CN113017598A - Method, device, equipment and medium for predicting and monitoring radio frequency energy deposition - Google Patents
Method, device, equipment and medium for predicting and monitoring radio frequency energy deposition Download PDFInfo
- Publication number
- CN113017598A CN113017598A CN202110265492.7A CN202110265492A CN113017598A CN 113017598 A CN113017598 A CN 113017598A CN 202110265492 A CN202110265492 A CN 202110265492A CN 113017598 A CN113017598 A CN 113017598A
- Authority
- CN
- China
- Prior art keywords
- radio frequency
- energy deposition
- scanning
- model
- channel
- 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.)
- Granted
Links
- 230000008021 deposition Effects 0.000 title claims abstract description 150
- 238000000034 method Methods 0.000 title claims abstract description 72
- 238000012544 monitoring process Methods 0.000 title claims abstract description 31
- 238000004088 simulation Methods 0.000 claims abstract description 69
- 230000011218 segmentation Effects 0.000 claims abstract description 25
- 210000001519 tissue Anatomy 0.000 claims description 44
- 239000011159 matrix material Substances 0.000 claims description 36
- 230000005684 electric field Effects 0.000 claims description 17
- 210000003491 skin Anatomy 0.000 claims description 13
- 238000003384 imaging method Methods 0.000 claims description 12
- 210000003205 muscle Anatomy 0.000 claims description 12
- 210000004556 brain Anatomy 0.000 claims description 11
- 238000000926 separation method Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 8
- 230000035699 permeability Effects 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 210000000988 bone and bone Anatomy 0.000 claims description 6
- 210000002356 skeleton Anatomy 0.000 claims description 4
- 238000012806 monitoring device Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 11
- 238000009826 distribution Methods 0.000 description 7
- 238000002595 magnetic resonance imaging Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 230000005672 electromagnetic field Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000005284 excitation Effects 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000011552 rat model Methods 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
- 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
- 238000005457 optimization Methods 0.000 description 1
- 238000003672 processing method Methods 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
Images
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
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Molecular Biology (AREA)
- Animal Behavior & Ethology (AREA)
- Pathology (AREA)
- Radiology & Medical Imaging (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Theoretical Computer Science (AREA)
- Surgery (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- High Energy & Nuclear Physics (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
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 predicting method comprises the following steps: acquiring a magnetic resonance scanning structural image of a scanning object, and performing tissue segmentation based on the structural 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; and establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and confirming a radio frequency energy deposition value by combining a biological electromagnetic simulation model and the multi-channel radio frequency coil model. The monitoring method comprises the following steps: and predicting the deposition value of the radio frequency energy according to the prediction method, and adjusting a scanning strategy according to the prediction result. The technical scheme of the embodiment realizes more accurate and personalized prediction of the SAR value of the scanned object in the scene of the multichannel transmitting coil parallel transmitting technology, and improves the safety of the scanned object in magnetic resonance scanning.
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 (MRI) systems, a patient absorbs the energy of radio frequency electromagnetic waves during examination to form human body radio frequency energy deposits, and the measurement unit is Specific Absorption Rate (SAR), i.e. the amount of radio frequency electromagnetic wave energy absorbed per unit mass of biological tissue per unit time (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 multi-channel 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 degree of freedom of control is improved and the space is optimized. However, the increase of the degree of freedom of the excitation source may cause the SAR distribution generated by the coil in the human body to become extremely complex, and the possibility of generating local hot spots is greatly increased, which may exceed the safety standard of SAR. More accurate prediction and monitoring of SAR values is required.
However, in the prior art, the SAR values are predicted based on a plurality of digital human body models in commercial electromagnetic field simulation software, individual differences cannot be considered sufficiently, and factors such as the size and shape of the body, the sex and the body fat distribution, the body posture and the position in the system and the like cause prediction errors and are inaccurate. And when SAR calculation is carried out, 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 method, a device, equipment and a medium for predicting and monitoring radio frequency energy deposition, which are used for more accurately and individually predicting the SAR value of a scanned object in a multi-channel transmitting coil parallel transmitting technical scene and improving the safety of receiving magnetic resonance scanning by the scanned object.
