WO2022188237A1 - 射频能量沉积预测及射频能量沉积监测方法、装置、设备和介质 - Google Patents

射频能量沉积预测及射频能量沉积监测方法、装置、设备和介质 Download PDF

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WO2022188237A1
WO2022188237A1 PCT/CN2021/087689 CN2021087689W WO2022188237A1 WO 2022188237 A1 WO2022188237 A1 WO 2022188237A1 CN 2021087689 W CN2021087689 W CN 2021087689W WO 2022188237 A1 WO2022188237 A1 WO 2022188237A1
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radio frequency
scanning
energy deposition
frequency energy
model
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PCT/CN2021/087689
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English (en)
French (fr)
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郑海荣
李烨
陈巧燕
杜凤
李楠
贺强
刘新
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中国科学院深圳先进技术研究院
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling

Definitions

  • the present application relates to the technical field of magnetic resonance imaging, for example, to a method, apparatus, device and medium for radio frequency energy deposition prediction and radio frequency energy deposition monitoring.
  • the patient will absorb the energy of radio frequency electromagnetic waves during the examination, forming the body's radio frequency energy deposition, the measurement unit is the specific absorption rate (Specific Absorption Rate, SAR) , that is, the radio frequency electromagnetic wave energy (unit: W/kg) absorbed per unit mass of biological tissue in unit time.
  • SAR Specific Absorption Rate
  • the multi-channel parallel transmission technology is used to independently control the amplitude, phase and even the RF pulse waveform of multiple unit coil excitation sources, thereby improving the Freedom of control and room for optimization.
  • the present application provides a method, device, equipment and medium for radio frequency energy deposition prediction and radio frequency energy deposition monitoring, so as to realize a more accurate and personalized analysis of the SAR value of a scanned object in the scenario of a multi-channel transmitting coil parallel transmission technology. Predict and improve the safety of scanning subjects receiving MRI scans.
  • a radio frequency energy deposition prediction method comprising:
  • a multi-channel radio frequency coil model is established, and the radio frequency energy deposition value of the scanned object is predicted by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model simulation.
  • radio frequency energy deposition monitoring method comprising:
  • the radio frequency energy deposition prediction method Based on the radio frequency magnetic field strength and the weighting factor matrix, by using the radio frequency energy deposition prediction method according to any one of the embodiments, predict the radio frequency energy deposition value that the scanning object is subjected to during scanning imaging;
  • the scanning strategy is adjusted according to the RF energy deposition value.
  • radio frequency energy deposition prediction device comprising:
  • a human body model establishment module configured to collect a magnetic resonance scanning structural image of the scanning object, and perform tissue segmentation based on the magnetic resonance scanning structural image to establish a three-dimensional biological model of the scanning object;
  • a biological electromagnetic simulation model establishment module configured to establish a biological electromagnetic simulation model according to the three-dimensional biological model
  • the radio frequency deposition prediction module is configured to establish a multi-channel radio frequency coil model according to a preset scanning strategy, and combine the biological electromagnetic simulation model and the multi-channel radio frequency coil model to simulate and predict the radio frequency energy deposition value of the scanning object.
  • radio frequency energy deposition monitoring device comprising:
  • the scanning strategy determination module is configured to obtain a preset scanning strategy of the scanning object, and calculate the radio frequency magnetic field strength of the scanning sequence according to the preset scanning strategy, and according to the voltage amplitude and phase of the multi-channel transmitting coils in the preset scanning strategy , determine the weighting factor matrix of the multi-channel RF coil;
  • a radio frequency energy deposition value determination module configured to predict, based on the radio frequency magnetic field strength and the weighting factor matrix, the radio frequency energy deposition prediction method according to any one of the embodiments, to predict the radio frequency that the scanning object is subjected to during scanning and imaging energy deposition value;
  • the scanning control module is configured to adjust the scanning strategy according to the radio frequency energy deposition value.
  • Also provided is a computer device comprising:
  • processors one or more processors
  • memory arranged to store one or more programs
  • the one or more processors When the one or more programs are executed by the one or more processors, the one or more processors implement the radio frequency energy deposition prediction method provided by any embodiment of the present application, or the radio frequency energy deposition monitoring method.
  • a computer-readable storage medium which stores a computer program, and when the computer program is executed by the processor, implements the radio frequency energy deposition prediction method or the radio frequency energy deposition monitoring method provided by any embodiment of the present application.
  • FIG. 2 is a schematic diagram of the establishment of a biological electromagnetic simulation model of a rat provided in Embodiment 1 of the present application;
  • FIG. 3 is a schematic diagram of a biological electromagnetic simulation model and a coil used for prediction provided in Embodiment 1 of the present application;
  • FIG. 4 is a schematic diagram of the distribution of the simulated magnetic field B1 provided by the first embodiment of the present application based on the method in this embodiment;
  • FIG. 5 is a schematic diagram of the distribution of the magnetic field B1 actually measured based on the method in this embodiment provided by Embodiment 1 of the present application;
  • FIG. 6 is a flowchart of a method for monitoring radio frequency energy deposition provided in Embodiment 2 of the present application;
  • FIG. 7 is a schematic structural diagram of a radio frequency energy deposition prediction device provided in Embodiment 3 of the present application.
  • FIG. 8 is a schematic structural diagram of a radio frequency energy deposition monitoring device provided in Embodiment 4 of the present application.
  • FIG. 9 is a schematic structural diagram of a computer device according to Embodiment 5 of the present application.
  • FIG. 1 is a flowchart of a method for predicting radio frequency energy deposition according to Embodiment 1 of the present application. This embodiment is applicable to the case of performing magnetic resonance scanning on a scanning object.
  • the method may be performed by a pre-position configured for radio frequency energy deposition, and the apparatus may be implemented in software and/or hardware, and integrated into an electronic device with an application development function.
  • the radio frequency energy deposition prediction method includes the following steps.
  • S110 collect the magnetic resonance scanning structural image of the scanning object, and perform tissue segmentation based on the magnetic resonance scanning structural image, and establish a three-dimensional biological model of the scanning object.
  • electromagnetic simulation software is used to simulate the model of the digital scanning object.
  • a personalized three-dimensional biological model of the scanned object is established, so that the three-dimensional biological model in the simulation process can be simulated.
  • the biological model better matches the scanned object.
  • the scanned object can be scanned to obtain the MRI scan structure image, including the acquisition of the water-fat separation MRI image of the scanned object and the T1/T2 contrast MRI image, so as to obtain fat, brain and muscle. organization information.
  • ultra-short echo sequences can also be used to obtain ultra-short echo images to obtain bone information.
  • the tissue segmentation of the scanned image of the scanned object can be performed based on the water-fat separation image, the T1 and T2 contrast image, and the ultrashort echo image to obtain tissue structures such as fat, brain, muscle, bone, and skin.
  • the skin may be skin information added outside the overall outline of the scanned object. Based on the structures of fat, brain, muscle and skin obtained by tissue segmentation, a three-dimensional biological model of the scanned object can be established.
  • the scanned object can be a human body or an animal.
