CN110542872B - Magnetic resonance imaging method and equipment, and phase information acquisition method and device - Google Patents
Magnetic resonance imaging method and equipment, and phase information acquisition method and device Download PDFInfo
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Abstract
The invention discloses a magnetic resonance imaging method and equipment, a phase information acquisition method and device, electronic equipment and a storage medium. The acquisition method is applied to a data processor of a Magnetic Resonance Imaging (MRI) system, and comprises the following steps: acquiring an MR signal for each coil channel of a radio frequency coil of MRI equipment, obtaining first sampling data, and constructing a K space according to the first sampling data; determining an interpolation convolution kernel for filling data in the K space according to the first sampling data of each coil channel; fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain second sampling data; phase information of the MR signal of each shot is determined from the second sampled data. The method can determine the phase information based on the acquired MR signals, does not need to apply 180-degree echo pulse to the detected object, further determines the phase information based on the acquired navigation echo signals, and greatly improves the speed and the accuracy.
Description
Technical Field
The present invention relates to the field of medical imaging technologies, and in particular, to a magnetic resonance imaging method and apparatus, a method and device for acquiring phase information, an electronic apparatus, and a storage medium.
Background
Magnetic Resonance Imaging (MRI) is one of the main Imaging modes in modern medical Imaging, and the basic working principle of MRI is to utilize the Magnetic Resonance phenomenon, excite hydrogen protons in a detected object, perform position encoding by using a gradient field, then receive signals with position information by using a receiving coil, and finally reconstruct image information by fourier transform.
In order to reconstruct a high-resolution image, a common method is an imaging technique using multiple excitation (Multi-shot). During imaging, phase information of MR signals acquired at each shot (shot) needs to be obtained. In the prior art, Navigator (navigation echo) signals are generally adopted to acquire phase information of MR signals acquired by each excitation, specifically, a detected object is subjected to one-time radio frequency excitation, 180-degree convergence pulses are applied after the acquisition of the MR signals is completed, so that the signals are converged, and then one navigation echo signal is acquired rapidly. The phase information obtained according to the method has larger error, the reconstructed image is easy to generate artifacts, and the method needs additional data acquisition and has lower efficiency.
Disclosure of Invention
The invention provides a magnetic resonance imaging method and equipment, a phase information acquisition method and device, electronic equipment and a storage medium, and aims to improve the speed and accuracy of phase information acquisition.
Specifically, the invention is realized by the following technical scheme:
in a first aspect, a method for acquiring magnetic resonance phase information is provided, which is applied to a data processor of a magnetic resonance imaging MRI system, the MRI system further includes an MRI apparatus, and the MRI apparatus includes a radio frequency coil; the radio frequency coil comprises a plurality of coil channels;
the acquisition method comprises the following steps:
acquiring an MR signal for each coil channel of the radio frequency coil, obtaining first sampling data, and constructing a K space according to the first sampling data;
determining an interpolation convolution kernel for performing data filling on the K space according to the first sampling data of each coil channel;
fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain second sampling data;
determining phase information of the MR signals of each excitation according to the second sampling data.
In a second aspect, there is provided a magnetic resonance imaging method comprising:
acquiring phase information of MR signals of a plurality of excitations by the acquisition method of magnetic resonance phase information of the first aspect;
calculating an interpolation convolution kernel for filling data in the K space according to the phase information and the first sampling data;
fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain full sampling data of the K space;
and carrying out Fourier transform on the full-acquisition data to obtain a magnetic resonance image.
In a third aspect, an apparatus for acquiring magnetic resonance phase information is provided, which is applied to a data processor of a magnetic resonance imaging MRI system, and the MRI system further includes an MRI apparatus including a radio frequency coil; the radio frequency coil comprises a plurality of coil channels;
the acquisition device includes:
the acquisition module is used for acquiring an MR signal for each coil channel of the radio frequency coil, acquiring first sampling data and constructing a K space according to the first sampling data;
the determining module is used for determining an interpolation convolution kernel for data filling of the K space according to the first sampling data of each coil channel;
the fitting module is used for fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain second sampling data;
the determining module is further configured to determine phase information of the MR signal of each excitation according to the second sampling data.
