Diffusion gradient calculation method of diffusion imaging sequence and magnetic resonance imaging method
By setting the same sequence diffusion sensitivity factor in the diffusion imaging sequence and calculating the readout segment and diffusion gradient parameters for each excitation, the imaging quality degradation caused by inaccurate diffusion gradient design is solved, and a high-quality imaging effect is achieved.
Patent Information
- Application Number
- CN202410007799.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-02
- Publication Date
- 2025-07-04
AI Technical Summary
In the existing diffusion imaging sequences collected by multi-excitation propeller, the diffusion gradient design of different excitations is inaccurate, resulting in a decrease in imaging quality.
Set the sequence diffusion sensitivity factors between different excitations in the diffusion imaging sequence to be the same. By obtaining the read gradient parameters of each excitation, calculate the diffusion sensitivity factor and diffusion gradient sensitivity factor of the readout segment, and accurately calculate the diffusion gradient amplitude of each excitation.
Ensure that the sequence diffusion sensitivity factors of different excitations are the same, improving imaging quality.
Smart Images
Figure CN120254729A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of magnetic resonance imaging, in particular to a diffusion gradient calculation method for a diffusion imaging sequence and a magnetic resonance imaging method. Background Art
[0002] For the existing diffusion imaging sequence with multi-excitation propeller acquisition, the diffusion gradients of different excitations are designed with the same diffusion gradient diffusion sensitivity factor; since the readout gradient diffusion sensitivity factors of different excitations in the diffusion imaging sequence with multi-excitation propeller acquisition are different (the sequence diffusion sensitivity factor is equal to the sum of the diffusion gradient diffusion sensitivity factor and the readout gradient diffusion sensitivity factor), there are differences in the sequence diffusion sensitivity factors between different excitations. The multi-excitation sequence is composed of data acquired from different excitations to jointly form the K-space, and the different sequence diffusion sensitivity factors between different excitations make it difficult to calculate the overall sequence diffusion sensitivity factor, resulting in a decline in imaging quality.
[0003] Aiming at the problem that the design of the diffusion gradient diffusion sensitivity factor of the diffusion gradient in different excitations in the related art is inaccurate, which further causes a decline in imaging quality, no effective solution has been proposed yet. Summary of the Invention
[0004] In this embodiment, a diffusion gradient calculation method for a diffusion imaging sequence and a magnetic resonance imaging method are provided to solve the problem that the design of the diffusion gradient diffusion sensitivity factor of the diffusion gradient in different excitations in the related art is inaccurate, which further causes a decline in imaging quality.
[0005] In the first aspect, in this embodiment, a diffusion gradient calculation method for a diffusion imaging sequence is provided, including:
[0006] Setting the sequence diffusion sensitivity factors between different excitations in the diffusion imaging sequence to be the same; the diffusion imaging sequence includes a diffusion preparation segment and a readout segment;
[0007] Obtaining the gradient parameters of the readout gradient corresponding to each excitation, and calculating the readout segment diffusion sensitivity factor of each excitation based on the gradient parameters of the readout gradient;
[0008] Determining the diffusion gradient sensitivity factor of each excitation according to the sequence diffusion sensitivity factor and the readout segment diffusion sensitivity factor of each excitation;
[0009] Calculating the diffusion gradient amplitude of each excitation based on the diffusion gradient sensitivity factor of each excitation.
[0010] In some of these embodiments, obtaining the gradient parameters of the readout gradient corresponding to each excitation includes:
[0011] Obtaining the amplitude, interval, and duration of the readout gradient corresponding to each excitation.
[0012] In some of these embodiments, calculating a diffusion sensitivity factor for each excitation readout segment based on the gradient parameters of the readout gradient includes:
[0013] Constructing a first formula from the amplitude of the readout gradient, the interval of the readout gradient, the duration of the readout gradient, the gyromagnetic ratio, and the diffusion sensitivity factor of the readout segment;
[0014] Substituting the amplitude, interval, and duration of the readout gradient into the first formula to calculate the diffusion sensitivity factor of each excitation readout segment.
[0015] In some of these embodiments, determining a diffusion gradient sensitivity factor for each excitation based on the sequence diffusion sensitivity factor and the diffusion sensitivity factor of each excitation readout segment includes:
[0016] Subtracting the diffusion sensitivity factor of each excitation readout segment from the sequence diffusion sensitivity factor to obtain the diffusion gradient sensitivity factor of each excitation.
