CN111257809B - Magnetic resonance imaging method, magnetic resonance imaging device, storage medium and medical equipment - Google Patents
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Abstract
The application provides a magnetic resonance imaging method, a magnetic resonance imaging device, a storage medium and medical equipment, which are used for improving imaging quality. The magnetic resonance imaging method comprises the following steps: acquiring a sampling point set; the sampling point set comprises a preset number of sampling points in a Cartesian coordinate system in a phase coding plane arranged according to a random time sequence; generating a spatial coding gradient according to the sampling point set and a preset sampling track for acquiring the magnetic resonance signal; based on the spatial coding gradient, in each repetition time, jointly collecting original navigation data which are under a non-Cartesian coordinate system and are located in a central area of a K space and imaging data which are under the Cartesian coordinate system and are located at the periphery of the K space; converting the original navigation data into converted navigation data under a Cartesian coordinate system; and carrying out compressed sensing reconstruction on the converted navigation data and the imaging data to obtain a three-dimensional dynamic magnetic resonance image.
Description
Technical Field
The present application relates to the field of magnetic resonance imaging technologies, and in particular, to a magnetic resonance imaging method and apparatus, a storage medium, and a medical device.
Background
Magnetic Resonance Imaging (MRI) is imaging using the principle of magnetic resonance, and enables people to obtain detailed diagnostic images of living organs and tissues without damage. However, MRI has a disadvantage in that the imaging speed is slow, and a Compressive Sensing (CS) method performs sampling at a frequency much lower than the nyquist frequency, so that the imaging speed can be greatly increased, and has recently received extensive attention from researchers.
At present, magnetic resonance imaging methods based on compressed sensing are mainly classified into two categories according to a coordinate system where a sampling trajectory is located: the method based on the cartesian coordinate system (e.g., K-t SPARSE-SENSE method, PS-SPARSE method) and the method based on the non-cartesian coordinate system (e.g., iGRASP method) are susceptible to motion artifacts due to the time difference between the acquisition of the data in the central area of K space and the acquisition of the data in the periphery of K space, and the method based on the non-cartesian coordinate system is not sensitive to motion, but the streak artifacts appearing in the image are difficult to eliminate, so that it is known that the imaging quality of the magnetic resonance imaging method based on compressive sensing in the related art is not high.
Disclosure of Invention
In view of the above, the present application provides a magnetic resonance imaging method, apparatus, storage medium and medical device for improving imaging quality.
In a first aspect, an embodiment of the present application provides a magnetic resonance imaging method, which is used in a magnetic resonance imaging system including a phased array coil including a plurality of receiving coil units, and the method includes:
acquiring a sampling point set; the sampling point set comprises a preset number of sampling points in a Cartesian coordinate system in a phase coding plane arranged according to a random time sequence;
generating a spatial coding gradient according to the sampling point set and a preset sampling track for acquiring the magnetic resonance signal;
based on the spatial coding gradient, in each repetition time, jointly collecting original navigation data which are under a non-Cartesian coordinate system and are located in a central area of a K space and imaging data which are under the Cartesian coordinate system and are located at the periphery of the K space;
converting the original navigation data into converted navigation data under a Cartesian coordinate system;
and carrying out compressed sensing reconstruction on the converted navigation data and the imaging data to obtain a three-dimensional dynamic magnetic resonance image.
In a possible implementation manner, the jointly acquiring, at each repetition time, raw navigation data in a non-cartesian coordinate system and located in a central region of the K space and imaging data in a cartesian coordinate system and located in a periphery of the K space based on the spatial coding gradient includes:
for each receiving coil unit, based on the spatial coding gradient, in each repetition time, performing data sampling on an imaging region of the detected body through the receiving coil unit, collecting original navigation data in a non-Cartesian coordinate system and imaging data corresponding to the sampling point in the Cartesian coordinate system together, and filling the imaging data to a K space line passing through the center of the K space, wherein the original navigation data is filled in the central region of the K space, and the imaging data is filled in the periphery of the K space;
wherein the three-dimensional dynamic magnetic resonance image comprises magnetic resonance images corresponding to a plurality of time frames;
and performing preset magnetic resonance signal acquisition in each time frame, wherein each magnetic resonance signal acquisition comprises preset magnetic resonance signal acquisition, each magnetic resonance signal acquisition acquires a sampling point in a phase encoding plane, and each magnetic resonance signal acquisition fills one K space line.
In a possible implementation manner, the sampling trajectory is a butterfly type and is a sampling trajectory set based on a butterfly type navigation technology.
In a possible implementation manner, the obtaining the set of sampling points includes:
bisecting the phase encoding plane into four quadrants;
for each quadrant, randomly sequencing sampling points in a Cartesian coordinate system in the quadrant, and taking a preset number of sampling points which are arranged in the quadrant according to a random time sequence as a subset of the sampling points corresponding to the quadrant;
and combining the sampling point subsets corresponding to the four quadrants to obtain the sampling point set.
