CN113017598B - Radio frequency energy deposition prediction and monitoring method, device, equipment and medium - Google Patents
Radio frequency energy deposition prediction and monitoring method, device, equipment and medium Download PDFInfo
- Publication number
- CN113017598B CN113017598B CN202110265492.7A CN202110265492A CN113017598B CN 113017598 B CN113017598 B CN 113017598B CN 202110265492 A CN202110265492 A CN 202110265492A CN 113017598 B CN113017598 B CN 113017598B
- Authority
- CN
- China
- Prior art keywords
- radio frequency
- scanning
- energy deposition
- model
- frequency energy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000008021 deposition Effects 0.000 title claims abstract description 150
- 238000000034 method Methods 0.000 title claims abstract description 78
- 238000012544 monitoring process Methods 0.000 title claims abstract description 29
- 238000004088 simulation Methods 0.000 claims abstract description 61
- 230000011218 segmentation Effects 0.000 claims abstract description 30
- 210000001519 tissue Anatomy 0.000 claims description 51
- 239000011159 matrix material Substances 0.000 claims description 37
- 230000005684 electric field Effects 0.000 claims description 17
- 210000003491 skin Anatomy 0.000 claims description 15
- 210000003205 muscle Anatomy 0.000 claims description 14
- 210000004556 brain Anatomy 0.000 claims description 13
- 238000003384 imaging method Methods 0.000 claims description 12
- 210000000988 bone and bone Anatomy 0.000 claims description 11
- 238000000926 separation method Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012806 monitoring device Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 11
- 238000002595 magnetic resonance imaging Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 238000009826 distribution Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 6
- 230000005672 electromagnetic field Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 230000005284 excitation Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000011552 rat model Methods 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
- 210000000577 adipose tissue Anatomy 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 210000004072 lung Anatomy 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- 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
Abstract
The embodiment of the invention discloses a method, a device, equipment and a medium for predicting and monitoring radio frequency energy deposition, wherein the method for predicting comprises the following steps: acquiring a magnetic resonance scanning structure image of a scanning object, and carrying out tissue segmentation based on the structure image to establish a three-dimensional biological model of the scanning object; establishing a biological electromagnetic simulation model according to the three-dimensional biological model; according to a preset scanning strategy, a multichannel radio frequency coil model is established, and a biological electromagnetic simulation model and the multichannel radio frequency coil model are combined to simulate and confirm a radio frequency energy deposition value. The monitoring method comprises the following steps: and predicting the radio frequency energy deposition value according to the prediction method, and adjusting the scanning strategy according to the prediction result. According to the technical scheme, under the technical scene of parallel transmission of the multichannel transmitting coils, SAR values of the scanning object are predicted more accurately and individually, and the safety of the scanning object in magnetic resonance scanning is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of magnetic resonance imaging, in particular to a method, a device, equipment and a medium for predicting and monitoring radio frequency energy deposition.
Background
In high-field and ultra-high-field magnetic resonance imaging (Magnetic Resonance Imaging, MRI) systems, the patient absorbs the energy of the rf electromagnetic waves during the examination, resulting in the formation of a human rf energy deposit, the measure being the specific absorption rate (specific absorption rate, SAR), i.e. the rf electromagnetic wave energy absorbed per unit mass of biological tissue per unit time (in W/kg). In high-field and ultra-high-field MRI systems, in order to generate uniform radio-frequency electromagnetic fields and reduce SAR values, a multichannel parallel transmission technology is generally adopted, and the amplitude, the phase and even the radio-frequency pulse waveform of each unit coil excitation source are independently controlled, so that the control freedom degree and the optimization space are improved. However, the increase of the degree of freedom of the excitation source can also lead to the fact that SAR distribution generated by the coil in the human body becomes extremely complex, the possibility of local hot spots is greatly increased, and the safety standard of SAR is possibly exceeded. More accurate prediction and monitoring of SAR values is required.
However, in the prior art, the prediction of SAR values based on a plurality of digital mannequins in commercial electromagnetic field simulation software fails to sufficiently consider individual differences, and factors such as body size and shape, sex and body fat distribution, and body posture and position in the system may lead to prediction errors, inaccuracy. And when SAR calculation is performed, a fixed radio frequency shimming model is adopted, so that the method is not suitable for a multi-channel radio frequency emission scene.
Disclosure of Invention
The embodiment of the invention provides a radio frequency energy deposition prediction and monitoring method, device, equipment and medium, which are used for realizing more accurate and personalized SAR value prediction of a scanning object under the technical scene of parallel transmission of a multi-channel transmitting coil and improving the safety of the receiving magnetic resonance scanning of the scanning object.
In a first aspect, an embodiment of the present invention provides a method for predicting deposition of radio frequency energy, where the method includes:
acquiring a magnetic resonance scanning structure image of a scanning object, and carrying out tissue segmentation based on the structure image to establish a three-dimensional biological model of the scanning object;
establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
according to a preset scanning strategy, a multichannel radio frequency coil model is established, and the biological electromagnetic simulation model and the multichannel radio frequency coil model are combined to simulate and predict the radio frequency energy deposition value of the scanning object.
Optionally, the structural image includes a water-fat separation image, a T1 and T2 contrast image, and an ultrashort echo image, and the tissue segmentation is performed based on the structural image, so as to establish a three-dimensional biological model of the scanned object, including:
performing tissue segmentation of the scanning image of the scanning object based on the water-fat separation image, the T1 and T2 contrast image and the ultra-short echo image to obtain fat, brain, muscle, bone and skin;
And combining the fat, brain, muscle, bone and skin tissues after tissue segmentation to establish the three-dimensional biological model.