[0017] In some of these embodiments, calculating a diffusion gradient amplitude for each excitation based on the diffusion gradient sensitivity factor of each excitation includes:
[0018] Constructing a second formula from the diffusion gradient sensitivity factor, the gyromagnetic ratio, the interval of the diffusion gradient, the duration of the diffusion gradient, and the amplitude of the diffusion gradient;
[0019] Obtaining the gradient parameters of the diffusion gradient for each excitation;
[0020] Substituting the diffusion gradient sensitivity factor of each excitation and the gradient parameters of the diffusion gradient into the second formula to calculate the diffusion gradient amplitude of each excitation.
[0021] In some of these embodiments, obtaining the gradient parameters of the diffusion gradient for each excitation includes:
[0022] Obtaining the interval and duration of the diffusion gradient for each excitation.
[0023] In a second aspect, a magnetic resonance imaging method is provided in this embodiment, including:
[0024] Obtaining the diffusion gradient amplitude of each excitation using the diffusion gradient calculation method of the diffusion imaging sequence as described in the first aspect above;
[0025] Based on the diffusion gradient amplitude of each excitation, performing a multi-excitation propeller-type acquisition of a diffusion imaging sequence to obtain image data acquired from different excitations;
[0026] Filling the image data into the k-space to obtain a magnetic resonance image.
[0027] In a third aspect, a magnetic resonance imaging system is provided in this embodiment, including: a magnetic resonance imaging device and a processing device; wherein, the magnetic resonance imaging device is connected to the processing device;
[0028] The magnetic resonance imaging device is used to scan an object;
[0029] The processing device is used to execute the diffusion gradient calculation method of the diffusion imaging sequence described in the first aspect above, or the magnetic resonance imaging method described in the second aspect above.
[0030] In a fourth aspect, a computer device is provided in this embodiment, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the diffusion gradient calculation method of the diffusion imaging sequence described in the first aspect above, or the magnetic resonance imaging method described in the second aspect above.
[0031] In a fifth aspect, a storage medium is provided in this embodiment, on which a computer program is stored. When the program is executed by a processor, it implements the diffusion gradient calculation method of the diffusion imaging sequence described in the first aspect above, or the magnetic resonance imaging method described in the second aspect above.
[0032] Compared with the related art, in the diffusion gradient calculation method of the diffusion imaging sequence and the magnetic resonance imaging method provided in this embodiment, the sequence diffusion sensitivity factors between different excitations in the diffusion imaging sequence are set to be the same; the diffusion imaging sequence includes a diffusion preparation segment and a readout segment; the gradient parameters of the readout gradient corresponding to each excitation are obtained, and based on the gradient parameters of the readout gradient, the diffusion sensitivity factor of the readout segment of each excitation is calculated; according to the sequence diffusion sensitivity factor and the diffusion sensitivity factor of the readout segment of each excitation, the diffusion gradient sensitivity factor of each excitation is determined; based on the diffusion gradient sensitivity factor of each excitation, the diffusion gradient amplitude of each excitation is calculated, which solves the problem that the design of the diffusion gradient sensitivity factor of the diffusion gradient of different excitations is inaccurate, thereby causing a decrease in imaging quality, and accurately calculates the diffusion gradient sensitivity factor of each excitation, so as to ensure that the sequence diffusion sensitivity factors of different excitations are the same, thereby improving the imaging quality.
[0033] Details of one or more embodiments of the present application are set forth in the following drawings and description, so that other features, objects, and advantages of the present application become more comprehensible. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:
[0035] Figure 1 It is a hardware structure block diagram of a terminal device for a diffusion gradient calculation method or a magnetic resonance imaging method of a diffusion imaging sequence provided by an embodiment of the present application;
[0036] Figure 2 It is a flowchart of a diffusion gradient calculation method of a diffusion imaging sequence provided by an embodiment of the present application;
[0037] Figure 3 It is a schematic diagram of a diffusion imaging sequence provided by an embodiment of the present application;
[0038] Figure 4 is Figure 2 The flowchart of step S240 in
[0039] Figure 5 It is a schematic flowchart of a diffusion gradient calculation method provided by a preferred embodiment of the present application;
[0040] Figure 6 It is a structure block diagram of a diffusion gradient calculation device of a diffusion imaging sequence provided by an embodiment of the present application;
[0041] Figure 7 It is a schematic structure diagram of a magnetic resonance imaging system provided by an embodiment of the present application.
