CN114325524A - Magnetic resonance image reconstruction method, device and system and storage medium - Google Patents
Magnetic resonance image reconstruction method, device and system and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a magnetic resonance image reconstruction method, a device, a system and a storage medium, wherein the method comprises the following steps: acquiring a K space to be processed, wherein the K space comprises a plurality of sampling points and a plurality of non-sampling points; determining a fitting mode corresponding to a plurality of non-sampling points, wherein the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point; setting a fitting mode of each non-sampling point according to the fitting mode corresponding to the plurality of non-sampling points; acquiring a fitting result of each non-sampling point by using a fitting mode of each non-sampling point, wherein the K space data of the plurality of sampling points and the fitting results of the non-sampling points form a fitting K space; reconstructing the fitted K-space to acquire a magnetic resonance image. The problem that the reconstruction speed of the magnetic resonance image cannot be remarkably improved due to the fact that the fitting speed of an unsampled point is low is solved.
Description
Technical Field
The embodiment of the invention relates to the field of medical images, in particular to a magnetic resonance image reconstruction method, a device, a system and a storage medium.
Background
The magnetic resonance imaging system comprises a main magnet, a gradient coil, a radio frequency transmitting coil, a radio frequency receiving coil and an image reconstruction unit. The spin of hydrogen nuclei in the human body can be equivalent to a small magnetic needle. In the strong magnetic field provided by the main magnet, the hydrogen nuclei are converted from a disordered thermal equilibrium state to a partially cis state and a partially trans state, and the difference between the directions of the hydrogen nuclei and the main magnetic field forms a net magnetization vector. The hydrogen nuclei precess around the main magnetic field with precession frequency proportional to magnetic field strength. The gradient units generate magnetic fields whose intensity varies with spatial position for spatial encoding of the signals. The radio frequency transmitting coil is used for turning the hydrogen atomic nucleus to a transverse plane from the direction of the main magnetic field, precessing around the main magnetic field, and finally inducing a current signal in the radio frequency receiving coil so as to obtain magnetic resonance data. The image reconstruction unit is used for carrying out image reconstruction on the magnetic resonance data to obtain a magnetic resonance image.
Because the fully-acquired magnetic resonance data is particularly huge, in order to increase the acquisition speed of the magnetic resonance data, the magnetic resonance data is usually acquired in an under-acquisition mode. After the undersampled magnetic resonance data is obtained, the image reconstruction unit needs to fit all the non-sampling points in the undersampled magnetic resonance data, and then performs image reconstruction on the fitted magnetic resonance data to obtain a magnetic resonance image. Since the more the number of the unsampled points, the more time is taken for fitting the unsampled points, and the more time is taken for undersampling the fitting, the slower the image reconstruction speed of the magnetic resonance data is, and the more difficult the substantial improvement is.
In conclusion, the prior art has the problem that the reconstruction speed of the magnetic resonance image cannot be obviously improved due to the fact that the fitting speed of the non-sampling points is low.
Disclosure of Invention
The embodiment of the invention provides a magnetic resonance image reconstruction method, a device, a system and a storage medium, and solves the problem that the reconstruction speed of a magnetic resonance image cannot be obviously improved due to the fact that the fitting speed of an unsampled point is low.
In a first aspect, an embodiment of the present invention provides a magnetic resonance image reconstruction method, including:
acquiring a K space to be processed, wherein the K space comprises a plurality of sampling points and non-sampling points;
determining a fitting mode corresponding to a plurality of non-sampling points, wherein the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point;
setting a fitting mode of each non-sampling point according to the fitting mode corresponding to the plurality of non-sampling points;
acquiring a fitting result of each non-sampling point by using a fitting mode of each non-sampling point, wherein the K space data of the plurality of sampling points and the fitting results of the non-sampling points form a fitting K space;
reconstructing the fitted K-space to acquire a magnetic resonance image.
In a second aspect, an embodiment of the present invention further provides a magnetic resonance image reconstruction apparatus, including:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a K space to be processed, and the K space comprises a plurality of sampling points and non-sampling points;
the fitting mode determining module is used for determining a fitting mode corresponding to the plurality of non-sampling points, and the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point;
the fitting mode determining module is used for setting the fitting mode of each non-sampling point according to the fitting mode corresponding to the plurality of non-sampling points;
the fitting module is used for acquiring the fitting result of each non-sampling point by using the fitting mode of each non-sampling point, and the K space data of the plurality of sampling points and the fitting result of the non-sampling point form a fitting K space;
a reconstruction module for reconstructing the fitted K space to acquire a magnetic resonance image.
In a third aspect, an embodiment of the present invention further provides a magnetic resonance system, including:
a radio frequency transmission coil for transmitting a radio frequency pulse to a scanning portion of a target object to excite nuclear spins of the scanning portion;
gradient coils for applying a slice selection gradient field, a phase encoding gradient field and a frequency encoding gradient field to the scan site to generate echo signals;
a radio frequency receive coil for receiving the echo signals to form magnetic resonance scan data;
the system comprises a processor, a data processing unit and a data processing unit, wherein the processor is used for acquiring a K space to be processed, and the K space comprises a plurality of sampling points and non-sampling points; determining a fitting mode corresponding to a plurality of non-sampling points, wherein the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point; setting a fitting mode of each non-sampling point according to the fitting mode corresponding to the plurality of non-sampling points; acquiring a fitting result of each non-sampling point by using a fitting mode of each non-sampling point, wherein the K space data of the plurality of sampling points and the fitting results of the non-sampling points form a fitting K space; reconstructing the fitted K-space to acquire a magnetic resonance image.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the magnetic resonance image reconstruction method according to any of the embodiments.
