阅读说明：本技术 干扰消除方法、介质及电子设备 (Interference cancellation method, medium, and electronic device ) 是由 刘懿龙 朱瑞星 于 2021-08-31 设计创作，主要内容包括：本申请涉及信号处理技术领域,公开了一种干扰消除方法、介质及设备。该方法包括：采集步骤,采集多个成像回波,并根据多个成像回波,得到原始k空间数据；构建步骤,构建分块Hankel矩阵；分解步骤,对分块Hankel矩阵进行特征值分解,通过秩截断的方式使分块Hankel矩阵满足低秩约束,得到低秩矩阵；恢复步骤,基于低秩矩阵进行恢复,得到满足低秩约束的当前k空间数据；更新步骤,根据原始k空间数据和当前k空间数据生成更新k空间数据；判断步骤,判断是否满足预定条件,当不满足预定条件时,返回构建步骤,当满足预定条件时,进入图像重建步骤；图像重建步骤,利用更新k空间数据进行图像重建。本发明可以消除磁场扰动对磁共振成像的影响。(The application relates to the technical field of signal processing, and discloses an interference elimination method, medium and equipment. The method comprises the following steps: an acquisition step, acquiring a plurality of imaging echoes, and acquiring original k-space data according to the plurality of imaging echoes; a construction step, namely constructing a partitioned Hankel matrix; a decomposition step, carrying out eigenvalue decomposition on the block Hankel matrix, and enabling the block Hankel matrix to meet low-rank constraint in a rank truncation mode to obtain a low-rank matrix; a recovery step, namely recovering based on the low-rank matrix to obtain current k space data meeting low-rank constraint; an updating step, namely generating updated k-space data according to the original k-space data and the current k-space data; a judging step, namely judging whether a preset condition is met, returning to the constructing step when the preset condition is not met, and entering an image reconstructing step when the preset condition is met; and an image reconstruction step of reconstructing an image by using the updated k-space data. The invention can eliminate the influence of magnetic field disturbance on magnetic resonance imaging.)

1. An interference cancellation method, characterized in that the method comprises:

an acquisition step, acquiring a plurality of imaging echoes, obtaining original k-space data according to the imaging echoes, and taking the original k-space data as k-space data for construction;

a building step, namely building a partitioned Hankel matrix based on the k-space data for building;

a decomposition step, which is to decompose the characteristic value of the block Hankel matrix and enable the block Hankel matrix to meet low-rank constraint in a rank truncation mode to obtain a low-rank matrix;

a recovery step, namely recovering based on the low-rank matrix to obtain current k space data meeting the low-rank constraint;

an updating step, namely generating updated k-space data according to the original k-space data and the current k-space data;

a judging step of judging whether a predetermined condition is satisfied, when the predetermined condition is not satisfied, using the updated k-space data as the k-space data for construction, returning to the constructing step, and when the predetermined condition is satisfied, entering an image reconstructing step;

and an image reconstruction step of reconstructing an image by using the updated k-space data.

2. The method according to claim 1, wherein the predetermined condition is that a predetermined number of iterations has been reached or that the updated k-space data has converged.

3. The method of claim 2, wherein the raw k-space data comprises data for a plurality of k-space lines in raw k-space each at a plurality of sampling points, and wherein the current k-space data comprises data for a plurality of k-space lines in k-space each at the plurality of sampling points that satisfy the low rank constraint.

4. The method of claim 3, wherein the updating step comprises:

generating a phase difference average value between the data of the plurality of sampling points of each k-space line in the original k-space and the data of the plurality of sampling points in the k-space satisfying the low rank constraint according to the original k-space data and the current k-space data;

filtering the phase difference average value of each of the plurality of k-space lines;

obtaining phase change estimation of each of the plurality of k-space lines at the plurality of sampling points based on the filtered average value of the phase difference;

and according to the phase change estimation, generating updating data of the plurality of k-space lines at the plurality of sampling points respectively to obtain the updating k-space data.