[0042] In the figure: 102, a processor; 104, a memory; 106, a transmission device; 108, an input / output device; 110, a magnetic resonance imaging device; 111, a mobile platform; 112, a detector assembly; 113, a scanning area; 120, a network; 130, a processing device; 210, a setting module; 220, an acquisition module; 230, a first processing module; 240, a second processing module. Detailed implementation manners
[0043] To understand the purpose, technical solution and advantages of the present application more clearly, the present application is described and illustrated below with reference to the accompanying drawings and embodiments.
[0044] Unless otherwise defined, technical or scientific terms used in this application shall have the ordinary meanings as understood by those of ordinary skill in the technical field to which this application belongs. In this application, words such as "a", "an", "one kind", "the", "these", etc. do not indicate a limitation in quantity and can be singular or plural. The terms "including", "containing", "having" and any variants thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device including a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent in these processes, methods, products or devices. The terms "connected", "coupled", etc. involved in this application do not limit to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" involved in this application means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. Usually, the character " / " indicates that the objects associated before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.
[0045] The method embodiments provided in this embodiment may be executed on a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 is a hardware structure block diagram of a terminal for the diffusion gradient calculation method of the diffusion imaging sequence in this embodiment, or a magnetic resonance imaging method. As Figure 1 shown, the terminal may include one or more ( Figure 1 only one is shown in the figure) processors 102 and a memory 104 for storing data. Among them, the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above terminal. For example, the terminal may further include more or fewer components than Figure 1 shown in the figure, or have a different configuration from Figure 1 shown in the figure.
[0046] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the diffusion gradient calculation method of the diffusion imaging sequence in this embodiment, or the computer program corresponding to the magnetic resonance imaging method. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above-mentioned method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the terminal through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof.
[0047] The transmission device 106 is used to receive or send data via a network. The above-mentioned network includes the wireless network provided by the communication provider of the terminal. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.
[0048] In this embodiment, a diffusion gradient calculation method for a diffusion imaging sequence is provided. Figure 2 It is a flowchart of the diffusion gradient calculation method of the diffusion imaging sequence in this embodiment, as Figure 2 shown, and the process includes the following steps:
[0049] Step S210, set the sequence diffusion sensitivity factors between different excitations in the diffusion imaging sequence to be the same; the diffusion imaging sequence includes a diffusion preparation segment and a readout segment;
[0050] Step S220, obtain the gradient parameters of the readout gradient corresponding to each excitation, and calculate the diffusion sensitivity factor of the readout segment of each excitation based on the gradient parameters of the readout gradient;
[0051] Step S230, determine the diffusion gradient sensitivity factor of each excitation according to the sequence diffusion sensitivity factor and the diffusion sensitivity factor of the readout segment of each excitation;
[0052] Step S240, calculate the diffusion gradient amplitude of each excitation based on the diffusion gradient sensitivity factor of each excitation.
[0053] Specifically, in combination with Figure 3A schematic diagram of the diffusion imaging sequence will be described. As shown in the figure, the diffusion imaging sequence includes a diffusion preparation segment and a readout segment. The sequence diffusion sensitivity factor between different excitations can be considered as the overall diffusion sensitivity factor of the sequence. That is to say, in the above steps, the sequence diffusion sensitivity factor between different excitations in the diffusion imaging sequence, this overall diffusion sensitivity factor (the sum of the readout segment diffusion sensitivity factor and the diffusion gradient sensitivity factor) is set to be the same. Thus, starting from the same sequence diffusion sensitivity factor between different excitations in the diffusion imaging sequence, the diffusion gradient sensitivity factor of each excitation is calculated, and then based on the diffusion gradient sensitivity factor of each excitation, the diffusion gradient amplitude of each excitation is calculated.
[0054] Among them, there will be a corresponding readout gradient for the diffusion gradient between the excitation pulse and the refocusing pulse; for each excitation, the parameters between the gradients are the same. In the readout gradient, the gradient parameters of the readout gradient can be obtained, and then the readout segment diffusion sensitivity factor of each excitation can be calculated using relevant formulas, algorithms or models. Since the diffusion sensitivity factor is equal to the sum of the readout segment diffusion sensitivity factor and the diffusion gradient sensitivity factor, the diffusion gradient sensitivity factor of each excitation can be determined according to the sequence diffusion sensitivity factor and the readout segment diffusion sensitivity factor of each excitation. Finally, based on the diffusion gradient sensitivity factor of each excitation, the diffusion gradient amplitude of each excitation is calculated using relevant formulas, algorithms or models. Under the condition of precise control using relevant formulas, algorithms or models, the sequence diffusion sensitivity factor of different excitations is ensured to be the same, thereby improving the imaging quality.