Compared with the prior art, the technical scheme of the magnetic resonance image reconstruction method provided by the embodiment of the invention determines the fitting modes corresponding to the plurality of non-sampling points, sets the fitting mode of each non-sampling point according to the fitting modes corresponding to the plurality of non-sampling points, and obtains the fitting result of each non-sampling point by using the fitting mode of each non-sampling point. The fitting time of the non-sampling points of different fitting modes is reduced by matching different fitting modes for different fitting modes, so that the fitting time of all the non-sampling points in the whole K space is reduced, the fitting speed of the non-sampling points in the K space is improved, and the technical effect of improving the reconstruction speed of the magnetic resonance image is achieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a magnetic resonance image reconstruction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of fitting an unsampled point according to an embodiment of the present invention;
FIG. 3A is a schematic diagram of a fitting model according to an embodiment of the present invention;
FIG. 3B is a schematic diagram of another fitting model provided in accordance with an embodiment of the present invention;
FIG. 3C is a schematic diagram of another fitting model provided in accordance with an embodiment of the present invention;
fig. 4 is a block diagram of a magnetic resonance image reconstruction apparatus according to a second embodiment of the present invention;
fig. 5 is a block diagram of a magnetic resonance system according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described through embodiments with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Fig. 1 is a flowchart of a magnetic resonance image reconstruction method according to an embodiment of the present invention. The invention provides a magnetic resonance image reconstruction method aiming at the problem of low reconstruction speed under the condition of multi-channel coil receiving during magnetic resonance imaging fast imaging, in particular 3D high-definition imaging. The method can be executed by a magnetic resonance image reconstruction device provided by the embodiment of the invention, and the device can be realized in a software and/or hardware manner and is configured to be applied in a processor of the processor. The method specifically comprises the following steps:
s101, obtaining a K space to be processed, wherein the K space comprises a plurality of sampling points and a plurality of non-sampling points.
The K space to be processed is the K space currently used for image reconstruction, and the K space comprises a plurality of sampling points and at least two non-sampling points.
S102, determining a fitting mode corresponding to the plurality of non-sampling points, wherein the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point. That is, the corresponding fitting pattern is determined by the pattern formed from the sampling points within a set range from each sampling point. As shown in fig. 2, the non-sampled points can be obtained by linear fitting from the nearby sampled points, and the fitting formula is as follows:
Smn＝∑i,jCi,jSi,j (1)
wherein S ismnRepresenting the value of the fitted unsampled point, wherein the fitted unsampled point corresponds to the mth channel and the K space coordinate is n; ci,jIs a weight coefficient, Si,jAnd the values of the sampling points in the fitting range of the non-sampling points are represented by i, the channels of the sampling points used for fitting are represented by j, the K space coordinates of the sampling points used for fitting are represented by j, the j is not equal to n, and the sampling points used for fitting are close to the K space coordinates n. Weight coefficient Ci,jThe calibration data may be calculated by using the prior art, which is not described herein again. In this embodiment, m and i are both positive integers, and in the case of single channel acquisition, m is i; for the case of multiple channel acquisition, i can take any channel value such as 1, 2, 3, etc., and m is one of multiple channels. In one embodiment, the coordinates of K-space may be any value from-127 to 128 along the phase encode direction, -127 ≦ j ≦ 128, and-127 ≦ n ≦ 128.
It can be seen that when the number and distribution of sampling points around an unsampled point are different, the weight coefficient matrix is also different, and the concept of the fitting pattern is introduced for this embodiment. The number and distribution of each sampling point are corresponding to a fitting mode, namely each weight coefficient matrix is corresponding to a fitting mode. The unsampled points (shaded points) in fig. 3A, 3B, and 3C correspond to three different fitting patterns, respectively.
It can be understood that if the sampling mode of the current K space is interlaced acquisition, the non-sampled points located in the middle portion of the K space all correspond to the same fitting mode, i.e., the fitting mode in fig. 3A. If the sampling mode of the current K-space data is random sampling, see fig. 3B and 3C, the fitting patterns corresponding to different non-sampled points in the K-space are generally different, or each fitting pattern corresponds to only one or a small number of non-sampled points.
In one embodiment, if the non-sampled points fit the regular distribution of sample points within the range, the non-sampled points correspond to the first fitting pattern, see fig. 3A; if the distribution of the sampled points within the fitting range of the unsampled points is irregular, the unsampled points correspond to the second fitting pattern, see fig. 3B and 3C. It is understood that the first fitting pattern contains different K-space position points than the second fitting pattern contains. The number of sampling points included in the first fitting pattern is different from the number of sampling points included in the second fitting pattern.
S103, setting a fitting mode of each non-sampling point according to the fitting mode corresponding to the plurality of non-sampling points. In this embodiment, the fitting manner of the unsampled points can also be expressed as a reconstruction method of the unsampled points, one reconstruction method is selected for each unsampled point from at least two different reconstruction methods according to the fitting patterns corresponding to the plurality of unsampled points, and the recovery process of the unsampled points of the entire K space uses a hybrid reconstruction method, that is, two or more reconstruction methods are used.