5. The method according to claim 4, wherein the phase difference Δ Φ between the plurality of sampling points in the original k-space and the plurality of sampling points in k-space satisfying the low rank constraint for each k-space line is obtained according to the following equation 1_est(t)，

ΔΦ_est(t)＝angle(S_img(t)×conj(S_LR(t))) formula 1

Wherein S is_img(t) is the data of the sampling point t of the nth k-space line in the original k-space, S_LR(t) is data of a sampling point t of an nth k-space line in k-space satisfying a low rank constraint, the sampling point t being relative to a time instant at which the radio frequency pulse is applied,

the phase difference Δ Φ is obtained according to the following equation 2_est(t) weight w (t),

w(t)＝abs(S_img(t)×conj(S_LR(t))) formula 2

Obtaining the average value phi of the phase differences of each k-space line according to the following formula 3₀，

Where TE is the echo time of each imaging echo.

6. The method of claim 5Characterized in that the mean value of the phase differences Φ for each of the plurality of k-space lines₀Filtering to obtain the filtered phase difference average value phi 'of each k-space line'₀，

And obtaining the phase change estimate Δ Φ according to the following equation 4_est‘(t)，

7. The method of claim 6, wherein the update data S 'at the plurality of sampling points for each k-space line is generated according to equation 5 below'_img(t)，

8. The method of any one of claims 1-7, wherein the plurality of imaging echoes are each filled into a corresponding k-space line of k-space, and each k-space line is sampled at a plurality of sampling points to obtain the raw k-space data.

9. The method of claim 8, wherein the filtering process is a weighted average filtering process or a kalman filter.

10. The method of claim 1, wherein the plurality of imaging echoes are obtained using a gradient echo sequence consisting of a radio frequency pulse, a slice-selective gradient, a slice-selective echo gradient, a phase encoding gradient, a pre-applied phase gradient, a readout gradient, a destruction gradient, a phase wrap-around gradient.

11. A computer-readable storage medium having stored thereon instructions that, when executed on a computer, cause the computer to perform the interference cancellation method of any one of claims 1 to 10.

12. An electronic device, comprising: one or more processors; one or more memories; the one or more memories store one or more programs that, when executed by the one or more processors, cause the electronic device to perform the interference cancellation method of any of claims 1-10.

Technical Field

The present disclosure relates to the field of signal processing technologies, and in particular, to an interference cancellation method, medium, and electronic device for magnetic resonance imaging.

Background

The magnetic field intensity and direction of an imaging area can be changed due to rail traffic (subway, train, etc.) or high-power electric appliances near the magnetic resonance imaging system. In the presence of magnetic field disturbances, the magnetic resonance signals received by the receiving coils are subject to phase changes, so that the data phases of different phase-encoded lines (k-space lines) in the entire k-space are inconsistent, thereby causing image artifacts. This phenomenon is particularly evident in low-field or ultra-low field systems where the main magnetic field strength is low. Furthermore, in clinical applications, patient motion (such as breathing, swallowing, etc.) can also cause magnetic field perturbations.

To eliminate the effect of the magnetic field disturbance, a fluxgate sensor may be used to detect the magnetic field disturbance and drive an active shield coil to counteract the effect of the disturbance, but this solution is costly.

Disclosure of Invention

The embodiment of the application provides an interference elimination method, an interference elimination device, a medium and equipment.

In a first aspect, an embodiment of the present application provides an interference cancellation method, including: an acquisition step, acquiring a plurality of imaging echoes, obtaining original k-space data according to the imaging echoes, and taking the original k-space data as k-space data for construction; a building step, namely building a partitioned Hankel matrix based on the k-space data for building; a decomposition step, which is to decompose the characteristic value of the block Hankel matrix and enable the block Hankel matrix to meet low-rank constraint in a rank truncation mode to obtain a low-rank matrix; a recovery step, namely recovering based on the low-rank matrix to obtain current k space data meeting the low-rank constraint; an updating step, namely generating updated k-space data according to the original k-space data and the current k-space data; a judging step of judging whether a predetermined condition is satisfied, when the predetermined condition is not satisfied, using the updated k-space data as the k-space data for construction, returning to the constructing step, and when the predetermined condition is satisfied, entering an image reconstructing step; and an image reconstruction step of reconstructing an image by using the updated k-space data.

In a possible implementation of the first aspect described above, the predetermined condition is that the updating of k-space data has converged or has reached a predetermined number of iterations.