In a first aspect, an embodiment of the present invention provides a radio frequency energy deposition prediction method, where the method includes:
acquiring a magnetic resonance scanning structure image of a scanning object, performing tissue segmentation based on the structure image, and establishing a three-dimensional biological model of the scanning object;
establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
and establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and simulating and predicting the radio frequency energy deposition value of the scanned object by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model.
Optionally, the structural image includes a water-fat separation image, a T1 and T2 contrast image, and an ultra-short echo image, and the performing tissue segmentation based on the structural image to establish a three-dimensional biological model of the scanned object includes:
performing tissue segmentation of the scanned image of the scanned object based on the water-fat separation image, the T1 and T2 contrast images and the ultra-short echo image to obtain fat, brain, muscle, bone and skin;
and establishing the three-dimensional biological model by combining the fat, brain, muscle, skeleton and skin tissues after tissue segmentation.
Optionally, the establishing a biological electromagnetic simulation model according to the three-dimensional biological model includes:
and carrying out corresponding dielectric constant, magnetic permeability 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 rf energy deposition value of the scanned object by combining the bioelectromagnetic simulation model and the multi-channel rf coil model includes:
performing simulation based on the biological electromagnetic simulation model and the multi-channel radio frequency coil model, and determining the value of the radio frequency magnetic field intensity and the electric field intensity of each channel in the multi-channel radio frequency coil;
and predicting to obtain 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 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 includes:
according to the formula
Calculating the 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 a multi-channel radio frequency coil, w 'is 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 a conjugate 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,
v represents the statistical volume of the rf energy deposition values for rf magnetic field strength.
In a second aspect, an embodiment of the present invention further provides a radio frequency energy deposition monitoring method, where the method includes:
acquiring a preset scanning strategy of a scanned 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 a multi-channel radio frequency coil according to the voltage amplitude and the phase of a multi-channel transmitting coil in the preset scanning strategy;
predicting the radio frequency energy deposition value borne by the scanning object when scanning imaging is carried out by the radio frequency energy deposition prediction method according to any embodiment on the basis of the radio frequency magnetic field intensity 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 when the radio frequency energy deposition value is larger than a preset upper limit energy deposition value, stopping the scanning imaging process and adjusting scanning sequence parameters in the preset scanning strategy.
In a third aspect, an embodiment of the present invention further provides an rf energy deposition prediction apparatus, where the apparatus includes:
the human body model building module is used for collecting a magnetic resonance scanning structure image of a scanning object, carrying out tissue segmentation based on the structure image and building a three-dimensional biological model of the scanning object;
the biological electromagnetic simulation model establishing module is used for establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
and the radio frequency deposition prediction module is used for establishing a multi-channel radio frequency coil model according to a preset scanning strategy and predicting the radio frequency energy deposition value of the scanned object by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model in a simulation manner.
Optionally, the human body model building module is specifically configured to:
performing tissue segmentation of the scanned image of the scanned object based on the water-fat separation image, the T1 and T2 contrast images and the ultra-short echo image to obtain fat, brain, muscle, bone and skin;
and establishing the three-dimensional biological model by combining the fat, brain, muscle, skeleton and skin tissues after tissue segmentation.
Optionally, the biological electromagnetic simulation model building module is specifically configured to:
and carrying out corresponding dielectric constant, magnetic permeability 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 is specifically configured to:
performing simulation based on the biological electromagnetic simulation model and the multi-channel radio frequency coil model, and determining the value of the radio frequency magnetic field intensity and the electric field intensity of each channel in the multi-channel radio frequency coil;
and predicting to obtain 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 rf deposition prediction module is specifically configured to:
according to the formula
Calculating the 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 a multi-channel radio frequency coil, w 'is 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 a conjugate 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,
v represents the statistical volume of the rf energy deposition values for rf magnetic field strength.
