperative magnetic resonance temperature measurement method based on an alternating direction implicit temperature calculation method provided in an embodiment of this application.

[0027] like Figure 1 As shown, the cooperative magnetic resonance thermometry method of this alternating direction implicit temperature calculation method includes the following steps:

[0028] In step S101, magnetic resonance images of human tissue are acquired.

[0029] It is understood that the embodiments of this application can acquire magnetic resonance images of human tissue, thereby obtaining the temperature prediction and temperature measurement values ​​in the following steps, ensuring high-quality temperature distribution while providing high-resolution images as a background to confirm the location of high-temperature targets, thereby improving the accuracy of temperature measurement.

[0030] In step S102, based on the magnetic resonance image, the temperature prediction value is obtained using a preset alternating direction implicit method, while the temperature measurement value is obtained using a preset water-fat mixture temperature measurement method.

[0031] It is understood that the embodiments of this application can obtain temperature prediction values ​​based on magnetic resonance images using a preset alternating direction implicit method. For example, ADI (Alternating Direction Implicit) can be used to quickly predict future temperature distribution, while a preset water-fat mixture temperature measurement method can be used to obtain temperature measurement values, thereby reducing temperature measurement errors and improving the accuracy of temperature measurement.

[0032] In one embodiment of this application, the alternating direction implicit method can calculate the temperature change along each direction in a multidimensional temperature distribution by using a tridiagonal matrix implicit algorithm and an explicit algorithm in other directions within a single step length (usually no more than 0.1 seconds) in each coordinate axis direction, and obtain the temperature prediction after the expected time after multiple iterations.

[0033] In one embodiment of this application, the water-fat mixed temperature measurement method can separate water and fat using magnetic resonance multi-echo data to obtain water signals, fat signals, and other related signals. For pixels with a fat content higher than a threshold, the temperature is calculated using a magnetic resonance temperature measurement algorithm based on the water proton resonance frequency. The discrete temperature results are fitted and smoothed to obtain a rough temperature distribution. Based on this temperature distribution, a magnetic resonance signal model is used to iteratively process and obtain the magnetic field drift and the amplitude and phase of the water and fat signals. The magnetic field drift is low-pass filtered and used as a known quantity to iterate the amplitude of the water signal, the amplitude of the fat signal, and the temperature distribution, finally obtaining an accurate temperature measurement result.

[0034] In one embodiment of this application, the temperature prediction value is obtained using a preset alternating direction implicit method, which includes: when performing multidimensional heat conduction calculation, calculating the first temperature conduction in each coordinate axis direction within a preset time step using a preset tridiagonal matrix implicit algorithm, and calculating the second temperature conduction in other directions using a preset explicit processing algorithm; obtaining the temperature prediction value after a single preset time step based on the first temperature conduction and the second temperature conduction, and obtaining the temperature prediction value after a preset time step after multiple iterations.

[0035] In practical implementation, the embodiments of this application can perform multidimensional heat conduction calculations, for example, by using ADI. When processing multidimensional heat conduction calculations, ADI can use a tridiagonal matrix implicit algorithm to calculate the first temperature conduction in each coordinate axis direction within a certain time step, and use an explicit algorithm to calculate the second temperature conduction in other directions. Thus, based on the first and second temperature conductions, the predicted temperature value after a single time step is obtained. After multiple iterations, the predicted temperature value after a certain time is obtained. This can reduce the pressure of directly solving algebraic equations, thereby shortening the calculation time, especially when processing three-dimensional temperature field calculations, it has a significant speed advantage.

[0036] Furthermore, taking the ADI algorithm in the two-dimensional case as an example, when the unit time step is Δt, for the first Δt / 2 time, the discrete heat transfer equation is used to calculate only the temperature conduction along the x-axis, and for the next Δt / 2 time, the discrete heat transfer equation is used to calculate only the temperature conduction along the y-axis. When Δt is less than 0.1 seconds, a high temperature prediction accuracy can be achieved, and the computational load is greatly reduced.

[0037] Because the ADI algorithm uses an implicit algorithm, it has fewer time step restrictions and can generally achieve a time step of 0.1 seconds. In contrast, the traditional finite element algorithm uses an explicit algorithm, which has higher requirements for the time step and can usually only achieve about 1 millisecond. Therefore, when predicting the temperature after the same amount of time, the ADI algorithm can use fewer calculations.