In one possible implementation of the first aspect, the raw k-space data includes data of a plurality of k-space lines in raw k-space each at a plurality of sampling points, and the current k-space data includes data of a plurality of k-space lines in k-space each at the plurality of sampling points that satisfy the low rank constraint.

In a possible implementation of the first aspect, the updating step includes: generating a phase difference average value between the data of the plurality of sampling points of each k-space line in the original k-space and the data of the plurality of sampling points in the k-space satisfying the low rank constraint according to the original k-space data and the current k-space data; filtering the phase difference average value of each of the plurality of k-space lines; obtaining phase change estimation of each of the plurality of k-space lines at the plurality of sampling points based on the filtered average value of the phase difference; and according to the phase change estimation, generating updating data of the plurality of k-space lines at the plurality of sampling points respectively to obtain the updating k-space data.

In a possible implementation of the first aspect, the phase difference Δ Φ between the plurality of sampling points in the original k-space and the plurality of sampling points in k-space satisfying the low rank constraint is obtained according to the following formula 1_est(t)，

ΔФ_est(t)＝angle(S_img(t)×conj(S_LR(t))) formula 1

Wherein S is_img(t) is the data of the sampling point t of the nth k-space line in the original k-space, S_LR(t) is data of a sampling point t of an nth k-space line in k-space satisfying a low rank constraint, the sampling point t being relative to a time instant at which the radio frequency pulse is applied,

obtaining the phase difference delta phi according to the following formula 2_est(t) weight w (t),

w(t)＝abs(S_img(t)×conj(S_LR(t))) formula 2

Obtaining the phase difference average value phi of each k-space line according to the following formula 3₀，

Where TE is the echo time of each imaging echo.

In one possible implementation of the first aspect, the average value of phase differences Φ for each of the plurality of k-space lines₀Filtering to obtain the filtered phase difference average value phi 'of each k-space line'₀，

And obtaining the phase change estimation delta phi 'according to the following formula 4'_est(t)，

In one possible implementation of the first aspect, the update data S 'of each k-space line at the plurality of sampling points is generated according to the following formula 5'_img(t)，

In a possible implementation of the first aspect, the multiple imaging echoes are respectively filled into corresponding k-space lines of a k-space, and each k-space line is sampled at multiple sampling points to obtain the original k-space data.

In one possible implementation of the first aspect described above, the filtering process is a weighted average filtering process or a kalman filter.

In one possible implementation of the first aspect, the multiple imaging echoes are obtained using a gradient echo sequence, the gradient echo sequence consisting of a radio frequency pulse, a slice selection gradient, a slice selection echo gradient, a phase encoding gradient, a pre-applied phase gradient, a readout gradient, a destruction gradient, and a phase wrap-around gradient.

In a second aspect, an embodiment of the present application provides an interference cancellation apparatus, where the acquisition module acquires a plurality of imaging echoes, obtains original k-space data according to the plurality of imaging echoes, and uses the original k-space data as k-space data for construction; the building module is used for building a partitioned Hankel matrix based on the k-space data for building; the decomposition module is used for carrying out eigenvalue decomposition on the block Hankel matrix, and enabling the block Hankel matrix to meet low-rank constraint in a rank truncation mode to obtain a low-rank matrix; the recovery module recovers based on the low-rank matrix to obtain current k space data meeting the low-rank constraint; an update module that generates updated k-space data from the original k-space data and the current k-space data; the judging module is used for judging whether a preset condition is met, taking the updated k-space data as the k-space data for construction when the preset condition is not met, returning the k-space data to the construction module, and entering the image reconstruction module when the preset condition is met; and the image reconstruction module is used for reconstructing an image by using the updated k-space data. For example, the acquisition module, the construction module, the decomposition module, the recovery module, the update module, the judgment module, and the image reconstruction module may be implemented by a processor having functions of these modules or units in an electronic device.

In a third aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored on the storage medium, and when executed on a computer, the instructions cause the computer to perform the interference cancellation method in the first aspect.

In a fourth aspect, an embodiment of the present application provides an electronic device, including: one or more processors; one or more memories; the one or more memories store one or more programs that, when executed by the one or more processors, cause the electronic device to perform the interference cancellation method of the first aspect.