[0055] In the related art, for the diffusion imaging sequence acquired by multi-excitation propeller, the diffusion gradient sensitivity factors of different excitations are designed to be the same; due to the different readout gradient sensitivity factors of different excitations in the diffusion imaging sequence acquired by multi-excitation propeller, there will be differences in the sequence diffusion sensitivity factors between different excitations. The multi-excitation sequence is composed of data acquired by different excitations to jointly form the K-space, and the different sequence diffusion sensitivity factors between different excitations will make it difficult to calculate the overall sequence diffusion sensitivity factor, resulting in a decline in imaging quality. In this embodiment, through the above steps, the sequence diffusion sensitivity factors between different excitations in the diffusion imaging sequence are set to be the same; the diffusion imaging sequence includes a diffusion preparation segment and a readout segment; the gradient parameters of the readout gradient corresponding to each excitation are obtained, and based on the gradient parameters of the readout gradient, the readout segment diffusion sensitivity factor of each excitation is calculated; according to the sequence diffusion sensitivity factor and the readout segment diffusion sensitivity factor of each excitation, the diffusion gradient sensitivity factor of each excitation is determined; based on the diffusion gradient sensitivity factor of each excitation, the diffusion gradient amplitude of each excitation is calculated, solving the problem that the design of the diffusion gradient sensitivity factor of the diffusion gradient of different excitations is inaccurate, thereby causing a decline in imaging quality. On the premise that the sequence diffusion sensitivity factors between different excitations in the diffusion imaging sequence are set to be the same, the diffusion gradient sensitivity factor of each excitation is accurately calculated to ensure that the sequence diffusion sensitivity factors of different excitations are the same, thereby improving the imaging quality.
[0056] In some of these embodiments, obtaining the gradient parameters of the readout gradient corresponding to each excitation in step S220 includes the following steps:
[0057] Step S221, obtaining the amplitude, interval, and duration of the readout gradient corresponding to each excitation.
[0058] Specifically, each excitation has a corresponding readout gradient, and the gradient parameters of the readout gradient include, but are not limited to, amplitude, interval, and duration, etc. Obtaining the amplitude, interval, and duration of the readout gradient corresponding to each excitation is for subsequent calculation of the readout segment diffusion sensitivity factor of each excitation. In this embodiment, the obtaining method is not limited, for example: it can be obtained by measurement; obtained by pre-calculation, etc.
[0059] In some of these embodiments, calculating the readout segment diffusion sensitivity factor of each excitation based on the gradient parameters of the readout gradient in step S220 includes the following steps:
[0060] Step S222, constructing a first formula from the amplitude of the readout gradient, the interval of the readout gradient, the duration of the readout gradient, the gyromagnetic ratio, and the readout segment diffusion sensitivity factor;
[0061] Step S223: Substitute the amplitude, interval, and duration of the readout gradient into the first formula to calculate the diffusion sensitivity factor of the readout segment for each excitation.
[0062] Specifically, the expression of the first formula is constructed from the amplitude of the readout gradient, the interval of the readout gradient, the duration of the readout gradient, the gyromagnetic ratio, and the diffusion sensitivity factor of the readout segment as follows:
[0063]
[0064] In the formula, G ro represents the amplitude of the readout gradient; b ro represents the diffusion sensitivity factor of the readout segment of the readout gradient; γ represents the gyromagnetic ratio; Δ ro represents the interval of the readout gradient; δ ro represents the duration of the readout gradient.
[0065] Then, substituting the amplitude, interval, and duration of the readout gradient into the first formula for calculation, the diffusion sensitivity factor of the readout segment for each excitation can be obtained.
[0066] Through this embodiment, the first formula is first constructed, and then the amplitude, interval, and duration of the readout gradient are substituted into the first formula to accurately and quickly calculate the diffusion sensitivity factor of the readout segment for each excitation.
[0067] In some of these embodiments, determining the diffusion gradient sensitivity factor for each excitation according to the sequence diffusion sensitivity factor and the diffusion sensitivity factor of the readout segment for each excitation in step S230 includes the following steps:
[0068] Step S231: Subtract the diffusion sensitivity factor of the readout segment for each excitation from the sequence diffusion sensitivity factor to obtain the diffusion gradient sensitivity factor for each excitation.