  • the magnetic resonance scanning structural image includes a water-fat separation image, a T1/T2 contrast image, and an ultra-short echo image, and the tissue segmentation is performed based on the magnetic resonance scanning structural image to establish the scanning object.
  • 3D biological models including:
  • tissue segmentation of the scanned image of the scanning object based on the water-fat separation image, the T1/T2 contrast image and the ultrashort echo image to obtain fat, brain, muscle, bone and skin tissue;
  • the three-dimensional biological model is established by combining the tissue-segmented fat, brain, muscle, bone and skin tissues.
  • the establishing a biological electromagnetic simulation model according to the three-dimensional biological model includes:
  • the corresponding dielectric constant, magnetic permeability and tissue density are assigned to a plurality of tissues in the three-dimensional biological model to obtain the biological electromagnetic simulation model.
  • the values of electrical conductivity, magnetic permeability and characteristic absorptivity of different tissues are different, after the 3D biological model of the scanned object is obtained, the corresponding permittivity, magnetic permeability and The tissue density is assigned, and the bioelectromagnetic simulation model can be obtained.
  • the dielectric constant, magnetic permeability and tissue density can be pre-stored in the memory of the simulation system.
  • the scanning strategy includes a plurality of parameters of the scanning sequence and scanning site information, voltage amplitudes and phases of the multi-channel transmitting coils. Parameters such as the weighting factor of the multi-channel coil can be obtained according to the voltage amplitude and phase of the multi-channel transmitting coil. The weighting factor of the multiple channels represents the contribution of the radio frequency signals of the multiple channels to the scanning result.
  • the multi-channel radio frequency transmission technology is adopted because in high-field and ultra-high-field magnetic resonance imaging systems, in order to generate a uniform radio frequency electromagnetic field and reduce the energy deposition value, multi-channel parallel transmission technology is generally used to independently control the excitation of multiple unit coils.
  • the amplitude, phase and even the RF pulse shape of the source can increase the degree of freedom and optimization space to control the transmit sequence.
  • the combination of the bio-electromagnetic simulation model and the multi-channel radio frequency coil model to simulate and predict the radio frequency energy deposition value of the scanned object includes:
  • the radio frequency energy deposition value is predicted and obtained according to the radio frequency magnetic field strength value and the electric field strengths of multiple channels in the multi-channel radio frequency coil.
  • the electric field values of multiple channels in the multi-channel radio frequency coil model and the radio frequency magnetic field strength can be determined, which can be used for Parameters for predicting RF energy deposition values.
  • predicting the radio frequency energy deposition value according to the radio frequency magnetic field strength value and the electric field strengths of multiple channels in the multi-channel radio frequency coil includes:
  • the RF energy deposition value can be predicted by the following formula:
  • the middle integral term Q(x, y, z) is independent of the voltage amplitude and phase weight vector w of each channel
  • Q(x, y, z) is an N*N Hermitian regular matrix, which It can be calculated from the square of the magnitude of the electric field.
  • the advantage of this formula is that the local SAR can be calculated by pre-simulation, and has nothing to do with the voltage amplitude and phase weighting of multiple channels.
  • the voltage amplitude and phase of multiple emission channels, as well as the radio frequency of the scanning sequence are considered. pulse energy the elements of.
  • a rat can be used as a scanning object for simulation, and a water-fat separation image of the rat is acquired on a magnetic resonance imaging system with a resolution of 0.60 ⁇ 0.60 ⁇ 1.00 mm 3 .
  • the rat image data was processed with Matlab, and the rat image data was divided into four tissues: skin, fat, lung and muscle, and converted into model files that could be recognized by the electromagnetic field simulation software (Computer Simulation Technology, CST).
  • the tissue parameters refer to the CST database.
  • the obtained simplified rat model was simulated and tested by self-made coils.
  • Figure 2 is the rat model obtained by segmentation
  • Figure 3 is the simulation model and the coil used for the test. The reliability of the model is verified by comparing the simulated and measured magnetic field distributions.
  • Figure 4 is the simulated magnetic field B1 distribution of the rat model established by this method
  • Figure 5 is the measured magnetic field B1 distribution. It can be seen from Figures 4 and 5 that the simulation results are consistent with the measured results, which verifies the correctness of the self-built electromagnetic field simulation model. .
  • a three-dimensional biological model of the scanned object is established by collecting a magnetic resonance scanning structural image of the scanning object, and performing tissue segmentation based on the structural image; establishing a biological electromagnetic simulation model according to the three-dimensional biological model; According to the preset scanning strategy, a multi-channel radio frequency coil model is established, and the radio frequency energy deposition value is confirmed by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model simulation; the problem of inaccurate prediction of radio frequency energy deposition is solved, and the reduction of radio frequency energy deposition is solved. Simulation error, more accurate prediction of local energy deposition value.
  • Embodiment 6 is a flowchart of a method for monitoring radio frequency energy deposition according to Embodiment 2 of the present application.
  • This embodiment is applicable to the case of performing magnetic resonance scanning on a scanning object, and belongs to the same method as the method for predicting radio frequency energy deposition in the foregoing embodiment. an idea.
  • the method can be performed by a radio frequency energy deposition monitoring device, and the device can be implemented by software and/or hardware, and integrated in a computer device or server device with an application development function.
  • the radio frequency energy deposition monitoring method includes the following steps.
  • the SAR prediction is performed before the sequence scan, that is, the SAR is estimated according to the energy expansion of the sequence, the calibration data and the SAR model, if the estimated SAR exceeds the regulatory limit. , then adjust the scanning strategy, such as adjusting the TR (transmission time of the radio frequency sequence) and the flip angle in the sequence parameters, so as to reduce the SAR, and then perform the magnetic resonance sequence scanning under the condition of ensuring the safety of the scanning object. If the prediction passes, the scan can be started directly.
  • the multi-channel transmitting coil may also be referred to as a multi-channel radio frequency coil.
  • the forward and reverse power are always collected in real time through the directional coupler, and the applied power collection can be carried out in real time in combination with the analog-to-digital converter.
  • the B1 + field strength of the magnetic resonance It can be obtained by calibrating the flip angle through a calibration sequence. With the input of power and field strength, from the integral of the pulse waveform energy of the scan sequence, the sequence's energy can be calculated. Or the total energy of the sequence, combined with the SAR model of the aforementioned simulation calculation, the whole body SAR, head SAR, partial body SAR and local SAR can be obtained. When it is detected that the SAR of any part exceeds the safe value, the scanning is stopped in time, and the scanning strategy can be adjusted to keep the SAR value of the corresponding part within the safe range.
  • the adjusting the scanning strategy according to the radio frequency energy deposition value includes:
  • the process of scanning imaging is stopped, and the scanning sequence parameters in the preset scanning strategy are adjusted.
  • a preset scanning strategy of the scanning object is acquired, and a multi-channel radio frequency coil model is established according to the emission amplitude and phase of the scanning sequence in the preset scanning strategy, and based on the multi-channel radio frequency coil model, Using the radio frequency energy deposition prediction method described in any one of the embodiments, predict the radio frequency energy deposition value that the scanning object bears when the predetermined scanning sequence is used to scan and image the scanning object.