Drawings

Figure 1 shows a schematic structural diagram of a magnetic resonance imaging apparatus, according to some embodiments of the present application;

fig. 2 illustrates a flow diagram of a method of interference cancellation, according to some embodiments of the present application;

FIG. 3 illustrates a timing diagram of a gradient echo sequence, according to some embodiments of the present application;

FIG. 4 is a schematic diagram showing k-space lines and phase differences in k-space before and after filtering, according to some embodiments;

figure 5 illustrates a block diagram of a computer of a magnetic resonance imaging device, according to some embodiments of the present application.

Detailed Description

Illustrative embodiments of the present application include, but are not limited to, interference cancellation methods, apparatus, media and devices for magnetic resonance imaging.

The interference elimination method provided by the embodiment of the application can be applied to Magnetic Resonance Imaging (MRI).

As an example, in a magnetic resonance imaging scenario, the electronic device may be a device with magnetic resonance imaging functionality, which is referred to herein as a magnetic resonance imaging device.

In the following embodiments, an interference cancellation method performed by a magnetic resonance imaging device in a magnetic resonance imaging scene is mainly taken as an example, and the interference cancellation method provided by the embodiments of the present application is described. Similarly, details of implementation of the interference cancellation method performed by the electronic device in other application scenarios will not be repeated here, and some descriptions may refer to related descriptions of the interference cancellation method performed by the magnetic resonance imaging device.

Magnetic resonance imaging techniques can generate medical images in medical or clinical application scenarios for disease diagnosis. Specifically, the magnetic resonance imaging technique can perform image reconstruction using signals generated by the resonance of atomic nuclei in a strong magnetic field, and can generate tomographic images of a cross section, a sagittal plane, a coronal plane, and various inclined planes of a subject such as a human body.

In the implementation of the application, the magnetic resonance imaging equipment can be low-field and ultra-low-field magnetic resonance imaging equipment, and can also be medium-field and high-field magnetic resonance imaging equipment. As an example, magnetic resonance imaging systems in clinical applications can be generally classified by magnetic field strength into high field (above 1T), medium field (0.3-1T), low field (0.1-0.3T), and ultra-low field (below 0.1T).

It can be understood that the embodiments of the present application are mainly applied to low-field or ultra-low-field magnetic resonance imaging devices, and magnetic field disturbance is eliminated in the magnetic resonance imaging process, so that artifacts existing in the magnetic resonance imaging are eliminated, and the quality of the magnetic resonance imaging is improved.

Embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

Fig. 1 is a schematic diagram of a possible structure of a magnetic resonance imaging apparatus provided in an embodiment of the present application. The magnetic resonance imaging apparatus 100 may include: computer 101, spectrometer 102, gradient amplifier 103, gradient coil 104, transmit radio frequency amplifier 105, transmit radio frequency coil (also referred to as transmit coil) 106, receive radio frequency amplifier 107, receive radio frequency coil 108 (also referred to as receive coil), and magnet 109.

Specifically, computer 101 is used to issue instructions to spectrometer 102 under the control of an operator to trigger spectrometer 102 to generate a waveform of a gradient signal and a waveform of a radio frequency signal according to the instructions. After the gradient signals generated by spectrometer 102 are amplified by gradient amplifier 103, gradient of the magnetic field is formed by gradient coil 104, so as to implement spatial gradient encoding for the magnetic resonance signals (specifically, magnetic resonance imaging signals). In particular, spatial gradient encoding is used to spatially localize the magnetic resonance signals, i.e. to distinguish the location of the source of the magnetic resonance signals. The radio frequency signals generated by spectrometer 102 are amplified by a transmission radio frequency amplifier 105 and transmitted by a transmission radio frequency coil 106, thereby exciting protons (hydrogen nuclei) in the imaging region. The excited protons may emit radio frequency signals, which may be received by the receiving coil 108, amplified by the receiving rf amplifier 107, converted into digital signals by the spectrometer 102, and transmitted to the computer 101 for processing, obtaining images, and displaying. Furthermore, the magnet 109 may be any suitable type of magnet capable of generating a main magnetic field.

In some embodiments, the receive coils 108 described above may be implemented using single or multiple phased array coils, which are widely used in modern medical magnetic resonance imaging.