According to the embodiment, the fitting mode corresponding to each fitting mode is determined according to the magnitude relation between the first fitting time and the second fitting time corresponding to each fitting mode, so that the fitting time of all non-sampling points corresponding to each fitting mode is the shortest.
The K-space fit corresponds to equation 1 and, without loss of generality, can be expressed as a convolution operation, as follows:
wherein S ismFitting results S for a plurality of unsampled pointsmnMatrix formed, C'iIs Ci,jMatrix obtained by coordinate inversion, SiIs Si,jThe constituent K-space data/matrices,
is a convolution operation.
According to the mathematical principle, the image domain fitting (image domain product fitting) corresponds to equation 2, and the convolution operation thereof can be rewritten as:
Sm＝FFT(∑iWi·Ii) (3)
wherein the content of the first and second substances,Wi＝IFFT(C'i)，Ii＝IFFT(Si) FFT represents fourier transform, IFFT represents inverse fourier transform, and "·" represents dot product operation.
It is verified that, for the uniformly undersampled K-space data, as shown in fig. 3A, sampling points and non-sampling points are distributed in an interlaced manner along a phase encoding direction (horizontal coordinate direction in the figure), a plurality of sampling points corresponding to the same phase encoding form a complete line, and a plurality of non-sampling points corresponding to the same phase encoding also form a complete line, and the operation speed of formula 3 is faster than that of formula 2, but under the condition that a data matrix contained in the K-space is smaller or the number of non-sampling points to be fitted is small, or under the condition of non-uniform sampling, as shown in fig. 3B and fig. 3C, the operation speed of formula 2 is faster. It can be seen that for any unsampled point, the two corresponding fitting times are different, so that the present embodiment classifies the fitting patterns corresponding to the multiple unsampled points, and the unsampled points belonging to the same fitting pattern are classified into one class, and the unsampled points having the same fitting pattern are counted; and calculating first fitting time corresponding to the non-sampling points in the same fitting mode in the K space fitting mode and second fitting time corresponding to the non-sampling points in the same fitting mode in the image domain mode, and then selecting the fitting mode corresponding to the small one of the first fitting time and the second fitting time as the fitting mode of each non-sampling point.
In order to further improve the fitting speed of the non-sampling points, if the distribution rule of the sampling points corresponding to the non-sampling points is detected, namely the first fitting mode corresponding to the non-sampling points is detected, the fitting mode is set as an image domain fitting mode. And if the sampling points corresponding to the non-sampling points are detected to be irregularly distributed, namely when the non-sampling points corresponding to the second fitting mode are detected, determining the fitting mode corresponding to each non-sampling point according to the size relation between the number of the sampling points corresponding to the fitting mode and the preset threshold value. Specifically, if the number of the non-sampling points in the same fitting mode is smaller than a set threshold, it can be known that the fitting time of the corresponding K-space fitting mode is smaller than the fitting time of the corresponding image domain, and then the fitting mode of the non-sampling points in the fitting mode is set as the K-space fitting mode; if the number of the non-sampling points in the same fitting mode is larger than the set threshold, the fitting time of the corresponding K space fitting mode is known to be larger than or equal to the fitting time of the corresponding image domain, and the fitting mode of the non-sampling points in the fitting mode is set as the image domain fitting mode.
In one embodiment, the preset threshold is determined by: according to a reconstruction computer with specific configuration and the size of a specific K space, different thresholds are selected from small to large, the threshold is equal to the number H of the non-sampling points of the K space needing fitting, and the time needed by respectively using the K space fitting and the image domain fitting for the H non-sampling points is calculated. When the time required for fitting using K-space is equal to the time required for fitting the image domain, the threshold in this case is the set threshold. The set threshold may be stored in the reconstruction code.
Considering that the phase-encoding lines or location points in the central region of K-space mainly determine the contrast of the image, while the phase-encoding lines filling the peripheral regions of K-space mainly determine the anatomical details of the image. The number of sampling points of the K space to be processed passing through the central area of the K space is larger than that of the sampling points of the peripheral area of the K space. In one embodiment, K space fitting or K space-based reconstruction is selected for the fitting mode/reconstruction method of the non-sampled points close to the central region of the K space, that is, the low-frequency non-sampled points are selected as the K space fitting mode; and selecting an image domain fitting mode or a reconstruction mode based on an image space for the fitting mode of the non-sampling points in the peripheral area of the K space, namely selecting the image domain fitting mode for the non-sampling points with high frequency.
It should be noted that, in this embodiment, according to whether the distribution of the sampling points in the fitting range of the non-sampling points is regular or not, the fitting pattern of the non-sampling points may be divided into a first fitting pattern and a second fitting pattern, specifically, if the distribution of the sampling points is regular, the fitting pattern of the non-sampling points corresponding to the sampling points is the first fitting pattern, if the distribution of the sampling points is irregular, the fitting pattern of the non-sampling points corresponding to the sampling points is the second fitting pattern, and then the second fitting pattern is subdivided according to the number and distribution of the sampling points, and the subdivided fitting patterns may be a 2A fitting pattern, a 2B fitting pattern, and the like.
Wherein the size of the set threshold is related to the image processing parameters of the processor. If the configuration of the image processing parameters of the processor is high, the image processing speed is high, and for a fitting mode with a few fitting points, the user does not need to do so much labor in a K-space fitting mode, the set threshold value is high, whereas, if the configuration of the performance parameters of the processor is low, the image processing speed is low, and for a fitting mode with a few fitting points, the fitting speed is quickly reduced in a K-space fitting mode, so the set threshold value is low.
S104, obtaining the fitting result of each non-sampling point by using the fitting mode of each non-sampling point, wherein the K space data of the plurality of sampling points and the fitting results of the non-sampling points form a fitting K space.
And fitting each non-sampling point by using the fitting mode of each non-sampling point to obtain the fitting result of each non-sampling point. And the fitting results of all the non-sampling points and the K space data of the plurality of sampling points form a fitting space.
In some embodiments, after the fitting mode corresponding to the non-sampling point of each fitting mode is determined, all the non-sampling points corresponding to each fitting mode are fitted by using the fitting mode corresponding to each fitting mode, so that the fitting results of all the non-sampling points corresponding to the same fitting mode are obtained at one time. And after the fitting operation is performed on all the non-sampling points corresponding to all the fitting modes, the fitting results of all the non-sampling points in the K space can be obtained.
In one embodiment, traversing all the fitting modes, determining the number of fitting points of the current fitting mode, determining the fitting mode corresponding to the current fitting mode according to the size relationship between the number of fitting points and the set threshold, and fitting all the non-sampling points corresponding to the current fitting mode by adopting the fitting mode to obtain the fitting results of all the non-sampling points corresponding to the current fitting mode.
For the image domain fitting mode, the direct fitting result is image domain data, the fitting result needs to be transformed to the K space to obtain a transformation result, fitting data of one or more non-sampling points corresponding to the corresponding fitting mode are extracted from the transformation result, and the extracted fitting data are respectively filled into the corresponding non-sampling points of the K space to update the fitting result corresponding to the current fitting mode.
And S105, reconstructing the fitting K space to acquire a magnetic resonance image.
After the fitted K space is obtained, image reconstruction is performed on the K space to obtain a magnetic resonance image.
Compared with the prior art, the technical scheme of the magnetic resonance image reconstruction method provided by the embodiment of the invention determines the fitting modes corresponding to the plurality of non-sampling points, sets the fitting mode of each non-sampling point according to the fitting modes corresponding to the plurality of non-sampling points, and obtains the fitting result of each non-sampling point by using the fitting mode of each non-sampling point. The fitting time of the non-sampling points of different fitting modes is reduced by matching different fitting modes for different fitting modes, so that the fitting time of all the non-sampling points in the whole K space is reduced, the fitting speed of the non-sampling points in the K space is improved, and the technical effect of improving the reconstruction speed of the magnetic resonance image is achieved.
Example two
Fig. 4 is a block diagram of a magnetic resonance image reconstruction apparatus according to an embodiment of the present invention. The apparatus is used for executing the magnetic resonance image reconstruction method provided by any of the above embodiments, and the apparatus can be implemented by software or hardware. The device includes:
the device comprises an acquisition module 11, a processing module and a processing module, wherein the acquisition module is used for acquiring a K space to be processed, and the K space comprises a plurality of sampling points and non-sampling points;
a fitting mode determining module 12, configured to determine a fitting mode corresponding to the plurality of non-sampling points, where the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point;
a fitting mode determining module 13, configured to set a fitting mode of each non-sampling point according to the fitting mode corresponding to the plurality of non-sampling points;
the fitting module 14 is configured to obtain a fitting result of each non-sampling point by using a fitting mode of each non-sampling point, and the K space data of the plurality of sampling points and the fitting results of the non-sampling points form a fitting K space;
a reconstruction module 15 for reconstructing the fitted K-space to acquire a magnetic resonance image.
Optionally, the fitting mode determining module 12 is configured to determine a sampling point within a set range from each non-sampling point by taking each non-sampling point as a center; and combining the relative positions of the sampling points within the set range from each non-sampling point to form a fitting mode.
Optionally, the position point of the K space included in the first fitting pattern is different from the position point of the K space included in the second fitting pattern.
Optionally, the number of sampling points included in the first fitting pattern is different from the number of sampling points included in the second fitting pattern.
Optionally, the fitting mode determining module is configured to classify fitting modes corresponding to the multiple non-sampling points, and non-sampling points belonging to the same fitting mode are classified into one class; when the number of the non-sampling points in the same fitting mode is smaller than a set threshold, setting a fitting mode corresponding to the non-sampling points as K-space fitting; and when the number of the non-sampling points in the same fitting mode is larger than a set threshold, setting a fitting mode corresponding to the non-sampling points as image domain fitting.
Optionally, the fitting mode setting module is configured to classify fitting modes corresponding to the multiple non-sampling points, and non-sampling points belonging to the same fitting mode are classified into one class; calculating first fitting time corresponding to non-sampling points in the same fitting mode in a K space fitting mode; calculating second fitting time corresponding to the non-sampling points in the same fitting mode in an image domain fitting mode; and selecting the fitting mode corresponding to the small one of the first fitting time and the second fitting time as the fitting mode of each non-sampling point.