In a fourth aspect, there is provided a magnetic resonance imaging apparatus comprising:
a calculating device, an imaging device and an acquiring device of the magnetic resonance phase information of the third aspect;
the acquisition device is used for acquiring the phase information of the MR signals excited for multiple times;
the computing device is used for computing an interpolation convolution kernel for data filling of the K space according to the phase information and the first sampling data, and fitting undersampled data of the K space according to the interpolation convolution kernel to obtain fully-sampled data of the K space;
the imaging device is used for carrying out Fourier transform on the full-acquisition data to obtain a magnetic resonance image.
In a fifth aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method for acquiring magnetic resonance phase information according to the first aspect is implemented.
A sixth aspect provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of acquiring magnetic resonance phase information of the first aspect.
In a seventh aspect, an electronic device is provided, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the magnetic resonance imaging method of the second aspect is implemented.
In an eighth aspect, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the magnetic resonance imaging method of the second aspect.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects: the method can determine the phase information based on the acquired MR signals, does not need to apply 180-degree echo pulse to the detected object, further determines the phase information based on the acquired navigation echo signals, and greatly improves the speed and the accuracy.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic block diagram of an MRI system according to an exemplary embodiment of the present invention;
figure 2A is a flow chart illustrating a method of acquiring magnetic resonance phase information in accordance with an exemplary embodiment of the present invention;
FIG. 2B is a flowchart illustrating step 202 of FIG. 2A in accordance with an exemplary embodiment of the present invention;
figure 2C is a flow chart illustrating another method of acquiring magnetic resonance phase information in accordance with an exemplary embodiment of the present invention;
FIG. 2D is a flowchart illustrating step 205 of FIG. 2C in accordance with an exemplary embodiment of the present invention;
figure 2E is a flow chart illustrating another method of acquiring magnetic resonance phase information in accordance with an exemplary embodiment of the present invention;
FIG. 3A is a schematic illustration of K-space of MR signals obtained for one coil channel of one scan slice in accordance with an exemplary embodiment of the present invention;
FIG. 3B shows fully sampled data for 3 RF channels in accordance with an exemplary embodiment of the present invention;
FIG. 3C is an illustration of undersampled data for 3 radio frequency channels in accordance with an exemplary embodiment of the present invention;
figure 4 is a flow chart of a magnetic resonance imaging method according to an exemplary embodiment of the present invention;
fig. 5A is a schematic structural diagram of an apparatus for acquiring magnetic resonance phase information according to an exemplary embodiment of the present invention;
fig. 5B is a schematic structural diagram of another magnetic resonance phase information acquisition apparatus according to an exemplary embodiment of the present invention;
fig. 6 is a schematic structural diagram of a magnetic resonance imaging apparatus according to an exemplary embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
Fig. 1 is a schematic structural diagram of an MRI system according to an exemplary embodiment, and as shown in fig. 1, the MRI system includes an MRI apparatus 110, a display operation apparatus 120, and a data processor 130. The MRI apparatus 110 includes a magnet 111, a gradient coil 112, and a radio frequency coil 113.
The magnet 111 generates a static magnetic field for adjusting the direction of the magnetic dipole moment of the atomic nuclei in the object to be detected (which may be a human or an animal, or a part of a human or an animal) to a constant direction.
The gradient coil 112 includes an X-coil, a Y-coil, and a Z-coil for generating magnetic field gradients in an X-axis direction, a Y-axis direction, and a Z-axis direction that intersect at right angles to each other. The gradient coil 112 may provide spatial localization information of the detected object by differently inducing resonance frequencies according to a region of the detected object. Specifically, a gradient magnetic field in one direction is used as a slice selection gradient to determine a scanning slice, then gradient magnetic fields in the other two directions are used to determine a coordinate position in the slice, and tomography of any slice can be realized through different combinations of the three gradient magnetic fields.