It is to be understood that in the embodiment of the present application, the design and layout (deployment position, deployment direction, etc.) of the receiving coils in the magnetic resonance imaging apparatus 100 are not particularly limited, and may be any realizable scheme.

For gradient echoes, the magnetic resonance signals received by the receive coils can be expressed as

Wherein s (t) is a magnetic resonance imaging signal received by the receiving coil at time t, ρ is a proton spin distribution (spin distribution), γ is a gyromagnetic ratio (gyromagnetic ratio), G is a Field intensity distribution at each position in a Field of View (FOV), and x represents a space vector coordinate. If a subway or the like passes nearby, magnetic field disturbances will occur and can be considered as a constant over the entire field of view, i.e. the disturbance is constant

G'_x＝G_c+ Δ G formula (2)

Then formula (1) can be rewritten as

Namely, it is

Thus, the phase of the magnetic resonance signal will change, which can be expressed as

Equation (5) for Δ Φ (t) ═ γ Δ Gt

Based on the above description, the main workflow of the magnetic resonance imaging apparatus 100 to execute the magnetic field disturbance interference elimination method is described in detail below. In particular, the technical details described above for the magnetic resonance imaging apparatus 100 shown in fig. 1 are still applicable in the following method flow, and some details will not be described again to avoid repetition. In some embodiments, the subject of execution of the magnetic field disturbance rejection method of the present application may be the magnetic resonance imaging apparatus 100, in particular the computer 101 in the magnetic resonance imaging apparatus 100. Fig. 2 is a schematic flow chart of an interference cancellation method provided in the present application, and fig. 3 is a timing chart of a gradient echo sequence. In this embodiment, the method is used to eliminate the effect of magnetic field disturbance on magnetic resonance imaging.

An acquisition step 201: the magnetic resonance imaging apparatus 100 acquires a plurality of imaging echoes, obtains raw k-space data from the plurality of imaging echoes, and uses the raw k-space data as k-space data for construction.

Specifically, the magnetic resonance imaging signals received by the receiving coil 108 are acquired, and a plurality of imaging echoes are obtained. Referring to fig. 3, prior to acquisition, the magnetic resonance imaging apparatus 100 applies radio frequency pulses while applying a slice selection gradient, and then applies a slice selection echo gradient while applying a phase encode gradient and a pre-applied phase gradient in the readout direction. Next, the magnetic resonance imaging apparatus 100 acquires magnetic resonance imaging signals while applying a readout gradient, thereby obtaining imaging echoes. After one imaging echo is acquired, the magnetic resonance imaging apparatus 100 applies a phase wrap gradient and a destruction gradient simultaneously for eliminating the influence of the residual transverse magnetization vector on the next acquired imaging echo.

The magnetic resonance imaging apparatus 100 fills an unfilled k-space line in k-space with an acquired imaging echo and then repeats the acquisition process until all k-space lines in k-space are filled with corresponding imaging echoes, thus obtaining a plurality of imaging echoes.

It can be understood that a plurality of imaging echoes are respectively filled into corresponding k-space lines of k-space, and each k-space line is sampled at a plurality of sampling points, so as to obtain original k-space data.

It will be appreciated that the raw k-space data comprises data for a plurality of k-space lines in raw k-space at a plurality of sampling points, respectively.

It will be appreciated that the plurality of imaging echoes are obtained using a gradient echo sequence comprised of a radio frequency pulse, a slice selection gradient, a slice selection echo gradient, a phase encoding gradient, a pre-applied phase gradient, a readout gradient, a destruction gradient, and a phase rewind gradient.

In a construction step 202, the magnetic resonance imaging apparatus 100 constructs a segmented Hankel matrix based on the k-space data for construction. It is understood that the k-space data used for construction at this time is the original k-space data.

And a decomposition step 203, performing eigenvalue decomposition on the block Hankel matrix by the magnetic resonance imaging device 100, and enabling the block Hankel matrix to meet low-rank constraint in a rank truncation mode to obtain a low-rank matrix. It will be appreciated that this low rank matrix approximates the block Hankel matrix.

In a recovery step 204, the magnetic resonance imaging apparatus 100 performs recovery based on the low rank matrix to obtain current k-space data satisfying the low rank constraint. It is to be understood that the current k-space data comprises data for a plurality of k-space lines in k-space, each at a plurality of sampling points, that satisfy a low rank constraint.