In a possible implementation manner, in each round of magnetic resonance signal acquisition, data acquisition is performed on at least one sampling point in each quadrant respectively, projections of the acquired original navigation data corresponding to the same quadrant in a phase encoding plane are the same, and projections of the original navigation data corresponding to each quadrant in the phase encoding plane are perpendicular to each other.
In a possible implementation manner, the performing compressed sensing reconstruction on the converted navigation data and the imaging data to obtain a three-dimensional dynamic magnetic resonance image includes:
determining a time domain basis matrix according to the converted navigation data;
acquiring sensitivity spectrum data of each receiving coil unit, wherein the sensitivity spectrum data is determined according to image data acquired by the receiving coil unit during pre-scanning;
determining a spatial domain basis matrix according to the time domain basis matrix, the sensitivity spectrum data of each receiving coil unit and the imaging data;
and performing matrix multiplication operation on the space domain basis matrix and the time domain basis matrix to obtain the three-dimensional dynamic magnetic resonance image.
In a possible implementation manner, each of the receiving coil units corresponds to one channel, and the determining a spatial domain basis matrix according to the time domain basis matrix, the sensitivity spectrum data of each of the receiving coil units, and the imaging data includes:
determining a spatial domain basis matrix by adopting a first formula according to the time domain basis matrix, the sensitivity distribution graph and the imaging data;
wherein the first formula is:
wherein,
is a space domain basis matrix, NcIs the number of channels, diImaging data corresponding to a sampling point under a Cartesian coordinate system and actually acquired by the ith channel, wherein omega is a down-sampling matrix, FsIs a Fourier transform matrix of k-space to the spatial domain, SiSensitivity spectrum data of a receiving coil unit corresponding to the ith channel,
for the constraint term, λ is the weight of the constraint term, and Φ is the sparse transform matrix.
In a second aspect, the present application further provides a magnetic resonance imaging apparatus, including means for performing the magnetic resonance imaging method in the first aspect or any possible implementation manner of the first aspect.
In a third aspect, the present application further provides a storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the magnetic resonance imaging method in the first aspect or any possible implementation manner of the first aspect.
In a fourth aspect, the present application further provides a medical apparatus, including a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the magnetic resonance imaging method in the first aspect or any possible implementation manner of the first aspect when executing the program.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
the scheme provided by the application adopts the original navigation data under a non-Cartesian coordinate system and positioned in the central area of K space in each repetition Time (TR), and imaging data under a Cartesian coordinate system and positioned at the periphery of the K space are collected together, the data of the central area of the K space and the data of the periphery of the K space have no time difference, the acquired data is less susceptible to motion, so that motion artifacts can be reduced, and, when the image reconstruction is carried out, the original navigation data under the non-Cartesian coordinate system is converted into the converted navigation data under the Cartesian coordinate system, then, compressed sensing reconstruction is carried out by utilizing the converted navigation data in the Cartesian coordinate system and the imaging data in the Cartesian coordinate system, so that compared with a method of a non-Cartesian coordinate system, the streak artifact can be well eliminated, and in conclusion, the scheme provided by the application can improve the imaging quality.
Drawings
Fig. 1 is a schematic flowchart of a magnetic resonance imaging method according to an embodiment of the present application;
FIG. 2 is a diagram illustrating a time k of data acquisition in a magnetic resonance imaging method according to an embodiment of the present applicationy-kzA schematic view of a plane;
fig. 3 is a schematic flowchart of compressed sensing reconstruction of an image in a magnetic resonance imaging method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a magnetic resonance imaging apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an image reconstruction module in a magnetic resonance imaging apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a medical device provided in an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
Magnetic Resonance Imaging (MRI) is imaging using the principle of magnetic resonance, and has the greatest advantage of no harm to the human body, and thus is widely used in clinical diagnosis. However, MRI has a disadvantage of slow imaging speed, and the theory of Compressed Sensing (CS) considers that the original data is down-sampled in K-space (the acquired data may be much smaller than the fully sampled data), so long as the obtained Magnetic Resonance (MR) image is sparse in a certain transform domain, the original image can be accurately recovered from the small K-space data, and therefore, the compressed sensing method can sample at a frequency far lower than the nyquist frequency, and thus, the imaging speed can be greatly improved.
At present, magnetic resonance imaging methods based on compressed sensing are mainly classified into two categories according to a coordinate system where a sampling trajectory is located:
(1) a cartesian coordinate system based method. For example, the K-t SPARSE-SENSE method, the PS-SPARSE method, etc., in which data is acquired separately from data of the K-space periphery, i.e., either data of the K-space center region or data of the K-space periphery is acquired once per repetition time, so that there is a time difference between the acquisition of the data of the K-space center region and the acquisition of the data of the K-space periphery, and thus the cartesian coordinate system-based method is susceptible to motion artifacts.