Optionally, the building a bioelectromagnetic simulation model according to the three-dimensional biological model includes:
and carrying out corresponding dielectric constant, magnetic conductivity and tissue density assignment on each tissue in the three-dimensional biological model to obtain the biological electromagnetic simulation model.
Optionally, the simulating and predicting the radio frequency energy deposition value of the scan object by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model includes:
simulating based on the biological electromagnetic simulation model and the multichannel radio frequency coil model, and determining a radio frequency magnetic field intensity value and the electric field intensity of each channel in the multichannel radio frequency coil;
and predicting the radio frequency energy deposition value according to the radio frequency magnetic field intensity value and the electric field intensity of each channel in the multi-channel radio frequency coil.
Optionally, the predicting the rf energy deposition value according to the rf magnetic field strength value and the electric field strength of each channel in the multi-channel rf coil includes:
according to the formulaCalculating a radio frequency energy deposition value of the scanning object under the multi-channel radio frequency coil model, wherein SAR (x, y, z) represents the radio frequency energy deposition value of a preset position (x, y, z) in the scanning object, w is a weighting factor matrix of each channel coil in the multi-channel radio frequency coil, w 'is a conjugated transpose matrix of w, E (x, y, z) is an electric field intensity matrix of each channel in the multi-channel radio frequency coil, E' (x, y, z) is a conjugated transpose matrix of E (x, y, z), ρ (x, y, z) is the body density of the scanning object, and σ (x, y, z) is the body conductivity of the scanning object >For the rf magnetic field strength, V represents the statistical volume of the rf energy deposition values.
In a second aspect, an embodiment of the present invention further provides a method for monitoring deposition of radio frequency energy, where the method includes:
acquiring a preset scanning strategy of a scanning object, calculating the radio frequency magnetic field intensity of a scanning sequence according to the preset scanning strategy, and determining a weighting factor matrix of the multichannel radio frequency coil according to the voltage amplitude and the phase of the multichannel transmitting coil in the preset scanning strategy;
predicting a radio frequency energy deposition value to which the scanned object is subjected when scanning imaging by the radio frequency energy deposition prediction method according to any one of the embodiments, based on the radio frequency magnetic field strength and the weighting factor matrix;
and adjusting a scanning strategy according to the radio frequency energy deposition value.
Optionally, the adjusting the scanning strategy according to the rf energy deposition value includes:
and stopping the scanning imaging process and adjusting the scanning sequence parameters in the preset scanning strategy when the radio frequency energy deposition value is larger than a preset upper limit energy deposition value.
In a third aspect, an embodiment of the present invention further provides a device for predicting deposition of radio frequency energy, where the device includes:
The human body model building module is used for collecting a magnetic resonance scanning structure image of a scanning object, and carrying out tissue segmentation based on the structure image to build a three-dimensional biological model of the scanning object;
the biological electromagnetic simulation model building module is used for building a biological electromagnetic simulation model according to the three-dimensional biological model;
the radio frequency deposition prediction module is used for establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and combining 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.
Optionally, the mannequin building module is specifically configured to:
performing tissue segmentation of the scanning image of the scanning object based on the water-fat separation image, the T1 and T2 contrast image and the ultra-short echo image to obtain fat, brain, muscle, bone and skin;
and combining the fat, brain, muscle, bone and skin tissues after tissue segmentation to establish the three-dimensional biological model.
Optionally, the biological electromagnetic simulation model building module is specifically configured to:
and carrying out corresponding dielectric constant, magnetic conductivity and tissue density assignment on each tissue in the three-dimensional biological model to obtain the biological electromagnetic simulation model.
Optionally, the radio frequency deposition prediction module is specifically configured to:
simulating based on the biological electromagnetic simulation model and the multichannel radio frequency coil model, and determining a radio frequency magnetic field intensity value and the electric field intensity of each channel in the multichannel radio frequency coil;
and predicting the radio frequency energy deposition value according to the radio frequency magnetic field intensity value and the electric field intensity of each channel in the multi-channel radio frequency coil.
Optionally, the radio frequency deposition prediction module is specifically configured to:
according to the formulaCalculating a radio frequency energy deposition value of the scanning object under the multi-channel radio frequency coil model, wherein SAR (x, y, z) represents the radio frequency energy deposition value of a preset position (x, y, z) in the scanning object, w is a weighting factor matrix of each channel coil in the multi-channel radio frequency coil, w 'is a conjugated transpose matrix of w, E (x, y, z) is an electric field intensity matrix of each channel in the multi-channel radio frequency coil, E' (x, y, z) is a conjugated transpose matrix of E (x, y, z), ρ (x, y, z) is the body density of the scanning object, and σ (x, y, z) is the body conductivity of the scanning object>For the rf magnetic field strength, V represents the statistical volume of the rf energy deposition values.
In a fourth aspect, an embodiment of the present invention further provides a device for monitoring deposition of radio frequency energy, where the device includes:
the scanning strategy determining module is used for acquiring a preset scanning strategy of a scanning object, calculating the radio frequency magnetic field intensity of a scanning sequence according to the preset scanning strategy, and determining a weighting factor matrix of the multichannel radio frequency coil according to the voltage amplitude and the phase of the multichannel transmitting coil in the preset scanning strategy;
the rf energy deposition value determining module configured to predict, based on the rf magnetic field strength and the weighting factor matrix, an rf energy deposition value to which the scan object is subjected when performing scan imaging by the rf energy deposition prediction method according to any one of the embodiments;
and the scanning control module is used for adjusting a scanning strategy according to the radio frequency energy deposition value.
Optionally, the scan control module is specifically configured to:
and stopping the scanning imaging process and adjusting the scanning sequence parameters in the preset scanning strategy when the radio frequency energy deposition value is larger than a preset upper limit energy deposition value.