An updating step in which the magnetic resonance imaging apparatus 100 generates updated k-space data from the original k-space data and the current k-space data.

The updating step includes generating a phase difference average between data of a plurality of sampling points of each k-space line in the original k-space and data of a plurality of sampling points in the k-space satisfying the low rank constraint, according to the original k-space data and the current k-space data.

Specifically, the phase difference Δ Φ between multiple sampling points of each k-space line in the original k-space and multiple sampling points in the k-space satisfying the low rank constraint is obtained according to the following formula 1_est(t)，

ΔФ_est(t)＝angle(S_img(t)×conj(S_LR(t))) formula 1

Wherein S is_img(t) is data of a sampling point t of the nth k-space line in the original k-space, S_LR(t) is data of a sampling point t of an nth k-space line in k-space satisfying a low rank constraint, the sampling point t being relative to a time instant at which a radio frequency pulse is applied, angle being a function of a phase angle, and conj being a function of a complex conjugate. It will be appreciated that with the application of the radio frequency pulse at time 0, the sampling point t is a time relative to time 0.

The phase difference Δ Φ is obtained according to the following formula 2_estWeight w (t) of (t))，

w(t)＝abs(S_img(t)×conj(S_LR(t))) formula 2

Where abs is a function of the amplitude.

Obtaining the phase difference average value phi of each k-space line according to the following formula 3₀，

Where TE is the echo time of each imaging echo.

Thus, the phase difference average value phi of each k-space line is obtained₀. Then, other a priori information may be introduced to reduce the influence of noise on the estimation accuracy of the phase difference. For example, if the magnetic field disturbance is considered to be low frequency, then the phase difference average Φ for each of the plurality of k-space lines is taken in order of acquisition time₀Rearranging and filtering.

Specifically, the phase difference average value Φ of each of the plurality of k-space lines is set₀Arranging according to the acquisition time of each imaging echo and filtering as a time sequence to obtain the filtered phase difference average value phi 'of each k-space line'₀。

In the embodiment of the present invention, the filtering process is a weighted average filtering process, kalman filtering, or other filtering process, without limitation.

The weighted average filtering can be expressed as:

where Δ Φ_iIs the phase change corresponding to the ith k-space line sequenced according to the acquisition time, delta phi'_nFor the estimation of the phase change corresponding to the n-th k-space line after filtering, w_iThe weight of the corresponding ith space line in the weighted average filtering process is obtained; the window width of the weighted average filtering is here 2m + 1. In general, the closer i is to n, the more weight,for example, the weights may be defined according to a gaussian function:

the weighted average filtering with weights defined by this gaussian function is gaussian filtering.

Fig. 4 is a diagram illustrating k-space lines and phase differences in k-space before and after filtering processing according to some embodiments, where the thin solid line is the estimate of the phase difference before filtering and the thick dashed line is the estimate of the phase difference after filtering. It can be seen that the influence of noise can be reduced by the above filtering process.

Next, based on the filtered average value of the phase differences, phase change estimates for a plurality of k-space lines at a plurality of sampling points, respectively, are obtained.

Specifically, phase change estimates Δ Φ 'of a plurality of k-space lines at a plurality of sampling points are obtained according to the following formula 4'_est(t)，

Next, update data of each of the plurality of k-space lines at the plurality of sampling points is generated based on the phase change estimation, and the update k-space data is obtained.

Specifically, the update data S 'of a plurality of sampling points per k-space line is generated according to the following formula 5'_img(t)，

It will be appreciated that updating k-space data comprises updating data S 'of a plurality of k-space lines at a plurality of sampling points, respectively'_img(t) of (d). Wherein i represents an imaginary number.

In the determination step 206, the magnetic resonance imaging apparatus 100 determines whether or not a predetermined condition is satisfied, and when the predetermined condition is not satisfied, the updated k-space data is used as the k-space data for construction and returns to the construction step 202, and when the predetermined condition is satisfied, the process proceeds to the image reconstruction step 207.

Wherein the predetermined condition may be that a predetermined number of iterations has been reached or that the updated k-space data has converged. It is to be understood that the predetermined condition may also be other suitable conditions without any limitation.