(2) Non-cartesian coordinate system based methods. For example, the iGRASP method, in which the acquired K-space line passes through the center of K-space, so that the acquired data is not easily affected by motion, and thus motion artifacts can be reduced, but in the method, data in a non-cartesian coordinate system is acquired, and image reconstruction is performed using the data in the non-cartesian coordinate system, so that streak artifacts appearing in the image are difficult to eliminate.
Therefore, the imaging quality of the magnetic resonance imaging method based on the compressed sensing in the related art is not high.
In order to solve the above problems, the present application provides a magnetic resonance imaging method, an apparatus, a storage medium, and a medical device.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, an embodiment of the present application provides a magnetic resonance imaging method, which is used in a magnetic resonance imaging system that includes a phased array coil that includes a plurality of receiving coil units (or called receivers), and one receiving coil unit is distributed on one channel, and the method may include the following steps:
s101, acquiring a sampling point set; the sampling point set comprises a preset number of sampling points in a Cartesian coordinate system in a phase coding plane arranged according to a random time sequence;
s102, generating a spatial coding gradient according to the sampling point set and a preset sampling track for acquiring a magnetic resonance signal;
wherein the spatial encoding gradients include inter-layer phase encoding gradients, intra-layer phase encoding gradients, and frequency encoding gradients (k)x) The interlayer phase encoding gradient is used for determining the phase encoding direction (k) of the magnetic resonance signal between the layersz) For determining the intra-layer phase encoding direction (k) of the magnetic resonance signalsy) For determining the magnetic resonance signals in the frequency encoding direction (k)x) Upper position.
In the embodiment of the present application, the phase encoding plane ky-kzOne sampling point in a Cartesian coordinate system in a plane and a phase coding plane corresponds to one edge kxA directional K-space line.
In the embodiment of the application, the sampling track can be spiral or butterfly-shaped, and the butterfly-shaped sampling track is a sampling track set based on a butterfly-shaped navigation technology.
The butterfly type navigation technology is based on the following principle: the pulse scanning sequence is modified to enable the pre-wrap gradient waveform and the wrap-around gradient waveform to be used as a navigator, repeated acquisition of data of a K space central region is realized under the condition of hardly increasing the repetition time TR, and further, the motion is detected and corrected, and the name of butterfly is derived from the shape of the sampling track.
In the embodiment of the present application, the set of sampling points may be generated in advance or generated in real time, which is not limited in the embodiment of the present application.
In some embodiments, obtaining the set of sample points in step S101 includes:
bisecting the phase encoding plane into four quadrants;
for each quadrant, randomly sequencing sampling points in a Cartesian coordinate system in the quadrant, and taking a preset number of sampling points which are arranged in the quadrant according to a random time sequence as a subset of the sampling points corresponding to the quadrant;
and combining the sampling point subsets corresponding to the four quadrants to obtain the sampling point set.
For example, assume ky-kzThe number of the sampling points to be collected in the plane is N, and the N sampling points can be divided into ky-kzFour quadrants of the plane, the number of sampling points to be collected in the ith quadrant is
For the ith quadrant, randomly ordering sampling points in a Cartesian coordinate system in the quadrant, and arranging N in the quadrant according to a random time sequenceiAnd taking the sampling points as a subset of sampling points corresponding to the ith quadrant.
Of course, in other embodiments, all sampling points in the phase encoding plane under the cartesian coordinate system may also be randomly ordered, and a preset number of sampling points arranged according to a random time sequence may be used as the set of sampling points.
S103, based on the spatial coding gradient, in each repetition time, jointly collecting original navigation data which are under a non-Cartesian coordinate system and are located in a central area of a K space and imaging data which are under the Cartesian coordinate system and are located on the periphery of the K space;
in some embodiments, the jointly acquiring, in each repetition time, the raw navigation data in the non-cartesian coordinate system and located in the central region of the K space and the imaging data in the cartesian coordinate system and located in the periphery of the K space based on the spatial encoding gradient in step S103 includes:
for each receiving coil unit, based on the spatial coding gradient, in each repetition time, data sampling is performed on an imaging area of the detected body through the receiving coil unit, original navigation data in a non-cartesian coordinate system and imaging data corresponding to the sampling points in the cartesian coordinate system are collected together and filled into a K space line passing through the center of K space, the original navigation data is filled in the central area of the K space, and the imaging data is filled in the periphery of the K space.
In an embodiment of the application, the three-dimensional dynamic magnetic resonance image may comprise magnetic resonance images corresponding to a plurality of time frames; each time frame is used for carrying out preset round magnetic resonance signal acquisition, each round of magnetic resonance signal acquisition comprises preset sub-magnetic resonance signal acquisition, and each time of magnetic resonance signal acquisition acquires a sampling point in a phase encoding plane (one sampling point in the phase encoding plane corresponds to one sampling point along k direction)xA set of directional sampling points) that fills one of the K-space lines per magnetic resonance signal acquisition.