  • the scanning strategy is adjusted according to the RF energy deposition value.
  • the following is an example of the radio frequency energy deposition prediction and monitoring device provided by the embodiment of the present application.
  • the device and the radio frequency energy deposition prediction and monitoring method of the above-mentioned various embodiments belong to the same concept, and can realize the radio frequency energy deposition of the above-mentioned various embodiments. Sedimentation prediction and monitoring methods. For details that are not described in detail in the embodiments of the radio frequency energy deposition prediction and monitoring apparatus, reference may be made to the above-mentioned embodiments of the radio frequency energy deposition prediction and monitoring method.
  • FIG. 7 is a schematic structural diagram of an apparatus for predicting radio frequency energy deposition according to Embodiment 3 of the present application. This embodiment is applicable to the case of performing magnetic resonance scanning on a scanning object.
  • the radio frequency energy deposition prediction apparatus includes a human body model establishment module 310 , a bioelectromagnetic simulation model establishment module 320 and a radio frequency deposition prediction module 330 .
  • the human body model building module 310 is configured to collect the magnetic resonance scanning structure image of the scanning object, and perform tissue segmentation based on the magnetic resonance scanning structure image to establish a three-dimensional biological model of the scanning object;
  • the biological electromagnetic simulation model building module 320 is configured to set In order to establish a biological electromagnetic simulation model according to the three-dimensional biological model;
  • the radio frequency deposition prediction module 330 is set to establish a multi-channel RF coil model according to a preset scanning strategy, and combine the biological electromagnetic simulation model and the multi-channel RF coil model. Simulation predicts RF energy deposition values for the scanned object.
  • a three-dimensional biological model of the scanned object is established by collecting a magnetic resonance scanning structural image of the scanning object, and performing tissue segmentation based on the structural image; establishing a biological electromagnetic simulation model according to the three-dimensional biological model; According to the preset scanning strategy, a multi-channel radio frequency coil model is established, and the radio frequency energy deposition value is confirmed by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model simulation; the problem of inaccurate prediction of radio frequency energy deposition is solved, and the reduction of radio frequency energy deposition is solved. Simulation error, more accurate prediction of local energy deposition value.
  • the magnetic resonance scanning structure image includes a water-fat separation image, a T1/T2 contrast image and an ultra-short echo image
  • the human body model building module 310 is set to:
  • the three-dimensional biological model is established by combining the tissue-segmented fat, brain, muscle, bone and skin tissue.
  • the biological electromagnetic simulation model building module 320 is set to:
  • the corresponding dielectric constant, magnetic permeability and tissue density are assigned to a plurality of tissues in the three-dimensional biological model to obtain the biological electromagnetic simulation model.
  • the radio frequency deposition prediction module 330 is configured to:
  • the radio frequency energy deposition value is predicted and obtained according to the radio frequency magnetic field intensity value and the electric field intensity of multiple channels in the multi-channel radio frequency coil.
  • the radio frequency deposition prediction module 330 is configured to:
  • the radio frequency energy deposition prediction apparatus provided by the embodiment of the present application can execute the radio frequency energy deposition prediction method provided by any embodiment of the present application, and has functional modules and effects corresponding to the execution method.
  • FIG. 8 is a schematic structural diagram of a radio frequency energy deposition monitoring device according to Embodiment 4 of the present application. This embodiment can be applied to the case of performing magnetic resonance scanning on a scanning object.
  • the radio frequency energy deposition monitoring device includes a scan strategy determination module 410 , a radio frequency energy deposition value determination module 420 and a scan control module 430 .
  • the scanning strategy determination module 410 is configured to obtain a preset scanning strategy of the scanning object, and calculate the radio frequency magnetic field strength of the scanning sequence according to the preset scanning strategy, and according to the voltage amplitude of the multi-channel transmitting coil in the preset scanning strategy and the phase, to determine the weighting factor matrix of the multi-channel radio frequency coil; the radio frequency energy deposition value determination module 420 is set to, based on the radio frequency magnetic field strength and the weighting factor matrix, through the radio frequency energy deposition prediction method as described in any embodiment, Predicting the radio frequency energy deposition value that the scanning object bears during scanning and imaging; the scan control module 430 is configured to adjust the scanning strategy according to the radio frequency energy deposition value.
  • the scanning control module 430 is set to:
  • the process of scanning imaging is stopped, and the scanning sequence parameters in the preset scanning strategy are adjusted.
  • a preset scanning strategy of the scanning object is acquired, and a multi-channel radio frequency coil model is established according to the emission amplitude and phase of the scanning sequence in the preset scanning strategy, and based on the multi-channel radio frequency coil model, Using the radio frequency energy deposition prediction method described in any one of the embodiments, predict the radio frequency energy deposition value that the scanning object bears when the predetermined scanning sequence is used to scan and image the scanning object.
  • the scanning strategy is adjusted according to the RF energy deposition value.
  • the radio frequency energy deposition monitoring device provided by the embodiment of the present application can execute the radio frequency energy deposition monitoring method provided by any embodiment of the present application, and has functional modules and effects corresponding to the execution method.
  • FIG. 9 is a schematic structural diagram of a computer device according to Embodiment 5 of the present application.
  • Figure 9 shows a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present application.
  • the computer device 12 shown in FIG. 9 is only an example, and should not impose any limitations on the functions and scope of use of the embodiments of the present application.
  • the computer device 12 can be any terminal device with computing capability, such as an intelligent controller, a server, a mobile phone and other terminal devices.
  • the computer equipment can be connected with the magnetic resonance imaging equipment, so as to cooperate with the magnetic resonance scanning process, execute the corresponding method steps, and realize the prediction of the radio frequency energy deposition.
  • computer device 12 takes the form of a general-purpose computing device.
  • the components of computer device 12 may include: one or more processors or processing units 16, system memory 28, and a bus 18 connecting various system components including system memory 28 and processing unit 16.
  • Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of a variety of bus structures.
  • these architectures include Industry Subversive Alliance (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) ) local bus and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
  • ISA Industry Subversive Alliance
  • MCA Micro Channel Architecture
  • VESA Video Electronics Standards Association
  • PCI peripheral component interconnect
  • Computer device 12 includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including both volatile and nonvolatile media, removable and non-removable media.
  • System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and/or cache 32 .
  • Computer device 12 may include other removable/non-removable, volatile/non-volatile computer system storage media.
  • storage system 34 may be used to read and write to non-removable, non-volatile magnetic media (not shown in Figure 9, commonly referred to as "hard drives").
  • a disk drive for reading and writing to removable non-volatile magnetic disks eg "floppy disks" and removable non-volatile optical disks (eg (Compact Disc Read-Only Memory) may be provided , CD-ROM), digital video disc (Digital Video Disc-Read Only Memory, DVD-ROM) or other optical media) optical disc drive for reading and writing.
  • each drive may be connected to bus 18 through one or more data media interfaces.
  • System memory 28 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of various embodiments of the present application.
  • a program/utility 40 having a set (at least one) of program modules 42, which may be stored, for example, in system memory 28, such program modules 42 including an operating system, one or more application programs, other program modules, and program data, which An implementation of a network environment may be included in each or a combination of the examples.
  • Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
  • Computer device 12 may also communicate with one or more external devices 14 (eg, keyboard, pointing device, display 24, etc.), may also communicate with one or more devices that enable a user to interact with computer device 12, and/or communicate with Any device (eg, network card, modem, etc.) that enables the computer device 12 to communicate with one or more other computing devices. Such communication may take place through an input/output (I/O) interface 22 . Also, computer device 12 may communicate with one or more networks (eg, Local Area Network (LAN), Wide Area Network (WAN), and/or public networks such as the Internet) through network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18 . Although not shown in FIG. 9, other hardware and/or software modules may be used in conjunction with computer device 12, including: microcode, device drivers, redundant processing units, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) ) systems, tape drives, and data backup storage systems.
  • RAID Redundant Arrays of Independent Disks
  • the processing unit 16 executes a variety of functional applications and data processing by running the program stored in the system memory 28, such as implementing a radio frequency energy deposition prediction method provided in this embodiment, including:
  • a multi-channel radio frequency coil model is established, and the radio frequency energy deposition value of the scanning object is predicted by simulation combined with the biological electromagnetic simulation model and the multi-channel radio frequency coil model.
  • the radio frequency energy deposition monitoring method provided by any embodiment of the present application can also be implemented, including:
  • the radio frequency energy deposition prediction method Based on the radio frequency magnetic field strength and the weighting factor matrix, by using the radio frequency energy deposition prediction method according to any one of the embodiments, predict the radio frequency energy deposition value that the scanning object is subjected to during scanning imaging;
  • the scanning strategy is adjusted according to the RF energy deposition value.
  • the sixth embodiment provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, implements the radio frequency energy deposition prediction method provided by any embodiment of the present application, including:
  • a multi-channel radio frequency coil model is established, and the radio frequency energy deposition value of the scanning object is predicted by simulation combined with the biological electromagnetic simulation model and the multi-channel radio frequency coil model.
  • the radio frequency energy deposition monitoring method provided by any embodiment of the present application may also be implemented, including:
  • the radio frequency energy deposition prediction method Based on the radio frequency magnetic field strength and the weighting factor matrix, by using the radio frequency energy deposition prediction method according to any one of the embodiments, predict the radio frequency energy deposition value that the scanning object is subjected to during scanning imaging;
  • the scanning strategy is adjusted according to the RF energy deposition value.
  • the computer storage medium of the embodiments of the present application may adopt any combination of one or more computer-readable media.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above.
  • Computer readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, RAM, Read-Only Memory (ROM), Erasable Programmable Read-Only Memory (Erasable Programmable Read-Only Memory) Only Memory, EPROM), flash memory, optical fiber, CD-ROM, optical storage devices, magnetic storage devices, or any suitable combination of the above.