[0038] It should be noted that the preset time step, single preset time step, preset time, first temperature conduction and second temperature conduction are set by those skilled in the art according to the actual situation, and are not specifically limited here.

[0039] In step S103, the global temperature field result is obtained by combining the predicted temperature value and the measured temperature value based on the alternating direction multiplier method.

[0040] It is understood that the embodiments of this application can combine the predicted value with the temperature measurement value obtained by the water-fat mixture thermometry method based on ADMM (Alternating Direction Method of Multipliers). While maintaining the measurement accuracy of temperature distribution near the heat source, it suppresses the temperature measurement error caused by phase disorder at tissue junctions and low signal-to-noise ratio areas, and can obtain a more accurate global temperature field result. Among them, the ADMM algorithm has fast synergistic improvement speed and high efficiency, and low memory consumption, effectively reducing the computational space occupation and improving computational efficiency.

[0041] In one embodiment of this application, the alternating direction multiplier method can calculate the temperature change along each direction in a multidimensional temperature distribution by using an implicit algorithm with a tridiagonal matrix and an explicit algorithm in other directions within a single step length (usually no more than 0.1 seconds) in each coordinate axis direction. After multiple iterations, the temperature prediction after the expected time can be obtained.

[0042] For example, in the heating experiment of water-lipid mixed agar phantom, the ADI algorithm can be used to predict the temperature distribution, and the result can be combined with the temperature measurement data through a collaborative enhancement method. This makes the final temperature field distribution more uniform, and the RMSE (Root Mean Square Error) decreases from 0.4114℃ to 0.2772℃. The collaborative enhancement algorithm has high robustness, thus obtaining a more accurate temperature field distribution. This can improve the signal-to-noise ratio of the magnetic resonance thermometry algorithm, suppress noise and artifacts in the field of view, and provide a powerful, high-precision, non-invasive temperature measurement tool in ablation surgery.

[0043] Among them, the ADI algorithm has lower computational complexity, which greatly shortens the time required for the collaborative improvement process compared to the finite element analysis method. It has good three-dimensional computing potential and is more likely to meet the requirements of near real-time temperature measurement.

[0044] Optionally, in one embodiment of this application, the global temperature field result is obtained based on the alternating direction multiplier method, combining the predicted temperature value and the measured temperature value. This includes: using the sum of the L2 norm of the difference between the final temperature distribution and the predicted temperature value, the L2 norm of the difference between the final temperature distribution and the measured temperature value, and the L1 norm of the discrete cosine change of the final temperature distribution as the loss function of the iterative equation, performing iterative calculation based on the alternating direction multiplier method until the preset iteration stopping condition is met, then stopping the iteration, and using the iterated final temperature distribution as the global temperature field result.

[0045] As one possible implementation, embodiments of this application can use the ADMM iterative algorithm to combine the temperature prediction value obtained by the ADI algorithm in the above steps with the magnetic resonance temperature measurement value, and finally obtain an accurate temperature distribution combined with the iterative equation. The sum of the L2 norm of the difference between the final temperature distribution and the ADI temperature prediction value, the L2 norm of the difference between the final temperature distribution and the temperature measurement value, and the L1 norm of the discrete cosine change of the final temperature distribution is used as the loss function of the iterative equation. According to the ADMM algorithm, the iteration stops when the preset iteration stopping condition in the following steps is met, so that the final temperature distribution after iteration is used as the global temperature field result, thereby improving the accuracy of temperature measurement and eliminating noise in the magnetic resonance temperature image, thus meeting the requirements of temperature measurement.

[0046] In one embodiment of this application, the preset iteration stopping condition is that the calculated value of the loss function is less than a preset threshold.

[0047] In some embodiments, the preset iteration stopping condition is that the calculated value of the loss function is less than a preset threshold, so that after multiple iterations, a final temperature distribution with a sufficiently small loss function can be obtained as the result of synergistic improvement, thereby improving the accuracy of the measurement results.

[0048] It should be noted that the preset threshold is set by those skilled in the art based on the actual situation, and no specific limitation is made here.