In a fourth aspect, an embodiment of the present invention further provides an rf energy deposition monitoring apparatus, including:
the scanning strategy determining module is used for acquiring a preset scanning strategy of a scanned 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 multi-channel radio frequency coil according to the voltage amplitude and the phase of a multi-channel transmitting coil in the preset scanning strategy;
a radio frequency energy deposition value determination module, configured to predict, based on the radio frequency magnetic field strength and the weighting factor matrix, a radio frequency energy deposition value that the scan object undergoes when performing scan imaging by using the radio frequency energy deposition prediction method according to any embodiment;
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 when the radio frequency energy deposition value is larger than a preset upper limit energy deposition value, stopping the scanning imaging process and adjusting scanning sequence parameters in the preset scanning strategy.
In a fifth aspect, an embodiment of the present invention further provides a computer device, where the computer device includes one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method for predicting rf energy deposition, or a method for monitoring rf energy deposition, as provided in any of the embodiments of the invention.
In a sixth aspect, embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the rf energy deposition prediction method or the rf energy deposition monitoring method provided in any of the embodiments of the present invention.
The embodiment of the invention has the following advantages or beneficial effects:
according to the embodiment of the invention, a three-dimensional biological model of a scanning object is established by acquiring 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 determining a radio frequency energy deposition value by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model; the problem of inaccurate prediction of the radio frequency energy deposition in the prior art is solved, the simulation error is reduced, and the local energy deposition value is predicted more accurately.
Drawings
FIG. 1 is a flow chart of a method for predicting RF energy deposition according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a biological electromagnetic simulation model for a rat according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a simulation model of biological electromagnetic system and coils for prediction according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a distribution of a simulated magnetic field B1 based on the method in the first embodiment of the present invention;
fig. 5 is a schematic diagram of a distribution of a magnetic field B1 actually measured based on the method in the embodiment according to the first embodiment of the present invention;
FIG. 6 is a flowchart of a RF energy deposition monitoring method according to a second embodiment of the present invention;
FIG. 7 is a schematic structural diagram of an RF energy deposition prediction apparatus according to a third embodiment of the present invention;
fig. 8 is a schematic structural diagram of an rf energy deposition monitoring apparatus 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 present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a method for predicting rf energy deposition according to an embodiment of the present invention, which is applicable to a magnetic resonance scan of a scanned object. The method can be executed by a device configured for radio frequency energy deposition prediction, and the device can be realized by software and/or hardware and is integrated in an electronic device with an application development function.
As shown in fig. 1, the method for predicting rf energy deposition includes the following steps:
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, and in order to reduce simulation errors and make a simulation result more accurate, a personalized three-dimensional biological model of the scanning object is established in the step, so that the three-dimensional biological model in the simulation process is more matched with the scanning object.
Specifically, the scanning object can be scanned by setting different magnetic resonance scanning parameters to obtain a scanning structure image, including acquiring a water-fat separation magnetic resonance image and a T1/T2 contrast magnetic resonance image of the scanning object, so as to obtain information of fat, brain and muscle tissues. Meanwhile, an ultrashort echo image can be obtained by using the ultrashort echo sequence so as to obtain bone information. Furthermore, tissue segmentation of the scanned image of the scanning object can be performed based on the water-fat separation image, the T1 and T2 contrast images, and the ultra-short echo image, so that 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 a scanning object can be established. The scanning object may be a human body, or an animal.
And S120, establishing a biological electromagnetic simulation model according to the three-dimensional biological model.
Since different values of the tissue conductivity, the magnetic permeability, the characteristic absorption rate and the like are different, specifically, after the three-dimensional biological model of the scanned object is obtained, the corresponding dielectric constant, the magnetic permeability 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 dielectric constant, the magnetic permeability and the tissue density can be stored in a simulation system memory in advance.
S130, establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and simulating and predicting a radio frequency energy deposition value of the scanned object by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model.
Specifically, the scanning strategy comprises parameters of a scanning sequence, information of scanning parts, and voltage amplitude and phase of the multichannel transmitting coil. Parameters such as coil weight factors of each channel can be obtained according to the voltage amplitude and the phase of the multi-channel transmitting coil, and the weight factors of each channel represent the contribution degree of radio-frequency signals of each channel to a scanning result.