In the embodiment of the present application, during each magnetic resonance signal acquisition, the K space center can be started, and according to a butterfly-shaped sampling trajectory, a part of original navigation data under a non-cartesian coordinate system is acquired first, and then imaging data corresponding to a sampling point under the cartesian coordinate system is acquired (for example, the imaging data corresponding to the sampling point under the cartesian coordinate system is acquired along KxImaging data corresponding to multiple sampling points of the direction) and then acquiring another part of the original navigation data in a non-cartesian coordinate system and finally returning to the center of K space.
In the embodiment of the present application, before data acquisition, the number of time frames T for sampling data of an imaging region of a subject, and the number of rounds P, k of signal acquisition in each time frame may be set in advancey-kzThe number of sampling points to be acquired in the plane is N, and the frequency of acquiring signals in each round is N/(T multiplied by P).
In some embodiments, in each round of magnetic resonance signal acquisition, data acquisition is performed on at least one sampling point in each quadrant, the projections of the acquired original navigation data corresponding to the same quadrant in the phase encoding plane are the same, and the projections of the original navigation data corresponding to each quadrant in the phase encoding plane are perpendicular to each other.
In this embodiment of the application, the four quadrants may be adopted in turn when data is acquired, and the acquisition order of the sampling points in each quadrant is arranged according to a random timing sequence, for example, when a first round of magnetic resonance signals are acquired within a 1 st time frame, the sampling order is the sampling point in the first quadrant, the sampling point in the second quadrant, the sampling point in the third quadrant, and the sampling point in the fourth quadrant in turn, and the sampling point in the ith quadrant may be, for example, the first sampling point in a subset of sampling points corresponding to the ith quadrant.
For example, in each magnetic resonance signal acquisition, if k is pairedy-kzEach quadrant of the plane is used for acquiring data of one sampling point, the number of times of acquiring signals in a round of magnetic resonance signal acquisition is 4 × 1-4, if P is 3, when a first round of magnetic resonance signal acquisition is performed in a 1 st time frame, a sampling point in an ith quadrant is a first sampling point in a sampling point subset corresponding to the ith quadrant, when a second round of magnetic resonance signal acquisition is performed in the 1 st time frame, a sampling point in the ith quadrant is a second sampling point in the sampling point subset corresponding to the ith quadrant, when a third round of magnetic resonance signal acquisition is performed in the 1 st time frame, a sampling point in the ith quadrant is a third sampling point in the sampling point subset corresponding to the ith quadrant, when a first round of magnetic resonance signal acquisition is performed in a 2 nd time frame, a sampling point in the ith quadrant is a fourth sampling point in the sampling point subset corresponding to the ith quadrant, by analogy, the sampling point for each magnetic resonance signal acquisition can be determined. As shown in fig. 2, fig. 2 shows an example of data acquisition when P is 3, the shaded portion in fig. 2 is the projection of the original navigation data corresponding to each quadrant in the phase encoding plane, and the dot with number in fig. 2 is the sampling point of the quadrant.
As another example, in each round of magnetic resonance signal acquisition, if k is pairedy-kzEach quadrant of the plane is used for acquiring data of two sampling points, the number of times of acquiring signals in one round of magnetic resonance signal acquisition is 4 multiplied by 2 to 8, if P is 3, when a first round of magnetic resonance signal acquisition is performed in a 1 st time frame, sampling points in an ith quadrant are sequentially a first sampling point and a second sampling point in a sampling point subset corresponding to the ith quadrant, and when a second round of magnetic resonance signal acquisition is performed in the 1 st time frame, sampling points in the ith quadrant are sequentially the ith quadrantThe sampling points in the ith quadrant are sequentially the seventh sampling point and the eighth sampling point in the sampling point subset corresponding to the ith quadrant during the first round of magnetic resonance signal acquisition in the 2 nd time frame, and the sampling points in the ith quadrant can be determined by analogy.
In some embodiments, the projections of the raw navigation data corresponding to each quadrant in the phase encoding plane are rotated by 90 °/P relative to the projections of the raw navigation data corresponding to each quadrant in the phase encoding plane in the magnetic resonance signal acquisition of the current round by controlling the gradient change in the next magnetic resonance signal acquisition. For example, the projection of the original navigation data corresponding to the first quadrant during the second round of magnetic resonance signal acquisition in the 1 st time frame in the phase encoding plane is rotated by 90 °/3 ° to 30 ° with respect to the projection of the original navigation data corresponding to the first quadrant during the first round of magnetic resonance signal acquisition in the 1 st time frame in the phase encoding plane.
S104, converting the original navigation data into converted navigation data in a Cartesian coordinate system;
in some embodiments, the raw navigation data in the non-cartesian coordinate system may be converted into converted navigation data in the cartesian coordinate system by performing a GROG process or a gridding process on the raw navigation data.