The radio frequency coil 113 may transmit an RF (radio frequency) signal to a subject and receive an MR (magnetic resonance) signal transmitted from the subject. Specifically, the radio frequency coil 113 generates an RF signal having a type corresponding to the type of the atomic nucleus, and applies the RF signal to the object to be detected so that the atomic nucleus transits from a low energy state to a high energy state. When the RF signal generated by the radio frequency coil 113 disappears, the atomic nuclei transit from the high energy state to the low energy state, thereby emitting electromagnetic waves (MR signals) having a larmor frequency. In other words, when the application of the RF signal to the nuclei is stopped, the energy level of the nuclei is changed from a high energy level to a low energy level, and thus the nuclei may emit electromagnetic waves having a larmor frequency, at which time the radio frequency coil 113 may receive the MR signal from the nuclei in the subject. Therein, the radio frequency coil 113 may have a plurality of coil channels, such as 8 coil channels, 16 coil channels, 32 coil channels, 72 coil channels, 144 coil channels, or the like.
With multiple excitation techniques, the radio frequency coil 113 transmits multiple RF signals to a single scan slice of the subject. Taking 4 excitations as an example, the rf coil will excite 4 times (not continuously) the same scan slice.
The data processor 130 may process the MR signals received by the radio frequency coil 113 to reconstruct an image. The display operating device 120 may display an image reconstructed by the data processor 130.
The following describes in detail an embodiment of the method for acquiring magnetic resonance phase information according to the present invention with reference to an MRI system shown in fig. 1, taking a diffusion direction and a scanning slice as examples. For multiple dispersion directions, multiple scan levels, can be considered a cyclic treatment for this case.
For each diffusion direction, each scan slice, as shown in fig. 2A, the method comprises the steps of:
The K space is a positioning space of the MR signals received by the coil channels of the radio frequency coil, and the MR signals can be acquired by adopting, but not limited to, an SE sequence, a GRE sequence or an EPI sequence to construct the K space. The sampled data includes the following parameters: readout encoding, phase encoding and coil channels. If there are L coil channels to receive MR signals, L K spaces can be obtained.
FIG. 3A is a schematic diagram of K-space, the x-axis (K) of K-space, of MR signals acquired by one coil channel for one scan slice, shown in accordance with an exemplary embodimentxAxis) represents the frequency encoding direction, y-axis (K)yAxis) represents the phase encode direction. In fig. 3A, white areas indicate lines that are fully sampled and MR signals are detected (first sampling data), and black areas indicate lines that are not sampled or MR signals are not detected (undersampled data). The K space is divided into a fully sampled region (target region) 21 and an under-sampled region 22. The full sampling region includes only a white region, and full sampling may be performed on MR signals corresponding to the full sampling region to obtain full sampling data in the full sampling region
1 ≦ i ≦ L, see FIG. 3B, which exemplarily shows the result of performing full sampling, FIG. 3B shows the full sampled data of 3 RF channels excited at a time, wherein one circle represents one acquired data; the undersampled region is a region other than the full sampling region in the K-space, and includes a white region and a black region, and undersampling may be performed on the MR signals corresponding to the undersampled region to obtain data corresponding to a portion (white region) of the undersampled region, and fig. 3C exemplarily shows the result of performing undersampling, where one unfilled circle represents one acquired data and one filled circle represents one unacquired data.
In MRI imaging, in order to accurately represent the spatial position of an object, the difference Δ K between two adjacent points in K-space needs to be equal to that in K-space encoding
By full sampling is meant that the difference ak between two adjacent points in K-space is always equal to
By undersampling is meant that the difference ak between two adjacent points in K-space during sampling is greater than
The direct fourier transformation at this point will cause the image to be folded. In the DWI (diffusion weighted imaging) data of multiple excitations, the difference value of two adjacent points in K space in the phase encoding direction of K space data of each excitation is Nshot*Δk，NshotIs the total number of excitations.
It should be noted that the same geometric parameters, including the same layer thickness, FOV (field of view), and layer number, are used for performing full sampling and undersampling on the target region.
According to the parallel imaging theory, for the data acquired by a plurality of coil channels, a certain incidence relation exists, an interpolation convolution kernel of a K space is determined, namely the incidence relation is solved, so that the data which are not acquired in an undersampled area of the K space are solved according to the incidence relation.
In one example, as shown in FIG. 2B, step 202 comprises:
step 202-1, merging the first sampling data of each coil channel to obtain merged data of a virtual channel.