The multichannel radio frequency transmitting technology is adopted, because in a high-field and ultrahigh-field magnetic resonance imaging system, in order to generate a uniform radio frequency electromagnetic field and reduce an energy deposition value, the multichannel parallel transmitting technology is generally adopted, the amplitude and the phase of each unit coil excitation source, even the radio frequency pulse waveform, are independently controlled, and the degree of freedom for controlling a transmitting sequence and the optimization space can be improved.
Furthermore, by combining the biological electromagnetic simulation model of the scanned object with the multi-channel radio frequency coil model for simulation, the electric field value of each channel in the multi-channel radio frequency coil model and the radio frequency magnetic field intensity, namely parameters capable of being used for predicting the radio frequency energy deposition value, can be determined
Based on the acquired parameters for predicting the rf energy deposition value, the rf energy deposition value can be predicted by the following formula:
according to the formula
Calculating the 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 a multi-channel radio frequency coil, w 'is 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 a conjugate 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 intensity of the RF field, V denotes the radiationThe statistical volume of energy deposition values may include whole body, head, part of body and part.
Further, using the Q matrix to bring the channel weighting factor w outside the integral equation, there are:
wherein the central integral term Q (x, y, z) is independent of each channel voltage amplitude and phase weight vector w, Q (x, y, z) being an N x N hermite regular 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, independent 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 scanning sequence are only taken into account during the energy deposition monitoring
Of (c) is determined.
In a specific example, a rat can be used as a scanning object to perform simulation, and a rat water-fat separation image is firstly acquired on a magnetic resonance imaging system with the resolution of 0.60 multiplied by 1.00mm3. Matlab is used for processing rat image data, the rat image data are divided into four tissues of skin, fat, lung and muscle, then the four tissues are converted into model files which can be identified by electromagnetic field simulation computing software CST, the tissue parameters refer to a CST database, and the obtained rat simplified model is subjected to simulation and experimental test by using a self-made coil. Fig. 2 shows a rat model obtained by segmentation, and fig. 3 shows a simulation model and coils used for the test. The reliability of the model is verified by comparing the simulated and measured magnetic field distributions. Fig. 4 is the distribution of the rat model simulation magnetic field B1 established by the method, fig. 5 is the distribution of the measured magnetic field B1, and it can be seen from fig. 4 and fig. 5 that the simulation is consistent with the measured result, and the correctness of the self-established electromagnetic field simulation model is verified.
According to the technical scheme of the embodiment, a three-dimensional biological model of a scanning object is established by acquiring a magnetic resonance scanning structure image of the scanning object and performing 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 determining a radio frequency energy deposition value by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model; the problem of inaccurate prediction of the radio frequency energy deposition in the prior art is solved, the simulation error is reduced, and the local energy deposition value is predicted more accurately.
Example two
Fig. 6 is a flowchart of a rf energy deposition monitoring method according to a second embodiment of the present invention, which is applicable to the second embodiment and belongs to the same inventive concept as the data processing method in the first embodiment. The method can be executed by a radio frequency energy deposition monitoring device, which can be realized by software and/or hardware, and is integrated in a computer device or a server device with an application development function.
As shown in fig. 6, the rf energy deposition monitoring method includes the following steps:
s210, acquiring a preset scanning strategy of a scanned 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 a multi-channel radio frequency coil according to the voltage amplitude and the phase of a multi-channel transmitting coil in the preset scanning strategy.
S220, predicting the radio frequency energy deposition value borne by the scanning object when scanning imaging is carried out 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.
And S230, adjusting a scanning strategy according to the radio frequency energy deposition value.
Specifically, in order to ensure the smooth proceeding of sequence scanning, the SAR prediction is performed before the sequence scanning, that is, the SAR prediction is performed according to the energy expansion of the sequence, the calibration data and the SAR model, if the predicted SAR exceeds the limit of the regulation, the scanning strategy is adjusted, for example, TR (transmission time of the radio frequency sequence), flip angle and the like in sequence parameters are adjusted, the SAR is further reduced, and the magnetic resonance sequence scanning is performed under the condition of ensuring the safety of a scanned object. If the prediction is passed, the scanning can be directly started.
In a preferred embodiment, forward and reverse power is always acquired in real time by the directional coupler, real time applied power acquisition is possible in combination with an analog-to-digital converter, and the B1+ field strength of the magnetic resonance can be obtained by calibrating the flip angle by a calibration sequence, in order to fully ensure patient safety. 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 model calculated by simulation. When the SAR of any part exceeds the safety value, the scanning is stopped in time, and the scanning strategy can be continuously adjusted to ensure that the SAR of the corresponding part is in the safety range.
According to the technical scheme of the embodiment, a multi-channel radio frequency coil model is established according to the emission amplitude and the phase of a scanning sequence in a preset scanning strategy by obtaining the preset scanning strategy of a scanned object, and the radio frequency energy deposition value borne by the scanned object when the scanned object is scanned and imaged by using the preset scanning sequence is predicted through the radio frequency energy deposition prediction method in any embodiment based on the multi-channel radio frequency coil model. And adjusting a scanning strategy according to the radio frequency energy deposition value. The method and the device realize the monitoring of the radio frequency energy deposition value under the multi-channel radio frequency emission technology and improve the safety in the magnetic resonance scanning process.
The following embodiments of the device for predicting and monitoring rf energy deposition provided in the embodiments of the present invention belong to the same inventive concept as the method for predicting and monitoring rf energy deposition in the above embodiments, and can implement the method for predicting and monitoring rf energy deposition in the above embodiments. For details not described in detail in the embodiments of the apparatus for predicting and monitoring rf energy deposition, reference may be made to the embodiments of the method for predicting and monitoring rf energy deposition described above.
EXAMPLE III
Fig. 7 is a schematic structural diagram of an rf energy deposition prediction apparatus according to a third embodiment of the present invention, which can be applied to this embodiment.
As shown in fig. 7, the rf energy deposition prediction apparatus includes a human body model building module 310, a biological electromagnetic simulation model building module 320, and an rf deposition prediction module 330.
The human body model establishing module 310 is configured to acquire a magnetic resonance scanning structure image of a scanned object, perform tissue segmentation based on the structure image, and establish a three-dimensional biological model of the scanned object; a biological electromagnetic simulation model establishing module 320, configured to establish a biological electromagnetic simulation model according to the three-dimensional biological model; and the radio frequency deposition predicting module 330 is configured to establish a multi-channel radio frequency coil model according to a preset scanning strategy, and simulate and predict a radio frequency energy deposition value of the scanned object by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model.
According to the technical scheme of the embodiment, a three-dimensional biological model of a scanning object is established by acquiring a magnetic resonance scanning structure image of the scanning object and performing 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 determining a radio frequency energy deposition value by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model; the problem of inaccurate prediction of the radio frequency energy deposition in the prior art is solved, the simulation error is reduced, and the local energy deposition value is predicted more accurately.
Optionally, the human body model building module 310 is specifically configured to:
performing tissue segmentation of the scanned image of the scanned object based on the water-fat separation image, the T1 and T2 contrast images and the ultra-short echo image to obtain fat, brain, muscle, bone and skin;
and establishing the three-dimensional biological model by combining the fat, brain, muscle, skeleton and skin tissues after tissue segmentation.
Optionally, the biological electromagnetic simulation model building module 320 is specifically configured to:
and carrying out corresponding dielectric constant, magnetic permeability 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:
performing simulation based on the biological electromagnetic simulation model and the multi-channel radio frequency coil model, and determining the value of the radio frequency magnetic field intensity and the electric field intensity of each channel in the multi-channel radio frequency coil;
and predicting to obtain 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 rf deposition prediction module is specifically configured to:
according to the formula
Calculating the 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 a multi-channel radio frequency coil, w 'is 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 a conjugate 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,
v represents the statistical volume of the rf energy deposition values for rf magnetic field strength.
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 corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 8 is a schematic structural diagram of a radio frequency energy deposition monitoring apparatus according to a fourth embodiment of the present invention, which is applicable to a case of performing a magnetic resonance scan on a scanned object.
As shown in fig. 8, the rf energy deposition monitoring apparatus includes a scanning strategy determination module 410, an rf energy deposition value determination module 420, and a scanning control module 430.
The scanning strategy determining module 410 is configured to obtain a preset scanning strategy of a scanned 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 a multi-channel radio frequency coil according to a voltage amplitude and a phase of a multi-channel transmitting coil in the preset scanning strategy; a rf energy deposition value determining module 420, configured to predict, based on the rf magnetic field strength and the weighting factor matrix, an rf energy deposition value that the scan object undergoes during scan imaging through an rf energy deposition prediction method according to any embodiment; and a scanning strategy adjusting module 430, configured to adjust a scanning strategy according to the rf energy deposition value.
Optionally, the scanning policy adjusting module 430 is specifically configured to:
and when the radio frequency energy deposition value is larger than a preset upper limit energy deposition value, stopping the scanning imaging process and adjusting scanning sequence parameters in the preset scanning strategy.
According to the technical scheme of the embodiment, a multi-channel radio frequency coil model is established according to the emission amplitude and the phase of a scanning sequence in a preset scanning strategy by obtaining the preset scanning strategy of a scanned object, and the radio frequency energy deposition value borne by the scanned object when the scanned object is scanned and imaged by using the preset scanning sequence is predicted through the radio frequency energy deposition prediction method in any embodiment based on the multi-channel radio frequency coil model. And adjusting a scanning strategy according to the radio frequency energy deposition value. The method and the device realize the monitoring of the radio frequency energy deposition value under the multi-channel radio frequency emission technology and improve 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 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 only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention. The computer device 12 may be any terminal device with computing capability, such as a terminal device of an intelligent controller, a server, a mobile phone, and the like. The computer device may be connected to a magnetic resonance imaging device for performing corresponding method steps in conjunction with a magnetic resonance scanning procedure to achieve a prediction of rf energy deposition.
As shown in FIG. 9, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
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. 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 and write to non-removable, nonvolatile magnetic media (not shown in FIG. 9, and commonly referred to as a "hard drive"). Although not shown in FIG. 9, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in 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 of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing a method for predicting rf energy deposition provided by the embodiment of the present invention, including:
acquiring a magnetic resonance scanning structure image of a scanning object, performing tissue segmentation based on the structure image, and establishing a three-dimensional biological model of the scanning object;
establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
and establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and simulating and predicting the radio frequency energy deposition value of the scanned object by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model.
Alternatively, the computer program, when executed by the processor, may further implement a method for rf energy deposition monitoring as provided in any of the embodiments of the present invention, including:
acquiring a preset scanning strategy of a scanned 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 a multi-channel radio frequency coil according to the voltage amplitude and the phase of a multi-channel transmitting coil in the preset scanning strategy;
predicting the radio frequency energy deposition value borne by the scanning object when scanning imaging is carried out by the radio frequency energy deposition prediction method according to any embodiment on the basis of the radio frequency magnetic field intensity and the weighting factor matrix;
and adjusting a scanning strategy according to the radio frequency energy deposition value.
EXAMPLE six
A sixth embodiment provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for predicting rf energy deposition according to any embodiment of the present invention, including:
acquiring a magnetic resonance scanning structure image of a scanning object, performing tissue segmentation based on the structure image, and establishing a three-dimensional biological model of the scanning object;
establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
and establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and simulating and predicting the radio frequency energy deposition value of the scanned object by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model.
Alternatively, the computer program, when executed by the processor, may further implement a method for rf energy deposition monitoring as provided in any of the embodiments of the present invention, including:
acquiring a preset scanning strategy of a scanned 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 a multi-channel radio frequency coil according to the voltage amplitude and the phase of a multi-channel transmitting coil in the preset scanning strategy;
predicting the radio frequency energy deposition value borne by the scanning object when scanning imaging is carried out by the radio frequency energy deposition prediction method according to any embodiment on the basis of the radio frequency magnetic field intensity and the weighting factor matrix;
and adjusting a scanning strategy according to the radio frequency energy deposition value.
Computer storage media for embodiments of the invention may employ 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 any combination thereof. 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 the context of 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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 for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It will be understood by those skilled in the art that the modules or steps of the invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented by program code executable by a computing device, such that it may be stored in a memory device and executed by a computing device, or it may be separately fabricated into various integrated circuit modules, or it may be fabricated by fabricating a plurality of modules or steps thereof into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. 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, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (11)
1. A method for predicting rf energy deposition, comprising:
acquiring a magnetic resonance scanning structure image of a scanning object, performing tissue segmentation based on the structure image, and establishing a three-dimensional biological model of the scanning object;
establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
and establishing a multi-channel radio frequency coil model according to a preset scanning strategy, and simulating and predicting the radio frequency energy deposition value of the scanned object by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model.
2. The method of claim 1, wherein the structural image comprises a water-fat separation image, a T1-T2 contrast image and an ultra-short echo image, and the performing tissue segmentation based on the structural image to build a three-dimensional biological model of the scanned object comprises:
performing tissue segmentation of the scanned image of the scanned object based on the water-fat separation image, the T1 and T2 contrast images and the ultra-short echo image to obtain fat, brain, muscle, bone and skin;
and establishing the three-dimensional biological model by combining the fat, brain, muscle, skeleton and skin tissues after tissue segmentation.
3. 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 permeability and tissue density assignment on each tissue in the three-dimensional biological model to obtain the biological electromagnetic simulation model.
4. The method of claim 1, wherein said simulating combined said bioelectromagnetic simulation model and said multi-channel radio frequency coil model to predict radio frequency energy deposition values of said scanned object comprises:
performing simulation based on the biological electromagnetic simulation model and the multi-channel radio frequency coil model, and determining the value of the radio frequency magnetic field intensity and the electric field intensity of each channel in the multi-channel radio frequency coil;
and predicting to obtain 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.
5. The method of claim 4, wherein predicting the RF energy deposition value based on the RF magnetic field strength value and the electric field strength of each channel of the multi-channel RF coil comprises:
according to the formula
Calculating the 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 body, w is a weighting factor matrix of each channel coil in the multi-channel radio frequency coil, w' is 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 a conjugate transpose of E (x, y, z), ρ (x, y, z) is the body density of the scanning subject, σ (x, y, z) is the body conductivity of the scanning subject,
v represents the statistical volume of the rf energy deposition values for rf magnetic field strength.
6. A method for monitoring rf energy deposition, comprising:
acquiring a preset scanning strategy of a scanned 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 a multi-channel radio frequency coil according to the voltage amplitude and the phase of a multi-channel transmitting coil in the preset scanning strategy;
predicting the RF energy deposition value of the scanned object when scanning and imaging are carried out by the RF energy deposition prediction method according to any one of claims 1-5 based on the RF magnetic field intensity and the weighting factor matrix;
and adjusting a scanning strategy according to the radio frequency energy deposition value.
7. The method of claim 6, wherein said adjusting a scanning strategy according to said RF energy deposition value comprises:
and when the radio frequency energy deposition value is larger than a preset upper limit energy deposition value, stopping the scanning imaging process and adjusting scanning sequence parameters in the preset scanning strategy.
8. An rf 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, carrying out tissue segmentation based on the structure image and building a three-dimensional biological model of the scanning object;
the biological electromagnetic simulation model establishing module is used for establishing a biological electromagnetic simulation model according to the three-dimensional biological model;
and the radio frequency deposition prediction module is used for establishing a multi-channel radio frequency coil model according to a preset scanning strategy and predicting the radio frequency energy deposition value of the scanned object by combining the biological electromagnetic simulation model and the multi-channel radio frequency coil model in a simulation manner.
9. A radio frequency energy deposition monitoring device, comprising:
the scanning strategy determining module is used for acquiring a preset scanning strategy of a scanned object, calculating the radio frequency magnetic field intensity of a scanning sequence according to the preset scanning strategy, and determining the weighting factor moment of the multi-channel radio frequency coil according to the voltage amplitude and the phase of the multi-channel transmitting coil in the preset scanning strategy;
a radio frequency energy deposition value determination module, configured to predict, based on the radio frequency magnetic field strength and the weighting factor matrix, 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 5;
and the scanning control module is used for adjusting a scanning strategy according to the radio frequency energy deposition value.
10. A computer device, characterized in that the computer device comprises:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the radio frequency energy deposition prediction method of any one of claims 1-5, or the radio frequency energy deposition monitoring method of any one of claims 6-7.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the rf energy deposition prediction method according to any one of claims 1 to 5, or the rf energy deposition monitoring method according to any one of claims 6 to 7.
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 true CN113017598A (en) | 2021-06-25 |
| CN113017598B 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) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114398803A (en) * | 2022-03-25 | 2022-04-26 | 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 |
| WO2023185116A1 (en) * | 2022-03-28 | 2023-10-05 | 中国科学院深圳先进技术研究院 | Method and apparatus for determining loss model of radio frequency transmitting coil, and device and medium |
Citations (8)
| 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 |
| US20160245882A1 (en) * | 2015-02-24 | 2016-08-25 | Siemens Aktiengesellschaft | Method and magnetic resonance apparatus for determining absolute receive sensitivity maps for reception coils |
| 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 (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 (8)
| 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 |
| US20160245882A1 (en) * | 2015-02-24 | 2016-08-25 | Siemens Aktiengesellschaft | Method and magnetic resonance apparatus for determining absolute receive sensitivity maps for reception coils |
| 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" * |
| 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" * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114398803A (en) * | 2022-03-25 | 2022-04-26 | 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 |
| WO2023178843A1 (en) * | 2022-03-25 | 2023-09-28 | 中国科学院深圳先进技术研究院 | Shimming joint simulation method and apparatus, electronic device, and storage medium |
| WO2023185116A1 (en) * | 2022-03-28 | 2023-10-05 | 中国科学院深圳先进技术研究院 | Method and apparatus for determining loss model of radio frequency transmitting coil, and device and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022188237A1 (en) | 2022-09-15 |
| CN113017598B (en) | 2023-11-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109709503B (en) | Magnetic resonance system control method, magnetic resonance system and computer equipment | |
| US8653818B2 (en) | Parallel transmission RF pulse design with local SAR constraints | |
| US7701211B2 (en) | Magnetic resonance imaging system and method | |
| CN109613461B (en) | Gradient echo sequence setting method, magnetic resonance imaging system scanning device, and medium | |
| CN113017598B (en) | Radio frequency energy deposition prediction and monitoring method, device, equipment and medium | |
| CN111417342B (en) | Brain network activity estimation system, method and storage medium | |
| US20080157765A1 (en) | Method and device for monitoring radio-frequency exposure in a magnetic resonance measurement | |
| US11830111B2 (en) | Magnetic resonance imaging method, device, medical device and storage medium | |
| JP2011517983A (en) | Real-time local and global SAR estimation for improved patient safety and scanning performance | |
| CN105308473B (en) | The Automatic Optimal of parallel imaging acceleration parameter | |
| US8975892B2 (en) | Method of optimizing magnetic resonance image contrast with MRI relaxation time scanning parameters correlated to age of a subject | |
| US9704275B2 (en) | Dielectric encoding of medical images | |
| CN114398803B (en) | Shimming joint simulation method and device, electronic equipment and storage medium | |
| WO2023185116A1 (en) | Method and apparatus for determining loss model of radio frequency transmitting coil, and device and medium | |
| CN109124635B (en) | Model generation method, magnetic resonance imaging scanning method and system | |
| US7741845B2 (en) | Imaging tissue deformation using strain encoded MRI | |
| CN117197521A (en) | Simulation image preprocessing method, local specific absorption rate estimation method and device | |
| US11519988B2 (en) | Method for adjusting at least one measurement parameter for a measurement protocol for a magnetic resonance examination | |
| 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 | |
| US20240192293A1 (en) | Sar value acquisition method in mri, sar value acquisition system, electronic apparatus, and storage medium | |
| Doran et al. | The impact of variations in subject geometry, respiration and coil repositioning on the specific absorption rate in parallel transmit abdominal imaging at 7 T | |
| CN113384260B (en) | Acceleration factor adjusting method, magnetic resonance imaging scanning method, device and equipment | |
| CN113495240B (en) | Method and device for generating scanning scheme, computer equipment and storage medium | |
| JP7221067B2 (en) | Medical image processing device, magnetic resonance imaging device, learning device, medical image processing method, and program | |
| CN116520225A (en) | Method, system, device and medium for generating magnetic resonance image |
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 |