CN116047388A - Magnetic resonance imaging method, apparatus, computer device and storage medium - Google Patents
Magnetic resonance imaging method, apparatus, computer device and storage medium Download PDFInfo
- Publication number
- CN116047388A CN116047388A CN202211725289.4A CN202211725289A CN116047388A CN 116047388 A CN116047388 A CN 116047388A CN 202211725289 A CN202211725289 A CN 202211725289A CN 116047388 A CN116047388 A CN 116047388A
- Authority
- CN
- China
- Prior art keywords
- signal
- data
- signal data
- data set
- magnetic resonance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
- G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
- G01R33/5602—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
Abstract
The present application relates to a magnetic resonance imaging method, apparatus, computer device and storage medium. The method comprises the following steps: acquiring 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 is obtained according to the magnetic resonance signal data of the first data point, and the second signal data set is obtained according to the magnetic resonance signal data of the second data point; obtaining a signal offset data set according to a preset signal offset parameter and a second signal data set; obtaining weight parameters according to the signal offset data set, the second signal data set and the first signal data set; the weight parameter is used for acquiring and processing the data points obtained by downsampling; and carrying out image reconstruction processing on the magnetic resonance signal data based on the weight parameters. By adopting the method, the accuracy of the magnetic resonance imaging can be improved, and the stability and the quality of the magnetic resonance imaging can be improved.
Description
Technical Field
The present disclosure relates to the field of magnetic resonance technology, and in particular, to a magnetic resonance imaging method, apparatus, computer device, and storage medium.
Background
Magnetic resonance imaging is widely used in medical diagnostics. In the current magnetic resonance imaging acceleration technology, the imaging reconstruction method for the downsampled signal data can be divided into two types of parallel reconstruction methods (such as SENSE) based on an image domain and parallel reconstruction methods (such as GRAPPA and spiit) based on a K space domain according to the type of data to be reconstructed.
Parallel magnetic resonance imaging reconstruction techniques, such as GRAPPA, SPIRiT, etc., are K-space domain based magnetic resonance image reconstruction techniques, and are currently widely used in various fast magnetic resonance imaging applications. In this type of parallel imaging reconstruction technique, usually, small-range full-sampling calibration data (Calibration lines) of a central part area of the acquired K space is used as a reference for recovering the non-acquired data, a weight (Weighting Kernel) capable of fitting to obtain the non-sampled data is calculated, and the weight Kernel is applied to the downsampled data in the next data synthesis process to synthesize the complete K space data.
However, the current magnetic resonance imaging reconstruction technology has the problem that the image quality obtained by magnetic resonance imaging is low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a magnetic resonance imaging method, apparatus, computer device, and storage medium capable of improving image quality.
In a first aspect, the present application provides a magnetic resonance imaging method comprising:
acquiring 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 is obtained according to the magnetic resonance signal data of the first data point, and the second signal data set is obtained according to the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling a plurality of data points, and the second data point is a data point which has a preset position relation with the first data point in the plurality of data points;
obtaining a signal offset data set according to a preset signal offset parameter and a second signal data set;
obtaining weight parameters according to the signal offset data set, the second signal data set and the first signal data set; the weight parameter is used for acquiring and processing the data points obtained by downsampling;
and carrying out image reconstruction processing on the magnetic resonance signal data based on the weight parameters.
In one embodiment, according to a preset signal offset parameter and a second signal data set, a signal offset data set is obtained, including:
obtaining signal offset data corresponding to each second signal data according to the signal offset parameters and each second signal data in the second signal data set;
And obtaining a signal offset data set according to each signal offset data.
In one embodiment, obtaining the weight parameter according to the signal offset data set, the second signal data set and the first signal data set includes:
obtaining a recombined signal data set according to the signal offset data set and the second signal data set;
and obtaining the weight parameter by utilizing the recombined signal data set and the first signal data set.
In one embodiment, the signal offset parameter is a linear phase parameter; the number of linear phase parameters is at least one;
before the signal offset data set is obtained according to the preset signal offset parameter and the second signal data set, the method further comprises the following steps:
determining at least one linear phase parameter according to a down-sampling multiple of down-sampling the plurality of data points;
obtaining a signal offset data set according to a preset signal offset parameter and a second signal data set, wherein the signal offset data set comprises:
and obtaining a signal offset data set according to the at least one linear phase parameter and the second signal data set.