  • a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a propagated data signal in baseband or as part of a carrier wave, with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms, including electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .
  • the program code embodied on the computer-readable medium may be transmitted by any suitable medium, including: wireless, wire, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the above.
  • any suitable medium including: wireless, wire, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the above.
  • Computer program code for carrying out the operations of the present application may be written in one or more programming languages, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional A procedural programming language, such as the "C" language or similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any kind of network, including a LAN or WAN, or may be connected to an external computer (eg, using an Internet service provider to connect through the Internet).
  • the above-mentioned multiple modules or multiple steps of the present application can be implemented by a general-purpose computing device, and they can be centralized on a single computing device, or distributed on a network composed of multiple computing devices. implemented by program code executable by a computer device so that they can be stored in a storage device and executed by a computing device, or they can be separately made into a plurality of integrated circuit modules, or a plurality of modules or steps in them can be made into a single integrated circuit modules.
  • the present application is not limited to any particular combination of hardware and software.

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Abstract

一种射频能量沉积预测及射频能量沉积监测方法、装置、设备和介质，其中，预测方法包括：采集扫描对象的磁共振扫描结构图像，并基于磁共振扫描结构图像进行组织分割，建立扫描对象的三维生物模型（S110）；根据三维生物模型建立生物电磁仿真模型（S120）；根据预设扫描策略，建立多通道射频线圈模型，并结合生物电磁仿真模型和多通道射频线圈模型仿真确认射频能量沉积值（S130）。监测方法包括：根据预测方法预测射频能量沉积值，并根据预测结果调整扫描策略。

Description

射频能量沉积预测及射频能量沉积监测方法、装置、设备和介质
本申请要求在2021年03月11日提交中国专利局、申请号为202110265492.7的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本申请涉及磁共振成像技术领域，例如涉及一种射频能量沉积预测及射频能量沉积监测方法、装置、设备和介质。
背景技术
在高场及超高场磁共振成像(Magnetic Resonance Imaging，MRI)系统中，病人在检查时会吸收射频电磁波的能量，形成人体射频能量沉积，量度单位是特定吸收率(Specific Absorption Rate，SAR)，即每单位质量的生物组织在单位时间内所吸收的射频电磁波能量(单位为W/kg)。在高场及超高场MRI系统中，为了产生均匀的射频电磁场，降低SAR值，采用多通道并行发射技术，独立控制多个单元线圈激励源的幅值、相位甚至射频脉冲波形，从而提高了控制的自由度和优化空间。然而激励源自由度的提高同时会导致线圈在人体内产生的SAR分布变得极为复杂，产生局部热点的可能性大幅提高，有可能超过SAR的安全标准，则需要更加准确的对SAR值进行预测及监测。
但是，在相关技术中，基于商用电磁场仿真软件中的多个数字人体模型对SAR值进行预测，未能充分考虑个体差异，身体大小和形状、性别和体脂分布以及系统中的身体姿势和位置等因素都会导致预测误差，不准确。且在进行SAR计算时，采用固定的射频匀场模型，不再适用于多通道射频发射的场景。
发明内容
本申请提供了一种射频能量沉积预测及射频能量沉积监测方法、装置、设备和介质，以实现在多通道发射线圈并行发射技术的场景下，更加精确且个性化的对扫描对象的SAR值进行预测，提高扫描对象接收磁共振扫描的安全性。
提供了一种射频能量沉积预测方法，该方法包括：
采集扫描对象的磁共振扫描结构图像，并基于所述磁共振扫描结构图像进行组织分割，建立所述扫描对象的三维生物模型；
根据所述三维生物模型建立生物电磁仿真模型；
根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真 模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值。
还提供了一种射频能量沉积监测方法，该方法包括：
获取扫描对象的预设扫描策略，并根据所述预设扫描策略计算扫描序列的射频磁场强度，根据所述预设扫描策略中多通道发射线圈的电压幅值与相位，确定多通道射频线圈的加权因子矩阵；
基于所述射频磁场强度和所述加权因子矩阵，通过如任一实施例所述的射频能量沉积预测方法，预测所述扫描对象在进行扫描成像时承受的射频能量沉积值；
根据所述射频能量沉积值调整扫描策略。
还提供了一种射频能量沉积预测装置，该装置包括：
人体模型建立模块，设置为采集扫描对象的磁共振扫描结构图像，并基于所述磁共振扫描结构图像进行组织分割，建立所述扫描对象的三维生物模型；
生物电磁仿真模型建立模块，设置为根据所述三维生物模型建立生物电磁仿真模型；
射频沉积预测模块，设置为根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值。
还提供了一种射频能量沉积监测装置，该装置包括：
扫描策略确定模块，设置为获取扫描对象的预设扫描策略，并根据所述预设扫描策略计算扫描序列的射频磁场强度，根据所述预设扫描策略中多通道发射线圈的电压幅值与相位，确定多通道射频线圈的加权因子矩阵；
射频能量沉积值确定模块，设置为基于所述射频磁场强度和所述加权因子矩阵，通过如任一实施例所述的射频能量沉积预测方法，预测所述扫描对象在进行扫描成像时承受的射频能量沉积值；
扫描控制模块，设置为根据所述射频能量沉积值调整扫描策略。
还提供了一种计算机设备，包括：
一个或多个处理器；
存储器，设置为存储一个或多个程序；
当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如本申请任意实施例所提供的射频能量沉积预测方法，或，射频能量沉积监测方法。
还提供了一种计算机可读存储介质，存储有计算机程序，该计算机程序被处理器执行时实现如本申请任意实施例所提供的射频能量沉积预测方法，或，射频能量沉积监测方法。
附图说明
图1是本申请实施例一提供的一种射频能量沉积预测方法的流程图；
图2是本申请实施例一提供的一种大鼠的生物电磁仿真模型建立的示意图；
图3是本申请实施例一提供的一种生物电磁仿真模型与预测所用的线圈的示意图；
图4是本申请实施例一提供的基于本实施例中方法仿真磁场B1分布示意图；
图5是本申请实施例一提供的基于本实施例中方法实测的磁场B1分布示意图；
图6是本申请实施例二提供的一种射频能量沉积监测方法的流程图；
图7是本申请实施例三提供的一种射频能量沉积预测装置的结构示意图；
图8是本申请实施例四提供的一种射频能量沉积监测装置的结构示意图；