[0069] Specifically, since the diffusion sensitivity factor is equal to the sum of the diffusion sensitivity factor of the readout segment and the diffusion gradient sensitivity factor, subtracting the diffusion sensitivity factor of the readout segment for each excitation from the sequence diffusion sensitivity factor can obtain the diffusion gradient sensitivity factor for each excitation. For example: There are two diffusion gradients and two readout segment gradients in the diffusion imaging sequence; the diffusion sensitivity factor is assumed to be 8; the diffusion sensitivity factor of the readout segment for each excitation is 1; then the diffusion gradient sensitivity factor for each excitation can be obtained as 3. Here, the diffusion sensitivity factor, the diffusion sensitivity factor of the readout segment, and the diffusion gradient sensitivity factor are only used for illustration, and the specific values can be determined according to the actual application scenario, and are not limited thereto.
[0070] Through this embodiment, the diffusion gradient sensitivity factor for each excitation can be calculated by simple mathematical conversion, reducing the complexity of the calculation.
[0071] In some of these embodiments, as Figure 4 shown, calculating the diffusion gradient amplitude for each excitation based on the diffusion gradient sensitivity factor for each excitation includes the following steps:
[0072] Step S241, constructing a second formula from the diffusion gradient sensitivity factor, the gyromagnetic ratio, the interval of the diffusion gradient, the duration of the diffusion gradient, and the amplitude of the diffusion gradient;
[0073] Step S242, obtaining the gradient parameters of the diffusion gradient for each excitation;
[0074] Step S243, substituting the diffusion gradient sensitivity factor for each excitation and the gradient parameters of the diffusion gradient into the second formula to calculate the diffusion gradient amplitude for each excitation.
[0075] Specifically, the expression for constructing the second formula from the diffusion gradient sensitivity factor, the gyromagnetic ratio, the interval of the diffusion gradient, the duration of the diffusion gradient, and the amplitude of the diffusion gradient is:
[0076]
[0077] In the formula, G diff represents the amplitude of the diffusion gradient; b diff represents the diffusion gradient sensitivity factor of the diffusion gradient; Δ diff represents the interval of the diffusion gradient; δ diff represents the duration of the diffusion gradient; where, Δ diff and δ diff can be set according to the requirements of actual applications.
[0078] After constructing the second formula, it is necessary to obtain the gradient parameters for calculating the diffusion gradient of each excitation. Among them, the gradient parameters of the diffusion gradient include but are not limited to the interval and duration of the diffusion gradient. For example: the gradient parameters can also include empirical parameters. The empirical parameters can be the gyromagnetic ratio, etc. Then, substituting the diffusion gradient sensitivity factor for each excitation and the gradient parameters of the diffusion gradient into the second formula for calculation can obtain the diffusion gradient amplitude for each excitation.
[0079] Through this embodiment, first construct the second formula, and then substitute the diffusion gradient sensitivity factor for each excitation and the gradient parameters of the diffusion gradient into the second formula to accurately and quickly calculate the diffusion gradient amplitude for each excitation.
[0080] In addition, in combination with the diffusion gradient calculation method of the diffusion imaging sequence provided in the above embodiments, a magnetic resonance imaging method can also be provided in this embodiment.
[0081] This magnetic resonance imaging method includes the following steps:
[0082] The diffusion gradient magnitude of each excitation is obtained by using the diffusion gradient calculation method of any one of the diffusion imaging sequences in the above embodiments;
[0083] Based on the diffusion gradient magnitude of each excitation, a multi-excitation propeller acquisition diffusion imaging sequence is performed to obtain image data acquired from different excitations;
[0084] The image data is filled into the k-space to obtain a magnetic resonance image.
[0085] Specifically, the diffusion imaging sequence acquired by multi-excitation propeller is composed of data acquired from different excitations to jointly form the k-space. Based on the diffusion gradient calculation method of any one of the diffusion imaging sequences in the above embodiments, the diffusion gradient magnitude of each excitation is obtained, and thus the sequence diffusion sensitivity factor of each excitation is made exactly the same. In a preferred diffusion gradient calculation method of the diffusion imaging sequence, as Figure 5 shown; after setting the sequence diffusion sensitivity factors between different excitations in the diffusion imaging sequence to be the same, calculations are performed separately for each excitation. For example: for excitation 1, first calculate the readout segment diffusion sensitivity factor of the readout gradient of excitation 1; then calculate the diffusion gradient sensitivity factor of the diffusion gradient, and finally obtain the diffusion gradient magnitude; thus completing the relevant calculations for each excitation.
[0086] Through this embodiment, since the sequence diffusion sensitivity factors of each excitation are exactly the same, the k-space data between different excitations has good self-consistency, thereby improving the diffusion imaging quality. The magnetic resonance imaging method of this embodiment can be applied to application scenarios such as cerebral infarction diagnosis, tumor diagnosis, or brain structure analysis.
[0087] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.
[0088] In this embodiment, a diffusion gradient calculation device for a diffusion imaging sequence is also provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated here. The following terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.
[0089] Figure 6 is the structural block diagram of the diffusion gradient calculation device for the diffusion imaging sequence of this embodiment, as Figure 6As shown, the device includes: a setting module 210, an acquisition module 220, a first processing module 230, and a second processing module 240;
[0090] The setting module 210 is configured to set the same sequence diffusion sensitivity factor between different excitations in the diffusion imaging sequence; the diffusion imaging sequence includes a diffusion preparation segment and a readout segment;
[0091] The acquisition module 220 is configured to acquire the gradient parameters of the readout gradient corresponding to each excitation, and calculate the diffusion sensitivity factor of the readout segment for each excitation based on the gradient parameters of the readout gradient;
[0092] The first processing module 230 is configured to determine the diffusion gradient sensitivity factor for each excitation according to the sequence diffusion sensitivity factor and the diffusion sensitivity factor of the readout segment for each excitation;
[0093] The second processing module 240 is configured to calculate the diffusion gradient amplitude for each excitation based on the diffusion gradient sensitivity factor for each excitation.
[0094] Through the above diffusion gradient calculation device, the problem that the design of the diffusion gradient sensitivity factor of the diffusion gradient for different excitations is inaccurate, resulting in a decrease in imaging quality, is solved. The diffusion gradient sensitivity factor for each excitation is accurately calculated to ensure that the sequence diffusion sensitivity factors for different excitations are the same, thereby improving the imaging quality.
[0095] In some embodiments, the acquisition module 220 is further configured to acquire the amplitude, interval, and duration of the readout gradient corresponding to each excitation.
[0096] In some embodiments, the acquisition module 220 is further configured to construct a first formula from the amplitude of the readout gradient, the interval of the readout gradient, the duration of the readout gradient, the gyromagnetic ratio, and the diffusion sensitivity factor of the readout segment;
[0097] Substitute the amplitude, interval, and duration of the readout gradient into the first formula to calculate the diffusion sensitivity factor of the readout segment for each excitation.
[0098] In some embodiments, the first processing module 230 is further configured to subtract the diffusion sensitivity factor of the readout segment for each excitation from the sequence diffusion sensitivity factor to obtain the diffusion gradient sensitivity factor for each excitation.
[0099] In some embodiments, the second processing module 240 is further configured to construct a second formula from the diffusion gradient sensitivity factor, the gyromagnetic ratio, the interval of the diffusion gradient, the duration of the diffusion gradient, and the amplitude of the diffusion gradient;
[0100] Acquire the gradient parameters of the diffusion gradient for each excitation;
[0101] Substitute each excited diffusion gradient sensitivity factor and the gradient parameters of the diffusion gradient into the second formula to calculate the diffusion gradient amplitude of each excitation.
[0102] In some of these embodiments, the second processing module 240 is further configured to obtain the interval and duration of the diffusion gradient of each excitation.
[0103] In this embodiment, a magnetic resonance imaging apparatus is further provided, which includes: an acquisition module, a collection module, and a filling module;
[0104] The acquisition module is configured to obtain the diffusion gradient amplitude of each excitation by using the diffusion gradient calculation method of any one of the diffusion imaging sequences in the above embodiments;
[0105] The collection module is configured to collect a diffusion imaging sequence in a multi-excitation propeller manner based on the diffusion gradient amplitude of each excitation to obtain image data collected in different excitations;
[0106] The filling module is configured to fill the image data into the K-space to obtain a magnetic resonance image.
[0107] Through this embodiment, since the sequence diffusion sensitivity factors of each excitation are exactly the same, the K-space data between different excitations has good self-consistency, thereby improving the diffusion imaging quality.
[0108] It should be noted that the above-mentioned various modules can be functional modules or program modules, and can be implemented either by software or by hardware. For the modules implemented by hardware, the above-mentioned various modules can be located in the same processor; or the above-mentioned various modules can also be located in different processors in any combined form.
[0109] In this embodiment, a magnetic resonance imaging system is further provided, as Figure 7 shown, the magnetic resonance imaging system includes: a magnetic resonance imaging device 110 and a processing device 130; wherein, the magnetic resonance imaging device 110 is connected to the processing device;
[0110] The magnetic resonance imaging device 110 is configured to scan an object;
[0111] The processing device is configured to execute the diffusion gradient calculation method of any one of the above-mentioned diffusion imaging sequences, or the magnetic resonance imaging method.
[0112] In some of these embodiments, the components of the magnetic resonance imaging system can be connected in various ways. Exemplarily, the magnetic resonance imaging device 110 can be connected to the processing device 130 through a network 120.
[0113] The magnetic resonance imaging device 110 can scan a target and / or generate scan data regarding the target. In some embodiments, the target can be a living being such as a patient or an animal, or an artificial object such as a phantom. The target can also be a specific part such as an organ and / or tissue of a patient. When the target needs to be scanned, it can be placed on the mobile platform 111 and move along the longitudinal direction of the magnetic resonance imaging device 110 and enter the scan region 113. Herein, the scan region 113 is a scan chamber. Exemplarily, the magnetic resonance imaging device 110 can be a medical imaging device or an animal magnetic resonance imaging device for scientific research. The magnetic resonance imaging device 110 can include a scan main body 112. After the target enters the scan region 113, the scan main body 112 can generate a magnetic field. In some embodiments, the scan main body 112 can include a magnet, gradient coils, etc. In some embodiments, the magnetic resonance imaging device 110 further includes a radio frequency coil. Among them, the radio frequency coil includes a transmitting coil and a receiving coil.
[0114] The network 120 includes any suitable network that can facilitate the exchange of information and / or data of the magnetic resonance imaging system. In some embodiments, one or more components of the magnetic resonance imaging system can transmit information and / or data to one or more other components of the magnetic resonance imaging system via the network 120. In some embodiments, examples of the network 120 include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. In some embodiments, the network 120 can include one or more network access points. For example, the network 120 can include wired and / or wireless network access points, such as base stations and / or Internet exchange points, through which one or more components of the magnetic resonance imaging system can be connected to the network 120 to exchange data and / or information.
[0115] In this embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.
[0116] Optionally, the above computer device can further include a transmission device and an input / output device. Among them, the transmission device is connected to the above processor, and the input / output device is connected to the above processor.
[0117] Optionally, in this embodiment, the above processor can be configured to execute the following steps through the computer program:
[0118] S1, set the sequence diffusion sensitivity factors between different excitations in the diffusion imaging sequence to be the same; the diffusion imaging sequence includes a diffusion preparation segment and a readout segment;
[0119] S2. Obtain the gradient parameters of the readout gradient corresponding to each excitation, and calculate the readout segment diffusion sensitivity factor for each excitation based on the gradient parameters of the readout gradient.
[0120] S3. Determine the diffusion gradient sensitivity factor for each excitation according to the sequence diffusion sensitivity factor and the readout segment diffusion sensitivity factor for each excitation.
[0121] S4. Calculate the diffusion gradient amplitude for each excitation based on the diffusion gradient sensitivity factor for each excitation.
[0122] Or, S5. Obtain the diffusion gradient amplitude for each excitation by using the diffusion gradient calculation method of any one of the diffusion imaging sequences in the above embodiments.
[0123] S6. Based on the diffusion gradient amplitude for each excitation, perform multi-excitation propeller acquisition of the diffusion imaging sequence to obtain image data acquired in different excitations.
[0124] S7. Fill the image data into the k-space to obtain a magnetic resonance image.
[0125] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be repeated in this embodiment.
[0126] In addition, in combination with the diffusion gradient calculation method of the diffusion imaging sequence provided in the above embodiments, or the magnetic resonance imaging method, a storage medium can also be provided to implement in this embodiment. A computer program is stored on the storage medium; when the computer program is executed by a processor, it implements the diffusion gradient calculation method of any one of the diffusion imaging sequences in the above embodiments, or the magnetic resonance imaging method.
[0127] It should be understood that the specific embodiments described here are only used to explain this application, rather than to limit it. According to the embodiments provided in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present application.
[0128] Obviously, the drawings are only some examples or embodiments of the present application. For those of ordinary skill in the art, the present application can also be applied to other similar situations based on these drawings without creative efforts. In addition, it can be understood that although the work done during the development process may be complex and time-consuming, for those of ordinary skill in the art, some design, manufacturing, or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be regarded as insufficient disclosure of the present application.
[0129] As used in this application, the term "embodiment" means that the specific features, structures, or characteristics described in connection with an embodiment may be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily mean the same embodiment, nor does it mean that it is independent or alternative to other embodiments and mutually exclusive. Those of ordinary skill in the art can clearly or implicitly understand that the embodiments described in this application can be combined with other embodiments without conflict.
[0130] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of patent protection. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all fall within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.
Claims
1. A method for calculating a diffusion gradient of a diffusion imaging sequence, characterized in that, Comprising: Setting the sequence diffusion sensitivity factors between different excitations in the diffusion imaging sequence to be the same; The diffusion imaging sequence includes a diffusion preparation segment and a readout segment; Obtaining the gradient parameters of the readout gradient corresponding to each excitation, and calculating the diffusion sensitivity factor of the readout segment for each excitation based on the gradient parameters of the readout gradient; Determining the diffusion gradient sensitivity factor for each excitation according to the sequence diffusion sensitivity factor and the diffusion sensitivity factor of the readout segment for each excitation; Calculating the diffusion gradient amplitude for each excitation based on the diffusion gradient sensitivity factor for each excitation.
2. The diffusion gradient calculation method of the diffusion imaging sequence according to claim 1, wherein, Obtaining the gradient parameters of the readout gradient corresponding to each excitation includes: Obtaining the amplitude, interval, and duration of the readout gradient corresponding to each excitation.
3. The diffusion gradient calculation method of the diffusion imaging sequence according to claim 2, characterized in that, Calculating the diffusion sensitivity factor of the readout segment for each excitation based on the gradient parameters of the readout gradient includes: Constructing a first formula from the amplitude of the readout gradient, the interval of the readout gradient, the duration of the readout gradient, the gyromagnetic ratio, and the diffusion sensitivity factor of the readout segment; Substituting the amplitude, interval, and duration of the readout gradient into the first formula to calculate the diffusion sensitivity factor of the readout segment for each excitation.
4. The diffusion gradient calculation method of the diffusion imaging sequence according to claim 1, wherein, Determining the diffusion gradient sensitivity factor for each excitation according to the sequence diffusion sensitivity factor and the diffusion sensitivity factor of the readout segment for each excitation includes: Subtracting the diffusion sensitivity factor of the readout segment for each excitation from the sequence diffusion sensitivity factor to obtain the diffusion gradient sensitivity factor for each excitation.
5. The diffusion gradient calculation method of the diffusion imaging sequence according to claim 1, wherein Calculating the diffusion gradient amplitude for each excitation based on the diffusion gradient sensitivity factor for each excitation includes: Constructing a second formula from the diffusion gradient sensitivity factor, the gyromagnetic ratio, the interval of the diffusion gradient, the duration of the diffusion gradient, and the amplitude of the diffusion gradient; Obtaining the gradient parameters of the diffusion gradient for each excitation; Substituting the diffusion gradient sensitivity factor for each excitation and the gradient parameters of the diffusion gradient into the second formula to calculate the diffusion gradient amplitude for each excitation.
6. The diffusion gradient calculation method of the diffusion imaging sequence according to claim 5, characterized in that, Obtaining the gradient parameters of the diffusion gradient for each excitation includes: Obtaining the interval and duration of the diffusion gradient for each excitation.
7. A magnetic resonance imaging method, characterized in that, Comprising: Obtaining the diffusion gradient amplitude for each excitation by using the diffusion gradient calculation method of the diffusion imaging sequence according to any one of claims 1 to 6; Based on the diffusion gradient amplitude for each excitation, performing a multi-excitation propeller acquisition of the diffusion imaging sequence to obtain image data acquired from different excitations; Filling the image data into the K-space to obtain a magnetic resonance image.
8. A magnetic resonance imaging system, characterized in that, Comprising: A magnetic resonance imaging device and a processing device; wherein, the magnetic resonance imaging device is connected to the processing device; The magnetic resonance imaging device is used to scan an object; The processing device is used to execute the diffusion gradient calculation method of the diffusion imaging sequence according to any one of claims 1 to 6, or the magnetic resonance imaging method according to claim 7.
9. A computer device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to run the computer program to execute the diffusion gradient calculation method of the diffusion imaging sequence according to any one of claims 1 to 6, or the magnetic resonance imaging method according to claim 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the diffusion gradient calculation method of the diffusion imaging sequence described in any one of claims 1 to 6, or the magnetic resonance imaging method described in claim 7.