In one embodiment, the first set of signal data is a first matrix of signal data; the first signal data matrix is obtained according to a plurality of first signal data and longitudinal arrangement; 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;
Obtaining weight parameters according to the signal offset data set, the second signal data set and the first signal data set, wherein the weight parameters comprise:
obtaining a signal offset data matrix according to the signal offset 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;
zero padding is carried out according to the number of the signal offset data matrixes based on the first signal data matrixes, so that a second reconstructed signal data matrix is obtained;
and obtaining weight parameters according to the first recombined signal data matrix and the second recombined signal data matrix.
In one embodiment, performing image reconstruction processing on magnetic resonance signal data based on weight parameters includes:
processing a plurality of downsampled data points by using the weight parameters to obtain target image reconstruction data;
and performing image reconstruction processing on the target image reconstruction data to obtain medical images corresponding to the magnetic resonance signal data.
In a second aspect, the present application also provides a magnetic resonance imaging apparatus, the apparatus comprising:
the data set acquisition module is used for acquiring 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 is obtained according to the magnetic resonance signal data of the first data point, and the second signal data set is obtained according to the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling a plurality of data points, and the second data point is a data point which has a preset position relation with the first data point in the plurality of data points;
The offset set acquisition module is used for acquiring a signal offset data set according to a preset signal offset parameter and a second signal data set;
the weight parameter acquisition module is used for acquiring weight parameters according to the signal offset data set, the second signal data set and the first signal data set; the weight parameter is used for acquiring and processing the data points obtained by downsampling;
and the imaging processing module is used for carrying out image reconstruction processing on the magnetic resonance signal data based on the weight parameters.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method described above.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described above.
The magnetic resonance imaging method, the magnetic resonance imaging device, the computer equipment and the storage medium acquire a first signal data set and a second signal data set from magnetic resonance signal data; the magnetic resonance signal data corresponds to a plurality of data points, the first signal data set is obtained according to the magnetic resonance signal data of the first data point, and the second signal data set is obtained according to the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling a plurality of data points, and the second data point is a data point which has a preset position relation with the first data point in the plurality of data points; obtaining a signal offset data set according to a preset signal offset parameter and a second signal data set; obtaining weight parameters according to the signal offset data set, the second signal data set and the first signal data set; the weight parameter is used for acquiring and processing the data points obtained by downsampling; and carrying out image reconstruction processing on the magnetic resonance signal data based on the weight parameters. The second signal data set is processed through the preset signal offset parameters, and the obtained signal offset data set calculates the weight parameters according to the signal data processed through the signal offset parameters, so that aliasing artifacts generated by image reconstruction can be eliminated, the accuracy of magnetic resonance imaging can be improved, and the stability and quality of the magnetic resonance imaging can be improved.
Drawings
FIG. 1 is a diagram of an application environment of a magnetic resonance imaging method in one embodiment;
FIG. 2 is a flow chart of a method of magnetic resonance imaging in one embodiment;
FIG. 3 is a flow chart illustrating a step of obtaining a signal offset data set in one embodiment;
FIG. 4 is a flowchart illustrating a step of obtaining weight parameters according to another embodiment;
FIG. 5 is a distribution diagram of sampling points in K space in one embodiment;
FIG. 6 is another distribution of sample points in K space in one embodiment;
FIG. 7 is a schematic diagram of a method of computing a weight kernel in one embodiment;
figure 8 is a block diagram of a magnetic resonance imaging apparatus in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The magnetic resonance imaging method provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The data storage system may store magnetic resonance signal data. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, a magnetic resonance imaging method is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
s202, acquiring a first signal data set and a second signal data set from magnetic resonance signal data; the magnetic resonance signal data corresponds to a plurality of data points, the first signal data set is obtained according to the magnetic resonance signal data of the first data point, and the second signal data set is obtained according to the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling a plurality of data points, and the second data point is a data point which has a preset position relation with the first data point in the plurality of data points.
The first data point may be any data point that is fully sampled, for example, a central region in the K space may be fully sampled, and then the data point that is fully sampled in the central region may be used as the first data point. The second data point may be a data point that is fully sampled, and the second data point may be a data point within a preset range of the first data point, for example, a data point in a central region in K space is fully sampled, and a certain data point is taken as the first data point in the central region, and then the first data point is taken as the center, and a data point within the preset range in which the first data point is taken as the center may be taken as the second data point. The first signal data set may be obtained from magnetic resonance signal data of the first data points, for example, the central region in the K space is fully sampled, and then the data points of the central region may be used as the first data points, and the first signal data set is formed by using magnetic resonance signal data of a plurality of first data points. The second signal data set may be obtained according to magnetic resonance signal data of the second data points, for example, one first data point may correspond to a plurality of second data points, and magnetic resonance signal data of the plurality of second data points may form a second signal data subset corresponding to the first data point; the plurality of first data points may correspond to a plurality of second signal data subsets, respectively, from which a second signal data set may be derived.
The first signal data set and the second signal data set may be matrices, and the weight parameter may be obtained through calculation between the matrices. The magnetic resonance signal data can be obtained by generating signal data for a region to be imaged, acquiring the generated signal data by using a magnetic resonance coil, and filling the acquired magnetic resonance signal data into a K space. The K space is provided with a plurality of data points, the data points in the K space can be respectively subjected to full sampling and downsampling, and the small-range full sampling calibration data is used as a reference for recovery of the undersampled data which is not acquired. The weight kernel for recovering the downsampled data may be calculated by fitting the fully sampled magnetic resonance signal data. The first signal data set and the second signal data set may be acquired from the magnetic resonance signal data; the weight kernel (weight parameter) for acquiring the non-sampled data can be obtained by performing data fitting calculation processing using the first signal data set and the second signal data set.
S204, obtaining a signal offset data set according to the preset signal offset parameter and the second signal data set.
The signal offset parameter may be a parameter that causes the magnetic resonance signal data to be offset in the process of performing image reconstruction on the magnetic resonance signal data in the K space; the signal offset parameter may be derived, for example, from 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 non-corresponding position of the magnetic resonance signal data in the reconstructed image.
The second signal data set is illustratively subjected to data processing by using a preset signal offset parameter to obtain a signal offset data set. Therefore, after the data processing is carried out through the signal offset parameters, the displacement of the magnetic resonance signal data on the image is eliminated, so that the aliasing artifact generated by image reconstruction can be eliminated, the accuracy of the magnetic resonance imaging can be improved, and the stability and the quality of the magnetic resonance imaging can be improved.
S206, obtaining weight parameters according to the signal offset data set, the second signal data set and the first signal data set; the weight parameter is used for acquiring and processing the data points obtained by downsampling.
Wherein the weight parameter may be used to recover the parameters of the downsampled data. For example, the magnetic resonance signal data of the sampled points in the downsampled region may be processed with the weight parameters to obtain the magnetic resonance signal data of the non-sampled points.
For example, the signal offset data set and the second signal data set may be processed to obtain a new signal data set, and the weight parameter may be calculated using the new signal data set and the first signal data set. Therefore, by the method for acquiring the weight parameters, the rapid calculation of the magnetic resonance signal data of the non-sampling points in the downsampling region can be realized, the workload of full sampling is reduced, and the efficiency of magnetic resonance imaging can be improved.
S208, performing image reconstruction processing on the magnetic resonance signal data based on the weight parameters.
By way of example, the weight parameter may be used to perform data processing on the magnetic resonance signal data of the sampled points in the downsampling region to obtain the magnetic resonance signal data of the non-sampled points in the downsampling region, so that all the magnetic resonance signal data in the K space may be obtained, and the magnetic resonance signal data in the K space may be used to perform data image reconstruction to obtain an image corresponding to the magnetic resonance imaging.
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 a plurality of data points, the first signal data set is obtained according to the magnetic resonance signal data of the first data point, and the second signal data set is obtained according to the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling a plurality of data points, and the second data point is a data point which has a preset position relation with the first data point in the plurality of data points; obtaining a signal offset data set according to a preset signal offset parameter and a second signal data set; obtaining weight parameters according to the signal offset data set, the second signal data set and the first signal data set; the weight parameter is used for acquiring and processing the data points obtained by downsampling; and carrying out image reconstruction processing on the magnetic resonance signal data based on the weight parameters. The second signal data set is processed through the preset signal offset parameters, and the obtained signal offset data set calculates the weight parameters according to the signal data processed through the signal offset parameters, so that aliasing artifacts generated by image reconstruction can be eliminated, the accuracy of magnetic resonance imaging can be improved, and the stability and quality of the magnetic resonance imaging can be improved.
In one embodiment, as shown in fig. 3, according to a preset signal offset parameter and a second signal data set, a signal offset data set is obtained, including:
s302, obtaining signal offset data corresponding to each second signal data according to the signal offset parameters and each second signal data in the second signal data set;
s304, obtaining a signal offset data set according to each signal offset data.
Wherein the second signal data refers to signal data contained in the second signal data set. The signal offset data refers to signal data contained in the signal offset data set.
The second signal data set may be obtained from a plurality of second signal data subsets, and each second signal data subset may include a plurality of second signal data. And processing each second signal data of the second signal data subsets by using the signal offset parameters to obtain each signal offset data corresponding to each second signal data, and further obtaining each signal offset data subset corresponding to each second signal data subset. And forming a signal offset data set according to the plurality of signal offset data subsets.
In this embodiment, by performing data processing on each second signal data in the second signal data set by using the signal offset parameter, accuracy of processing the second signal data set can be improved, and accuracy of eliminating aliasing artifacts generated by image reconstruction can be improved, so that stability and quality of magnetic resonance imaging are further improved.
In one embodiment, obtaining the weight parameter from the signal offset data set, the second signal data set, and the first signal data set includes:
obtaining a recombined signal data set according to the signal offset data set and the second signal data set;
and obtaining the weight parameter by utilizing the recombined signal data set and the first signal data set.
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.
The first set of signal data may be, for example, 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. And combining the signal offset data matrix and the second signal data matrix to obtain a recombined signal data matrix. The weight parameters are calculated by using the recombined signal data matrix and the first signal data matrix, for example, by performing fitting calculation by using the recombined signal data matrix and the weight parameters, the first signal data matrix can be obtained, and the weight parameters can be calculated in the case that the first signal data matrix and the recombined signal data matrix can be known.
In this embodiment, the weight parameter is obtained by calculating the recombined signal data set obtained by combining the signal offset data set and the second signal data set with the first signal data set, so that the weight parameter can be obtained based on the magnetic resonance signal data after the aliasing artifact is eliminated, and the accuracy of the weight parameter can be improved.
In one embodiment, the signal offset parameter is a linear phase parameter; the number of linear phase parameters is at least one;
before the signal offset data set is obtained according to the preset signal offset parameter and the second signal data set, the method further comprises the following steps:
determining at least one linear phase parameter according to a down-sampling multiple of down-sampling the plurality of data points;
obtaining a signal offset data set according to a preset signal offset parameter and a second signal data set, wherein the signal offset data set comprises:
and obtaining a signal offset data set according to the at least one linear phase parameter and the second signal data set.
Wherein the linear phase parameter may be a linear phase term in the K-space domain (or frequency domain).
Illustratively, at least one linear phase term may be obtained according to a downsampling multiple in the downsampling process, and each second signal data in the second signal data set may be processed by using the at least one linear phase term, for example, product processing may be performed, to obtain a signal offset data set.
In this embodiment, the linear phase parameter is determined by using the downsampling multiple, so that an error generated in the process of recovering the downsampled magnetic resonance signal data can be eliminated, and thus, an aliasing artifact generated by image reconstruction can be eliminated, and the stability and quality of magnetic resonance imaging can be improved.
In one embodiment, as shown in fig. 4, the first set of signal data is a first matrix of signal data; the first signal data matrix is obtained according to a plurality of first signal data and longitudinal arrangement; 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;
obtaining weight parameters according to the signal offset data set, the second signal data set and the first signal data set, wherein the weight parameters comprise:
s402, obtaining a signal offset data matrix according to the signal offset parameter and the second signal data matrix;
s404, obtaining a first recombined signal data matrix according to the signal offset data matrix and the second signal data matrix;
s406, based on the first signal data matrix, carrying out zero padding according to the number of the signal offset data matrices to obtain a second reconstructed signal data matrix;
S408, obtaining weight parameters according to the first recombined signal data matrix and the second recombined signal data matrix.
Wherein the second letterThe number data matrix may be obtained by a plurality of second signal data subsets. The first signal data matrix may be obtained from a plurality of first signal data. For example, the magnetic resonance signal data of any one first data point in the central full sampling region in K space is represented by T j Meaning j= … N, N is the total number of full sample area data points. In addition { S }, use j，m And a second subset of signal data including magnetic resonance signal data of all second data points around the jth data point within the size of the kernel calculation range, including data obtained from different magnetic resonance coil channels, but excluding the j first data point itself, and M ranging from 1 to M (M being the number of all second data points within the kernel calculation range excluding the j first data point).
Illustratively, the second signal data matrix may be multiplied by a linear phase parameter to obtain a signal offset data matrix. And recombining the signal offset data matrix and the second signal data matrix to obtain a first recombined signal data matrix. And based on the first signal data matrix, carrying out zero padding according to the number of the signal offset data matrixes to obtain a second reconstructed signal data matrix. And calculating the weight parameters by using the first recombined signal data matrix and the second recombined signal data matrix, and obtaining the weight parameters through calculation.
In this embodiment, the weight parameter is obtained by performing calculation processing on the first recombined signal data matrix obtained by combining the signal offset data matrix and the second signal data matrix and the second recombined signal data matrix obtained by zero padding based on the first signal data matrix, and the weight parameter can be obtained based on the magnetic resonance signal data after the aliasing artifact is eliminated, so that the accuracy of the weight parameter can be improved.
In a particular embodiment, for example, { S j，m The row vectors S may be formed sequentially j，1 ,S j，2 ，…S j，m ，…,S j，M ]Then the position of the target point j is moved in the central full sampling area to traverse all points in the calibration area, forming a series of row directionsAll row vectors are arranged in the longitudinal direction to obtain a matrix, denoted a.
At the same time, all T are as described above j A column vector is formed in a longitudinal arrangement and denoted b.
Each element S in the matrix A j，m Multiplying the linear phase term to obtain Sv j，m ，Sv j，m A virtual matrix a' is formed.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
for the linear phase term, R is a downsampling multiple, R is an integer from 1 to R-1, and K is the index value of the acquired first data point in K space.
By thus adding an additional linear phase to the K-space magnetic resonance signal data to form a Sv j,m Then by Sv j,m The generated image is compared with the original signal S j,m The difference between the generated images is that the images form a certain displacement in a preset direction. And Sv j,m The position of the image displacement formed by the data is exactly the position where the aliasing artifact appears, so that the aliasing artifact can be eliminated, and the stability and the quality of the magnetic resonance imaging can be improved.
When the weight parameters are calculated, the original calculation matrix A and one or more virtual matrices A 'can be combined to form a larger matrix according to the longitudinal direction, meanwhile, the original matrix b is filled with 0 for expansion, and the optimized weight kernel W' can be obtained by solving a new linear equation set (5). After the optimized weight kernel W 'is obtained, the weight kernel W' is applied to the downsampled data in the next data synthesis process so as to synthesize more accurate and complete K space data.
In one embodiment, performing image reconstruction processing on magnetic resonance signal data based on weight parameters includes:
processing a plurality of downsampled data points by using the weight parameters to obtain target image reconstruction data;
and performing image reconstruction processing on the target image reconstruction data to obtain medical images corresponding to the magnetic resonance signal data.
Wherein the target image reconstruction data may be complete magnetic resonance signal data in K-space.
The sampled magnetic resonance signal data in the downsampling region is processed by using the weight parameters to obtain the magnetic resonance signal data of the non-sampling 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, and the medical image corresponding to the magnetic resonance signal data is obtained.
In this embodiment, the weight parameter is used to obtain the magnetic resonance signal data of the downsampling region, so that the sampling efficiency can be improved, and the efficiency of magnetic resonance imaging can be improved.
In a specific embodiment, as shown in fig. 5, the parallel image reconstruction is performed by 2-fold downsampling using the K-space domain based reconstruction technique SPIRiT. The magnetic resonance signal data of the K space used for reconstruction is subjected to data downsampling according to a specific mode so as to increase the sampling speed. Fig. 5 and 6 show schematic diagrams of full sampled calibration data versus down sampled data point distributions. The fully sampled calibration region and downsampled data in fig. 5 employ a discrete acquisition mode; the fully sampled calibration data and the downsampled data in fig. 6 are scanned in a combined mode.
In fig. 5, solid dots represent collected data points, and open dots represent non-collected data points. The calibration data and the downsampled data are collected in two separate acquisitions. FIG. 5 (a) is a distribution of calibration data for a full sample of the K-space center region; fig. 5 (b) is a distribution of downsampled data in k-space. In fig. 6, solid dots represent collected data points, and open dots represent non-collected data points. The calibration data and the downsampled data are combined in one acquisition. In the figure, the K center dashed box is full sampling calibration data.
In a specific embodiment, the SPIRiT is a K-space based parallel reconstruction method, which may be roughly divided into two steps for image reconstruction. Firstly, using collected small-range full-sampling calibration data of a central part area of a K space as a reference for recovery of uncollected data, calculating a weight kernel (weight parameter) capable of fitting to obtain the uncollected data, and then applying the weight kernel to downsampled data in the next data synthesis process to synthesize complete K space data.
The calculation method of the weight kernel may be as shown in fig. 7, where fig. 7 is a K-space full sampling center area, and the dashed box defines the range size of the data used for calculating the weight parameter, such as the 3×3 kernel calculation range size shown in fig. 5. The center point within the dashed box may be the first data point and the surrounding points within the dashed box except the center point may be the second data point.
For magnetic resonance signal data of any one first data point in a central full sampling area in K space, T is used j Meaning j= … N, N is the total number of full sample area data points. In addition { S }, use j，m A second subset of signal data including magnetic resonance signal data of all second data points around the jth data point within the size of the nuclear calculation range, including data obtained from different magnetic resonance coil channels, excluding the first data point itself, mThe range of values ranges from 1 to M (M being the number of all second data points within the range of the core calculation after the first data point, j, is excluded).
{S j，m The row vectors S may be formed sequentially j，1 ,S j，2 ，…S j，m ，…，S j，M ]Then the position of the target point j is moved in the central full sampling area, so that the target point j traverses all points in the calibration area to form a series of row vectors, and all the row vectors are arranged longitudinally to obtain a matrix which is marked as A.
At the same time, all T are as described above j A column vector is formed in a longitudinal arrangement and denoted b.
Each element S in the matrix A j，m Multiplying the linear phase term to obtain Sv j，m ，Sv j，m A virtual matrix a' is formed.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
for the linear phase term, R is a downsampling multiple, R is an integer from 1 to R-1, and K is the index value of the acquired first data point in K space.
By thus adding an additional linear phase to the K-space magnetic resonance signal data to form a Sv j,m Then by Sv j,m The generated image is compared with the original signal S j,m The difference between the generated images is that the images form a certain displacement in a preset direction. And Sv j,m The position of the image displacement formed by the data is exactly the position where the aliasing artifact appears, so that the aliasing artifact can be eliminated, and the stability and the quality of the magnetic resonance imaging can be improved.
When the weight parameters are calculated, the original calculation matrix A and one or more virtual matrices A 'can be combined to form a larger matrix according to the longitudinal direction, meanwhile, the original matrix b is filled with 0 for expansion, and the optimized weight kernel W' can be obtained by solving a new linear equation set (5). After the optimized weight kernel W 'is obtained, the weight kernel W' is applied to the downsampled data in the next data synthesis process so as to synthesize more accurate and complete K space data.
In one embodiment, a magnetic resonance imaging method is provided comprising the steps of:
1. the calibration data for the full sampling of the K-space center region is arranged in the manner of equations (1) and (2) to form matrix a and column vector b.
2. Based on the matrix A, one or more virtual matrices A' are calculated according to the formula (3).
3. Combining a with one or more a' forms a larger matrix and expands the b vector in a 0-filled manner.
4. And (5) calculating to obtain a weight kernel W'.
5. And applying the weight kernel to the downsampled data, and fitting to obtain complete K space data.
6. And reconstructing the complete K space data to obtain an image.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiments of the present application also provide a magnetic resonance imaging apparatus for implementing the above-mentioned related magnetic resonance imaging method. The implementation of the solution provided by the apparatus is similar to that described in the above method, so specific limitations in one or more embodiments of the magnetic resonance imaging apparatus provided below may be found in the limitations of the magnetic resonance imaging method described above, and will not be repeated here.
In one embodiment, as shown in fig. 8, there is provided a magnetic resonance imaging apparatus 800 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:
a data set acquisition module 810 for acquiring 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 is obtained according to the magnetic resonance signal data of the first data point, and the second signal data set is obtained according to the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling a plurality of data points, and the second data point is a data point which has a preset position relation with the first data point in the plurality of data points.
The offset set obtaining module 820 is configured to obtain a signal offset data set according to a preset signal offset parameter and the second signal data set.
The weight parameter obtaining module 830 is configured to obtain weight parameters according to the signal offset data set, the second signal data set, and the first signal data set; the weight parameter is used for acquiring and processing the data points obtained by downsampling.
The imaging processing module 840 is configured to perform image reconstruction processing on the magnetic resonance signal data based on the weight parameters.
In one embodiment, the offset set acquisition module includes an offset data acquisition unit and an offset data set acquisition unit.
The offset data acquisition unit is used for acquiring 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 used for acquiring a signal offset data set according to each signal offset data.
In one embodiment, the weight parameter acquisition module includes a reorganization set acquisition unit and a weight acquisition unit.
The reorganization set acquisition unit is used for acquiring a reorganization signal data set according to the signal offset data set and the second signal data set. The weight acquisition unit is used for acquiring weight parameters by utilizing the recombined signal data set and the first signal data set.
In one embodiment, the signal offset parameter is a linear phase parameter; the number of linear phase parameters is at least one; the apparatus also includes a linear phase module.
The linear phase module is used for determining at least one linear phase parameter according to the down-sampling multiple of down-sampling processing on the plurality of data points. The offset data set acquisition unit is used for obtaining a signal offset data set according to at least one linear phase parameter and the second signal data set.
In one embodiment, the first set of signal data is a first matrix of signal data; the first signal data matrix is obtained according to a plurality of first signal data and longitudinal arrangement; 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.
The weight parameter acquisition module comprises a signal offset matrix unit, a first recombinant foot matrix unit and a second recombinant foot matrix unit.
The signal offset matrix unit is used for obtaining a signal offset data matrix according to the signal offset parameter and the second signal data matrix. The first recombined foot matrix unit is used for obtaining a first recombined signal data matrix according to the signal offset data matrix and the second signal data matrix. The second reconstruction sufficient matrix unit is used for carrying out zero padding according to the number of the signal offset data matrixes based on the first signal data matrix to obtain a second reconstruction signal data matrix. The weight acquisition unit is used for acquiring weight parameters according to the first recombined signal data matrix and the second recombined signal data matrix.
In one embodiment, the imaging processing module includes a target data acquisition unit and an image reconstruction unit.
The target data acquisition unit is used for processing the plurality of downsampled data points by utilizing the weight parameters to obtain target image reconstruction data. The image reconstruction unit is used for performing image reconstruction processing on the target image reconstruction data to obtain medical images corresponding to the magnetic resonance signal data.
The various modules in the magnetic resonance imaging apparatus described above may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing magnetic resonance signal data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a magnetic resonance imaging method.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of magnetic resonance imaging, the method comprising:
acquiring 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 is obtained according to the magnetic resonance signal data of the first data point, and the second signal data set is obtained according to the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling the plurality of data points, and the second data point is a data point which has a preset position relation with the first data point in the plurality of data points;
Obtaining a signal offset data set according to a preset signal offset parameter and the second signal data set;
obtaining weight parameters according to the signal offset data set, the second signal data set and the first signal data set; the weight parameter is used for acquiring data points obtained through downsampling and processing the data points;
and carrying out image reconstruction processing on the magnetic resonance signal data based on the weight parameters.
2. The method of claim 1, wherein the obtaining the signal offset data set according to the preset signal offset parameter and the second signal data set includes:
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;
and obtaining the signal offset data set according to each signal offset data.
3. The method of claim 1, wherein the deriving weight parameters from the signal offset data set, the second signal data set, and the first signal data set comprises:
obtaining a recombined signal data set according to the signal offset data set and the second signal data set;
And obtaining the weight parameter by using the recombined signal data set and the first signal data set.
4. The method of claim 1, wherein the signal offset parameter is a linear phase parameter; the number of the linear phase parameters is at least one;
before the signal offset data set is obtained according to the preset signal offset parameter and the second signal data set, the method further comprises:
determining at least one linear phase parameter according to a down-sampling multiple of down-sampling the plurality of data points;
the obtaining the signal offset data set according to the preset signal offset parameter and the second signal data set includes:
and obtaining the signal offset data set according to the at least one linear phase parameter and the second signal data set.
5. The method of claim 1, wherein the first set of signal data is a first matrix of signal data; the first signal data matrix is obtained according to a plurality of first signal data and longitudinal arrangement; 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;
The obtaining a weight parameter according to the signal offset data set, the second signal data set and the first signal data set includes:
obtaining a signal offset data matrix according to the signal offset 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;
zero padding is carried out according to the number of the signal offset data matrixes based on the first signal data matrixes, so that a second reconstructed signal data matrix is obtained;
and obtaining the weight parameter according to the first recombined signal data matrix and the second recombined signal data matrix.