图9是本申请实施例五提供的一种计算机设备的结构示意图。
具体实施方式
下面结合附图和实施例对本申请进行说明。此处所描述的实施例仅仅用于解释本申请，而非对本申请的限定。为了便于描述，附图中仅示出了与本申请相关的部分而非全部结构。
实施例一
图1为本申请实施例一提供的一种射频能量沉积预测方法的流程图，本实施例可适用于对扫描对象进行磁共振扫描的情况。该方法可以由配置于射频能量沉积预测置执行，该装置可以由软件和/或硬件的方式来实现，集成于具有应用开发功能的电子设备中。
如图1所示，射频能量沉积预测方法包括以下步骤。
S110、采集扫描对象的磁共振扫描结构图像，并基于所述磁共振扫描结构 图像进行组织分割，建立所述扫描对象的三维生物模型。
通常采用电磁仿真软件，对数字扫描对象的模型进行仿真，为了能够减小仿真误差，使仿真结果更加准确，在本步骤中要建立扫描对象的个性化的三维生物模型，使仿真过程中的三维生物模型与扫描对象更加匹配。
可以通过设置不同的磁共振扫描参数，对扫描对象进行扫描，得到磁共振扫描结构图像，包括采集扫描对象的水脂分离磁共振图像以及T1/T2对比磁共振图像，从而获得脂肪、大脑和肌肉组织的信息。同时，还可利用超短回波序列获得超短回波图像，以获取骨骼信息。进而可以基于水脂分离图像、T1与T2对比图像及超短回波图像进行扫描对象的扫描图像的组织分割，得到脂肪、大脑、肌肉、骨骼及皮肤等组织结构。其中，皮肤可以是在扫描对象整体轮廓外侧增加的皮肤信息。基于组织分割得到的脂肪、大脑、肌肉及皮肤等结构，可以建立扫描对象的三维生物模型。扫描对象可以是人体，或者动物。
在一实施例中，所述磁共振扫描结构图像包括水脂分离图像、T1/T2对比图像及超短回波图像，所述基于所述磁共振扫描结构图像进行组织分割，建立所述扫描对象的三维生物模型，包括：
基于所述水脂分离图像、所述T1/T2对比图像及所述超短回波图像进行所述扫描对象的扫描图像的组织分割，得到脂肪、大脑、肌肉、骨骼及皮肤组织；
结合经过组织分割后的脂肪、大脑、肌肉、骨骼及皮肤组织建立所述三维生物模型。
S120、根据所述三维生物模型建立生物电磁仿真模型。
在一实施例中，所述根据所述三维生物模型建立生物电磁仿真模型，包括：
为所述三维生物模型中多个组织进行对应的介电常数、磁导率及组织密度赋值，得到所述生物电磁仿真模型。
由于不同的组织的导电率、磁导率及特性吸收率等数值是不同的，在得到了扫描对象的三维生物模型之后，为三维生物模型中多个组织对应的介电常数、磁导率及组织密度进行赋值，便可以得到生物电磁仿真模型。其中，介电常数、磁导率及组织密度可以是预先存储在仿真系统的内存中的。
S130、根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值。
在一实施例中，扫描策略包括扫描序列的多个参数及扫描部位信息，多通道发射线圈的电压幅值与相位。可以根据多通道发射线圈的电压幅值与相位得 到多通道线圈的权重因子等参数，多个通道的权重因子表示了多个通道射频信号对扫描结果的贡献度。
采用多通道的射频发射技术，是因为在高场及超高场磁共振成像系统中，为了产生均匀的射频电磁场，降低能量沉积值，一般采用多通道并行发射技术，独立控制多个单元线圈激励源的幅值、相位甚至射频脉冲波形，可以提高控制发射序列的自由度和优化空间。
在一实施例中，所述结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值，包括：
基于所述生物电磁仿真模型和所述多通道射频线圈模型进行仿真，确定射频磁场强度数值及多通道射频线圈中多个通道的电场强度；
根据所述射频磁场强度数值及所述多通道射频线圈中多个通道的电场强度，预测得到所述射频能量沉积值。
在一实施例中，通过结合扫描对象的生物电磁仿真模型和多通道射频线圈模型，进行仿真，可以确定多通道射频线圈模型中的多个通道的电场值，以及射频磁场强度，即能够用于预测射频能量沉积值的参数。
在一实施例中，所述根据所述射频磁场强度数值及所述多通道射频线圈中多个通道的电场强度，预测得到所述射频能量沉积值，包括：
根据公式
Figure PCTCN2021087689-appb-000001
计算在所述多通道射频线圈模型下，所述扫描对象的射频能量沉积值。
基于已经获取到的用于预测射频能量沉积值的参数，可以通过如下公式对射频能量沉积值进行预测：
根据公式
Figure PCTCN2021087689-appb-000002
计算在所述多通道射频线圈模型下，所述扫描对象的射频能量沉积值，其中，SAR(x,y,z)表示所述扫描对象体内预设位置(x,y,z)的射频能量沉积值，w为多通道射频线圈中多通道线圈的加权因子矩阵，w′为w的共轭转置矩阵，E(x,y,z)为所述多通道射频线圈中多个通道的电场强度矩阵，E′(x,y,z)为E(x,y,z)的共轭转置矩阵，ρ(x,y,z)为所述扫描对象的身体密度，σ(x,y,z)为所述扫描对象的身体电导率，
Figure PCTCN2021087689-appb-000003
为射频磁场强度，V表示所述射频能量沉积值的统计体积， 可以包括全身，头部，部分身体和局部。
在一实施例中，即利用Q矩阵把通道加权因子w提到积分公式的外面，有：
Figure PCTCN2021087689-appb-000004
其中，中间的积分项Q(x,y,z)独立于每个通道的电压幅度和相位权重向量w，Q(x,y,z)是一个N*N的埃尔米特正则矩阵，它可以从电场幅度的平方计算得到。这个公式的好处在于局部的SAR可以通过预先仿真计算得到，与多个通道的电压幅度、相位加权无关，在能量沉积监控时，才考虑多个发射通道的电压幅度和相位，以及扫描序列的射频脉冲能量
Figure PCTCN2021087689-appb-000005
的因素。
在一个实例中，可以大鼠作为扫描对象进行仿真，在磁共振成像系统上采集大鼠水脂分离图像，分辨率为0.60×0.60×1.00mm 3。用Matlab处理大鼠图像数据，将大鼠图像数据分割为皮肤、脂肪、肺和肌肉四种组织，转换为电磁场仿真计算软件(Computer Simulation Technology，CST)可识别的模型文件，组织参数参考CST数据库，将得到的大鼠简化模型利用自制线圈进行仿真和实验测试。图2是分割得到的大鼠模型，图3是仿真模型与测试所用的线圈。通过对比仿真与实测的磁场分布，来验证该模型的可靠性。图4是本方法建立的大鼠模型仿真磁场B1分布，图5是实测的磁场B1分布，从图4和图5可以看出，仿真与实测结果一致，验证了自建电磁场仿真模型的正确性。
本实施例的技术方案，通过采集扫描对象的磁共振扫描结构图像，并基于所述结构图像进行组织分割，建立所述扫描对象的三维生物模型；根据所述三维生物模型建立生物电磁仿真模型；根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真确认射频能量沉积值；解决了射频能量沉积预测不准确的问题，实现了减少仿真误差，更准确预测局部能量沉积值。
实施例二
图6为本申请实施例二提供的一种射频能量沉积监测方法的流程图，本实施例可适用于对扫描对象进行磁共振扫描的情况，与上述实施例中的射频能量沉积预测方法属于同一个构思。该方法可以由射频能量沉积监测装置执行，该装置可以由软件和/或硬件的方式来实现，集成于具有应用开发功能的计算机设备或服务器设备中。
如图6所示，射频能量沉积监测方法包括以下步骤。
S210、获取扫描对象的预设扫描策略，并根据所述预设扫描策略计算扫描序列的射频磁场强度，根据所述预设扫描策略中多通道发射线圈的电压幅值与相位，确定多通道射频线圈的加权因子矩阵。
S220、基于所述射频磁场强度和所述加权因子矩阵，通过如任一实施例所述的射频能量沉积预测方法，预测所述扫描对象在进行扫描成像时承受的射频能量沉积值。
S230、根据所述射频能量沉积值调整扫描策略。
在一实施例中，为了保证序列扫描的顺利进行，在序列扫描前会进行SAR预测，即根据序列的能量展开、校准数据和SAR模型进行SAR的预估，如果预估的SAR超出法规的限制，则调整扫描策略，如调整序列参数中TR(射频序列的发射时间)、和翻转角等，降低SAR，在保证扫描对象安全的条件下再进行磁共振序列扫描。如果预测通过则可以直接开始本次扫描。其中，多通道发射线圈也可称为多通道射频线圈。
在一种可选实施例中，为了完全保证患者的安全，通过定向耦合器一直实时采集前向和反向功率，结合模数转换器可以进行实时的施加功率采集，磁共振的B1 +场强可以通过校准序列校准翻转角得到。有了功率和场强的输入，根据扫描序列的脉冲波形能量的积分，可以计算序列的
Figure PCTCN2021087689-appb-000006
或序列总能量，再结合前述仿真计算的SAR模型，可以得到全身SAR，头部SAR，部分身体SAR和局部SAR。当监测到任意部位SAR超过安全值时及时停止扫描，可继续调整扫描策略，使相应部位的SAR值在安全范围内。
在一实施例中，所述根据所述射频能量沉积值调整扫描策略，包括：
当所述射频能量沉积值大于预设上限能量沉积值时，停止扫描成像的过程，并调整所述预设扫描策略中的扫描序列参数。