It is understood that, for example, the predetermined number of iterations (repetitions) is 10, and if it is determined that the current number of iterations has not reached 10, the obtained updated k-space data is used as the k-space data for construction, and the construction step 202 is returned, and the step 202 and 206 are repeated. If the current iteration number is judged to reach 10 times, the iteration process is stopped, and the step 207 of image reconstruction is carried out.

It is understood that if it is determined that the updated k-space data has not converged, the obtained updated k-space data is used as the k-space data for construction, and the construction step 202 is returned, and the step 202 and 206 are repeated. If it is determined that the updated k-space data has converged, the image reconstruction step 207 is entered.

An image reconstruction step 207 performs an image reconstruction with the magnetic resonance imaging apparatus 100 using the updated k-space data.

It can be understood that the invention can utilize the Hankel matrix to obtain a low-rank matrix for correcting the phase change of k-space lines in k-space, thereby eliminating the influence of magnetic field disturbance on magnetic resonance imaging, eliminating artifacts existing in the magnetic resonance imaging and improving the quality of the magnetic resonance imaging.

Further, as is clear from the above equation (5), the phase change due to the magnetic field disturbance becomes more significant as the echo time becomes longer, and therefore, the phase change due to the magnetic field disturbance can be reduced as much as possible by reducing TE.

If the magnetic resonance imaging system is in a non-electromagnetic shielding environment, the influence of electromagnetic interference needs to be removed (refer to the filed patent "interference elimination method, medium and equipment", CN113176528A/CN113180636A/CN113203969A, which can be incorporated herein by reference), and then the method proposed by the present invention is used to eliminate the influence of magnetic field disturbance.

Similarly, for other scenarios applied in the embodiment of the present application, the electronic device may also implement the interference cancellation method according to steps similar to the aforementioned steps 201-207, except that the implementation subject is different.

Referring now to fig. 5, shown is a block diagram of a computer in a magnetic resonance imaging apparatus 100 in accordance with one embodiment of the present application. FIG. 5 schematically illustrates an example computer 1400 in accordance with various embodiments. In one embodiment, system 1400 may include one or more processors 1404, system control logic 1408 coupled to at least one of processors 1404, system memory 1412 coupled to system control logic 1408, non-volatile memory (NVM)1416 coupled to system control logic 1408, and a network interface 1420 coupled to system control logic 1408.

In some embodiments, processor 1404 may include one or more single-core or multi-core processors. In some embodiments, processor 1404 may include any combination of general-purpose processors and dedicated processors (e.g., graphics processors, application processors, baseband processors, etc.). In embodiments where system 1400 employs an eNB (enhanced Node B) 101 or RAN (Radio Access Network) controller 102, processor 1404 may be configured to perform various consistent embodiments, e.g., the embodiment shown in fig. 2.

In some embodiments, system control logic 1408 may include any suitable interface controllers to provide any suitable interface to at least one of processors 1404 and/or to any suitable device or component in communication with system control logic 1408.

In some embodiments, system control logic 1408 may include one or more memory controllers to provide an interface to system memory 1412. System memory 1412 may be used to load and store data and/or instructions. Memory 1412 of system 1400 may include any suitable volatile memory, such as suitable Dynamic Random Access Memory (DRAM), in some embodiments.

NVM/memory 1416 may include one or more tangible, non-transitory computer-readable media for storing data and/or instructions. In some embodiments, the NVM/memory 1416 may include any suitable non-volatile memory such as flash memory and/or any suitable non-volatile storage device such as at least one of a HDD (Hard Disk Drive), CD (Compact Disc) Drive, DVD (Digital Versatile Disc) Drive.

The NVM/memory 1416 may comprise a portion of the storage resources on the device on which the system 1400 is installed, or it may be accessible by, but not necessarily a part of, the device. For example, the NVM/memory 1416 may be accessible over a network via the network interface 1420.

In particular, system memory 1412 and NVM/storage 1416 may each include: a temporary copy and a permanent copy of instructions 1424. Instructions 1424 may include: instructions that, when executed by at least one of the processors 1404, cause the computer 1400 to perform the method illustrated in fig. 2. In some embodiments, instructions 1424, hardware, firmware, and/or software components thereof may additionally/alternatively be located in system control logic 1408, network interface 1420, and/or processor 1404.