And S105, carrying out compressed sensing reconstruction on the converted navigation data and the imaging data to obtain a three-dimensional dynamic magnetic resonance image.
In some embodiments, as shown in fig. 3, the performing compressed sensing reconstruction on the transformed navigation data and the imaging data in step S105 to obtain a three-dimensional dynamic magnetic resonance image includes:
s105-1, determining a time domain basis matrix according to the converted navigation data;
in the embodiment of the application, the corresponding conversion of each time frame can be realizedThe navigation data are respectively transformed to an image domain through Fourier transformation, and then a dynamic series matrix with low spatial resolution and high time resolution can be obtained
Where M is a width determination based on a projection of the original navigation data in the phase encoding plane, NcRepresenting the channel number, T representing the time frame number, and then performing singular value decomposition on the dynamic series matrix Q to obtain a time domain basis matrix
Where L represents the order, or number of eigenvalues, of the dynamic series matrix Q.
S105-2, acquiring sensitivity spectrum data of each receiving coil unit, wherein the sensitivity spectrum data is determined according to image data acquired by the receiving coil units during pre-scanning;
in the embodiment of the present application, there may be multiple ways for acquiring sensitivity spectrum data, which are exemplified below.
In some embodiments, the acquiring sensitivity spectrum data of each of the receiving coil units in step S105-2 includes:
and for any receiving coil unit in the plurality of receiving coil units, obtaining sensitivity spectrum data of the receiving coil unit according to the image data acquired by the receiving coil unit during pre-scanning.
For example, the image data acquired by the receiving coil unit is fiReceiving sensitivity spectrum data S of the coil uniti＝fi/SOS(fi)，
Wherein m is the total number of receiving coil units in the phased array coil, and sensitivity spectrum data can be obtained by the formula for other receiving coil units.
In other embodiments, the magnetic resonance imaging system includes a Quadrature Body Coil (QBC), and the acquiring sensitivity spectrum data of each of the receiving Coil units in step S105-2 includes:
for any receiving coil unit in the multiple receiving coil units, obtaining sensitivity spectrum data of the receiving coil unit according to image data acquired by the receiving coil unit during pre-scanning and image data acquired by the orthogonal general coil.
For example, the image data acquired by the receiving coil unit is fiAnd the image data acquired by the orthogonal general coil is Q, the sensitivity spectrum data S of the receiving coil uniti＝fiand/Q, sensitivity spectrum data can be obtained by the formula for other receiving coil units.
In some other embodiments, the acquiring sensitivity spectrum data of each of the receiving coil units in step S105-2 includes:
and for any receiving coil unit in the plurality of receiving coil units, obtaining sensitivity spectrum data of the receiving coil unit according to the imaging data acquired by the receiving coil unit during actual dynamic scanning.
For example, sampling points at the same positions acquired by the receiving coil unit are averaged in the time axis direction, and then fourier transform is performed on the averaged K-space data to obtain f image data corresponding to the receiving coil unitiReceiving sensitivity spectrum data S of the coil uniti＝fi/SOS(fi)，
Wherein m is the total number of receiving coil units in the phased array coil, and sensitivity spectrum data can be obtained by the formula for other receiving coil units.
S105-3, determining a spatial domain basis matrix according to the time domain basis matrix, the sensitivity spectrum data of each receiving coil unit and the imaging data;
in some embodiments, each of the receiving coil units corresponds to a channel, and determining a spatial domain basis matrix according to the time domain basis matrix, the sensitivity spectrum data of each of the receiving coil units, and the imaging data includes:
determining a spatial domain basis matrix by adopting a first formula according to the time domain basis matrix, the sensitivity distribution graph and the imaging data;
wherein the first formula is:
wherein,
is a space domain basis matrix, NcIs the number of channels, diImaging data corresponding to a sampling point under a Cartesian coordinate system and actually acquired by the ith channel, wherein omega is a down-sampling matrix, FsIs a Fourier transform matrix of k-space to space domain, SiSensitivity spectrum data of a receiving coil unit corresponding to the ith channel,
for the constraint term, λ is the weight of the constraint term, and Φ is the sparse transform matrix.
And S105-4, performing matrix multiplication on the space domain basis matrix and the time domain basis matrix to obtain the three-dimensional dynamic magnetic resonance image.
In the embodiment of the application, three-dimensional dynamic magnetic resonance image
For example, the space domain base matrix
And the time domain base matrix
The product of (a) and (b),namely, it is
Based on the same inventive concept, the present application further provides a magnetic resonance imaging apparatus, which is used in a magnetic resonance imaging system, the magnetic resonance imaging system includes a phased array coil, the phased array coil includes a plurality of receiving coil units, and referring to fig. 4, the apparatus includes: the device comprises a sampling point set generation module 11, a spatial coding gradient generation module 12, a data acquisition module 13, a data conversion module 14 and an image reconstruction module 15.
A sampling point set generating module 11 configured to obtain a set of sampling points; the sampling point set comprises a preset number of sampling points in a Cartesian coordinate system in a phase coding plane arranged according to a random time sequence;
a spatial encoding gradient generating module 12 configured to generate a spatial encoding gradient according to the set of sampling points and a preset sampling trajectory for acquiring the magnetic resonance signal;
a data acquisition module 13 configured to jointly acquire, at each repetition time, original navigation data in a non-cartesian coordinate system and located in a central region of K space and imaging data in a cartesian coordinate system and located at a periphery of K space, based on the spatial encoding gradient;
a data conversion module 14 configured to convert the original navigation data into converted navigation data in a cartesian coordinate system;
and the image reconstruction module 15 is configured to perform compressed sensing reconstruction on the converted navigation data and the imaging data to obtain a three-dimensional dynamic magnetic resonance image.
In a possible implementation manner, the sampling trajectory is a butterfly type and is a sampling trajectory set based on a butterfly type navigation technology.
In a possible implementation, the sampling point set generating module 11 is configured to:
bisecting the phase encoding plane into four quadrants;
for each quadrant, randomly sequencing sampling points in a Cartesian coordinate system in the quadrant, and taking a preset number of sampling points which are arranged in the quadrant according to a random time sequence as a subset of the sampling points corresponding to the quadrant;
and combining the sampling point subsets corresponding to the four quadrants to obtain the sampling point set.
In a possible implementation, the data acquisition module 13 is configured to:
for each receiving coil unit, based on the spatial coding gradient, in each repetition time, performing data sampling on an imaging region of the detected body through the receiving coil unit, collecting original navigation data in a non-Cartesian coordinate system and imaging data corresponding to the sampling point in the Cartesian coordinate system together, and filling the imaging data to a K space line passing through the center of the K space, wherein the original navigation data is filled in the central region of the K space, and the imaging data is filled in the periphery of the K space;
wherein the three-dimensional dynamic magnetic resonance image comprises magnetic resonance images corresponding to a plurality of time frames;
and performing preset magnetic resonance signal acquisition in each time frame, wherein each magnetic resonance signal acquisition comprises preset magnetic resonance signal acquisition, each magnetic resonance signal acquisition acquires a sampling point in a phase encoding plane, and each magnetic resonance signal acquisition fills one K space line.
In a possible implementation manner, in each round of magnetic resonance signal acquisition, data acquisition is performed on at least one sampling point in each quadrant respectively, projections of the acquired original navigation data corresponding to the same quadrant in a phase encoding plane are the same, and projections of the original navigation data corresponding to each quadrant in the phase encoding plane are perpendicular to each other.
In a possible implementation manner, as shown in fig. 5, the image reconstruction module 15 includes:
a time domain basis matrix determination sub-module 151 configured to determine a time domain basis matrix from the converted navigation data;
a sensitivity spectrum data acquisition sub-module 152 configured to acquire sensitivity spectrum data of each of the receiving coil units, the sensitivity spectrum data being determined from image data acquired by the receiving coil units at the time of pre-scanning;
a spatial domain basis matrix determination sub-module 153 configured to determine a spatial domain basis matrix from the temporal domain basis matrix, the sensitivity spectrum data of each of the receiving coil units, and the imaging data;
a dynamic image determination sub-module 154 configured to multiply the spatial domain basis matrix and the time domain basis matrix to obtain the three-dimensional dynamic magnetic resonance image.
In a possible implementation manner, each of the receiving coil units corresponds to one channel, and the spatial domain basis matrix determining submodule 153 is configured to:
determining a spatial domain basis matrix by adopting a first formula according to the time domain basis matrix, the sensitivity distribution graph and the imaging data;
wherein the first formula is:
wherein,
is a space domain basis matrix, NcIs the number of channels, diImaging data corresponding to a sampling point under a Cartesian coordinate system and actually acquired by the ith channel, wherein omega is a down-sampling matrix, FsIs a Fourier transform matrix of k-space to the spatial domain, SiSensitivity spectrum data of a receiving coil unit corresponding to the ith channel,
for the constraint term, λ is the weight of the constraint term, and Φ is the sparse transform matrix.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement without inventive effort.
Based on the same inventive concept, the present application further provides a storage medium, on which a computer program is stored, and the program, when executed by a processor, implements the steps of the magnetic resonance imaging method in any possible implementation manner.
Alternatively, the storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Based on the same inventive concept, referring to fig. 6, the present application further provides a medical apparatus, which includes a memory 71 (e.g., a non-volatile memory), a processor 72, and a computer program stored on the memory 71 and executable on the processor 72, wherein the processor 72 executes the program to implement the steps of the magnetic resonance imaging method in any possible implementation manner. The medical device may be, for example, an MRI system.
As shown in fig. 6, the medical device may also generally include: a memory 73, a network interface 74, and an internal bus 75. In addition to these components, other hardware may be included, which is not described in detail.