本实施例的技术方案，通过获取扫描对象的预设扫描策略，并根据所述预设扫描策略中扫描序列的发射幅值与相位建立多通道射频线圈模型，基于所述多通道射频线圈模型，通过任一实施例所述的射频能量沉积预测方法预测使用所述预设扫描序列对所述扫描对象进行扫描成像时所述扫描对象承受的射频能量沉积值。根据所述射频能量沉积值调整扫描策略。本实施例实现了在多通道射频发射技术下，对射频能量沉积值的监测，提高了磁共振扫描过程中的安全性。
以下是本申请实施例提供的射频能量沉积预测及监测装置的实施例，该装 置与上述多个实施例的射频能量沉积预测及监测方法属于同一个构思，可实现上述多个实施例的射频能量沉积预测及监测方法。在射频能量沉积预测及监测装置的实施例中未详尽描述的细节内容，可以参考上述射频能量沉积预测及监测方法的实施例。
实施例三
图7为本申请实施例三提供的一种射频能量沉积预测装置的结构示意图，本实施例可适用于对扫描对象进行磁共振扫描的情况。
如图7所示，射频能量沉积预测装置包括人体模型建立模块310、生物电磁仿真模型建立模块320和射频沉积预测模块330。
人体模型建立模块310，设置为采集扫描对象的磁共振扫描结构图像，并基于所述磁共振扫描结构图像进行组织分割，建立所述扫描对象的三维生物模型；生物电磁仿真模型建立模块320，设置为根据所述三维生物模型建立生物电磁仿真模型；射频沉积预测模块330，设置为根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值。
本实施例的技术方案，通过采集扫描对象的磁共振扫描结构图像，并基于所述结构图像进行组织分割，建立所述扫描对象的三维生物模型；根据所述三维生物模型建立生物电磁仿真模型；根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真确认射频能量沉积值；解决了射频能量沉积预测不准确的问题，实现了减少仿真误差，更准确预测局部能量沉积值。
可选的，所述磁共振扫描结构图像包括水脂分离图像、T1/T2对比图像及超短回波图像，所述人体模型建立模块310是设置为：
基于所述水脂分离图像、T1/T2对比图像及超短回波图像进行所述扫描对象的扫描图像的组织分割，得到脂肪、大脑、肌肉、骨骼及皮肤组织；
结合经过组织分割后的脂肪、大脑、肌肉、骨骼及皮肤组织建立所述三维生物模型。
可选的，所述生物电磁仿真模型建立模块320是设置为：
为所述三维生物模型中多个组织进行对应的介电常数、磁导率及组织密度赋值，得到所述生物电磁仿真模型。
可选的，所述射频沉积预测模块330是设置为：
基于所述生物电磁仿真模型和所述多通道射频线圈模型进行仿真，确定射 频磁场强度数值及所述多通道射频线圈中多个通道的电场强度；
根据射频磁场强度数值及所述多通道射频线圈中多个通道的电场强度，预测得到所述射频能量沉积值。
可选的，所述射频沉积预测模块330是设置为：
根据公式
Figure PCTCN2021087689-appb-000007
计算在所述多通道射频线圈模型下，所述扫描对象的射频能量沉积值，其中，SAR(x,y,z)表示所述扫描对象体内预设位置(x,y,z)的射频能量沉积值，w为多通道射频线圈中多通道线圈的加权因子矩阵，w′为w的共轭转置矩阵，E(x,y,z)为所述多通道射频线圈中多个通道的电场强度矩阵，E′(x,y,z)为E(x,y,z)的共轭转置矩阵，ρ(x,y,z)为所述扫描对象的身体密度，σ(x,y,z)为所述扫描对象的身体电导率，
Figure PCTCN2021087689-appb-000008
为射频磁场强度，V表示所述射频能量沉积值的统计体积。
本申请实施例所提供的射频能量沉积预测装置可执行本申请任意实施例所提供的射频能量沉积预测方法，具备执行方法相应的功能模块和效果。
实施例四
图8为本申请实施例四提供的一种射频能量沉积监测装置的结构示意图，本实施例可适用于对扫描对象进行磁共振扫描的情况。
如图8所示，射频能量沉积监测装置包括扫描策略确定模块410、射频能量沉积值确定模块420和扫描控制模块430。
扫描策略确定模块410，设置为获取扫描对象的预设扫描策略，并根据所述预设扫描策略计算扫描序列的射频磁场强度，根据所述预设扫描策略中多通道发射线圈的电压幅值与相位，确定多通道射频线圈的加权因子矩阵；射频能量沉积值确定模块420，设置为基于所述射频磁场强度和所述加权因子矩阵，通过如任一实施例所述的射频能量沉积预测方法，预测所述扫描对象在进行扫描成像时承受的射频能量沉积值；扫描控制模块430，设置为根据所述射频能量沉积值调整扫描策略。
可选的，所述扫描控制模块430，是设置为：
当所述射频能量沉积值大于预设上限能量沉积值时，停止扫描成像的过程，并调整所述预设扫描策略中的扫描序列参数。
本实施例的技术方案，通过获取扫描对象的预设扫描策略，并根据所述预设扫描策略中扫描序列的发射幅值与相位建立多通道射频线圈模型，基于所述多通道射频线圈模型，通过任一实施例所述的射频能量沉积预测方法预测使用所述预设扫描序列对所述扫描对象进行扫描成像时所述扫描对象承受的射频能量沉积值。根据所述射频能量沉积值调整扫描策略。本实施例实现了在多通道射频发射技术下，对射频能量沉积值的监测，提高了磁共振扫描过程中的安全性。
本申请实施例所提供的射频能量沉积监测装置可执行本申请任意实施例所提供的射频能量沉积监测方法，具备执行方法相应的功能模块和效果。
实施例五
图9为本申请实施例五提供的一种计算机设备的结构示意图。图9示出了适于用来实现本申请实施方式的示例性计算机设备12的框图。图9显示的计算机设备12仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。计算机设备12可以为任意具有计算能力的终端设备，如智能控制器、服务器和手机等终端设备。该计算机设备可以与磁共振成像设备相连接，以配合磁共振扫描过程，执行相应的方法步骤，实现射频能量沉积的预测。
如图9所示，计算机设备12以通用计算设备的形式表现。计算机设备12的组件可以包括：一个或者多个处理器或者处理单元16，系统存储器28，连接不同系统组件(包括系统存储器28和处理单元16)的总线18。
总线18表示几类总线结构中的一种或多种，包括存储器总线或者存储器控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些体系结构包括工业标准体系结构(Industry Subversive Alliance，ISA)总线，微通道体系结构(Micro Channel Architecture，MCA)总线，增强型ISA总线、视频电子标准协会(Video Electronics Standards Association，VESA)局域总线以及外围组件互连(Peripheral Component Interconnect，PCI)总线。
计算机设备12包括多种计算机系统可读介质。这些介质可以是任何能够被计算机设备12访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
系统存储器28可以包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(Random Access Memory，RAM)30和/或高速缓存32。计算机设备12可以包括其它可移动/不可移动的、易失性/非易失性计算机系统存储介质。仅作为举例，存储系统34可以用于读写不可移动的、非易失性磁介质(图 9未显示，通常称为“硬盘驱动器”)。尽管图9中未示出，可以提供用于对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及对可移动非易失性光盘(例如(Compact Disc Read-Only Memory，CD-ROM)，数字视盘(Digital Video Disc-Read Only Memory，DVD-ROM)或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过一个或者多个数据介质接口与总线18相连。系统存储器28可以包括至少一个程序产品，该程序产品具有一组(例如至少一个)程序模块，这些程序模块被配置以执行本申请多个实施例的功能。
具有一组(至少一个)程序模块42的程序/实用工具40，可以存储在例如系统存储器28中，这样的程序模块42包括操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或一种组合中可能包括网络环境的实现。程序模块42通常执行本申请所描述的实施例中的功能和/或方法。
计算机设备12也可以与一个或多个外部设备14(例如键盘、指向设备、显示器24等)通信，还可与一个或者多个使得用户能与该计算机设备12交互的设备通信，和/或与使得该计算机设备12能与一个或多个其它计算设备进行通信的任何设备(例如网卡，调制解调器等等)通信。这种通信可以通过输入/输出(Input/Output，I/O)接口22进行。并且，计算机设备12还可以通过网络适配器20与一个或者多个网络(例如局域网(Local Area Network，LAN)，广域网(Wide Area Network，WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器20通过总线18与计算机设备12的其它模块通信。尽管图9中未示出，可以结合计算机设备12使用其它硬件和/或软件模块，包括：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、磁盘阵列(Redundant Arrays of Independent Disks，RAID)系统、磁带驱动器以及数据备份存储系统等。