6. The method of claim 1, wherein performing image reconstruction processing on the magnetic resonance signal data based on the weight parameters comprises:
processing a plurality of down-sampled data points by utilizing the weight parameters to obtain target image reconstruction data;
and performing image reconstruction processing on the target image reconstruction data to obtain medical images corresponding to the magnetic resonance signal data.
7. A magnetic resonance imaging apparatus, the apparatus comprising:
The data set acquisition module is used for acquiring 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 is obtained according to the magnetic resonance signal data of the first data point, and the second signal data set is obtained according to the magnetic resonance signal data of the second data point; the first data point is a data point obtained by fully sampling the plurality of data points, and the second data point is a data point which has a preset position relation with the first data point in the plurality of data points;
the offset set acquisition module is used for acquiring a signal offset data set according to a preset signal offset parameter and the second signal data set;
the weight parameter acquisition module is used for acquiring weight parameters according to the signal offset data set, the second signal data set and the first signal data set; the weight parameter is used for acquiring data points obtained through downsampling and processing the data points;
and the imaging processing module is used for carrying out image reconstruction processing on the magnetic resonance signal data based on the weight parameters.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211725289.4A CN116047388A (en) | 2022-12-30 | 2022-12-30 | Magnetic resonance imaging method, apparatus, computer device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211725289.4A CN116047388A (en) | 2022-12-30 | 2022-12-30 | Magnetic resonance imaging method, apparatus, computer device and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116047388A true CN116047388A (en) | 2023-05-02 |
Family
ID=86132473
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211725289.4A Pending CN116047388A (en) | 2022-12-30 | 2022-12-30 | Magnetic resonance imaging method, apparatus, computer device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116047388A (en) |
-
2022
- 2022-12-30 CN CN202211725289.4A patent/CN116047388A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hammernik et al. | Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination | |
| CN108896943B (en) | Magnetic resonance quantitative imaging method and device | |
| Cole et al. | Unsupervised MRI reconstruction with generative adversarial networks | |
| US10489943B2 (en) | System and method for sparse image reconstruction | |
| Chang et al. | Nonlinear GRAPPA: A kernel approach to parallel MRI reconstruction | |
| US20170309019A1 (en) | System, method and computer-accessible medium for learning an optimized variational network for medical image reconstruction | |
| WO2020114329A1 (en) | Fast magnetic resonance parametric imaging and device | |
| Cole et al. | Analysis of deep complex-valued convolutional neural networks for MRI reconstruction | |
| KR20200025179A (en) | Methoed for removing ghost artifact of echo planar imaging by using neural network and apparatus therefor | |
| JPH04117944A (en) | Data processing method for nuclear magnetoresonance device | |
| CN112037298A (en) | Image reconstruction method and device, computer equipment and storage medium | |
| CN112329920A (en) | Unsupervised training method and unsupervised training device for magnetic resonance parameter imaging model | |
| CN111223046B (en) | Image super-resolution reconstruction method and device | |
| CN116047388A (en) | Magnetic resonance imaging method, apparatus, computer device and storage medium | |
| Ehrhardt | MR data acquisition and reconstruction using efficient sampling schemes | |
| CN115424038A (en) | Multi-scale image processing method, system and device and computer equipment | |
| CN113298901B (en) | Method for reconstructing magnetic resonance image in convoluted field of view, computer device and storage medium | |
| KR101340944B1 (en) | Parallel mr imaging method for radial trajectory | |
| CN114675221A (en) | Magnetic resonance gradient correction compensation factor determination method, correction method and device | |
| CN113050009A (en) | Three-dimensional magnetic resonance rapid parameter imaging method and device | |
| Sun et al. | DIR3D: Cascaded Dual-Domain Inter-Scale Mutual Reinforcement 3D Network for highly accelerated 3D MR image reconstruction | |
| CN116699490B (en) | Magnetic resonance imaging method, device, computer equipment and magnetic resonance imaging system | |
| CN115690253A (en) | Magnetic resonance image reconstruction method and image reconstruction device | |
| Feng et al. | Fast direct fourier reconstruction of radial and PROPELLER MRI data using the chirp transform algorithm on graphics hardware | |
| US6157191A (en) | Method for fast Fourier transformation for nuclear magnetic resonance signals arranged in a matrix having an arbitrary matrix size |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |