CN107092805B - Magnetic resonance parallel imaging device - Google Patents
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Abstract
The invention provides a magnetic resonance parallel imaging device, comprising: an acquisition unit for acquiring k-space data, the k-space data comprising acquisition data and calibration data; the calculation unit is used for carrying out data extraction on the acquired data to obtain extracted data, and calculating the coil combination coefficient according to the extracted data and the calibration data; the filling unit is used for filling to obtain complete k space data according to the coil merging coefficient and the acquired data; and the imaging unit is used for transforming the complete k-space data to an image domain to obtain an image. According to the technical scheme provided by the invention, the data re-refining is carried out on the convolution kernel selected in the magnetic resonance parallel acquisition and reconstruction process, so that the data volume after refining is reduced, the signal characteristic is enhanced, the refined convolution kernel is utilized to carry out data filling, and the obtained new convolution kernel is optimized compared with the original convolution kernel.
Description
The application is a division of a chinese patent application filed on 9.1.2014.in chinese patent office under the invention name of "magnetic resonance coil combination coefficient calculation method, magnetic resonance imaging method and apparatus thereof" with the application number of 201410010303.1.
Technical Field
The invention relates to the field of magnetic resonance imaging, in particular to a magnetic resonance parallel imaging device.
Background
In magnetic resonance imaging, the speed of imaging is an important criterion for measuring the imaging method. Important factors limiting the imaging speed are data acquisition, and k-space filling. In a general data acquisition mode, full k-space data is acquired, and then reconstruction is performed to obtain an image. The magnetic resonance parallel acquisition reconstruction technology is characterized in that undersampled data are filled in by utilizing a coil recombination and combination mode, and the k space data which are completely filled in are utilized for reconstruction. In this way, only a part of the k-space data can be acquired according to the requirements, and the whole k-space is not required to be acquired. Such a method can greatly increase the speed of imaging.
One of the more common parallel reconstruction methods is GRAPPA. The conventional GRAPPA algorithm is shown in fig. 1, and black solid points represent actually acquired k-space data; the white empty points are data to be filled in the undersampling process; the grey solid dots represent the calibration data for the appropriate amount of full sampling for calculating the coil merging parameters. The GRAPPA algorithm recognizes that any open dot in the figure can be represented as a linear superposition of surrounding black solid dots, which is equivalent to combining data of multiple coils. And the coil combination coefficient nij(i-th coil, j-th position, FIG. 1) can be simulated by black solid dotsAnd combining gray points to determine. After the coil combination coefficient is determined, other white hollow points can be obtained by calculation and filling according to the obtained combination coefficient and black real points, and therefore complete k-space data are obtained through reconstruction.
The coil combining coefficients may also be referred to as convolution kernels, and in the conventional method, the computation direction of the convolution kernels is added only to the phase encoding direction, and the channel direction. In order to optimize the effect, in recent years, many methods have introduced other directions such as the frequency encoding direction, and the time direction in k-t, etc. The introduction of the dimensions increases the information of a convolution kernel, but with the excessive introduction, the information can be redundant, and the calculated coefficient is still influenced due to the influence of noise; and the convolution kernel is too large, so that the coefficient to be fitted is increased, and instability of calculation is brought. And if the convolution kernel is too small, the data information is insufficient, and the calculation process is not accurate enough.
Disclosure of Invention
The invention aims to provide a magnetic resonance parallel imaging device, which solves the problems that in the process of selecting a convolution kernel, the information quantity is increased, the information is redundant, the noise is too much, the fitting coefficient is increased, the calculation is unstable, and when the selection of the convolution kernel is too small, the calculated data is insufficient, and the result is inaccurate.
The invention provides a magnetic resonance parallel imaging device, comprising:
an acquisition unit adapted to acquire k-space data, the k-space data comprising acquisition data and calibration data;
the calculation unit is suitable for carrying out data refinement on the acquired data to obtain refined data, the dimensionality of the acquired data in a refinement direction is n, the dimensionality of the refined data in the refinement direction is t, both t and n are positive integers, and t < n, and coil merging coefficients are calculated from the refined data and the calibration data;
the filling unit is suitable for filling to obtain complete k space data according to the coil merging coefficient and the acquired data;
an imaging unit adapted to transform the complete k-space data to an image domain resulting in an image;
the computing unit is adapted to:
taking a first direction vector of the collected data as a basic vector, wherein the first direction is vertical to a refining direction;
calculating a covariance matrix of the acquired data, and selecting the first t values with larger eigenvalue in the covariance matrix normalization orthogonal eigenvector to form a refinement coefficient;
and calculating by the refining coefficient to obtain refining data.
Preferably, the calculation unit is adapted to: taking a first direction vector of the collected data as a basic vector, wherein the first direction is vertical to a refining direction; classifying the acquired data according to the same data direction to obtain various first direction vectors Obk _ Sx1, Obk _ Sx2, … and Obk _ Sxi; calculating various sample first direction vector mean values Obk _ Sm1, Obk _ Sm2, …, Obk _ Smi and total sample first direction vector mean values Obk _ Sm from the various first direction vectors, and further calculating sample intra-class dispersion matrixes Obk _ Ss1, Obk _ Ss2, …, Obk _ Ssi and sample inter-dispersion matrixes Obk _ Ssb; obk _ Ssi-1 × Obk _ Ssb, the eigenvectors corresponding to the first t maximum eigenvalues are wi1, wi2, … and wit respectively, and wi1 and wi2 to wit are combined into a matrix to serve as the refined data.
Preferably, the data direction of the k-space data comprises any one or more of: a frequency encoding direction, a phase encoding direction, a channel direction, a second phase encoding direction in 3d scanning, or a direction in which the above directions are combined.
Preferably, the refinement coefficients are determined by a matrix dimension reduction method.
Preferably, the first direction is a column vector direction or a row vector direction of the acquired data.
According to the technical scheme provided by the invention, the data re-refining is carried out on the convolution kernel selected in the magnetic resonance parallel acquisition and reconstruction process, so that the data volume after refining is reduced, the signal characteristic is enhanced, the refined convolution kernel is utilized to carry out data filling, and the obtained new convolution kernel is optimized compared with the original convolution kernel. On one hand, the influence of noise can be removed to a certain degree, so that the signal characteristics are strengthened, and the accuracy and the stability of coefficient calculation are optimized; further, the choice of convolution kernel can be made easier.
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FIG. 1 is a schematic diagram of prior art coil combination coefficient calculation;
FIG. 2 is a flow chart of a method for calculating the combination coefficients of the MRI coils according to the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
The invention provides a method for calculating a merging coefficient of a magnetic resonance parallel imaging coil, fig. 2 is a flow chart thereof, please refer to fig. 2, and the method comprises the following steps:
s101, collecting k-space data, wherein the k-space data comprise collected data and calibration data;
s102, data refining is carried out on the collected data to obtain refined data, the dimensionality of the collected data in a refining direction is n, the dimensionality of the refined data in the refining direction is t, both t and n are positive integers, and t is less than n;
and S103, calculating coil merging coefficients according to the refined data and the calibration data.
Step S101 is performed first, and k-space data is acquired, and in the conventional GRAPPA technique, the acquired data is divided into acquired data and calibration data. The acquired data is acquired in a parallel acquisition manner, as shown in fig. 1, the acquired data is interlaced data in k-space, the blank part is not acquired under-acquired data, and the calibration data is data of a central region part of the k-space acquired through full acquisition. When calculating the coil combination coefficient, the relationship between the collected data and the calibration data is:
Obk_S*Cft＝Obk_Sacs [1]
where Obk _ S represents acquisition data, Obk _ Sacs represents calibration data, and Cft represents coil combining coefficients to be found in the GRAPPA algorithm.
As mentioned in the background section, the acquired k-space data typically contains a plurality of data directions, the conventional data directions being the phase encoding direction and the channel direction, and in recent years for the purpose of expanding the data, the frequency encoding direction, the time direction in k-t, and the second phase encoding direction when performing a 3d scan have also been used. The data direction of the K-space data may also be a direction in which one or more of the above-described directions are combined. For the embodiments of the present invention, the following description will introduce that the number of data directions does not affect the implementation of the present invention.
And executing S102, performing data refinement on the collected data to obtain refined data, wherein the dimensionality of the collected data in a refinement direction is n, the dimensionality of the refined data in the refinement direction is t, and both t and n are positive integers and t < n.
Here, the collected data Obk _ S needs to be refined to obtain refined data Dbk _ S, and the relationship between the collected data Obk _ S and the refined data Dbk _ S is:
Dbk_S＝Obk_S*Rft [2]
wherein Rft is a refinement coefficient.
The idea of the method for determining the refined coefficients is to use the first direction vector in the collected data Obk _ S as a base vector to perform principal component analysis and extraction, and to remove the correlation between vectors, where the first direction vector may be a column vector of each column or a row vector of each row in the collected data matrix.
The K L T algorithm is one of the more commonly used algorithms for feature extraction [ see, documents 1.M.Turk and A.Pentland, "Eigenfaces for recognitions," J.Cogn.Neurosci.3, 71-86 (1991), 2.R.Everson and L. "Sirovich, Karhenen-L even procedure for gap data" Vol.12, No.8/August 1995/J.Opt.Soc.Am.A ].