In a fifth aspect, embodiments of the present invention also provide a computer device, the computer device having one or more processors;
A memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method of radio frequency energy deposition prediction, or a method of radio frequency energy deposition monitoring, as provided by any of the embodiments of the present invention.
In a sixth aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for predicting radiofrequency energy deposition as provided by any embodiment of the present invention, or a method for monitoring radiofrequency energy deposition.
The embodiments of the above invention have the following advantages or benefits:
according to the embodiment of the invention, a three-dimensional biological model of a scanned object is established by collecting a magnetic resonance scanning structure image of the scanned object and carrying out tissue segmentation based on the structure image; establishing a biological electromagnetic simulation model according to the three-dimensional biological model; establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model to simulate and confirm a radio frequency energy deposition value; the method solves the problem of inaccurate radio frequency energy deposition prediction in the prior art, reduces simulation errors, and predicts local energy deposition values more accurately.
Drawings
FIG. 1 is a flowchart of a method for predicting deposition of RF energy according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a biological electromagnetic simulation model establishment of a rat according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a coil used for biological electromagnetic simulation model and prediction according to a first embodiment of the present invention;
FIG. 4 is a schematic diagram of a simulated magnetic field B1 according to the method of the first embodiment of the present invention;
FIG. 5 is a schematic diagram showing the distribution of the magnetic field B1 based on the actual measurement of the method according to the first embodiment of the present invention;
FIG. 6 is a flowchart of a method for monitoring deposition of RF energy according to a second embodiment of the present invention;
fig. 7 is a schematic structural diagram of a device for predicting deposition of rf energy according to a third embodiment of the present invention;
fig. 8 is a schematic structural diagram of a monitoring device for rf energy deposition according to a fourth embodiment of the present invention;
fig. 9 is a schematic structural diagram of a computer device according to a fifth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a method for predicting rf energy deposition according to an embodiment of the present invention, where the embodiment is applicable to a case of performing magnetic resonance scanning on a scanned object. The method can be performed by a device configured for deposition of radiofrequency energy, which can be implemented in software and/or hardware, integrated in an electronic device with application development functions.
As shown in fig. 1, the rf energy deposition prediction method includes the steps of:
s110, acquiring a magnetic resonance scanning structure image of a scanning object, and performing tissue segmentation based on the structure image to establish a three-dimensional biological model of the scanning object.
At present, electromagnetic simulation software is generally adopted to simulate a model of a digital scanning object, so that simulation errors can be reduced, a simulation result is more accurate, a personalized three-dimensional biological model of the scanning object is established in the step, and the three-dimensional biological model in the simulation process is more matched with a scanning object.
Specifically, the scanning structure image can be obtained by setting different magnetic resonance scanning parameters to scan the scanning object, and the method comprises the steps of collecting a water-fat separation magnetic resonance image and a T1/T2 contrast magnetic resonance image of the scanning object, so that the information of fat, brain and muscle tissue is obtained. Meanwhile, an ultra-short echo image can be obtained by using the ultra-short echo sequence so as to obtain skeleton information. And the tissue segmentation of the scanning image of the scanning object can be performed based on the water-fat separation image, the T1 and T2 contrast image and the ultra-short echo image, so that the tissue structures such as fat, brain, muscle, bone and skin can be obtained. Wherein the skin may be skin information added outside the overall contour of the scanned object. Based on the structures of fat, brain, muscle, skin and the like obtained by tissue segmentation, a three-dimensional biological model of the scanned object can be established. The scan object may be a human body or an animal.
S120, building a biological electromagnetic simulation model according to the three-dimensional biological model.
Because the values of the conductivity, the magnetic conductivity, the characteristic absorptivity and the like of different tissues are different, specifically, after the three-dimensional biological model of the scanned object is obtained, the corresponding dielectric constant, the magnetic conductivity and the tissue density of each tissue in the three-dimensional biological model are assigned, so that the biological electromagnetic simulation model can be obtained. The permittivity, permeability and tissue density may be pre-stored in the simulation system memory.
S130, a multi-channel radio frequency coil model is established according to a preset scanning strategy, and the biological electromagnetic simulation model and the multi-channel radio frequency coil model are combined to simulate and predict the radio frequency energy deposition value of the scanning object.
Specifically, the scanning strategy includes each parameter of the scanning sequence and the scanning position information, and the voltage amplitude and the phase of the multichannel transmitting coil. Parameters such as a weight factor of each channel coil can be obtained according to the voltage amplitude and the phase of the multichannel transmitting coil, and the weight factor of each channel represents the contribution degree of each channel radio frequency signal to the scanning result.
The multichannel radio frequency emission technology is adopted, because in order to generate uniform radio frequency electromagnetic fields and reduce energy deposition values in a high-field and ultra-high-field magnetic resonance imaging system, the multichannel parallel emission technology is generally adopted, and the amplitude, the phase and even the radio frequency pulse waveform of each unit coil excitation source are independently controlled, so that the degree of freedom of controlling emission sequences and the optimization space can be improved.
Further, by combining the biological electromagnetic simulation model of the scanned object and the multi-channel radio frequency coil model, the electric field value and the radio frequency magnetic field strength of each channel in the multi-channel radio frequency coil model can be determined, namely, parameters for predicting the radio frequency energy deposition value can be obtained
Based on the parameters for predicting the radio frequency energy deposition value that have been acquired, the radio frequency energy deposition value may be predicted by the following formula:
according to the formulaCalculating a radio frequency energy deposition value of the scanning object under the multi-channel radio frequency coil model, wherein SAR (x, y, z) represents the radio frequency energy deposition value of a preset position (x, y, z) in the scanning object, w is a weighting factor matrix of each channel coil in the multi-channel radio frequency coil, w 'is a conjugated transpose matrix of w, E (x, y, z) is an electric field intensity matrix of each channel in the multi-channel radio frequency coil, E' (x, y, z) is a conjugated transpose matrix of E (x, y, z), ρ (x, y, z) is the body density of the scanning object, and σ (x, y, z) is the body conductivity of the scanning object>For rf magnetic field strength, V represents the statistical volume of the rf energy deposition values and may include whole body, head, part of body and part.