It should be noted that the magnetic resonance imaging apparatus can be implemented by software, which is a logical apparatus formed by reading computer program instructions stored in a nonvolatile memory into a memory 73 for execution by a processor 72 of a medical device in which the apparatus is located.
Embodiments of the subject matter and the functional operations described in this specification can be implemented in: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on a tangible, non-transitory program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode and transmit information to suitable receiver apparatus for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Computers suitable for executing computer programs include, for example, general and/or special purpose microprocessors, or any other type of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory and/or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer does not necessarily have such a device. Moreover, a computer may be embedded in another device, e.g., a mobile telephone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.
Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., an internal hard disk or a removable disk), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. In other instances, features described in connection with one embodiment may be implemented as discrete components or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Further, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be advantageous.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (16)
1. A magnetic resonance imaging method for use in a magnetic resonance imaging system including a phased array coil including a plurality of receive coil units, the method comprising:
acquiring a sampling point set; the sampling point set comprises a preset number of sampling points in a Cartesian coordinate system in a phase coding plane arranged according to a random time sequence;
generating a spatial coding gradient according to the sampling point set and a preset sampling track for acquiring the magnetic resonance signal;
based on the spatial coding gradient, in each repetition time, jointly collecting original navigation data which are under a non-Cartesian coordinate system and are located in a central area of a K space and imaging data which are under the Cartesian coordinate system and are located at the periphery of the K space;
converting the original navigation data into converted navigation data under a Cartesian coordinate system;
and carrying out compressed sensing reconstruction on the converted navigation data and the imaging data to obtain a three-dimensional dynamic magnetic resonance image.
2. The method of claim 1, wherein the co-acquiring, at each repetition time, raw navigation data in a non-cartesian coordinate system and located in a central region of K-space and imaging data in a cartesian coordinate system and located at a periphery of K-space based on the spatial encoding gradients comprises:
for each receiving coil unit, based on the spatial coding gradient, in each repetition time, performing data sampling on an imaging region of the detected body through the receiving coil unit, collecting original navigation data in a non-Cartesian coordinate system and imaging data corresponding to the sampling point in the Cartesian coordinate system together, and filling the imaging data to a K space line passing through the center of the K space, wherein the original navigation data is filled in the central region of the K space, and the imaging data is filled in the periphery of the K space;
wherein the three-dimensional dynamic magnetic resonance image comprises magnetic resonance images corresponding to a plurality of time frames;
and performing preset magnetic resonance signal acquisition in each time frame, wherein each magnetic resonance signal acquisition comprises preset magnetic resonance signal acquisition, each magnetic resonance signal acquisition acquires a sampling point in a phase encoding plane, and each magnetic resonance signal acquisition fills one K space line.
3. The method according to claim 1 or 2, wherein the sampling trajectory is of a butterfly type and is a sampling trajectory set based on a butterfly type navigation technique.
4. The method of claim 3, wherein obtaining the set of sample points comprises:
bisecting the phase encoding plane into four quadrants;
for each quadrant, randomly sequencing sampling points in a Cartesian coordinate system in the quadrant, and taking a preset number of sampling points which are arranged in the quadrant according to a random time sequence as a subset of the sampling points corresponding to the quadrant;
and combining the sampling point subsets corresponding to the four quadrants to obtain the sampling point set.
5. The method according to claim 4, wherein in each round of magnetic resonance signal acquisition, data acquisition is performed on at least one sampling point in each quadrant, the projections of the acquired raw navigation data corresponding to the same quadrant in the phase encoding plane are the same, and the projections of the raw navigation data corresponding to each quadrant in the phase encoding plane are perpendicular to each other.
6. The method of claim 1, wherein the compressed sensing reconstruction of the transformed navigation data and the imaging data to obtain a three-dimensional dynamic magnetic resonance image comprises:
determining a time domain basis matrix according to the converted navigation data;
acquiring sensitivity spectrum data of each receiving coil unit, wherein the sensitivity spectrum data is determined according to image data acquired by the receiving coil unit during pre-scanning;
determining a spatial domain basis matrix according to the time domain basis matrix, the sensitivity spectrum data of each receiving coil unit and the imaging data;
and performing matrix multiplication operation on the space domain basis matrix and the time domain basis matrix to obtain the three-dimensional dynamic magnetic resonance image.
7. The method of claim 6, wherein each of the receiver coil units corresponds to a channel, and wherein determining a spatial domain basis matrix from the time domain basis matrix, the sensitivity spectrum data of each of the receiver coil units, and the imaging data comprises:
determining a spatial domain basis matrix by adopting a first formula according to the time domain basis matrix, the sensitivity distribution graph and the imaging data;
wherein the first formula is:
wherein,
is a space domain basis matrix, NcIs the number of channels, diImaging data corresponding to a sampling point under a Cartesian coordinate system and actually acquired by the ith channel, wherein omega is a down-sampling matrix, FsIs a Fourier transform matrix of k-space to the spatial domain, SiSensitivity spectrum data of a receiving coil unit corresponding to the ith channel,
is a constraint term, λ is the weight of the constraint term, Φ is the sparse transform matrix,
is a time domain base matrix.