[0049] The collaborative magnetic resonance thermometry method based on the alternating direction implicit temperature calculation method proposed in this application can acquire magnetic resonance images of human tissues and obtain temperature prediction values ​​using a preset alternating direction implicit method, while simultaneously obtaining temperature measurement values ​​using a preset water-lipid mixture thermometry method. Based on the alternating direction multiplier method, the global temperature field result is obtained by combining the temperature prediction and temperature measurement values, thereby effectively reducing temperature measurement errors and improving the accuracy of the measurement results. Furthermore, it can collaboratively enhance the temperature distribution in three-dimensional cases, improving the applicability of temperature collaborative enhancement. This solves the problems in related technologies where the computational load of the Kalman filter collaborative enhancement algorithm is too large, resulting in Kalman filtering only being able to handle temperature collaborative enhancement in two-dimensional cases, exhibiting limitations, low efficiency and accuracy, and failing to meet the needs of temperature collaborative enhancement.

[0050] Next, referring to the accompanying drawings, a cooperative magnetic resonance temperature measuring device based on the alternating direction implicit temperature calculation method proposed in the embodiments of this application is described.

[0051] Figure 2 This is a block diagram of a cooperative magnetic resonance temperature measurement device for the alternating direction implicit temperature calculation method according to an embodiment of this application.

[0052] like Figure 2As shown, the cooperative magnetic resonance temperature measurement device 10 of the alternating direction implicit temperature calculation method includes: a data acquisition module 100, a first calculation module 200, and a second calculation module 300.

[0053] Specifically, the acquisition module 100 is used to acquire magnetic resonance images of human tissues.

[0054] The first calculation module 200 is used to obtain the temperature prediction value based on the magnetic resonance image using a preset alternating direction implicit method, and at the same time obtain the temperature measurement value using a preset water-fat mixture temperature measurement method.

[0055] The second calculation module 300 is used to obtain the global temperature field result based on the alternating direction multiplier method, combining the predicted temperature value and the measured temperature value.

[0056] Optionally, in one embodiment of this application, the first calculation module 200 includes: a first calculation unit and an acquisition unit.

[0057] The first calculation unit is used to calculate the first temperature conduction in each coordinate axis direction within a preset time step using a preset tridiagonal matrix implicit algorithm, and to calculate the second temperature conduction in other directions using a preset explicit processing algorithm when performing multidimensional heat conduction calculation.

[0058] The acquisition unit is used to obtain the temperature prediction value after a single preset time step based on the first temperature conduction and the second temperature conduction, and to obtain the temperature prediction value after a preset time after multiple iterations.

[0059] Optionally, in one embodiment of this application, the second computing module 300 includes a second computing unit.

[0060] The second calculation unit is used to use the sum of the L2 norm of the difference between the final temperature distribution and the predicted temperature value, the L2 norm of the difference between the final temperature distribution and the measured temperature value, and the L1 norm of the discrete cosine change of the final temperature distribution as the loss function of the iterative equation. It performs iterative calculation based on the alternating direction multiplier method until the preset iteration stopping condition is met, at which point the iteration stops and the final temperature distribution after iteration is taken as the global temperature field result.

[0061] Optionally, in one embodiment of this application, the preset iteration stopping condition is that the calculated value of the loss function is less than a preset threshold.

[0062] It should be noted that the explanation of the above-described embodiment of the cooperative magnetic resonance temperature measurement method of the alternating direction implicit temperature calculation method also applies to the cooperative magnetic resonance temperature measurement device of the alternating direction implicit temperature calculation method in this embodiment, and will not be repeated here.

[0063] The collaborative magnetic resonance thermometry device based on the alternating direction implicit temperature calculation method proposed in this application can acquire magnetic resonance images of human tissues. While obtaining predicted temperature values ​​using a preset alternating direction implicit method, it can also obtain measured temperature values ​​using a preset water-lipid mixture thermometry method. Based on the alternating direction multiplier method, it combines the predicted and measured temperature values ​​to obtain the global temperature field result, effectively reducing temperature measurement errors and improving the accuracy of the measurement results. Furthermore, it can collaboratively enhance the temperature distribution in three-dimensional cases, improving the applicability of temperature collaborative enhancement. This solves the problems in related technologies where the computational load of the Kalman filter collaborative enhancement algorithm is too large, resulting in Kalman filtering only being able to handle temperature collaborative enhancement in two-dimensional cases, exhibiting limitations, low efficiency and accuracy, and failing to meet the needs of temperature collaborative enhancement.

[0064] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0065] The memory 301, the processor 302, and the computer program stored on the memory 301 and capable of running on the processor 302.

[0066] When the processor 302 executes the program, it implements the cooperative magnetic resonance temperature measurement method of the alternating direction implicit temperature calculation method provided in the above embodiments.

[0067] Furthermore, electronic devices also include:

[0068] Communication interface 303 is used for communication between memory 301 and processor 302.

[0069] The memory 301 is used to store computer programs that can run on the processor 302.

[0070] The memory 301 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0071] If the memory 301, processor 302, and communication interface 303 are implemented independently, then the communication interface 303, memory 301, and processor 302 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0072] Optionally, in a specific implementation, if the memory 301, processor 302, and communication interface 303 are integrated on a single chip, then the memory 301, processor 302, and communication interface 303 can communicate with each other through an internal interface.

[0073] Processor 302 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0074] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described cooperative magnetic resonance temperature measurement method using the alternating direction implicit temperature calculation method.

[0075] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0076] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0077] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0078] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0079] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0080] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0081] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0082] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A cooperative magnetic resonance temperature measurement device based on an alternating direction implicit temperature calculation method, characterized in that, include: The acquisition module is used to acquire magnetic resonance images of human tissues; The first calculation module is used to obtain a temperature prediction value based on the magnetic resonance image using a preset alternating direction implicit method, and to obtain a temperature measurement value using a preset water-fat mixture thermometry method. as well as The second calculation module is used to obtain the global temperature field result based on the alternating direction multiplier method, combining the temperature prediction value and the temperature measurement value. The first computing module includes: The first calculation unit is used to calculate the first temperature conduction in each coordinate axis direction within a preset time step using a preset tridiagonal matrix implicit algorithm, and to calculate the second temperature conduction in other directions using a preset explicit processing algorithm when performing multidimensional heat conduction calculations. The acquisition unit is used to acquire the temperature prediction value after a single preset time step based on the first temperature conduction and the second temperature conduction, and to obtain the temperature prediction value after a preset time after multiple iterations. The second calculation module includes: The second calculation unit is used to use the sum of the L2 norm of the difference between the final temperature distribution and the predicted temperature value, the L2 norm of the difference between the final temperature distribution and the measured temperature value, and the L1 norm of the discrete cosine change of the final temperature distribution as the loss function of the iterative equation, and to perform iterative calculation based on the alternating direction multiplier method until the preset iteration stopping condition is met, at which point the iteration stops and the final temperature distribution after iteration is taken as the global temperature field result. The preset iteration stopping condition is that the calculated value of the loss function is less than a preset threshold.

2. A cooperative magnetic resonance thermometry method based on an alternating direction implicit temperature calculation method, wherein the apparatus as described in claim 1 is used to execute the method, characterized in that, Includes the following steps: Acquire magnetic resonance images of human tissues; Based on the magnetic resonance image, a predicted temperature value is obtained using a preset alternating direction implicit method, while a measured temperature value is obtained using a preset water-fat mixture thermometry method; and The global temperature field result is obtained by combining the predicted temperature value and the measured temperature value based on the alternating direction multiplier method.

3. The method according to claim 2, characterized in that, The method of obtaining the temperature prediction value using a preset alternating direction implicit method includes: When performing multidimensional heat conduction calculations, the first temperature conduction is calculated using a preset tridiagonal matrix implicit algorithm in each coordinate axis direction within a preset time step, and the second temperature conduction is calculated using a preset explicit processing algorithm in other directions. The temperature prediction value is obtained after a single preset time step based on the first temperature conduction and the second temperature conduction, and the temperature prediction value is obtained after multiple iterations after a preset time.

4. The method according to claim 2, characterized in that, The global temperature field result obtained by combining the temperature prediction value and the temperature measurement value based on the alternating direction multiplier method includes: The loss function of the iterative equation is the sum of the L2 norm of the difference between the final temperature distribution and the predicted temperature value, the L2 norm of the difference between the final temperature distribution and the measured temperature value, and the L1 norm of the discrete cosine change of the final temperature distribution. Iterative calculation is performed based on the alternating direction multiplier method until the preset iteration stopping condition is met. The iteration is then stopped, and the final temperature distribution after iteration is taken as the result of the global temperature field.

5. The method according to claim 4, characterized in that, The preset iteration stopping condition is that the calculated value of the loss function is less than a preset threshold.

6. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the cooperative magnetic resonance thermometry method of the alternating direction implicit temperature calculation method as described in any one of claims 2-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the cooperative magnetic resonance thermometry method of the alternating direction implicit temperature calculation method as described in any one of claims 2-5.

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