Compared with the prior art, the technical scheme of the magnetic resonance image reconstruction device provided by the embodiment of the invention determines the fitting modes corresponding to a plurality of non-sampling points, sets the fitting mode of each non-sampling point according to the fitting modes corresponding to the plurality of non-sampling points, and obtains the fitting result of each non-sampling point by using the fitting mode of each non-sampling point. The fitting time of the non-sampling points of different fitting modes is reduced by matching different fitting modes for different fitting modes, so that the fitting time of all the non-sampling points in the whole K space is reduced, the fitting speed of the non-sampling points in the K space is improved, and the technical effect of improving the reconstruction speed of the magnetic resonance image is achieved.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a magnetic resonance system according to a third embodiment of the present invention, as shown in fig. 5
An embodiment of the present invention provides a magnetic resonance system, as shown in fig. 5, the system includes a scanning apparatus 110, the scanning apparatus 110 includes a radio frequency transmitting coil 111, a gradient coil 112, a radio frequency receiving coil 113, and a processor 120, the radio frequency transmitting coil 111 is configured to transmit a radio frequency pulse to a scanning region of a target object to excite nuclear spins in the scanning region; the gradient coils 112 are used for applying a slice selection gradient field, a phase encoding gradient field and a frequency encoding gradient field to the scanning part to generate echo signals; a radio frequency receive coil 113 for receiving echo signals to form magnetic resonance scan data; the processor 120 is configured to obtain a K space to be processed, where the K space includes a plurality of sampling points and a plurality of non-sampling points; determining a fitting mode corresponding to the plurality of non-sampling points, wherein the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point; setting a fitting mode of each non-sampling point according to a fitting mode corresponding to the plurality of non-sampling points; acquiring a fitting result of each non-sampling point by using a fitting mode of each non-sampling point, wherein the K space data of a plurality of sampling points and the fitting results of the non-sampling points form a fitting K space; the fitting K-space is reconstructed to acquire the magnetic resonance image.
The K space to be processed is the K space currently used for image reconstruction, and the K space comprises a plurality of sampling points and at least two non-sampling points.
As shown in fig. 2, the non-sampled points can be obtained by linear fitting from the nearby sampled points, and the fitting formula is as follows:
Smn＝∑i,jCi,jSi,j (1)
wherein S ismnRepresenting the value of the fitted unsampled point, wherein the fitted unsampled point corresponds to the mth channel and the K space coordinate is n; ci,jIs a weight coefficient, Si,jAnd the values of the sampling points in the fitting range of the non-sampling points are represented by i, the channels of the sampling points used for fitting are represented by j, the K space coordinates of the sampling points used for fitting are represented by j, the j is not equal to n, and the sampling points used for fitting are close to the K space coordinates n. Weight coefficient Ci,jThe calibration data may be calculated by using the prior art, which is not described herein again. In this embodiment, m and i are both positive integers, and in the case of single channel acquisition, m is i; for the case of multiple channel acquisition, i can take any channel value such as 1, 2, 3, etc., and m is one of multiple channels. In one embodiment, the coordinates of K-space may be any value from-127 to 128 along the phase encode direction, -127 ≦ j ≦ 128, and-127 ≦ n ≦ 128.
It can be seen that when the number and distribution of sampling points around an unsampled point are different, the weight coefficient matrix is also different, and the concept of the fitting pattern is introduced for this embodiment. The number and distribution of each sampling point are corresponding to a fitting mode, namely each weight coefficient matrix is corresponding to a fitting mode. The unsampled points (shaded points) in fig. 3A, 3B, and 3C correspond to three different fitting patterns, respectively.
It can be understood that if the sampling mode of the current K space is interlaced acquisition, the non-sampled points located in the middle portion of the K space all correspond to the same fitting mode, i.e., the fitting mode in fig. 3A. If the sampling mode of the current K-space data is random sampling, see fig. 3B and 3C, the fitting patterns corresponding to different non-sampled points in the K-space are generally different, or each fitting pattern corresponds to only one or a small number of non-sampled points.
In one embodiment, if the non-sampled points fit the regular distribution of sample points within the range, the non-sampled points correspond to the first fitting pattern, see fig. 3A; if the distribution of the sampled points within the fitting range of the unsampled points is irregular, the unsampled points correspond to the second fitting pattern, see fig. 3B and 3C. It is understood that the first fitting pattern contains different K-space position points than the second fitting pattern contains. The number of sampling points included in the first fitting pattern is different from the number of sampling points included in the second fitting pattern.
According to the embodiment, the fitting mode corresponding to each fitting mode is determined according to the magnitude relation between the first fitting time and the second fitting time corresponding to each fitting mode, so that the fitting time of all non-sampling points corresponding to each fitting mode is the shortest.
The K-space fit corresponds to equation 1 and, without loss of generality, can be expressed as a convolution operation, as follows:
wherein S ismFitting results S for a plurality of unsampled pointsmnMatrix formed, C'iIs Ci,jMatrix obtained by coordinate inversion, SiIs Si,jThe constituent K-space data/matrices,
is a convolution operation.
According to the mathematical principle, the image domain fitting (image domain product fitting) corresponds to equation 2, and the convolution operation thereof can be rewritten as:
Sm＝FFT(∑iWi·Ii) (3)
wherein, Wi＝IFFT(C'i)，Ii＝IFFT(Si) FFT represents fourier transform, IFFT represents inverse fourier transform, and "·" represents dot product operation.
Through verification, for the uniform undersampled K-space data, for example, as shown in fig. 3A, sampling points and non-sampling points are distributed in an interlaced manner along a phase encoding direction (horizontal coordinate direction in the figure), a plurality of sampling points corresponding to the same phase encoding form a complete line, and a plurality of non-sampling points corresponding to the same phase encoding also form a complete line, the operation speed of formula 3 is faster than that of formula 2, but the operation speed of formula 2 is faster under the condition that a data matrix contained in the K-space is smaller or the number of non-sampling points to be fitted is small, or under the condition of non-uniform sampling, as shown in fig. 3B and fig. 3C, the operation speed of formula 2 is faster.
It can be seen that for any unsampled point, the magnitude relationship between the two corresponding fitting times is related to the number of its corresponding sampled points, or whether the distribution of its corresponding sampled points is regular. For a plurality of non-sampling points with irregularly distributed sampling points, the embodiment classifies the fitting patterns corresponding to the plurality of non-sampling points, and the non-sampling points belonging to the same fitting pattern are classified into one class; and calculating first fitting time corresponding to the non-sampling points in the same fitting mode in the K space fitting mode and second fitting time corresponding to the non-sampling points in the same fitting mode in the image domain mode, and then selecting the fitting mode corresponding to the small one of the first fitting time and the second fitting time as the fitting mode of each non-sampling point.
And if the sampling points corresponding to the non-sampling points are in a regular distribution, directly judging that the non-sampling points correspond to the first fitting mode, and setting the fitting mode of the first fitting mode as an image domain fitting mode.
If the distribution of the sampling points corresponding to the fitting pattern is irregular, the fitting time is related to the number of the sampling points corresponding to the fitting pattern. Therefore, after the fitting mode of each non-sampling point is determined, the fitting mode corresponding to each non-sampling point needs to be determined according to the size relationship between the number of sampling points corresponding to the fitting mode and the preset threshold value. Specifically, if the number of the non-sampling points in the same fitting mode is smaller than a set threshold, it can be known that the fitting time of the corresponding K-space fitting mode is smaller than the fitting time of the corresponding image domain, and then the fitting mode of the non-sampling points in the fitting mode is set as the K-space fitting mode; if the number of the non-sampling points in the same fitting mode is larger than the set threshold, the fitting time of the corresponding K space fitting mode is known to be larger than or equal to the fitting time of the corresponding image domain, and the fitting mode of the non-sampling points in the fitting mode is set as the image domain fitting mode.
Wherein the size of the set threshold is related to the image processing parameters of the processor. If the configuration of the image processing parameters of the processor is high, the image processing speed is high, and for a fitting mode with a few fitting points, the user does not need to do so much labor in a K-space fitting mode, the set threshold value is high, whereas, if the configuration of the performance parameters of the processor is low, the image processing speed is low, and for a fitting mode with a few fitting points, the fitting speed is quickly reduced in a K-space fitting mode, so the set threshold value is low.
And fitting each non-sampling point by using the fitting mode of each non-sampling point to obtain the fitting result of each non-sampling point. And the fitting results of all the non-sampling points and the K space data of the plurality of sampling points form a fitting space.
In some embodiments, after the fitting mode corresponding to the non-sampling point of each fitting mode is determined, all the non-sampling points corresponding to each fitting mode are fitted by using the fitting mode corresponding to each fitting mode, so that the fitting results of all the non-sampling points corresponding to the same fitting mode are obtained at one time. And after the fitting operation is performed on all the non-sampling points corresponding to all the fitting modes, the fitting results of all the non-sampling points in the K space can be obtained.
In one embodiment, traversing all the fitting modes, determining the number of fitting points of the current fitting mode, determining the fitting mode corresponding to the current fitting mode according to the size relationship between the number of fitting points and the set threshold, and fitting all the non-sampling points corresponding to the current fitting mode by adopting the fitting mode to obtain the fitting results of all the non-sampling points corresponding to the current fitting mode.
For the image domain fitting mode, the direct fitting result is image domain data, the fitting result needs to be transformed to the K space to obtain a transformation result, fitting data of one or more non-sampling points corresponding to the corresponding fitting mode are extracted from the transformation result, and the extracted fitting data are respectively filled into the corresponding non-sampling points of the K space to update the fitting result corresponding to the current fitting mode.
After the fitted K space is obtained, image reconstruction is performed on the K space to obtain a magnetic resonance image.
Compared with the prior art, the technical scheme of the magnetic resonance image reconstruction method provided by the embodiment of the invention determines the fitting modes corresponding to the plurality of non-sampling points, sets the fitting mode of each non-sampling point according to the fitting modes corresponding to the plurality of non-sampling points, and obtains the fitting result of each non-sampling point by using the fitting mode of each non-sampling point. The fitting time of the non-sampling points of different fitting modes is reduced by matching different fitting modes for different fitting modes, so that the fitting time of all the non-sampling points in the whole K space is reduced, the fitting speed of the non-sampling points in the K space is improved, and the technical effect of improving the reconstruction speed of the magnetic resonance image is achieved.
As shown in fig. 5, the magnetic resonance imaging system 100 further includes a controller 130, an input device 140, and an output device 150, wherein the controller 130 may include one or a combination of several of a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphic Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field-Programmable Gate Array (FPGA), a Gate controller (ARM), and the like.
An output device 150, such as a display, may display a magnetic resonance image of the region of interest. Further, the output device 150 can also display the height, weight, age, imaging part of the subject, and the operating state of the scanning device 110, etc. The output device 150 may be one or a combination of Cathode Ray Tube (CRT) output device, liquid crystal output device (LCD), organic light emitting output device (OLED), plasma output device, and the like. In one embodiment, the display responds to the scanning protocol parameter to enable the target object to have a slice to be scanned with PNS exceeding the limit in the magnetic resonance data acquisition process, and displays the recommended value of the protocol parameter, wherein the recommended value comprises: one or more of a gradient rotation angle recommendation value, a gradient recommendation mode, a bandwidth recommendation value, a readout resolution recommendation value, and a phase resolution recommendation value.
The magnetic resonance imaging system 100 may be connected to a Local Area Network (LAN), Wide Area Network (WAN), Public Network, private Network, Public Switched Telephone Network (PSTN), the internet, wireless Network, virtual Network, or any combination thereof.
The scanning apparatus 110 includes an MR signal acquisition module, an MR control module, and an MR data storage module. Wherein the MR signal acquisition module comprises a magnet unit and a radio frequency unit. The magnet unit mainly comprises a main magnet generating a B0 main magnetic field and gradient components generating gradients. The main magnet contained in the magnet unit may be a permanent magnet or a superconducting magnet, the gradient assembly mainly includes a gradient current Amplifier (AMP), a gradient coil, and may further include three independent channels Gx, Gy, Gz, each gradient amplifier excites a corresponding one of the gradient coils in the gradient coil set to generate a gradient field for generating a corresponding spatial encoding signal to spatially locate the magnetic resonance signal. The radio frequency unit mainly comprises a radio frequency transmitting coil and a radio frequency receiving coil, the radio frequency transmitting coil is used for transmitting radio frequency pulse signals to a detected person or a human body, the radio frequency receiving coil is used for receiving magnetic resonance signals collected from the human body, and the radio frequency coils forming the radio frequency unit can be divided into a body coil and a local coil according to different functions. In one embodiment, the type of body coil or local coil may be a birdcage coil, a solenoid coil, a saddle coil, a Helmholtz coil, an array coil, a loop coil, or the like. In one embodiment, the local coils are arranged as array coils, and the array coils can be arranged in a 4-channel mode, an 8-channel mode, or a 16-channel mode. The magnet unit and the radio frequency unit can form an open low-field magnetic resonance device or a closed superconducting magnetic resonance device.
The MR control module can monitor an MR signal acquisition module and an MR data processing module which comprise a magnet unit and a radio frequency unit. Specifically, the MR control module may receive information or pulse parameters sent by the MR signal acquisition module; in addition, the MR control module can also control the processing of the MR data processing module. In one embodiment, the MR control module is further connected to a controller including a pulse sequence generator, a gradient waveform generator, a transmitter, a receiver, etc. for controlling the magnetic field module to execute a corresponding scan sequence after receiving a command from a console.
Illustratively, the specific process of generating MR data by the scanning device 110 in the embodiment of the present invention includes: a main magnet generates a B0 main magnetic field, and atomic nuclei in a body of a detected person generate precession frequency under the action of the main magnetic field, wherein the precession frequency is in direct proportion to the strength of the main magnetic field; the MR control module stores and sends a command of a scanning sequence (scan sequence) to be executed, the pulse sequence generator controls the gradient waveform generator and the transmitter according to the scanning sequence command, the gradient waveform generator outputs a gradient pulse signal with a preset time sequence and waveform, the signal passes through Gx, Gy and Gz gradient current amplifiers and then passes through three independent channels Gx, Gy and Gz in the gradient assembly, each gradient amplifier excites a corresponding gradient coil in the gradient coil group to generate a gradient field for generating a corresponding spatial coding signal so as to spatially position a magnetic resonance signal; the pulse sequence generator also executes a scanning sequence, outputs data including timing, strength, shape and the like of radio frequency transmitted radio frequency pulses and timing of radio frequency receiving and the length of a data acquisition window to the transmitter, simultaneously the transmitter sends corresponding radio frequency pulses to a body transmitting coil in the radio frequency unit to generate B1 fields, signals emitted by atomic nuclei excited in a patient body under the action of the B1 fields are sensed by a receiving coil in the radio frequency unit, then the signals are transmitted to the MR data processing module through a transmitting/receiving switch, and the signals are subjected to digital processing such as amplification, demodulation, filtering, AD conversion and the like and then transmitted to the MR data storage module. After the MR data storage module acquires a set of raw k-space data, the scan is complete. The original k-space data is rearranged into separate k-space data sets corresponding to each image to be reconstructed, and each k-space data set is input to the array controller for image reconstruction and then combined with the magnetic resonance signals to form a set of image data.
Example four
Embodiments of the present invention also provide a storage medium containing computer-executable instructions which, when executed by a computer processor, perform a method of magnetic resonance image reconstruction, the method comprising:
acquiring all non-sampling points of a K space, and determining at least one fitting mode corresponding to all the non-sampling points in the K space, wherein the K space is one of at least one target block contained in current K space data;
determining the minimum fitting time of at least two optional fitting times corresponding to each fitting mode, and taking the fitting mode corresponding to the minimum fitting time as the fitting mode of each fitting mode;
fitting all the non-sampling points corresponding to each fitting mode by adopting a fitting mode corresponding to each fitting mode to obtain fitting results of all the non-sampling points in the K space;
and repeating the steps until fitting results of all non-sampling points in the current K space data are obtained, so as to update the current K space data, and carrying out image reconstruction on the updated K space data to obtain a magnetic resonance image.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the magnetic resonance image reconstruction method provided by any embodiments of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the magnetic resonance image reconstruction method according to the embodiments of the present invention.
It should be noted that, in the embodiment of the magnetic resonance image reconstruction apparatus, the included units and modules are only divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A magnetic resonance image reconstruction method, comprising:
acquiring a K space to be processed, wherein the K space comprises a plurality of sampling points and a plurality of non-sampling points;
determining a fitting mode corresponding to a plurality of non-sampling points, wherein the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point;
setting a fitting mode of each non-sampling point according to the fitting mode corresponding to the plurality of non-sampling points;
acquiring a fitting result of each non-sampling point by using a fitting mode of each non-sampling point, wherein the K space data of the plurality of sampling points and the fitting results of the non-sampling points form a fitting K space;
reconstructing the fitted K-space to acquire a magnetic resonance image.
2. The method of claim 1, wherein setting the fitting pattern of each unsampled point according to the fitting pattern corresponding to the plurality of unsampled points comprises:
and selecting one reconstruction method for each non-sampling point from at least two different reconstruction methods according to the fitting mode corresponding to the plurality of non-sampling points, wherein the K space reconstruction process at least uses two different reconstruction methods.
3. The method of claim 1, wherein the fitting pattern of the plurality of non-sampled point correspondences is determined by:
determining sampling points within a set range from each non-sampling point by taking each non-sampling point as a center;
and combining the relative positions of the sampling points within the set range of each non-sampling point to form a fitting mode.
4. The method of claim 1, wherein the fitting patterns corresponding to the plurality of unsampled points comprise a first fitting pattern and a second fitting pattern, the first fitting pattern corresponding to a regular pattern and the second fitting pattern corresponding to an irregular pattern.
5. The method of claim 4, wherein the first fitting pattern comprises a different K-space position point than the second fitting pattern comprises a different K-space position point.
6. The method according to claim 1, wherein setting the fitting pattern of each unsampled point according to the fitting pattern corresponding to the plurality of unsampled points comprises:
counting the number of non-sampling points with the same fitting pattern;
when the number of the non-sampling points in the same fitting mode is smaller than a set threshold, setting a fitting mode corresponding to the non-sampling points as K-space fitting;
and when the number of the non-sampling points in the same fitting mode is larger than a set threshold, setting a fitting mode corresponding to the non-sampling points as image domain fitting.
7. The method according to claim 1, wherein setting the fitting pattern of each unsampled point according to the fitting pattern corresponding to the plurality of unsampled points comprises:
classifying the fitting modes corresponding to the plurality of non-sampling points, and dividing the non-sampling points belonging to the same fitting mode into a class;
calculating first fitting time corresponding to non-sampling points in the same fitting mode in a K space fitting mode;
calculating second fitting time corresponding to the non-sampling points in the same fitting mode in an image domain fitting mode;
and selecting the fitting mode corresponding to the small one of the first fitting time and the second fitting time as the fitting mode of each non-sampling point.
8. A magnetic resonance image reconstruction apparatus, characterized by comprising:
the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a K space to be processed, and the K space comprises a plurality of sampling points and non-sampling points;
the fitting mode determining module is used for determining a fitting mode corresponding to the plurality of non-sampling points, and the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point;
the fitting mode determining module is used for setting the fitting mode of each non-sampling point according to the fitting mode corresponding to the plurality of non-sampling points;
the fitting module is used for acquiring the fitting result of each non-sampling point by using the fitting mode of each non-sampling point, and the K space data of the plurality of sampling points and the fitting result of the non-sampling point form a fitting K space;
a reconstruction module for reconstructing the fitted K space to acquire a magnetic resonance image.
9. A magnetic resonance system, comprising:
a radio frequency transmission coil for transmitting a radio frequency pulse to a scanning portion of a target object to excite nuclear spins of the scanning portion;
gradient coils for applying a slice selection gradient field, a phase encoding gradient field and a frequency encoding gradient field to the scan site to generate echo signals;
a radio frequency receive coil for receiving the echo signals to form magnetic resonance scan data;
the system comprises a processor, a data processing unit and a data processing unit, wherein the processor is used for acquiring a K space to be processed, and the K space comprises a plurality of sampling points and non-sampling points; determining a fitting mode corresponding to a plurality of non-sampling points, wherein the fitting mode is a pattern formed by sampling points within a set range from each non-sampling point; setting a fitting mode of each non-sampling point according to the fitting mode corresponding to the plurality of non-sampling points; acquiring a fitting result of each non-sampling point by using a fitting mode of each non-sampling point, wherein the K space data of the plurality of sampling points and the fitting results of the non-sampling points form a fitting K space; reconstructing the fitted K-space to acquire a magnetic resonance image.
10. A storage medium containing computer executable instructions for performing the magnetic resonance image reconstruction method of any one of claims 1-7 when executed by a computer processor.
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