Network interface 1420 may include a transceiver to provide a radio interface for system 1400 to communicate with any other suitable device (e.g., front end module, antenna, etc.) over one or more networks. In some embodiments, network interface 1420 may be integrated with other components of system 1400. For example, network interface 1420 may be integrated with at least one of processor 1404, system memory 1412, NVM/storage 1416, and a firmware device (not shown) having instructions that, when executed by at least one of processors 1404, cause computer 1400 to implement the method shown in fig. 2.

Network interface 1420 may further include any suitable hardware and/or firmware to provide a multiple-input multiple-output radio interface. For example, network interface 1420 may be a network adapter, a wireless network adapter, a telephone modem, and/or a wireless modem.

In one embodiment, at least one of the processors 1404 may be packaged together with logic for one or more controllers of system control logic 1408 to form a System In Package (SiP). In one embodiment, at least one of processors 1404 may be integrated on the same die with logic for one or more controllers of system control logic 1408 to form a system on a chip (SoC).

The computer 1400 may further include: input/output (I/O) devices 1432. The I/O device 1432 may include a user interface to enable a user to interact with the computer 1400; the design of the peripheral component interface enables peripheral components to also interact with the computer 1400. In some embodiments, the computer 1400 further includes sensors for determining at least one of environmental conditions and location information associated with the computer 1400.

In some embodiments, the user interface may include, but is not limited to, a display (e.g., a liquid crystal display, a touch screen display, etc.), a speaker, a microphone, one or more cameras (e.g., still image cameras and/or video cameras), a flashlight (e.g., a light emitting diode flash), and a keyboard. For example, the user interface described above may be used to display an imaging image of a magnetic resonance imaging procedure, an image of k-space, and the like.

Embodiments of the mechanisms disclosed herein may be implemented in hardware, software, firmware, or a combination of these implementations. Embodiments of the application may be implemented as computer programs or program code executing on programmable systems comprising at least one processor, a storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device.

Program code may be applied to input instructions to perform the functions described herein and generate output information. The output information may be applied to one or more output devices in a known manner. For purposes of this application, a processing system includes any system having a processor such as, for example, a Digital Signal Processor (DSP), a microcontroller, an Application Specific Integrated Circuit (ASIC), or a microprocessor.

The program code may be implemented in a high level procedural or object oriented programming language to communicate with a processing system. The program code can also be implemented in assembly or machine language, if desired. Indeed, the mechanisms described in this application are not limited in scope to any particular programming language. In any case, the language may be a compiled or interpreted language.

In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or via other computer readable media. Thus, a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including, but not limited to, floppy diskettes, optical disks, read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROMs), Random Access Memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or a tangible machine-readable memory for transmitting information (e.g., carrier waves, infrared signal digital signals, etc.) using the internet in electrical, optical, acoustical or other forms of propagated signals. Thus, a machine-readable medium includes any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

In the drawings, some features of the structures or methods may be shown in a particular arrangement and/or order. However, it is to be understood that such specific arrangement and/or ordering may not be required. Rather, in some embodiments, the features may be arranged in a manner and/or order different from that shown in the illustrative figures. In addition, the inclusion of a structural or methodical feature in a particular figure is not meant to imply that such feature is required in all embodiments, and in some embodiments, may not be included or may be combined with other features.

It should be noted that, in the embodiments of the apparatuses in the present application, each unit/module is a logical unit/module, and physically, one logical unit/module may be one physical unit/module, or may be a part of one physical unit/module, and may also be implemented by a combination of multiple physical units/modules, where the physical implementation manner of the logical unit/module itself is not the most important, and the combination of the functions implemented by the logical unit/module is the key to solve the technical problem provided by the present application. Furthermore, in order to highlight the innovative part of the present application, the above-mentioned device embodiments of the present application do not introduce units/modules which are not so closely related to solve the technical problems presented in the present application, which does not indicate that no other units/modules exist in the above-mentioned device embodiments.

It is noted that, in the examples and descriptions of this patent, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the use of the verb "comprise a" to define an element does not exclude the presence of another, same element in a process, method, article, or apparatus that comprises the element.

While the present application has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application.