Magnetic resonance imaging method, apparatus, computer equipment and storage medium

Abstract

The present application relates to a magnetic resonance imaging method, apparatus, computer device, and storage medium. The method includes: obtaining a first signal data set and a second signal data set from magnetic resonance signal data; the magnetic resonance signal data corresponds to multiple data points, the first signal data set is obtained based on the magnetic resonance signal data of the first data point, and the second signal data set is obtained based on the magnetic resonance signal data of the second data point; obtaining a signal offset data set based on a preset signal offset parameter and the second signal data set; obtaining a weight parameter based on the signal offset data set, the second signal data set, and the first signal data set; the weight parameter is used to obtain downsampled data points for processing; and image reconstruction processing is performed on the magnetic resonance signal data based on the weight parameter. The present method can improve the accuracy of magnetic resonance imaging and improve the stability and quality of magnetic resonance imaging.

Description

Technical Field

[0001] The present application relates to the field of magnetic resonance technology, and in particular to a magnetic resonance imaging method, apparatus, computer equipment, and storage medium. Background Art

[0002] Magnetic resonance imaging (MRI) is widely used in medical diagnosis. Current MRI acceleration technologies for reconstructing downsampled signal data can be categorized by the type of reconstruction input: image-domain parallel reconstruction methods (e.g., SENSE) and k-space-domain parallel reconstruction methods (e.g., GRAPPA and SPIRiT).

[0003] Parallel MRI reconstruction techniques, such as GRAPPA and SPIRiT, are based on the K-space domain and are currently widely used in various fast MRI applications. These techniques typically utilize a small, fully sampled calibration line from the central region of K-space as a baseline for recovering unsampled data. Weighting kernels are then calculated to fit the unsampled data. These weighting kernels are then applied to the downsampled data in the next step of data synthesis to synthesize the complete K-space data.

[0004] However, current magnetic resonance imaging reconstruction technology has the problem of low quality of images obtained by magnetic resonance imaging. Summary of the Invention

[0005] Based on this, it is necessary to provide a magnetic resonance imaging method, apparatus, computer equipment and storage medium that can improve image quality in order to address the above technical problems.

[0006] In a first aspect, the present application provides a magnetic resonance imaging method, the method comprising:

[0007] Obtaining a first signal data set and a second signal data set from the magnetic resonance signal data; the magnetic resonance signal data corresponds to a plurality of data points, the first signal data set being obtained based on the magnetic resonance signal data of the first data point, and the second signal data set being obtained based on the magnetic resonance signal data of the second data point; the first data point being a data point obtained by fully sampling the plurality of data points, and the second data point being a data point among the plurality of data points that has a preset positional relationship with the first data point;

[0008] Obtaining a signal offset data set according to a preset signal offset parameter and a second signal data set;

[0009] Obtaining weight parameters based on the signal offset data set, the second signal data set, and the first signal data set; the weight parameters are used to obtain downsampled data points for processing;

[0010] Based on the weight parameters, image reconstruction processing is performed on the magnetic resonance signal data.

[0011] In one embodiment, obtaining a signal offset data set according to a preset signal offset parameter and a second signal data set includes:

[0012] Obtaining signal offset data corresponding to each second signal data according to the signal offset parameter and each second signal data in the second signal data set;

[0013] A signal offset data set is obtained according to each signal offset data.

[0014] In one embodiment, obtaining a weight parameter according to the signal offset data set, the second signal data set, and the first signal data set includes:

[0015] Obtaining a recombined signal data set according to the signal offset data set and the second signal data set;

[0016] A weight parameter is obtained using the reorganized signal data set and the first signal data set.

[0017] In one embodiment, the signal offset parameter is a linear phase parameter; the number of the linear phase parameter is at least one;

[0018] Before obtaining the signal offset data set according to the preset signal offset parameter and the second signal data set, the method further includes:

[0019] Determining at least one linear phase parameter according to a downsampling multiple of the plurality of data points;

[0020] According to the preset signal offset parameter and the second signal data set, a signal offset data set is obtained, including:

[0021] A signal offset data set is obtained based on the at least one linear phase parameter and the second signal data set.