处理单元16通过运行存储在系统存储器28中的程序，从而执行多种功能应用以及数据处理，例如实现本实施例所提供的一种射频能量沉积预测方法，包括：
采集扫描对象的磁共振扫描结构图像，并基于所述磁共振扫描结构图像进行组织分割，建立所述扫描对象的三维生物模型；
根据所述三维生物模型建立生物电磁仿真模型；
根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值。
或者，计算机程序被处理器执行时还可实现如本申请任意实施例所提供的射频能量沉积监测方法，包括：
获取扫描对象的预设扫描策略，并根据所述预设扫描策略计算扫描序列的 射频磁场强度，根据所述预设扫描策略中多通道发射线圈的电压幅值与相位，确定多通道射频线圈的加权因子矩阵；
基于所述射频磁场强度和所述加权因子矩阵，通过如任一实施例所述的射频能量沉积预测方法，预测所述扫描对象在进行扫描成像时承受的射频能量沉积值；
根据所述射频能量沉积值调整扫描策略。
实施例六
本实施例六提供了一种计算机可读存储介质，存储有计算机程序，该计算机程序被处理器执行时实现如本申请任意实施例所提供的射频能量沉积预测方法，包括：
采集扫描对象的磁共振扫描结构图像，并基于所述磁共振扫描结构图像进行组织分割，建立所述扫描对象的三维生物模型；
根据所述三维生物模型建立生物电磁仿真模型；
根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值。
或者，计算机程序被处理器执行时还可实现如本申请任意实施例所提供的射频能量沉积监测方法，包括：
获取扫描对象的预设扫描策略，并根据所述预设扫描策略计算扫描序列的射频磁场强度，根据所述预设扫描策略中多通道发射线圈的电压幅值与相位，确定多通道射频线圈的加权因子矩阵；
基于所述射频磁场强度和所述加权因子矩阵，通过如任一实施例所述的射频能量沉积预测方法，预测所述扫描对象在进行扫描成像时承受的射频能量沉积值；
根据所述射频能量沉积值调整扫描策略。
本申请实施例的计算机存储介质，可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是：电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、RAM、只读存储器(Read-Only Memory，ROM)、可擦式可编程只读存储器(Erasable Programmable Read-Only Memory，EPROM)、闪存、光纤、CD-ROM、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任 何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括：无线、电线、光缆、射频(Radio Frequency，RF)等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本申请操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言，诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言，诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络，包括LAN或WAN，连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
上述的本申请的多个模块或多个步骤可以用通用的计算装置来实现，它们可以集中在单个计算装置上，或者分布在多个计算装置所组成的网络上，可选地，他们可以用计算机装置可执行的程序代码来实现，从而可以将它们存储在存储装置中由计算装置来执行，或者将它们分别制作成多个集成电路模块，或者将它们中的多个模块或步骤制作成单个集成电路模块来实现。这样，本申请不限制于任何特定的硬件和软件的结合。

Claims (11)

  1. 一种射频能量沉积预测方法，包括：
    采集扫描对象的磁共振扫描结构图像，并基于所述磁共振扫描结构图像进行组织分割，建立所述扫描对象的三维生物模型；
    根据所述三维生物模型建立生物电磁仿真模型；
    根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值。
  2. 根据权利要求1所述的方法，其中，所述磁共振扫描结构图像包括水脂分离图像、T1/T2对比图像及超短回波图像，所述基于所述磁共振扫描结构图像进行组织分割，建立所述扫描对象的三维生物模型，包括：
    基于所述水脂分离图像、所述T1/T2对比图像及所述超短回波图像进行所述扫描对象的扫描图像的组织分割，得到脂肪、大脑、肌肉、骨骼及皮肤组织的图像；
    结合经过组织分割后的脂肪、大脑、肌肉、骨骼及皮肤组织的图像建立所述三维生物模型。
  3. 根据权利要求1所述的方法，其中，所述根据所述三维生物模型建立生物电磁仿真模型，包括：
    为所述三维生物模型中多个组织进行对应的介电常数、磁导率及组织密度赋值，得到所述生物电磁仿真模型。
  4. 根据权利要求1中所述的方法，其中，所述结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值，包括：
    基于所述生物电磁仿真模型和所述多通道射频线圈模型进行仿真，确定射频磁场强度数值及多通道射频线圈中多个通道的电场强度；
    根据所述射频磁场强度数值及所述多通道射频线圈中多个通道的电场强度，预测得到所述射频能量沉积值。
  5. 根据权利要求4所述的方法，其中，所述根据所述射频磁场强度数值及所述多通道射频线圈中多个通道的电场强度，预测得到所述射频能量沉积值，包括：
    根据公式
    Figure PCTCN2021087689-appb-100001
    计算在所述多通道射频线圈模型下，所述扫描对象的射频能量沉积值，其中，SAR(x,y,z)表示所述扫描对象体内预设位置(x,y,z)的射频能量沉积值，w为所 述多通道射频线圈中多通道射频线圈的加权因子矩阵，w′为w的共轭转置矩阵，E(x,y,z)为所述多通道射频线圈中多个通道的电场强度矩阵，E′(x,y,z)为E(x,y,z)的共轭转置矩阵，ρ(x,y,z)为所述扫描对象的身体密度，σ(x,y,z)为所述扫描对象的身体电导率，
    Figure PCTCN2021087689-appb-100002
    为射频磁场强度，V表示所述射频能量沉积值的统计体积。
  6. 一种射频能量沉积监测方法，包括：
    获取扫描对象的预设扫描策略，并根据所述预设扫描策略计算扫描序列的射频磁场强度，根据所述预设扫描策略中多通道射频线圈的电压幅值与相位，确定多通道射频线圈的加权因子矩阵；
    基于所述射频磁场强度和所述加权因子矩阵，通过如权利要求1-5中任一项所述的射频能量沉积预测方法，预测所述扫描对象在进行扫描成像的情况下承受的射频能量沉积值；
    根据所述射频能量沉积值调整扫描策略。
  7. 根据权利要求6所述的方法，其中，所述根据所述射频能量沉积值调整扫描策略，包括：
    在所述射频能量沉积值大于预设上限能量沉积值的情况下，停止扫描成像的过程，并调整所述预设扫描策略中的扫描序列参数。
  8. 一种射频能量沉积预测装置，包括：
    人体模型建立模块，设置为采集扫描对象的磁共振扫描结构图像，并基于所述磁共振扫描结构图像进行组织分割，建立所述扫描对象的三维生物模型；
    生物电磁仿真模型建立模块，设置为根据所述三维生物模型建立生物电磁仿真模型；
    射频沉积预测模块，设置为根据预设扫描策略，建立多通道射频线圈模型，并结合所述生物电磁仿真模型和所述多通道射频线圈模型仿真预测所述扫描对象的射频能量沉积值。
  9. 一种射频能量沉积监测装置，包括：
    扫描策略确定模块，设置为获取扫描对象的预设扫描策略，并根据所述预设扫描策略计算扫描序列的射频磁场强度，根据所述预设扫描策略中多通道射频线圈的电压幅值与相位，确定多通道射频线圈的加权因子矩阵；
    射频能量沉积值确定模块，设置为基于所述射频磁场强度和所述加权因子矩阵，通过如权利要求1-5中任一项所述的射频能量沉积预测方法，预测所述扫描对象在进行扫描成像的情况下承受的射频能量沉积值；
    扫描控制模块，设置为根据所述射频能量沉积值调整扫描策略。
  10. 一种计算机设备，包括：
    一个或多个处理器；
    存储器，设置为存储一个或多个程序；
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如权利要求1-5中任一项所述的射频能量沉积预测方法，或，权利要求6-7中任一项所述的射频能量沉积监测方法。
  11. 一种计算机可读存储介质，存储有计算机程序，其中，所述计算机程序被处理器执行时实现如权利要求1-5中任一项所述的射频能量沉积预测方法，或，权利要求6-7中任一项所述的射频能量沉积监测方法。
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