8. An apparatus for magnetic resonance imaging, the apparatus being used in a magnetic resonance imaging system comprising a phased array coil including a plurality of receive coil units, the apparatus comprising:
a sampling point set generating module configured to obtain a set of sampling points; the sampling point set comprises a preset number of sampling points in a Cartesian coordinate system in a phase coding plane arranged according to a random time sequence;
a spatial encoding gradient generating module configured to generate a spatial encoding gradient according to the set of sampling points and a preset sampling trajectory for acquiring a magnetic resonance signal;
the data acquisition module is configured to perform joint acquisition on original navigation data which are under a non-Cartesian coordinate system and are positioned in a central area of the K space and imaging data which are under the Cartesian coordinate system and are positioned at the periphery of the K space in each repetition time based on the spatial coding gradient;
a data conversion module configured to convert the original navigation data into converted navigation data in a Cartesian coordinate system;
and the image reconstruction module is configured to perform compressed sensing reconstruction on the converted navigation data and the imaging data to obtain a three-dimensional dynamic magnetic resonance image.
9. The apparatus of claim 8, wherein the data acquisition module is configured to:
for each receiving coil unit, based on the spatial coding gradient, in each repetition time, performing data sampling on an imaging area of the detected body through the receiving coil unit, collecting original navigation data under a non-Cartesian coordinate system and imaging data corresponding to the sampling point under the Cartesian coordinate system together, and filling the imaging data to a K space line passing through the center of K space, wherein the original navigation data is filled in the central area of the K space, and the imaging data is filled in the periphery of the K space;
wherein the three-dimensional dynamic magnetic resonance image comprises magnetic resonance images corresponding to a plurality of time frames;
and performing preset magnetic resonance signal acquisition in each time frame, wherein each magnetic resonance signal acquisition comprises preset magnetic resonance signal acquisition, each magnetic resonance signal acquisition acquires a sampling point in a phase encoding plane, and each magnetic resonance signal acquisition fills one K space line.
10. The apparatus according to claim 8 or 9, wherein the sampling trajectory is of a butterfly type and is a sampling trajectory set based on a butterfly type navigation technology.
11. The apparatus of claim 10, wherein the set of sampling points generation module is configured to:
bisecting the phase encoding plane into four quadrants;
for each quadrant, randomly sequencing sampling points in a Cartesian coordinate system in the quadrant, and taking a preset number of sampling points which are arranged in the quadrant according to a random time sequence as a subset of the sampling points corresponding to the quadrant;
and combining the sampling point subsets corresponding to the four quadrants to obtain the sampling point set.
12. The apparatus according to claim 11, wherein in each magnetic resonance signal acquisition, data acquisition is performed on at least one sampling point in each quadrant, and projections of the acquired raw navigation data corresponding to the same quadrant in the phase encoding plane are the same, and projections of the raw navigation data corresponding to each quadrant in the phase encoding plane are perpendicular to each other.
13. The apparatus of claim 8, wherein the image reconstruction module comprises:
a time domain basis matrix determination submodule configured to determine a time domain basis matrix from the converted navigation data;
a sensitivity spectrum data acquisition sub-module configured to acquire sensitivity spectrum data of each of the receiving coil units, the sensitivity spectrum data being determined according to image data acquired by the receiving coil units at the time of pre-scanning;
a spatial domain basis matrix determination sub-module configured to determine a spatial domain basis matrix from the time domain basis matrix, the sensitivity spectrum data of each of the receive coil units, and the imaging data;
and the dynamic image determining submodule is configured to perform matrix multiplication on the space domain basis matrix and the time domain basis matrix to obtain the three-dimensional dynamic magnetic resonance image.
14. The apparatus of claim 13, wherein each of the receive coil units corresponds to one channel, and wherein the spatial domain basis matrix determination submodule is configured to:
determining a spatial domain basis matrix by adopting a first formula according to the time domain basis matrix, the sensitivity distribution graph and the imaging data;
wherein the first formula is:
wherein,
is a space domain basis matrix, NcIs the number of channels, diImaging data corresponding to a sampling point under a Cartesian coordinate system and actually acquired by the ith channel, wherein omega is a down-sampling matrix, FsIs a Fourier transform matrix of k-space to the spatial domain, SiSensitivity spectrum data of a receiving coil unit corresponding to the ith channel,
is a constraint term, λ is the weight of the constraint term, Φ is the sparse transform matrix,
is a time domain base matrix.
15. A storage medium having a computer program stored thereon, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
16. A medical device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any one of claims 1-7 are implemented when the program is executed by the processor.
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