In step 202-1, the purpose of data combination is to combine data in this dimension of the coil channels, and specifically, the first sampling data of each coil channel may be combined based on a solution SENSE algorithm. The virtual channels are obtained by expanding MR signals received by b channels excited a times, namely, MR signals of b channels excited 1 times are used as signals of virtual channels 1-8, MR signals of b channels excited 2 times are used as signals of new virtual channels 9-16, MR signals of b channels excited i times are used as signals of new virtual channels (i-1) b + 1-i b, and MR signals of b channels excited a times are used as signals of new virtual channels (a-1) b + 1-a b.
It is understood that the MR signal mapped to K-space contains two phases, one is the phase of the MR signal transmitted by the subject and the other is the phase of the coil channel, the sum of the phases of the two phases is the phase of the data acquired from the coil channel, and the process of de-SENSE is to subtract the phase information of the coil channel in each channel data by using the coil sensitivity map (containing the phase information of each coil channel), only the phase information of the MR signal transmitted by the subject is retained, then all the coil channel data are added and combined into the combined data of a virtual channel, and the combined data is transformed to K-space domain by FFT to obtain the MR signal mapped to K-space domain
Step 202-2, converting the merged data into K space data.
Since the SENSE process is performed in the image domain, the merged data obtained in step 202-1 is image domain data, and the full sampling data is K-space data, in order to facilitate the step 202-3, the merged data needs to be converted into K-space data, and specifically, the conversion of the data type can be implemented based on an inverse fourier transform or an inverse fast fourier transform.
Step 202-3, determining an interpolation convolution kernel according to the first sampling data of each coil channel and the K space data of the combined data.
Wherein, the formula of the training model is as follows:
the above formula describes the interpolation relationship from the undersampled K space to the virtually fully sampled K space, where Si(ky-bAΔky) Representing first sample data; s' (k)y-mΔky) Representing the merged data; w (b, i, m) represents the association relationship between the two; l represents the number of coil channels; a represents the undersampling multiple, which is also the number of excitations; m is 0,1 … a-1; n is a radical ofbMean thatIs defined by kyIn the phase encoding direction NbFitting of adjacent data, S, because the data is undersampledi(ky-bAΔky) And (4) showing.
Training interpolation, namely solving w (b, i, m) in a formula, wherein w (b, i, m) can represent the full sampling data acquired by each coil channel
Merging data with virtual channels
The correlation between the K-space and the K-space can be used to solve for the data not acquired in the K-space under-sampled region, i.e., the data corresponding to the filled circles in fig. 3C. As can be seen from the above equation, the relationship is for a specific number of channels, a specific down-sampling multiple, and the relationship is recalculated when the number of channels and the down-sampling multiple change.
And step 203, fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain second sampling data.
Fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain imaging data of multiple times of excitation aiming at each K space; the second sampling data is full sampling data of a plurality of K spaces, which has no channel information but data on a virtual channel. Referring to fig. 3A and 3C, each coil channel has uncollected data, and the uncollected data in each coil channel is calculated according to interpolation fitting undersampled data, that is, according to the merged data of the virtual channels and the interpolation w (b, i, m), so as to obtain the K-space full-sampling data acquired by each coil channel.
And step 204, determining the phase information of the MR signals of each excitation according to the second sampling data.
Specifically, in step 204, full-acquisition data of K space of each coil channel is merged based on a SENSE solution algorithm to obtain full-acquisition data of a virtual channel, and the full-acquisition data of the virtual channel is subjected to image phase, so that phase information of the MR signal excited each time can be obtained, and the phase information is used for constructing a phase map.
In the embodiment, the phase information of the MR signals can be determined based on the acquired MR signals, additional data acquisition is not needed, and the speed and the accuracy are greatly improved.
On the basis of fig. 2A, fig. 2C shows a flowchart of another embodiment of the method for acquiring magnetic resonance phase information according to the present invention, in this embodiment, iterative computation is performed on the phase information, that is, the phase information calculated last time is taken as an input, and the phase information is optimized to improve the accuracy of the computation. Referring to fig. 2C, the method further comprises:
and step 205, reconstructing the K space according to the first sampling data of each coil channel and the phase information obtained in the step 204. Then returning to step 202, the step of determining the phase information is executed iteratively until the iteration number is equal to the number threshold or the difference between the result of the previous iteration and the result of the current iteration is less than the threshold.
In one example, as shown in fig. 2D, step 205 includes:
and step 205-1, sequentially selecting the MR excited once from the MR signals excited many times as a target MR signal, and subtracting the phase information of the target MR signal from the phase information of the MR signals excited other times to obtain new phase information.
In step 205-1, that is, one of the MR signals obtained in step 204 is sequentially selected as the target MR signal, and the phase information of the selected MR signal is subtracted from the phase information of the other MR signals obtained in step 204 to obtain new phase information
For example, if the MR signal of the first excitation is used as the target MR signal, then
Representing the phase difference of the first excited MR signal and the first excited MR signal,
representing the phase of the MR signal of the first excitation with the MR signal of the second excitationA potential difference.
And step 205-2, performing transformation expansion on the first sampling data according to the new phase information and the first sampling data acquired in the step 201 to construct a new K space. And then returns to step 202.
In step 205-2, for convenience of calculation, the dimension of the excitation times is extended to the coil channels, that is, the MR signals acquired by exciting each coil channel at each time are extended
(including a full sampling area and an undersampled area) to carry out rewriting numbering to obtain a New MR signal New _ RawjJ is L (q-1) + i, i is not less than 1 and not more than L, and q is not less than 1 and not more than M. Taking L as 8 and M as 4 as an example, the MR signals received by the 8 channels excited 4 times are extended to 32 new virtual channels, that is, the MR signals of the 8 channels excited 1 st time are used as the signals of the new virtual channels 1 to 8, the MR signals of the 8 channels excited 2 nd time are used as the signals of the new virtual channels 9 to 16, the MR signals of the 8 channels excited 3 rd time are used as the signals of the new virtual channels 17 to 24, and the MR signals of the 8 channels excited 4 th time are used as the signals of the new virtual channels 25 to 32. It should be noted that the signal of the new virtual channel is the same as the original MR signal.
And then, according to the new phase information obtained in the step 205-1 and the MR signals of 32 new virtual channels, performing transformation expansion on the first sampling data to construct a new K space. Specifically, the new multi-channel K space of the structure is represented as K'j(l, n), j is Index of the reference excitation, l is the original coil channel Index, n is Index of the number of excitations:
K′j(l′)＝K′j(l，n)l′:{1～L*Nshot}
l′＝l+n*Nshot。
on the basis of fig. 2A, fig. 2E shows a flowchart of another embodiment of the method for acquiring magnetic resonance phase information, in this embodiment, final phase information is obtained through two iterations, referring to fig. 2E, after obtaining initial phase information in step 204, the method further includes:
and step 205', according to the initial phase information, performing transformation expansion on the first sampling data to obtain expanded data, and constructing a new K space according to the expanded data.
The specific implementation manner of performing transform expansion on the first sample data is similar to that in step 205-1 and step 205-2, and is not described herein again.
And step 206, determining a second interpolation convolution kernel for data filling of the newly constructed K space according to the extension data, and fitting the undersampled data of the newly constructed K space according to the second interpolation convolution kernel to obtain third sampling data of the newly constructed K space.
Wherein the third sample data has no channel information but data on the virtual channel, similar to the second sample data.
And step 207, determining final phase information of the MR signals of each excitation according to the third sampling data.
Because the interpolation capability of the convolution kernel is related to the number of coil channels and the down-sampling multiple, theoretically, the acceleration multiple of the K space in one direction is limited by the distribution number of the coil channels in the direction, otherwise, the interpolation cannot correctly restore the data, when the number of shots (for the data of each shot, the shot number is the down-sampling multiple) is large, the original channel information is only utilized, the data cannot be correctly deconvoluted, and the phase information of the shots is utilized, after the channels are expanded, the data of all the shots are regarded as the acquisition of different channels of the shots, and the increase of the number of the channels can help to calculate the convolution kernel more accurately and restore the data, so that the phase of the shot data calculated by channel expansion can be obviously improved compared with the initial phase obtained by the first calculation.
The interpolation convolution kernel calculated after the channel is expanded depends on the phase accuracy of each shot data, so that the interpolation convolution kernel calculated once again by using a more accurate phase is more accurate, the accuracy of the convolution kernel improves the reduction degree of the data, better phase information is obtained, and the accuracy of the result is further improved.
It should be noted that iteration is time-consuming, and the number of iterations can be set according to actual requirements. Generally, better phase information can be obtained by solving a new interpolation convolution kernel after channel expansion again, and the two times of the interpolation convolution kernel can be terminated. Of course, the condition that the difference between the result of the previous iteration and the result of the current iteration is smaller than the threshold value may also be used as the iteration stop condition.
Fig. 4 is a flow chart of a magnetic resonance imaging method according to an exemplary embodiment, the method comprising the steps of:
In step 401, phase information of the MR signal is acquired by using the method for acquiring magnetic resonance phase information shown in any of the above embodiments. If two iterations are used to acquire phase information, in step 301, the results of the two iterations are acquired as final phase information, and image reconstruction is performed.
And 403, fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain the full sampling data of the K space.
The specific implementation manner of step 402 and step 403 is similar to that of step 202 and step 203, and is not described here again.
And step 404, performing Fourier transform on the full-acquisition data to obtain a magnetic resonance image.
Corresponding to the embodiments of the magnetic resonance phase information acquisition method and the magnetic resonance imaging method, the invention also provides embodiments of a magnetic resonance phase information acquisition device and a magnetic resonance imaging device.
Fig. 5A is a schematic structural diagram illustrating an apparatus for acquiring magnetic resonance phase information according to an exemplary embodiment, the apparatus being applied to a data processor of a magnetic resonance imaging MRI system, the MRI system further including an MRI apparatus, the MRI apparatus including a radio frequency coil; the radio frequency coil includes a plurality of coil channels. Referring to fig. 5A, the acquisition means includes: an acquisition module 51, a determination module 52 and a fitting module 53.
The acquisition module 51 is configured to acquire an MR signal for each coil channel of the radio frequency coil, obtain first sampling data, and construct a K space according to the first sampling data;
the determining module 52 is configured to determine an interpolation convolution kernel for data padding on the K space according to the first sampling data of each coil channel;
the fitting module 53 is configured to fit the undersampled data of the K space according to the interpolation convolution kernel to obtain second sampled data;
the determination module 52 is further configured to determine phase information of the MR signal of each shot according to the second sampling data.
Optionally, when determining an interpolation convolution kernel for data padding on the K space according to the first sampling data of each coil channel, the determining module is specifically configured to:
merging the first sampling data of each coil channel to obtain merged data of a virtual channel;
and determining an interpolation convolution kernel according to the first sampling data and the combined data of each coil channel.
Optionally, when determining the phase information of the MR signal of each excitation from the second sampling data, the determining module is specifically configured to:
fourier transforming the second sampled data to an image domain;
phase information of the MR signals of each shot is acquired from the second sampled data of the image domain.
On the basis of the schematic structural diagram of the apparatus for acquiring magnetic resonance phase information shown in fig. 5A, referring to fig. 5B, a schematic structural diagram of another apparatus for acquiring magnetic resonance phase information is shown, which further includes: an iteration module 54.
The iteration module 54 is configured to construct a new K space according to the first sampling data and the phase information of each coil channel, and then iteratively execute the step of determining the phase information until the iteration number is equal to a number threshold or a difference between a result of a previous iteration and a result of the current iteration is smaller than a threshold.
Optionally, the iteration module is specifically configured to:
sequentially selecting the MR signals excited for one time from the MR signals excited for multiple times as target MR signals, and subtracting the phase information of the target MR signals from the phase information of other excited MR signals to obtain new phase information;
and constructing a new K space according to the new phase information and the first sampling data of each coil channel. Fig. 6 is a schematic configuration diagram of a magnetic resonance imaging apparatus according to an exemplary embodiment, the magnetic resonance imaging apparatus including: a computing means 61, an imaging means 62 and an acquisition means 63 of magnetic resonance phase information as shown in any of the embodiments described above.
The acquiring device 63 is used for acquiring phase information of the MR signals of multiple excitations;
the calculating device 61 is used for calculating an interpolation convolution kernel for data filling of the K space according to the phase information and the first sampling data, and fitting undersampled data of the K space according to the interpolation convolution kernel to obtain fully-sampled data of the K space;
the imaging device 62 is used to perform fourier transform on the full acquisition data to obtain a magnetic resonance image.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, which shows a block diagram of an exemplary electronic device 70 suitable for implementing an embodiment of the present invention. The electronic device 70 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in FIG. 7, the electronic device 70 may take the form of a general purpose computing device, which may be a server device, for example. The components of the electronic device 70 may include, but are not limited to: the at least one processor 71, the at least one memory 72, and a bus 73 connecting the various system components (including the memory 72 and the processor 71).
The bus 73 includes a data bus, an address bus, and a control bus.
The memory 72 may include volatile memory, such as Random Access Memory (RAM)721 and/or cache memory 722, and may further include Read Only Memory (ROM) 723.
The processor 71 executes computer programs stored in the memory 72 to perform various functional applications and data processing, such as the acquisition method of magnetic resonance phase information provided by any of the above embodiments.
The electronic device 70 may also communicate with one or more external devices 74 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 75. Also, the model-generating electronic device 70 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 76. As shown, the network adapter 76 communicates with the other modules of the model-generating electronic device 70 via a bus 73. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating electronic device 70, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the method for acquiring magnetic resonance phase information according to any one of the above embodiments.
An embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the magnetic resonance imaging method according to any of the above embodiments is implemented.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the magnetic resonance imaging method according to any one of the above embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (14)
1. A method for acquiring magnetic resonance phase information is applied to a data processor of a Magnetic Resonance Imaging (MRI) system, the MRI system further comprises an MRI device, and the MRI device comprises a radio frequency coil; the radio frequency coil comprises a plurality of coil channels;
the acquisition method comprises the following steps:
acquiring an MR signal for each coil channel of the radio frequency coil, obtaining first sampling data, and constructing a K space according to the first sampling data, wherein the first sampling data are full sampling data;
determining an interpolation convolution kernel for performing data filling on the K space according to the first sampling data of each coil channel;
fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain second sampling data;
determining phase information of the MR signals of each excitation according to the second sampling data;
the determining an interpolation convolution kernel for data padding of the K space according to the first sampling data of each coil channel includes:
combining the first sampling data of each coil channel to obtain combined data of a virtual channel, wherein the virtual channel is obtained by expanding MR signals received by a channel b after a time of excitation;
and determining the interpolation convolution kernel according to the first sampling data of each coil channel and the combined data converted into the K space data.
2. A method of acquiring magnetic resonance phase information as claimed in claim 1, wherein determining phase information of the MR signal for each shot from the second sampled data comprises:
fourier transforming the second sampled data to an image domain;
phase information of the MR signals of each shot is acquired from the second sampled data of the image domain.
3. The method of acquiring magnetic resonance phase information as set forth in any one of claims 1-2, further including:
and constructing a new K space according to the first sampling data of each coil channel and the phase information, and then iteratively executing the step of determining the phase information until the iteration number is equal to a number threshold or the difference between the result of the previous iteration and the result of the current iteration is less than the threshold.
4. A method of acquiring magnetic resonance phase information according to claim 3, wherein constructing a new K-space from the first sampled data of each coil channel and the phase information comprises:
sequentially selecting the MR signals excited for one time from the MR signals excited for multiple times as target MR signals, and subtracting the phase information of the target MR signals from the phase information of other excited MR signals to obtain new phase information;
constructing a new K space according to the new phase information and the first sampling data of each coil channel, including:
expanding the MR signals excited for multiple times to obtain MR signals of a new virtual channel;
according to the new phase information and the MR signal of the new virtual channel, carrying out transformation expansion on the first sampling data to construct a new K space;
wherein the new K space of construction is represented as K'j(L, n), j is the Index of the reference excitation, L is the original coil channel Index, n is the Index of the number of excitations, L represents the number of coil channels,
representing first sampled data, N, corresponding to coil channel lshotFor the total number of excitations:
l′＝l+n*Nshot。
5. a magnetic resonance imaging method, characterized in that it comprises:
acquiring phase information of MR signals of a plurality of excitations by the acquisition method of magnetic resonance phase information of any one of claims 1-4;
calculating an interpolation convolution kernel for filling data in the K space according to the phase information and the first sampling data;
fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain full sampling data of the K space;
and carrying out Fourier transform on the full-acquisition data to obtain a magnetic resonance image.