[0022] In one embodiment, the first signal data set is a first signal data matrix; the first signal data matrix is ​​obtained by vertically arranging the plurality of first signal data; the second signal data set is a second signal data matrix; the elements of each row in the second signal data matrix are the second signal data corresponding to each first signal data;

[0023] Obtaining weight parameters according to the signal offset data set, the second signal data set, and the first signal data set, including:

[0024] Obtaining a signal offset data matrix according to the signal offset parameter and the second signal data matrix;

[0025] Obtain a first recombined signal data matrix according to the signal offset data matrix and the second signal data matrix;

[0026] Based on the first signal data matrix, zero padding is performed according to the number of signal offset data matrices to obtain a second recombined signal data matrix;

[0027] A weight parameter is obtained according to the first reorganized signal data matrix and the second reorganized signal data matrix.

[0028] In one embodiment, performing image reconstruction processing on magnetic resonance signal data based on the weight parameters includes:

[0029] Using weight parameters, multiple downsampled data points are processed to obtain target image reconstruction data;

[0030] Perform image reconstruction processing on the target image reconstruction data to obtain a medical image corresponding to the magnetic resonance signal data.

[0031] In a second aspect, the present application further provides a magnetic resonance imaging device, comprising:

[0032] a data set acquisition module, configured to acquire a first signal data set and a second signal data set from the magnetic resonance signal data; the magnetic resonance signal data corresponding to a plurality of data points, the first signal data set being acquired based on the magnetic resonance signal data of the first data point, and the second signal data set being acquired based on the magnetic resonance signal data of the second data point; the first data point being a data point acquired by fully sampling the plurality of data points, and the second data point being a data point among the plurality of data points that has a preset positional relationship with the first data point;

[0033] An offset set acquisition module, configured to obtain a signal offset data set based on a preset signal offset parameter and a second signal data set;

[0034] A weight parameter acquisition module is used to obtain weight parameters based on the signal offset data set, the second signal data set and the first signal data set; the weight parameters are used to obtain the data points obtained by downsampling for processing;

[0035] The imaging processing module is used to perform image reconstruction processing on the magnetic resonance signal data based on the weight parameters.

[0036] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0037] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0038] In a fifth aspect, the present application further provides a computer program product, which includes a computer program that implements the steps of the above method when executed by a processor.

[0039] The magnetic resonance imaging method, apparatus, computer device, and storage medium described above acquire a first signal data set and a second signal data set from magnetic resonance signal data; the magnetic resonance signal data corresponds to multiple data points, the first signal data set is acquired based on the magnetic resonance signal data of the first data point, and the second signal data set is acquired based on the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling the multiple data points, and the second data point is a data point in the multiple data points that has a preset positional relationship with the first data point; a signal offset data set is acquired based on a preset signal offset parameter and the second signal data set; a weight parameter is acquired based on the signal offset data set, the second signal data set, and the first signal data set; the weight parameter is used to acquire and process the downsampled data points; and image reconstruction processing is performed on the magnetic resonance signal data based on the weight parameter. The signal offset data set obtained by processing the second signal data set using the preset signal offset parameter is used to calculate the weight parameter based on the signal data processed using the signal offset parameter, thereby eliminating aliasing artifacts generated by image reconstruction, thereby improving the accuracy, stability, and quality of magnetic resonance imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A diagram of an application environment of a magnetic resonance imaging method according to an embodiment;

[0041] Figure 2 is a schematic flow chart of a magnetic resonance imaging method according to an embodiment;

[0042] Figure 3 FIG. 1 is a flow chart of steps for obtaining a signal offset data set in one embodiment;

[0043] Figure 4 Schematic diagram of a flow chart of a step of obtaining weight parameters in another embodiment;

[0044] Figure 5 is a distribution diagram of sampling points in K space in a specific embodiment;

[0045] Figure 6 is another distribution diagram of sampling points in K space in a specific embodiment;

[0046] Figure 7 Schematic diagram of a method for calculating a weight kernel in a specific embodiment;

[0047] Figure 8 is a structural block diagram of a magnetic resonance imaging device in one embodiment;

[0048] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0050] The magnetic resonance imaging method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store data that server 104 needs to process. The data storage system can be integrated with server 104 or placed on a cloud or other network server. The data storage system can store magnetic resonance signal data. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. Server 104 can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0051] In one embodiment, Figure 2 As shown, a magnetic resonance imaging method is provided, which is applied to Figure 1 The following steps are used as an example to illustrate the server in the example:

[0052] S202, obtaining a first signal data set and a second signal data set from the magnetic resonance signal data; the magnetic resonance signal data corresponds to multiple data points, the first signal data set is obtained based on the magnetic resonance signal data of the first data point, and the second signal data set is obtained based on the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling the multiple data points, and the second data point is a data point among the multiple data points that has a preset positional relationship with the first data point.

[0053] The first data point can be any fully sampled data point. For example, if the central region of K space is fully sampled, then the data point in the fully sampled central region can be used as the first data point. The second data point can be any fully sampled data point. The second data point can be a data point within a preset range of the first data point. For example, if the central region of K space is fully sampled, and a data point in the central region is used as the first data point, then the data points within the preset range centered on the first data point can be used as the second data point. The first signal data set can be obtained based on the magnetic resonance signal data of the first data point. For example, if the central region of K space is fully sampled, then all data points in the central region can be used as the first data point. The magnetic resonance signal data of multiple first data points form the first signal data set. The second signal data set can be obtained based on the magnetic resonance signal data of the second data point. For example, one first data point can correspond to multiple second data points. The magnetic resonance signal data of the multiple second data points can form a second signal data subset corresponding to the first data point. The multiple first data points can each correspond to multiple second signal data subsets. The second signal data set can be obtained from the multiple second signal data subsets.

[0054] Exemplarily, both the first signal data set and the second signal data set can be matrices, and weight parameters can be obtained by calculations between matrices. Signal data can be generated for the area to be imaged, and the generated signal data can be collected using a magnetic resonance coil to obtain magnetic resonance signal data, and the collected magnetic resonance signal data can be filled into the K space. There are multiple data points in the K space, and the multiple data points in the K space can be fully sampled and downsampled respectively. The small-range fully sampled calibration data is used as a benchmark for recovering the uncollected downsampled data. The weight kernel for recovering the downsampled data can be obtained by fitting the fully sampled magnetic resonance signal data. The first signal data set and the second signal data set can be obtained from the magnetic resonance signal data; the first signal data set and the second signal data set are used to perform data fitting calculations to obtain the weight kernel (weight parameters) for obtaining the unsampled data.

[0055] S204: Obtain a signal offset data set according to a preset signal offset parameter and the second signal data set.

[0056] The signal offset parameter may be a parameter that causes an offset in the magnetic resonance signal data during image reconstruction of the magnetic resonance signal data in K space. For example, the signal offset parameter may be obtained based on a displacement parameter of the magnetic resonance signal data in the reconstructed image. The displacement of the magnetic resonance signal data in the reconstructed image may be understood as the magnetic resonance signal data appearing at a non-corresponding position in the reconstructed image.

[0057] Exemplarily, the second signal data set is processed using preset signal offset parameters to obtain a signal offset data set. In this manner, after data processing using the signal offset parameters, displacement of the magnetic resonance signal data on the image can be eliminated, thereby eliminating aliasing artifacts generated by image reconstruction, thereby improving the accuracy, stability, and quality of magnetic resonance imaging.

[0058] S206 , obtaining weight parameters according to the signal offset data set, the second signal data set, and the first signal data set; the weight parameters are used to obtain down-sampled data points for processing.

[0059] The weight parameters can be used to restore the parameters of the downsampled data. For example, the weight parameters can be used to process the magnetic resonance signal data of the sampled points in the downsampled region to obtain the magnetic resonance signal data of the unsampled points.

[0060] For example, the signal offset data set and the second signal data set can be processed to obtain a new signal data set. The new signal data set and the first signal data set can then be used to calculate weight parameters. Thus, by obtaining weight parameters, rapid calculation of magnetic resonance signal data for unsampled points in the downsampled region can be achieved, reducing the workload of full sampling and thereby improving the efficiency of magnetic resonance imaging.

[0061] S208 , performing image reconstruction processing on the magnetic resonance signal data based on the weight parameters.

[0062] For example, the weight parameters can be used to process the magnetic resonance signal data of the sampled points in the down-sampling area to obtain the magnetic resonance signal data of the unsampled points in the down-sampling area. In this way, all the magnetic resonance signal data in the K space can be obtained, and the magnetic resonance signal data in the K space can be used to perform data image reconstruction to obtain an image corresponding to the magnetic resonance imaging.

[0063] In this embodiment, a first signal data set and a second signal data set are obtained from magnetic resonance signal data; the magnetic resonance signal data corresponds to multiple data points, the first signal data set is obtained based on the magnetic resonance signal data of the first data point, and the second signal data set is obtained based on the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling the multiple data points, and the second data point is a data point in the multiple data points that has a preset positional relationship with the first data point; a signal offset data set is obtained based on a preset signal offset parameter and the second signal data set; a weight parameter is obtained based on the signal offset data set, the second signal data set, and the first signal data set; the weight parameter is used to obtain and process the downsampled data points; and image reconstruction processing is performed on the magnetic resonance signal data based on the weight parameter. After processing the second signal data set using the preset signal offset parameter, the signal offset data set is obtained. The weight parameter is calculated based on the signal data processed using the signal offset parameter, thereby eliminating aliasing artifacts generated by image reconstruction, thereby improving the accuracy, stability, and quality of magnetic resonance imaging.

[0064] In one embodiment, Figure 3 As shown, according to the preset signal offset parameter and the second signal data set, a signal offset data set is obtained, including:

[0065] S302, obtaining signal offset data corresponding to each second signal data according to the signal offset parameter and each second signal data in the second signal data set;

[0066] S304: Obtain a signal offset data set according to each signal offset data.

[0067] The second signal data refers to the signal data included in the second signal data set, and the signal offset data refers to the signal data included in the signal offset data set.

[0068] Exemplarily, the second signal data set may be obtained from multiple second signal data subsets, each of which may contain multiple pieces of second signal data. Using a signal offset parameter, each piece of second signal data in the second signal data subset is processed to obtain respective pieces of signal offset data corresponding to each piece of second signal data. Furthermore, respective signal offset data subsets corresponding to each piece of second signal data subset may be obtained. A signal offset data set is formed based on the multiple signal offset data subsets.

[0069] In this embodiment, by utilizing the signal offset parameter, data processing is performed on each second signal data in the second signal data set, which can improve the accuracy of processing the second signal data set and improve the accuracy of eliminating aliasing artifacts generated by image reconstruction, thereby further improving the stability and quality of magnetic resonance imaging.

[0070] In one embodiment, obtaining weight parameters according to the signal offset data set, the second signal data set, and the first signal data set includes:

[0071] Obtaining a recombined signal data set according to the signal offset data set and the second signal data set;

[0072] A weight parameter is obtained using the reorganized signal data set and the first signal data set.

[0073] The recombined signal data set refers to a signal data set obtained by combining the signal offset data set and the second signal data set.

[0074] For example, the first signal data set may be a first signal data matrix. The signal offset data set may be a signal offset data matrix, and the second signal data set may be a second signal data matrix. By combining the signal offset data matrix and the second signal data matrix, a recombined signal data matrix may be obtained. The weight parameters may be calculated using the recombined signal data matrix and the first signal data matrix. For example, the first signal data matrix may be obtained by performing a fitting calculation using the recombined signal data matrix and the weight parameters. If the first signal data matrix and the recombined signal data matrix are known, the weight parameters may be calculated.

[0075] In this embodiment, the recombined signal data set obtained by combining the signal offset data set and the second signal data set is calculated with the first signal data set to obtain a weight parameter. The weight parameter can be obtained based on the magnetic resonance signal data after eliminating aliasing artifacts, thereby improving the accuracy of the weight parameter.

[0076] In one embodiment, the signal offset parameter is a linear phase parameter; the number of the linear phase parameter is at least one;

[0077] Before obtaining the signal offset data set according to the preset signal offset parameter and the second signal data set, the method further includes:

[0078] Determining at least one linear phase parameter according to a downsampling multiple of the plurality of data points;

[0079] According to the preset signal offset parameter and the second signal data set, a signal offset data set is obtained, including:

[0080] A signal offset data set is obtained based on the at least one linear phase parameter and the second signal data set.

[0081] The linear phase parameter may be a linear phase term in the K-space domain (or frequency domain).

[0082] Exemplarily, at least one linear phase term can be obtained based on the downsampling multiple in the downsampling process, and each second signal data in the second signal data set can be processed using the at least one linear phase term. For example, a multiplication process can be performed to obtain a signal offset data set.

[0083] In this embodiment, the linear phase parameter is determined by using the downsampling multiple, which can eliminate errors generated in the process of restoring the downsampled magnetic resonance signal data, thereby eliminating aliasing artifacts generated by image reconstruction and improving the stability and quality of magnetic resonance imaging.

[0084] In one embodiment, Figure 4 As shown, the first signal data set is a first signal data matrix; the first signal data matrix is ​​obtained by vertically arranging multiple first signal data; the second signal data set is a second signal data matrix; the elements of each row in the second signal data matrix are second signal data corresponding to each first signal data;

[0085] Obtaining weight parameters according to the signal offset data set, the second signal data set, and the first signal data set, including:

[0086] S402, obtaining a signal offset data matrix according to the signal offset parameter and the second signal data matrix;

[0087] S404, obtaining a first recombined signal data matrix according to the signal offset data matrix and the second signal data matrix;

[0088] S406, padding the first signal data matrix with zeros according to the number of signal offset data matrices to obtain a second recombined signal data matrix;

[0089] S408: Obtain weight parameters according to the first reorganized signal data matrix and the second reorganized signal data matrix.

[0090] The second signal data matrix can be obtained by using multiple second signal data subsets. The first signal data matrix can be obtained by using multiple first signal data. For example, the magnetic resonance signal data of any first data point in the central full sampling area in the K space is obtained by using T j Indicates that j = 1...N, where N is the total number of data points in the entire sampling area. In addition, {S j，m} represents the second signal data subset, which includes the magnetic resonance signal data of all second data points within the kernel calculation range around the j-th data point, including data obtained from different magnetic resonance coil channels, but excluding the first data point j itself. The value range of m is from 1 to M (M is the number of all second data points within the kernel calculation range after excluding the first data point j).

[0091] For example, the second signal data matrix can be multiplied using linear phase parameters to obtain a signal offset data matrix. The signal offset data matrix and the second signal data matrix are recombined to obtain a first recombined signal data matrix. Based on the first signal data matrix, zero padding is performed according to the number of signal offset data matrices to obtain a second recombined signal data matrix. Weight parameters are calculated using the first and second recombined signal data matrices to obtain the weight parameters.

[0092] In this embodiment, weight parameters are obtained by calculating and processing a first recombined signal data matrix obtained by combining the signal offset data matrix and the second signal data matrix, and a second recombined signal data matrix obtained by zero-padding based on the first signal data matrix. The weight parameters can be obtained based on the magnetic resonance signal data after eliminating aliasing artifacts, thereby improving the accuracy of the weight parameters.

[0093] In a specific embodiment, for example, {S j，m} can be used to form a row vector [S j，1 ,S j，2 ,…S j，m ,…,S j，M ], and then move the position of the target point j in the central full sampling area so that it traverses all points in the calibration area, forming a series of row vectors. Arrange all the row vectors vertically to obtain a matrix, which is recorded as A.

[0094]

[0095] At the same time, all the above T j Arrange them vertically to form a column vector, denoted as b.

[0096]

[0097] Each element S in the matrix A j，m Multiplying by the linear phase term gives Sv j，m , Sv j，m Form a virtual matrix A′.

[0098]

[0099]

[0100] in, is the linear phase term, R is the downsampling multiple, r is an integer from 1 to R-1, and k is the index value of the first data point in K space.

[0101] By adding an additional linear phase to the magnetic resonance signal data in K space, Sv is formed. j,m , and then by Sv j,m The generated image is the same as the original signal S j,m The difference in the generated images is that the images form a certain displacement in the preset direction. j,m The position of the image displacement formed by the data is exactly the position where the aliasing artifact appears, thereby eliminating the aliasing artifact and improving the stability and quality of magnetic resonance imaging.

[0102] When calculating the weight parameters, we can first combine the original calculation matrix A with one or more virtual matrices A′ in the longitudinal direction to form a larger matrix, and at the same time expand the original matrix b by filling it with zeros. By solving the new linear equation system (5), we can obtain the optimized weight kernel W′. After obtaining the optimized weight kernel W′, in the next step of data synthesis, the weight kernel W′ is applied to the downsampled data to synthesize more accurate and complete K-space data.

[0103]

[0104] In one embodiment, performing image reconstruction processing on magnetic resonance signal data based on the weight parameters includes:

[0105] Using weight parameters, multiple downsampled data points are processed to obtain target image reconstruction data;

[0106] Perform image reconstruction processing on the target image reconstruction data to obtain a medical image corresponding to the magnetic resonance signal data.

[0107] The target image reconstruction data may be complete magnetic resonance signal data in the K space.

[0108] Exemplarily, the weight parameters are used to process the sampled magnetic resonance signal data in the downsampling area to obtain the magnetic resonance signal data of the unsampled points, so that the complete magnetic resonance signal data in the K space can be obtained. The complete magnetic resonance signal data in the K space is subjected to image reconstruction processing, for example, the magnetic resonance signal data in the K space is subjected to inverse Fourier transform processing to obtain the medical image corresponding to the magnetic resonance signal data.

[0109] In this embodiment, the weight parameters are used to acquire the magnetic resonance signal data of the down-sampling region, which can improve the sampling efficiency and thus improve the efficiency of magnetic resonance imaging.

[0110] In a specific embodiment, Figure 5 As shown in FIG, the K-space domain-based reconstruction technology SPIRiT is used as an example to perform parallel image reconstruction by downsampling by a factor of 2. The magnetic resonance signal data used for reconstructing the K-space is downsampled according to a specific pattern to speed up the sampling speed. Figure 5 and Figure 6 A schematic diagram showing the distribution of fully sampled calibration data and downsampled data points. Figure 5 The fully sampled calibration area and downsampled data are collected separately. Figure 6 The fully sampled calibration data and downsampled data in the are scanned together in a merged manner.

[0111] Figure 5 In the figure, solid points represent collected data points, and hollow points represent uncollected data points. The calibration data and downsampled data were collected in two batches. Figure 5 (a) is the calibration data distribution of the full sampling in the central area of ​​K space; Figure 5 (b) is the distribution of downsampled data in k-space. Figure 6 In the figure, solid dots represent collected data points, and hollow dots represent uncollected data points. The calibration data and downsampled data are combined into a single acquisition. The fully sampled calibration data is within the dashed box at the center of K in the figure.

[0112] In a specific embodiment, SPIRiT is a parallel reconstruction method based on K-space. The SPIRiT reconstruction method can be roughly divided into two steps for image reconstruction. First, the fully sampled calibration data of a small area in the central region of the acquired K-space is used as a reference for recovering the unacquired data. The weight kernel (weight parameters) that can fit the unsampled data is calculated. Then, in the next step of data synthesis, this weight kernel is applied to the downsampled data to synthesize the complete K-space data.

[0113] The weight kernel can be calculated as Figure 7 As shown, Figure 7 The dotted box defines the range of data used to calculate the weight parameters. Figure 5 The calculation range of the 3×3 kernel is shown. The center point in the dotted box can be used as the first data point, and the surrounding points in the dotted box except the center point can be used as the second data points.

[0114] For the magnetic resonance signal data of any first data point in the central full sampling area in the K space, use T jIndicates that j = 1...N, where N is the total number of data points in the entire sampling area. In addition, {S j，m} represents the second signal data subset, which includes the magnetic resonance signal data of all second data points within the kernel calculation range around the j-th data point, including data obtained from different magnetic resonance coil channels, but excluding the first data point j itself. The value range of m is from 1 to M (M is the number of all second data points within the kernel calculation range after excluding the first data point j).

[0115] {S j，m} can be used to form a row vector [S j，1 ,S j，2 ,…S j，m ,…,S j，M ], and then move the position of the target point j in the central full sampling area so that it traverses all points in the calibration area, forming a series of row vectors. Arrange all the row vectors vertically to obtain a matrix, which is recorded as A.

[0116]

[0117] At the same time, all the above T j Arrange them vertically to form a column vector, denoted as b.

[0118]

[0119] Each element S in the matrix A j，m Multiplying by the linear phase term gives Sv j，m , Sv j，m Form a virtual matrix A′.

[0120]

[0121]

[0122] in, is the linear phase term, R is the downsampling multiple, r is an integer from 1 to R-1, and k is the index value of the first data point in K space.

[0123] By adding an additional linear phase to the magnetic resonance signal data in K space, Sv is formed. j,m , and then by Sv j,m The generated image is the same as the original signal S j,m The difference in the generated images is that the images form a certain displacement in the preset direction. j,m The position of the image displacement formed by the data is exactly the position where the aliasing artifact appears, thereby eliminating the aliasing artifact and improving the stability and quality of magnetic resonance imaging.

[0124] When calculating the weight parameters, we can first combine the original calculation matrix A with one or more virtual matrices A′ in the longitudinal direction to form a larger matrix, and at the same time expand the original matrix b by filling it with zeros. By solving the new linear equation system (5), we can obtain the optimized weight kernel W′. After obtaining the optimized weight kernel W′, in the next step of data synthesis, the weight kernel W′ is applied to the downsampled data to synthesize more accurate and complete K-space data.

[0125]

[0126] In one embodiment, a magnetic resonance imaging method is provided, comprising the following steps:

[0127] 1. The calibration data of the full sampling of the central area of ​​K space are arranged according to formulas (1) and (2) to form matrix A and column vector b.

[0128] 2. Based on the matrix A, one or more virtual matrices A′ are calculated according to formula (3).

[0129] 3. Combine A with one or more A′ to form a larger matrix and expand the b vector by filling it with zeros.

[0130] 4. Calculate the weight kernel W′ according to formula (5).

[0131] 5. Apply the weight kernel to the downsampled data to fit the complete K-space data.

[0132] 6. Reconstruct the complete K-space data to obtain an image.

[0133] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0134] Based on the same inventive concept, embodiments of the present application further provide a magnetic resonance imaging apparatus for implementing the aforementioned magnetic resonance imaging method. The solution provided by this apparatus is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more magnetic resonance imaging apparatus embodiments provided below can be found in the above-described limitations of the magnetic resonance imaging method and will not be further elaborated here.

[0135] In one embodiment, Figure 8 As shown, a magnetic resonance imaging apparatus 800 is provided, comprising: a data set acquisition module 810, an offset set acquisition module 820, a weight parameter acquisition module 830 and an imaging processing module 840, wherein:

[0136] The data set acquisition module 810 is used to obtain a first signal data set and a second signal data set from the magnetic resonance signal data; the magnetic resonance signal data corresponds to multiple data points, the first signal data set is obtained based on the magnetic resonance signal data of the first data point, and the second signal data set is obtained based on the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling the multiple data points, and the second data point is a data point among the multiple data points that has a preset positional relationship with the first data point.

[0137] The offset set acquisition module 820 is configured to obtain a signal offset data set according to a preset signal offset parameter and the second signal data set.

[0138] The weight parameter acquisition module 830 is used to obtain weight parameters according to the signal offset data set, the second signal data set and the first signal data set; the weight parameters are used to obtain the data points obtained by downsampling for processing.

[0139] The imaging processing module 840 is configured to perform image reconstruction processing on the magnetic resonance signal data based on the weight parameters.

[0140] In one embodiment, the offset set acquisition module includes an offset data acquisition unit and an offset data set acquisition unit.

[0141] The offset data acquisition unit is configured to obtain signal offset data corresponding to each second signal data according to the signal offset parameter and each second signal data in the second signal data set. The offset data set acquisition unit is configured to obtain a signal offset data set according to each signal offset data.

[0142] In one embodiment, the weight parameter acquisition module includes a recombinant set acquisition unit and a weight acquisition unit.

[0143] The recombined signal data set acquisition unit is used to obtain a recombined signal data set according to the signal offset data set and the second signal data set. The weight acquisition unit is used to obtain a weight parameter using the recombined signal data set and the first signal data set.

[0144] In one embodiment, the signal offset parameter is a linear phase parameter; the number of the linear phase parameter is at least one; and the apparatus further includes a linear phase module.

[0145] The linear phase module is used to determine at least one linear phase parameter according to the downsampling multiple of the multiple data points. The offset data set acquisition unit is used to obtain a signal offset data set according to the at least one linear phase parameter and the second signal data set.

[0146] In one embodiment, the first signal data set is a first signal data matrix; the first signal data matrix is ​​obtained based on multiple first signal data and arranged vertically; the second signal data set is a second signal data matrix; the elements of each row in the second signal data matrix are the second signal data corresponding to each first signal data.

[0147] The weight parameter acquisition module includes a signal offset matrix unit, a first reorganization foot matrix unit and a second reorganization foot matrix unit.

[0148] The signal offset matrix unit is configured to obtain a signal offset data matrix based on the signal offset parameter and the second signal data matrix. The first reorganization matrix unit is configured to obtain a first reorganized signal data matrix based on the signal offset data matrix and the second signal data matrix. The second reorganization matrix unit is configured to perform zero padding based on the first signal data matrix according to the number of signal offset data matrices to obtain a second reorganized signal data matrix. The weight acquisition unit is configured to obtain weight parameters based on the first reorganized signal data matrix and the second reorganized signal data matrix.

[0149] In one embodiment, the imaging processing module includes a target data acquisition unit and an image reconstruction unit.

[0150] The target data acquisition unit is used to process the multiple downsampled data points using weight parameters to obtain target image reconstruction data. The image reconstruction unit is used to perform image reconstruction processing on the target image reconstruction data to obtain a medical image corresponding to the magnetic resonance signal data.

[0151] Each module in the aforementioned magnetic resonance imaging apparatus may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0152] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store magnetic resonance signal data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a magnetic resonance imaging method is implemented.

[0153] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0154] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0155] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0156] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0157] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0158] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0159] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A magnetic resonance imaging method, characterized in that: The method comprises: Obtaining a first signal data matrix and a second signal data matrix from magnetic resonance signal data; the magnetic resonance signal data corresponds to a plurality of data points, the first signal data matrix is ​​obtained based on the magnetic resonance signal data of the first data point, and the second signal data matrix is ​​obtained based on the magnetic resonance signal data of the second data point; the first data point is a data point obtained by sampling all of the plurality of data points, and the second data point is a data point among the plurality of data points that has a preset positional relationship with the first data point; Determine at least one value among positive integers smaller than the downsampling multiple, and obtain a ratio of the at least one value to the downsampling multiple; For any ratio, take the ratio, -2, π 、 i and k The product of is the exponential and takes the natural constant e as the base to obtain the corresponding linear phase parameter; i is an imaginary number, k Obtaining the first data point index value in K space; obtaining at least one signal offset data matrix according to a product of at least one linear phase parameter and the second signal data matrix; Obtaining a first recombined signal data matrix according to the signal offset data matrix and the second signal data matrix; Based on the first signal data matrix, padding the matrix with zeros according to the number of the signal offset data matrix to obtain a second recombined signal data matrix; Obtaining weight parameters based on the first recombined signal data matrix and the second recombined signal data matrix; the weight parameters are used to obtain down-sampled data points for processing; Image reconstruction processing is performed on the magnetic resonance signal data based on the weight parameters.

2. The method according to claim 1, characterized in that The obtaining of at least one signal offset data matrix according to the product of at least one linear phase parameter and the second signal data matrix comprises: For any linear phase parameter, obtaining signal offset data corresponding to each second signal data according to a product of the linear phase parameter and each second signal data in the second signal data matrix; According to the signal offset data corresponding to each second signal data, a signal offset data matrix corresponding to the linear phase parameter is obtained.

3. The method according to claim 1, characterized in that The downsampling multiple is a downsampling multiple for downsampling the plurality of data points.

4. The method according to claim 1, wherein The first signal data matrix is ​​obtained by arranging a plurality of first signal data in a vertical arrangement; the elements of each row in the second signal data matrix are second signal data corresponding to each first signal data.

5. The method according to claim 1, wherein The performing image reconstruction processing on the magnetic resonance signal data based on the weight parameter includes: Using the weight parameters, a plurality of downsampled data points are processed to obtain target image reconstruction data; Perform image reconstruction processing on the target image reconstruction data to obtain a medical image corresponding to the magnetic resonance signal data.

6. A magnetic resonance imaging apparatus, characterized in that: The device comprises: a data set acquisition module, configured to acquire a first signal data matrix and a second signal data matrix from magnetic resonance signal data; the magnetic resonance signal data corresponding to a plurality of data points, the first signal data matrix being acquired based on the magnetic resonance signal data of the first data point, and the second signal data matrix being acquired based on the magnetic resonance signal data of the second data point; the first data point being a data point acquired by sampling all of the plurality of data points, and the second data point being a data point among the plurality of data points that has a preset positional relationship with the first data point; The linear phase module is used to determine at least one value among the positive integers less than the downsampling multiple, and obtain the ratio of at least one value to the downsampling multiple; for any ratio, the ratio, -2, π 、 i and k The product of is the exponential and takes the natural constant e as the base to obtain the corresponding linear phase parameter; i is an imaginary number, k Obtaining the first data point index value in K space; an offset set acquisition module, configured to obtain at least one signal offset data matrix according to a product of at least one linear phase parameter and the second signal data matrix; a weight parameter acquisition module, configured to obtain a first recombined signal data matrix based on the signal offset data matrix and the second signal data matrix; perform zero padding based on the first signal data matrix according to the number of the signal offset data matrix to obtain a second recombined signal data matrix; and obtain weight parameters based on the first recombined signal data matrix and the second recombined signal data matrix; the weight parameters are used to obtain data points obtained by downsampling for processing; An imaging processing module is used to perform image reconstruction processing on the magnetic resonance signal data based on the weight parameters.

7. The device according to claim 6, characterized in that The offset set acquisition module includes an offset data acquisition unit and an offset data set acquisition unit; an offset data acquiring unit, configured to obtain, for any linear phase parameter, signal offset data corresponding to each second signal data according to a product of the linear phase parameter and each second signal data in the second signal data matrix; The offset data set acquisition unit is configured to obtain a signal offset data matrix corresponding to the linear phase parameter according to the signal offset data corresponding to each second signal data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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