Taking the K L T algorithm as an example to perform dimensionality reduction of the collected data matrix, and further determine the refinement coefficients Rft, the implementation process is to take the column vector of the collected data Obk _ S as an extraction object, the column vector corresponds to a corresponding column of black point data in fig. 1, first, obtain the covariance matrix C _ Obk _ S of the collected data Obk _ S, then obtain the normalized orthogonal eigenvectors q (assuming n in total) of C _ Obk _ S, select the first T (T < n) q with larger eigenvalues, to form Rft, which is the refinement coefficients.
Taking the L DA algorithm as an example, the specific implementation process of the technique of the present invention is to classify the column vectors in the acquired data Obk _ S, and record the vectors with the same data direction (e.g., frequency encoding direction and channel direction) as Obk _ Sx1, Obk _ Sx2, …, Obk _ Sxi, wherein the acquisition matrices have c classes and N vectors.
Recording the mean vectors of various samples as Obk _ Sm1, Obk _ Sm2, … and Obk _ Smi, and calculating the mean vectors by the following steps:
the total sample mean Obk _ Sm is calculated as:
the intra-sample class dispersion matrix Obk _ Ss1, Obk _ Ss2, …, Obk _ Ssi is calculated by the following steps:
the sample inter-class divergence matrix Obk _ Ssb is calculated as:
in the above formulas, x represents one of i-th column vectors Ri, and c, N, i, and Ni are the same as defined above (…)TRepresented as the inverse of the matrix.
Memory wi1, wi2 … wit is the matrix Obk _ Ssi-1Obk _ Ssb feature vectors corresponding to the first t maximum feature values, combining feature vector sets obtained from all classes into a matrix, namely refining data Dbk _ S, and using a formula [2]]The refinement factor Rft can be obtained.
When the column vector is used as the basic vector, the row direction is the refining direction, the data dimension in the refining direction is n, and the data dimension of the refined data after refining is T, wherein T is less than n.
And finally, executing step S103, and calculating coil merging coefficients by using the refined data and the calibration data.
After the refinement in the previous step, the final calculation coil merging coefficient base set comprises two sets of data, namely refined data Dbk _ S and calibration data Obk _ Sacs;
the corresponding relationship between the refining data Dbk _ S and the calibration data Obk _ Sacs is as follows:
Dbk_S*Cft_new＝Obk_Sacs [7]
here the process is transformed to solve Cft _ new.
From the collected data Obk _ S and the calibration data Obk _ Sacs collected in step S101, and the corresponding equations [2] and [7], it can be known that:
Obk_S*Rft*Cft_new＝Obk_Sacs [8]
for simplicity of calculation, it is simplified here as:
Cft＝Rft*Cft_new [9]
thus, then:
Obk_S*Cft＝Obk_Sacs [10]
cft _ new in the above formula is the coil combination coefficient obtained by the technical solution of the present invention, and the process of filling up to obtain the complete k-space is performed by using the coil combination coefficient Cft _ new and the acquired data Obk _ S.
As can be seen from the above description of the technical solution of the present invention, in step S102, the dimension of the coil merging coefficient Cft _ new corresponding to the refined data Dbk _ S is controlled accordingly through the data refining process, so that when the collected data is selected in step S101, relatively large collected data can be simply selected for processing, and it is not necessary to change the size of the collected data according to the situation of the data. I.e. making the selection of the acquired data simpler, as illustrated below: the common method selects the collected data, and assumes that the collected data contains two data directions, namely nx x ny (such as 3 x 4); due to the limited amount of calibration data, nx ny is not suitable to be too large in the fitting process; on the other hand, however, we want nx ny large enough to contain as much information as possible, which requires balancing the size of the convolution kernels. Therefore, by using the method, a collected data with a larger nx ny (such as 30 x 4) can be selected, and then by using the refining method, useful information in the nx ny is extracted (the size of the data is reduced from nx ny to 3 x 4), so that the finally obtained refined data is reduced in the refining process.
The invention also provides a magnetic resonance parallel imaging method based on the magnetic resonance parallel imaging coil combination coefficient calculation method, which comprises the steps of obtaining coil combination coefficients by the magnetic resonance parallel imaging coil combination coefficient calculation method, carrying out parallel acceleration data reconstruction on collected data, filling to obtain complete k-space data, and then transforming the k-space data to an image domain to obtain a magnetic resonance image.
The invention also provides a magnetic resonance parallel imaging device corresponding to the magnetic resonance imaging method, which comprises the following steps:
an acquisition unit adapted to acquire k-space data, the k-space data comprising acquisition data and calibration data;
the calculation unit is suitable for carrying out data refinement on the acquired data to obtain refined data, the dimensionality of the acquired data in a refinement direction is n, the dimensionality of the refined data in the refinement direction is t, both t and n are positive integers, and t < n, and coil merging coefficients are calculated from the refined data and the calibration data;
the filling unit is suitable for filling to obtain complete k space data according to the coil merging coefficient and the acquired data;
an imaging unit adapted to transform the complete k-space data to an image domain resulting in an image.
Wherein the calculation unit is adapted to: taking a first direction vector of the collected data as a basic vector, wherein the first direction is vertical to a refining direction; calculating a covariance matrix of the acquired data, and selecting the first t values with larger eigenvalue in the covariance matrix normalization orthogonal eigenvector to form a refinement coefficient; and calculating by the refining coefficient to obtain refining data.
Optionally, the computing unit is adapted to: taking a first direction vector of the collected data as a basic vector, wherein the first direction is vertical to a refining direction; classifying the acquired data according to the same data direction to obtain various first direction vectors Obk _ Sx1, Obk _ Sx2, … and Obk _ Sxi; calculating various sample first direction vector mean values Obk _ Sm1, Obk _ Sm2, …, Obk _ Smi and total sample first direction vector mean values Obk _ Sm from the various first direction vectors, and further calculating sample intra-class dispersion matrixes Obk _ Ss1, Obk _ Ss2, …, Obk _ Ssi and sample inter-dispersion matrixes Obk _ Ssb; obk _ Ssi-1 × Obk _ Ssb, the eigenvectors corresponding to the first t maximum eigenvalues are wi1, wi2, … and wit respectively, and wi1 and wi2 to wit are combined into a matrix to serve as the refined data.
The specific implementation process of the magnetic resonance imaging method and the magnetic resonance imaging apparatus may refer to the implementation process of the magnetic resonance parallel imaging coil combination coefficient calculation method, and details are not repeated here.
Although the present invention has been described with respect to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A magnetic resonance parallel imaging apparatus, comprising:
an acquisition unit adapted to acquire k-space data, the k-space data comprising acquisition data and calibration data;
the calculation unit is suitable for carrying out data refinement on the acquired data to obtain refined data, the dimensionality of the acquired data in a refinement direction is n, the dimensionality of the refined data in the refinement direction is t, both t and n are positive integers, and t < n, and coil merging coefficients are calculated from the refined data and the calibration data;
the filling unit is suitable for filling to obtain complete k space data according to the coil merging coefficient and the acquired data;
an imaging unit adapted to transform the complete k-space data to an image domain resulting in an image;
the computing unit is adapted to:
taking a first direction vector of the collected data as a basic vector, wherein the first direction is vertical to a refining direction;
calculating a covariance matrix of the acquired data, and selecting the first t values with larger eigenvalue in the covariance matrix normalization orthogonal eigenvector to form a refinement coefficient;
and calculating by the refining coefficient to obtain refining data.
2. The magnetic resonance parallel imaging apparatus as set forth in claim 1, wherein the computing unit is adapted to:
taking a first direction vector of the collected data as a basic vector, wherein the first direction is vertical to a refining direction;
classifying the acquired data according to the same data direction to obtain various first direction vectors Obk _ Sx1, Obk _ Sx2, … and Obk _ Sxi;
calculating various sample first direction vector mean values Obk _ Sm1, Obk _ Sm2, …, Obk _ Smi and total sample first direction vector mean values Obk _ Sm from the various first direction vectors, and further calculating sample intra-class dispersion matrixes Obk _ Ss1, Obk _ Ss2, …, Obk _ Ssi and sample inter-dispersion matrixes Obk _ Ssb;
Obk_Ssi-1and the eigenvectors corresponding to the first t maximum eigenvalues of Obk _ Ssb are wi1, wi2, … and wit respectively, and wi1, wi2 to wit are combined into a matrix to serve as the refined data.
3. The magnetic resonance parallel imaging apparatus as set forth in claim 1, wherein the data direction of the k-space data includes any one or more of: a frequency encoding direction, a phase encoding direction, a channel direction, a second phase encoding direction in 3d scanning, or a direction in which the above directions are combined.
4. The magnetic resonance parallel imaging apparatus as set forth in claim 1, wherein the refinement coefficients are determined using a matrix dimension reduction method.
5. The magnetic resonance parallel imaging apparatus of claim 1, wherein the first direction is a column vector direction or a row vector direction of the acquired data.
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