6. An apparatus for acquiring magnetic resonance phase information, wherein the apparatus is applied to a data processor of a Magnetic Resonance Imaging (MRI) system, the MRI system further comprises an MRI device, and the MRI device comprises a radio frequency coil; the radio frequency coil comprises a plurality of coil channels;
the acquisition device includes:
the acquisition module is used for acquiring an MR signal for each coil channel of the radio frequency coil, acquiring first sampling data, and constructing a K space according to the first sampling data, wherein the first sampling data are full sampling data;
the determining module is used for determining an interpolation convolution kernel for data filling of the K space according to the first sampling data of each coil channel;
the fitting module is used for fitting the undersampled data of the K space according to the interpolation convolution kernel to obtain second sampling data;
the determining module is further used for determining phase information of the MR signals of each excitation according to the second sampling data;
when determining an interpolation convolution kernel for data padding on the K space according to the first sampling data of each coil channel, the determining module is specifically configured to:
combining the first sampling data of each coil channel to obtain combined data of a virtual channel, wherein the virtual channel is obtained by expanding MR signals received by b channels excited for a time;
and determining the interpolation convolution kernel according to the first sampling data of each coil channel and the combined data converted into the K space data.
7. An apparatus for acquiring magnetic resonance phase information as claimed in claim 6, wherein, in determining the phase information of the MR signal of each excitation from the second sampled data, the determining means is specifically configured to:
fourier transforming the second sampled data to an image domain;
phase information of the MR signals of each shot is acquired from the second sampled data of the image domain.
8. The apparatus for acquiring magnetic resonance phase information as set forth in claim 6 or 7, further comprising:
and the iteration module is used for constructing a new K space according to the first sampling data of each coil channel and the phase information, and then iterating and executing the step of determining the phase information until the iteration number is equal to a number threshold or the difference value between the result of the previous iteration and the result of the current iteration is smaller than the threshold.
9. The apparatus for acquiring magnetic resonance phase information as set forth in claim 8, wherein the iteration module is specifically configured to:
sequentially selecting the MR signals excited for one time from the MR signals excited for multiple times as target MR signals, and subtracting the phase information of the target MR signals from the phase information of other excited MR signals to obtain new phase information;
constructing a new K space according to the new phase information and the first sampling data of each coil channel, including:
expanding the MR signals excited for multiple times to obtain MR signals of a new virtual channel;
according to the new phase information and the MR signal of the new virtual channel, carrying out transformation expansion on the first sampling data to construct a new K space;
wherein the new K space of construction is represented as K'j(L, n), j is the Index of the reference excitation, L is the original coil channel Index, n is the Index of the number of excitations, L represents the number of coil channels,
representing first sampled data, N, corresponding to coil channel lshotFor the total number of excitations:
l′＝l+n*Nshot。
10. a magnetic resonance imaging apparatus, characterized in that the magnetic resonance imaging apparatus comprises:
a computing device, an imaging device and an acquisition device of magnetic resonance phase information according to any one of claims 6 to 9;
the acquisition device is used for acquiring the phase information of the MR signals excited for multiple times;
the computing device is used for computing an interpolation convolution kernel for data filling of the K space according to the phase information and the first sampling data, and fitting undersampled data of the K space according to the interpolation convolution kernel to obtain fully-sampled data of the K space;
the imaging device is used for carrying out Fourier transform on the full-acquisition data to obtain a magnetic resonance image.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of acquiring magnetic resonance phase information of any one of claims 1 to 4 when executing the computer program.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of acquisition of magnetic resonance phase information of any one of claims 1 to 4.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the magnetic resonance imaging method as claimed in claim 5 when executing the computer program.
14. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the magnetic resonance imaging method as set forth in claim 5.
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