Further, i.e. the channel weighting factor w is extracted outside the integral formula by using the Q matrix, there are:
wherein the intermediate integral term Q (x, y, z) is independent of each channel voltage amplitude and phase weight vectorw, Q (x, y, z) is a hermitian canonical matrix, which can be calculated from the square of the electric field amplitude. The advantage of this formula is that the local SAR can be calculated by pre-simulation, irrespective of the voltage amplitude and phase weighting of the individual channels, and the voltage amplitude and phase of the individual transmit channels, and the RF pulse energy of the scan sequence, are taken into account during the energy deposition monitoringFactors of (3).
In a specific example, a rat can be used as a scanning object for simulation, and a rat water-fat separation image with resolution of 0.60×0.60×1.00mm is acquired on a magnetic resonance imaging system 3 . The method comprises the steps of processing rat image data by Matlab, dividing the rat image data into four tissues of skin, fat, lung and muscle, converting the tissues into a model file which can be identified by electromagnetic field simulation calculation software CST, referencing a CST database by tissue parameters, and carrying out simulation and experimental test on the obtained simplified rat model by using a self-made coil. Fig. 2 is a rat model obtained by segmentation, and fig. 3 is a simulation model and a coil used for testing. The reliability of the model is verified by comparing the simulated and measured magnetic field distributions. Fig. 4 shows the simulated magnetic field B1 distribution of the rat model established by the method, fig. 5 shows the actually measured magnetic field B1 distribution, and as can be seen from fig. 4 and 5, the simulation is consistent with the actually measured result, and the correctness of the self-built electromagnetic field simulation model is verified.
According to the technical scheme, a three-dimensional biological model of a scanning object is established by collecting a magnetic resonance scanning structure image of the scanning object and carrying out tissue segmentation based on the structure image; establishing a biological electromagnetic simulation model according to the three-dimensional biological model; establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model to simulate and confirm a radio frequency energy deposition value; the method solves the problem of inaccurate radio frequency energy deposition prediction in the prior art, reduces simulation errors, and predicts local energy deposition values more accurately.
Example two
Fig. 6 is a flowchart of a method for monitoring deposition of rf energy according to a second embodiment of the present invention, where the present embodiment is applicable, and the method for processing data in the foregoing embodiment is the same as the method for processing data in the foregoing embodiment. The method may be performed by a radio frequency energy deposition monitoring device, which may be implemented in software and/or hardware, integrated in a computer device or server device with application development functionality.
As shown in fig. 6, the rf energy deposition monitoring method includes the steps of:
s210, acquiring a preset scanning strategy of a scanning object, calculating the radio frequency magnetic field intensity of a scanning sequence according to the preset scanning strategy, and determining a weighting factor matrix of the multichannel radio frequency coil according to the voltage amplitude and the phase of the multichannel transmitting coil in the preset scanning strategy.
S220, predicting a radio frequency energy deposition value born by the scanning object when scanning imaging by the radio frequency energy deposition prediction method according to any embodiment based on the radio frequency magnetic field intensity and the weighting factor matrix.
S230, adjusting a scanning strategy according to the radio frequency energy deposition value.
Specifically, in order to ensure smooth sequence scanning, SAR prediction is performed before sequence scanning, that is, SAR prediction is performed according to energy expansion and calibration data of the sequence and SAR model, if the estimated SAR exceeds the limit of the regulations, a scanning strategy is adjusted, for example, TR (transmission time of radio frequency sequence), flip angle, etc. in sequence parameters are adjusted, SAR is further reduced, and magnetic resonance sequence scanning is performed under the condition of ensuring the safety of a scanning object. If the prediction passes, the scanning can be directly started.
In a preferred embodiment, in order to fully ensure patient safety, forward and reverse power is always acquired in real time through the directional coupler, real-time applied power acquisition can be performed in combination with the analog-to-digital converter, and the b1+ field strength of the magnetic resonance can be obtained by calibrating the flip angle through a calibration sequence. With the input of power and field intensity, B1+RMS of the sequence or total energy of the sequence can be calculated according to the integral of pulse waveform energy of the scanning sequence, and specific whole body SAR, head SAR, partial body SAR and local SAR can be obtained by combining the SAR models calculated through simulation. When the SAR of any part is monitored to exceed the safety value, the scanning is stopped in time, and the scanning strategy can be continuously adjusted so that the SAR value of the corresponding part is within the safety range.
According to the technical scheme of the embodiment, a multichannel radio frequency coil model is built by acquiring a preset scanning strategy of a scanning object and according to the emission amplitude and the phase of a scanning sequence in the preset scanning strategy, and the radio frequency energy deposition value born by the scanning object when the scanning object is scanned and imaged by the preset scanning sequence is predicted by the radio frequency energy deposition prediction method according to any embodiment based on the multichannel radio frequency coil model. And adjusting a scanning strategy according to the radio frequency energy deposition value. The method realizes the monitoring of the deposition value of the radio frequency energy under the multichannel radio frequency emission technology, and improves the safety in the magnetic resonance scanning process.
The following is an embodiment of the apparatus for predicting and monitoring rf energy deposition provided by the embodiments of the present invention, where the apparatus and the method for predicting and monitoring rf energy deposition of the embodiments described above belong to the same inventive concept, and the method for predicting and monitoring rf energy deposition of the embodiments described above may be implemented. Details of the rf energy deposition prediction and monitoring apparatus are not described in detail in the embodiments of the rf energy deposition prediction and monitoring method described above.
Example III
Fig. 7 is a schematic structural diagram of a device for predicting deposition of rf energy according to a third embodiment of the present invention, which is applicable to the present invention.
As shown in fig. 7, the rf energy deposition prediction apparatus includes a mannequin build module 310, a bioelectromagnetic simulation model build module 320, and an rf deposition prediction module 330.
The human body model building module 310 is configured to collect a magnetic resonance scanning structure image of a scanning object, and perform tissue segmentation based on the structure image to build a three-dimensional biological model of the scanning object; a bioelectromagnetic simulation model building module 320, configured to build a bioelectromagnetic simulation model according to the three-dimensional bioelectromagnetic model; the rf deposition prediction module 330 is configured to establish a multi-channel rf coil model according to a preset scanning strategy, and simulate and predict an rf energy deposition value of the scanned object by combining the bioelectromagnetic simulation model and the multi-channel rf coil model.
According to the technical scheme, a three-dimensional biological model of a scanning object is established by collecting a magnetic resonance scanning structure image of the scanning object and carrying out tissue segmentation based on the structure image; establishing a biological electromagnetic simulation model according to the three-dimensional biological model; establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model to simulate and confirm a radio frequency energy deposition value; the method solves the problem of inaccurate radio frequency energy deposition prediction in the prior art, reduces simulation errors, and predicts local energy deposition values more accurately.
Optionally, the mannequin building module 310 is specifically configured to:
performing tissue segmentation of the scanning image of the scanning object based on the water-fat separation image, the T1 and T2 contrast image and the ultra-short echo image to obtain fat, brain, muscle, bone and skin;
and combining the fat, brain, muscle, bone and skin tissues after tissue segmentation to establish the three-dimensional biological model.
Optionally, the bioelectromagnetic simulation model building module 320 is specifically configured to:
and carrying out corresponding dielectric constant, magnetic conductivity and tissue density assignment on each tissue in the three-dimensional biological model to obtain the biological electromagnetic simulation model.
Optionally, the rf deposition prediction module 330 is specifically configured to:
simulating based on the biological electromagnetic simulation model and the multichannel radio frequency coil model, and determining a radio frequency magnetic field intensity value and the electric field intensity of each channel in the multichannel radio frequency coil;
and predicting the radio frequency energy deposition value according to the radio frequency magnetic field intensity value and the electric field intensity of each channel in the multi-channel radio frequency coil.
Optionally, the radio frequency deposition prediction module is specifically configured to:
according to the formulaCalculating a radio frequency energy deposition value of the scanning object under the multi-channel radio frequency coil model, wherein SAR (x, y, z) represents the radio frequency energy deposition value of a preset position (x, y, z) in the scanning object, w is a weighting factor matrix of each channel coil in the multi-channel radio frequency coil, w 'is a conjugated transpose matrix of w, E (x, y, z) is an electric field intensity matrix of each channel in the multi-channel radio frequency coil, E' (x, y, z) is a conjugated transpose matrix of E (x, y, z), ρ (x, y, z) is the body density of the scanning object, and σ (x, y, z) is the body conductivity of the scanning object >For the rf magnetic field strength, V represents the statistical volume of the rf energy deposition values.
The radio frequency energy deposition prediction device provided by the embodiment of the invention can execute the radio frequency energy deposition prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 8 is a schematic structural diagram of a radio frequency energy deposition monitoring device according to a fourth embodiment of the present invention, where the present embodiment is applicable to a case of performing magnetic resonance scanning on a scanned object.
As shown in fig. 8, the rf energy deposition monitoring apparatus includes a scan strategy determination module 410, an rf energy deposition value determination module 420, and a scan control module 430.
The scanning strategy determining module 410 is configured to obtain a preset scanning strategy of a scanning object, calculate a radio frequency magnetic field strength of a scanning sequence according to the preset scanning strategy, and determine a weighting factor matrix of the multichannel radio frequency coil according to a voltage amplitude and a phase of the multichannel transmitting coil in the preset scanning strategy; a radio frequency energy deposition value determining module 420, configured to predict a radio frequency energy deposition value that the scan object undergoes when performing scan imaging by the radio frequency energy deposition prediction method according to any of the embodiments, based on the radio frequency magnetic field strength and the weighting factor matrix; the scan strategy adjustment module 430 is configured to adjust a scan strategy according to the rf energy deposition value.
Optionally, the scan policy adjustment module 430 is specifically configured to:
and stopping the scanning imaging process and adjusting the scanning sequence parameters in the preset scanning strategy when the radio frequency energy deposition value is larger than a preset upper limit energy deposition value.
According to the technical scheme of the embodiment, a multichannel radio frequency coil model is built by acquiring a preset scanning strategy of a scanning object and according to the emission amplitude and the phase of a scanning sequence in the preset scanning strategy, and the radio frequency energy deposition value born by the scanning object when the scanning object is scanned and imaged by the preset scanning sequence is predicted by the radio frequency energy deposition prediction method according to any embodiment based on the multichannel radio frequency coil model. And adjusting a scanning strategy according to the radio frequency energy deposition value. The method realizes the monitoring of the deposition value of the radio frequency energy under the multichannel radio frequency emission technology, and improves the safety in the magnetic resonance scanning process.
The radio frequency energy deposition monitoring device provided by the embodiment of the invention can execute the radio frequency energy deposition monitoring method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 9 is a schematic structural diagram of a computer device according to a fifth embodiment of the present invention. Fig. 9 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in fig. 9 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention. The computer device 12 may be any terminal device with computing power, such as an intelligent controller, a server, a mobile phone, and the like. The computer device may be connected to the magnetic resonance imaging device for performing the corresponding method steps in cooperation with the magnetic resonance scanning process for realizing the prediction of the radio frequency energy deposition.
As shown in fig. 9, the computer device 12 is in the form of a general purpose computing device. Components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The 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 memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 9, commonly referred to as a "hard disk drive"). Although not shown in fig. 9, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be appreciated that although not shown in fig. 9, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, to implement a method for predicting deposition of rf energy provided in the present embodiment, including:
acquiring a magnetic resonance scanning structure image of a scanning object, and carrying out tissue segmentation based on the structure image to establish a three-dimensional biological model of the scanning object;
establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
according to a preset scanning strategy, a multichannel radio frequency coil model is established, and the biological electromagnetic simulation model and the multichannel radio frequency coil model are combined to simulate and predict the radio frequency energy deposition value of the scanning object.
Alternatively, the computer program may further implement a method for monitoring deposition of rf energy according to any embodiment of the present invention, when executed by a processor, including:
acquiring a preset scanning strategy of a scanning object, calculating the radio frequency magnetic field intensity of a scanning sequence according to the preset scanning strategy, and determining a weighting factor matrix of the multichannel radio frequency coil according to the voltage amplitude and the phase of the multichannel transmitting coil in the preset scanning strategy;
predicting a radio frequency energy deposition value to which the scanned object is subjected when scanning imaging by the radio frequency energy deposition prediction method according to any one of the embodiments, based on the radio frequency magnetic field strength and the weighting factor matrix;
And adjusting a scanning strategy according to the radio frequency energy deposition value.
Example six
The sixth embodiment provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for predicting deposition of radio frequency energy as provided in any embodiment of the present invention, including:
acquiring a magnetic resonance scanning structure image of a scanning object, and carrying out tissue segmentation based on the structure image to establish a three-dimensional biological model of the scanning object;
establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
according to a preset scanning strategy, a multichannel radio frequency coil model is established, and the biological electromagnetic simulation model and the multichannel radio frequency coil model are combined to simulate and predict the radio frequency energy deposition value of the scanning object.
Alternatively, the computer program may further implement a method for monitoring deposition of rf energy according to any embodiment of the present invention, when executed by a processor, including:
acquiring a preset scanning strategy of a scanning object, calculating the radio frequency magnetic field intensity of a scanning sequence according to the preset scanning strategy, and determining a weighting factor matrix of the multichannel radio frequency coil according to the voltage amplitude and the phase of the multichannel transmitting coil in the preset scanning strategy;
Predicting a radio frequency energy deposition value to which the scanned object is subjected when scanning imaging by the radio frequency energy deposition prediction method according to any one of the embodiments, based on the radio frequency magnetic field strength and the weighting factor matrix;
and adjusting a scanning strategy according to the radio frequency energy deposition value.
The computer storage media of embodiments of the invention may take the form of 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, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. 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. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It will be appreciated by those of ordinary skill in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device, or distributed over a network of computing devices, or they may alternatively be implemented in program code executable by a computer device, such that they are stored in a memory device and executed by the computing device, or they may be separately fabricated as individual integrated circuit modules, or multiple modules or steps within them may be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A method for predicting deposition of radio frequency energy, comprising:
acquiring a magnetic resonance scanning structure image of a scanning object, and carrying out tissue segmentation based on the structure image to establish a three-dimensional biological model of the scanning object;
establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
according to a preset scanning strategy, a multichannel radio frequency coil model is established, and the biological electromagnetic simulation model and the multichannel radio frequency coil model are combined to simulate and predict a radio frequency energy deposition value of the scanning object;
the structure image comprises a water-fat separation image, a T1 and T2 contrast image and an ultra-short echo image, the structure image is based on tissue segmentation, and a three-dimensional biological model of the scanning object is established, and the structure image comprises:
performing tissue segmentation of the scanning image of the scanning object based on the water-fat separation image, the T1 and T2 contrast image and the ultra-short echo image to obtain fat, brain, muscle, bone and skin;
the three-dimensional biological model is built by combining fat, brain, muscle, bone and skin tissues after tissue segmentation;
the performing tissue segmentation of the scanned image of the scanned object includes: and automatically dividing the tissues by computer software on the obtained scanning structure image.
2. The method of claim 1, wherein said building a bioelectromagnetic simulation model from said three-dimensional biological model comprises:
and carrying out corresponding dielectric constant, magnetic conductivity and tissue density assignment on each tissue in the three-dimensional biological model to obtain the biological electromagnetic simulation model.
3. The method of claim 1, wherein said simulating predicting the rf energy deposition value of the scanned object in combination with the bioelectromagnetic simulation model and the multichannel rf coil model comprises:
simulating based on the biological electromagnetic simulation model and the multichannel radio frequency coil model, and determining a radio frequency magnetic field intensity value and the electric field intensity of each channel in the multichannel radio frequency coil;
and predicting the radio frequency energy deposition value according to the radio frequency magnetic field intensity value and the electric field intensity of each channel in the multi-channel radio frequency coil.
4. The method of claim 3, wherein predicting the rf energy deposition value based on the rf magnetic field strength value and the electric field strength of each channel in the multi-channel rf coil comprises:
according to the formulaCalculating a radio frequency energy deposition value of the scanning object under the multi-channel radio frequency coil model, wherein SAR (x, y, z) represents the radio frequency energy deposition value of a preset position (x, y, z) in the scanning object, a weighting factor matrix of each channel coil in w multi-channel radio frequency coils, and w ′ A conjugate transpose matrix of w, E (x, y, z) is an electric field intensity matrix of each channel in the multi-channel radio frequency coil, E ′ (x, y, z) is the conjugate transpose of E (x, y, z), ρ (x, y, z) is the body density of the scan subject, σ (x, y, z) is the body conductivity of the scan subject>V represents the statistical volume of the deposition value of the radio frequency energy for the radio frequency magnetic field strength。
5. A method for monitoring deposition of radio frequency energy, comprising:
acquiring a preset scanning strategy of a scanning object, calculating the radio frequency magnetic field intensity of a scanning sequence according to the preset scanning strategy, and determining a weighting factor matrix of the multichannel radio frequency coil according to the voltage amplitude and the phase of the multichannel transmitting coil in the preset scanning strategy;
predicting a radio frequency energy deposition value to which the scanning object is subjected when scanning imaging by the radio frequency energy deposition prediction method according to any one of claims 1 to 4 based on the radio frequency magnetic field intensity and the weighting factor matrix;
and adjusting a scanning strategy according to the radio frequency energy deposition value.
6. The method of claim 5, wherein adjusting the scanning strategy based on the rf energy deposition value comprises:
And stopping the scanning imaging process and adjusting the scanning sequence parameters in the preset scanning strategy when the radio frequency energy deposition value is larger than a preset upper limit energy deposition value.
7. A radio frequency energy deposition prediction apparatus, comprising:
the human body model building module is used for collecting a magnetic resonance scanning structure image of a scanning object, and carrying out tissue segmentation based on the structure image to build a three-dimensional biological model of the scanning object;
the biological electromagnetic simulation model building module is used for building a biological electromagnetic simulation model according to the three-dimensional biological model;
the radio frequency deposition prediction module is used for establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model to simulate and predict a radio frequency energy deposition value of the scanning object;
the human body model building module is specifically used for:
performing tissue segmentation of the scanning image of the scanning object based on the water-fat separation image, the T1 and T2 contrast image and the ultra-short echo image to obtain fat, brain, muscle, bone and skin;
the three-dimensional biological model is built by combining fat, brain, muscle, bone and skin tissues after tissue segmentation;
The human body model building module comprises a tissue segmentation unit which is used for automatically carrying out tissue segmentation on the obtained scanning structure image through computer software.
8. A radio frequency energy deposition monitoring device, comprising:
the scanning strategy determining module is used for acquiring a preset scanning strategy of a scanning object, calculating the radio frequency magnetic field intensity of a scanning sequence according to the preset scanning strategy, and determining a weighting factor matrix of the multichannel radio frequency coil according to the voltage amplitude and the phase of the multichannel transmitting coil in the preset scanning strategy;
a radio frequency energy deposition value determining module, configured to predict a radio frequency energy deposition value to which the scan object is subjected when performing scan imaging by the radio frequency energy deposition prediction method according to any one of claims 1 to 4, based on the radio frequency magnetic field strength and the weighting factor matrix;
and the scanning control module is used for adjusting a scanning strategy according to the radio frequency energy deposition value.
9. A computer device, the computer device comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, the one or more programs cause the one or more processors to implement the method of radio frequency energy deposition prediction as claimed in any one of claims 1-4, or the method of radio frequency energy deposition monitoring as claimed in any one of claims 5-6.
10. A computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of radio frequency energy deposition prediction as claimed in any one of claims 1 to 4 or the method of radio frequency energy deposition monitoring as claimed in any one of claims 5 to 6.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110265492.7A CN113017598B (en) | 2021-03-11 | 2021-03-11 | Radio frequency energy deposition prediction and monitoring method, device, equipment and medium |
| PCT/CN2021/087689 WO2022188237A1 (en) | 2021-03-11 | 2021-04-16 | Radio frequency energy deposition prediction method and apparatus, radio frequency energy deposition monitoring method and apparatus, and device and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110265492.7A CN113017598B (en) | 2021-03-11 | 2021-03-11 | Radio frequency energy deposition prediction and monitoring method, device, equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113017598A CN113017598A (en) | 2021-06-25 |
| CN113017598B true CN113017598B (en) | 2023-11-21 |
Family
ID=76469695
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110265492.7A Active CN113017598B (en) | 2021-03-11 | 2021-03-11 | Radio frequency energy deposition prediction and monitoring method, device, equipment and medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN113017598B (en) |
| WO (1) | WO2022188237A1 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114398803B (en) * | 2022-03-25 | 2022-07-12 | 中国科学院深圳先进技术研究院 | Shimming joint simulation method and device, electronic equipment and storage medium |
| CN114417639B (en) * | 2022-03-28 | 2022-08-12 | 中国科学院深圳先进技术研究院 | Method, device, equipment and medium for determining loss model of radio frequency transmitting coil |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101933807A (en) * | 2009-06-26 | 2011-01-05 | 西门子公司 | The specific absorption rate that is used for the multi-channel magnetic resonance transmitting system calculates |
| CN102123661A (en) * | 2008-08-15 | 2011-07-13 | 皇家飞利浦电子股份有限公司 | Method and monitoring device for performing an RF-safe MIT scan |
| CN102736045A (en) * | 2011-04-08 | 2012-10-17 | 西门子公司 | Parallel transmission RF pulse design with local sar constraints |
| CN103284723A (en) * | 2013-06-02 | 2013-09-11 | 南方医科大学 | High-field magnetic resonance scanning safety testing body model system, high-field magnetic resonance scanning safety testing method and magnetic resonance system |
| CN103698732A (en) * | 2013-12-12 | 2014-04-02 | 深圳先进技术研究院 | Magnetic-resonance radio-frequency coil performance evaluation method and system |
| CN105572612A (en) * | 2014-12-31 | 2016-05-11 | 中国科学院深圳先进技术研究院 | Method of improving multichannel radio frequency coil performances |
| CN107137083A (en) * | 2017-06-16 | 2017-09-08 | 上海联影医疗科技有限公司 | A kind of magnetic resonance scanning method, system and computer-readable recording medium |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102015203306A1 (en) * | 2015-02-24 | 2016-08-25 | Siemens Healthcare Gmbh | Method for determining absolute reception sensitivity maps for receiving coils in a magnetic resonance device and magnetic resonance device |
| CN107495967B (en) * | 2017-08-24 | 2020-06-19 | 上海联影医疗科技有限公司 | Method, device and system for predicting and controlling radio frequency energy deposition and storage medium |
| EP3469990A1 (en) * | 2017-10-16 | 2019-04-17 | Koninklijke Philips N.V. | Determination of a subject profile with a camera |
-
2021
- 2021-03-11 CN CN202110265492.7A patent/CN113017598B/en active Active
- 2021-04-16 WO PCT/CN2021/087689 patent/WO2022188237A1/en active Application Filing
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102123661A (en) * | 2008-08-15 | 2011-07-13 | 皇家飞利浦电子股份有限公司 | Method and monitoring device for performing an RF-safe MIT scan |
| CN101933807A (en) * | 2009-06-26 | 2011-01-05 | 西门子公司 | The specific absorption rate that is used for the multi-channel magnetic resonance transmitting system calculates |
| CN102736045A (en) * | 2011-04-08 | 2012-10-17 | 西门子公司 | Parallel transmission RF pulse design with local sar constraints |
| CN103284723A (en) * | 2013-06-02 | 2013-09-11 | 南方医科大学 | High-field magnetic resonance scanning safety testing body model system, high-field magnetic resonance scanning safety testing method and magnetic resonance system |
| CN103698732A (en) * | 2013-12-12 | 2014-04-02 | 深圳先进技术研究院 | Magnetic-resonance radio-frequency coil performance evaluation method and system |
| CN105572612A (en) * | 2014-12-31 | 2016-05-11 | 中国科学院深圳先进技术研究院 | Method of improving multichannel radio frequency coil performances |
| CN107137083A (en) * | 2017-06-16 | 2017-09-08 | 上海联影医疗科技有限公司 | A kind of magnetic resonance scanning method, system and computer-readable recording medium |
Non-Patent Citations (2)
| Title |
|---|
| 李华勇 等.磁共振六通道线圈阵列的数字化仿真与设计.中国生物医学工程学报.2017,第36卷(第06期),711-717. * |
| 黄绮华 等.高场和超高场MR下人体内B1场均匀性及SAR 随场强变化规律的研究.中国生物医学工程学报.2013,第32卷(第01期),21-27. * |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022188237A1 (en) | 2022-09-15 |
| CN113017598A (en) | 2021-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113017598B (en) | Radio frequency energy deposition prediction and monitoring method, device, equipment and medium | |
| CN102736045B (en) | Parallel transmission RF pulse design with local SAR constraints | |
| US7622921B2 (en) | Method and device for monitoring radio-frequency exposure in a magnetic resonance measurement | |
| EP1831712B1 (en) | Magnetic resonance imaging system and method | |
| CN109709503A (en) | A kind of magnetic resonance system control method, magnetic resonance system, computer equipment | |
| US9625541B2 (en) | Safety tests for systems monitoring local SAR | |
| CN109613461B (en) | Gradient echo sequence setting method, magnetic resonance imaging system scanning device, and medium | |
| US20130063143A1 (en) | Local SAR Constrained Parallel Transmission RF Pulse in Magnetic Resonance Imaging | |
| CN101396265B (en) | Magnetic resonance imaging apparatus and analysis method for fat suppression effect | |
| JP6884344B2 (en) | Brain network activity estimation system, brain network activity estimation method, brain network activity estimation program, and learned brain activity estimation model | |
| CN103777159B (en) | The control method of the high-frequency radiator of MRT system | |
| CN105308473B (en) | The Automatic Optimal of parallel imaging acceleration parameter | |
| CN104736050B (en) | The Forecasting Methodology of MR imaging apparatus and SAR | |
| JP2013516208A (en) | Method for calculating local energy specific absorption rate (SAR) in nuclear magnetic resonance | |
| WO2023185116A1 (en) | Method and apparatus for determining loss model of radio frequency transmitting coil, and device and medium | |
| WO2010001747A1 (en) | Magnetic resonance imaging apparatus and method of deciding execution order | |
| Silemek et al. | Rapid safety assessment and mitigation of radiofrequency induced implant heating using small root mean square sensors and the sensor matrix Qs | |
| US11428767B2 (en) | Computer-implemented method for providing an actuation sequence and method for acquiring measurement data by a magnetic resonance device | |
| CN117197521A (en) | Simulation image preprocessing method, local specific absorption rate estimation method and device | |
| CN109124635B (en) | Model generation method, magnetic resonance imaging scanning method and system | |
| US20080315877A1 (en) | Imaging Tissue Deformation Using Strain Encoded Mri | |
| CN104434102B (en) | Method for measuring HF driving pulses | |
| US20150260817A1 (en) | Noise optimization for a magnetic resonance system | |
| CN103837848B (en) | Radio frequency antenna device for magnetic resonance system and method | |
| Kozlov et al. | Modeling electromagnetic exposure in humans inside a whole-body birdcage coil excited by a two-channel parallel transmitter operated at 123 MHz |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |