CN115598575A - Magnetic resonance imaging method, magnetic resonance imaging apparatus, computer device, and storage medium - Google Patents
Magnetic resonance imaging method, magnetic resonance imaging apparatus, computer device, and storage medium Download PDFInfo
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Abstract
The present application relates to a magnetic resonance imaging method, apparatus, computer device and storage medium. The method comprises the following steps: determining a plurality of sampling trajectories in non-cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold; acquiring signals according to the plurality of sampling tracks, and filling the acquired echo signals into the non-Cartesian k space; rearranging the sampled data filled in the non-cartesian k-space into cartesian k-space; and performing image reconstruction processing based on the rearranged data filled in the Cartesian k space to obtain a magnetic resonance image. By adopting the method, the residual artifacts can be eliminated.
Description
Technical Field
The present application relates to the field of magnetic resonance imaging technology, and in particular, to a magnetic resonance imaging method, apparatus, computer device, and storage medium.
Background
Magnetic Resonance Imaging (MRI) Imaging is one of the most advanced medical Imaging methods today and is finding increasingly widespread use in clinical and scientific research.
In cartesian k-space magnetic resonance sampling, double oversampling is usually used to eliminate wrap-around artifacts when acquiring signals corresponding to frequency-encoded readout gradients. However, in the non-cartesian k-space magnetic resonance sampling method, double oversampling is also used in signal acquisition, and thus some artifacts remain in the magnetic resonance image. Therefore, how to eliminate artifacts in a non-cartesian k-space magnetic resonance sampling method is a technical problem to be solved.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a magnetic resonance imaging method, apparatus, computer device and storage medium capable of eliminating residual artifacts.
A magnetic resonance imaging method, the method comprising:
determining a plurality of sampling trajectories in non-cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold;
carrying out signal acquisition according to a plurality of sampling tracks, and filling the acquired echo signals into a non-Cartesian k space;
rearranging the sampled data filled in the non-cartesian k-space into cartesian k-space;
and performing image reconstruction processing based on the rearrangement data filled in the Cartesian k space to obtain a magnetic resonance image.
In one embodiment, the determining the plurality of sampling trajectories in the non-cartesian k-space includes:
for each sampling track, acquiring a starting point position, a rotation angular velocity, a radial screw-out velocity, a scanning time length and an oversampling multiple;
rotating at a rotation angular velocity and a radial unscrewing velocity from the starting point position to obtain a spiral line;
and determining the number of sampling points in the spiral line according to the scanning time length and the oversampling multiple to obtain a sampling track.
In one embodiment, the acquiring signals according to a plurality of sampling trajectories and filling the acquired echo signals into a non-cartesian k-space includes:
for each sampling track, determining excitation pulses and oscillation gradients on each logic axis of magnetic resonance according to the position of the sampling point in the sampling track to obtain a scanning sequence;
scanning a detection object by using a scanning sequence, and acquiring an echo signal generated by the detection object;
and filling the echo signals into corresponding sampling points of the non-Cartesian k-space.
In one embodiment, the scan sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.
In one embodiment, the rearranging of the sample data filled in the non-cartesian k-space into the cartesian k-space includes:
for each target fill position of a Cartesian k-space, obtaining a plurality of target sample data in a non-Cartesian k-space corresponding to the target fill position;
and performing data rearrangement processing on the plurality of target sampling data by using a preset data rearrangement algorithm, and filling the rearranged data obtained by processing into the target filling position.
In one embodiment, the acquiring a plurality of target sample data in non-cartesian k-space corresponding to the target filling position includes:
calculating the relevance between each sampling point and the target filling position in the non-Cartesian k space by utilizing a preset relevance function;
and determining the sampling point with the relevance conforming to the preset condition as a target sampling point corresponding to the target filling position, and determining the sampling data filled in the target sampling point as target sampling data.
In one embodiment, the performing, by using a preset data rearrangement algorithm, data rearrangement processing on a plurality of target sample data includes:
acquiring a weight coefficient corresponding to each target sampling data;
and calculating the multiple target sampling data by using a data rearrangement algorithm and the weight coefficient to obtain rearranged data.
A magnetic resonance imaging apparatus, the apparatus comprising:
a sampling trajectory determination module for determining a plurality of sampling trajectories in non-Cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold;
the signal filling module is used for carrying out signal acquisition according to a plurality of sampling tracks and filling the acquired echo signals into a non-Cartesian k space;
the data rearrangement module is used for rearranging the sampling data filled in the non-Cartesian k space into a Cartesian k space;
and the image reconstruction module is used for carrying out image reconstruction processing on the basis of the rearrangement data filled in the Cartesian k space to obtain a magnetic resonance image.
In one embodiment, the sampling trajectory determining module is specifically configured to, for each sampling trajectory, obtain a start point position, a rotation angular velocity, a radial swing-out velocity, a scanning duration, and an oversampling multiple; rotating at a rotation angular velocity and a radial unscrewing velocity from the starting point position to obtain a spiral line; and determining the number of sampling points in the spiral line according to the scanning time length and the oversampling multiple to obtain a sampling track.
In one embodiment, the signal filling module is specifically configured to determine, for each sampling trajectory, an excitation pulse and an oscillation gradient on each logical axis of magnetic resonance according to a sampling point position in the sampling trajectory to obtain a scanning sequence; scanning a detection object by using a scanning sequence, and acquiring an echo signal generated by the detection object; and filling the echo signals into corresponding sampling points of the non-Cartesian k-space.
In one embodiment, the scan sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.
In one embodiment, the data reordering module includes:
the data acquisition sub-module is used for acquiring a plurality of target sampling data in a non-Cartesian k space corresponding to each target filling position of the Cartesian k space;
and the rearrangement processing submodule is used for carrying out data rearrangement processing on the plurality of target sampling data by utilizing a preset data rearrangement algorithm and filling the rearranged data obtained by processing into the target filling position.
In one embodiment, the data obtaining sub-module is specifically configured to calculate a correlation between each sampling point and a target filling position in a non-cartesian k-space by using a preset correlation function; and determining the sampling points with the association degrees meeting the preset conditions as target sampling points corresponding to the target filling positions, and determining the sampling data filled in the target sampling points as target sampling data.
In one embodiment, the rearrangement processing sub-module is specifically configured to obtain a weight coefficient corresponding to each target sample data; and calculating the multiple target sampling data by using a data rearrangement algorithm and the weight coefficient to obtain rearranged data.
A computer device comprising a memory storing a computer program and a processor implementing the following steps when the computer program is executed:
determining a plurality of sampling trajectories in non-cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold;
carrying out signal acquisition according to a plurality of sampling tracks, and filling the acquired echo signals into a non-Cartesian k space;
rearranging the sampled data filled in the non-cartesian k-space into cartesian k-space;
and performing image reconstruction processing based on the rearranged data filled in the Cartesian k space to obtain a magnetic resonance image.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
determining a plurality of sampling trajectories in non-cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold;
carrying out signal acquisition according to a plurality of sampling tracks, and filling the acquired echo signals into a non-Cartesian k space;
rearranging the sampled data filled in the non-Cartesian k space into a Cartesian k space;
and performing image reconstruction processing based on the rearrangement data filled in the Cartesian k space to obtain a magnetic resonance image.
The magnetic resonance imaging method, the magnetic resonance imaging device, the computer equipment and the storage medium determine a plurality of sampling tracks in a non-Cartesian k space; carrying out signal acquisition according to a plurality of sampling tracks, and filling the acquired echo signals into a non-Cartesian k space; rearranging the sampled data filled in the non-Cartesian k space into a Cartesian k space; and performing image reconstruction processing based on the rearranged data filled in the Cartesian k space to obtain a magnetic resonance image. In the embodiment of the disclosure, because the sampling trajectory in the non-cartesian k-space is a spiral line, and the collection of a plurality of spiral lines starts from the center of the k-space, the high-density sampling of the center of the k-space can be used for eliminating the convolution artifact; in addition, because the number of sampling points in each sampling track is greater than a preset number threshold, the non-Cartesian k space provides enough sampling data for data rearrangement, and then a magnetic resonance image is generated according to the Cartesian k space, so that residual artifacts can be further eliminated, and the quality of the magnetic resonance image is improved.
Drawings
FIG. 1 is a diagram of an embodiment of a magnetic resonance imaging method;
figure 2 is a flow chart illustrating a magnetic resonance imaging method according to an embodiment;
FIG. 3 is a diagram illustrating spatial correspondence and data reordering in one embodiment;
FIG. 4 is a flowchart illustrating the step of determining a plurality of sampling trajectories in non-Cartesian k-space in one embodiment;
figure 5 is a schematic illustration of magnetic resonance image contrast in one embodiment;
FIG. 6 is a schematic flow chart of the signal acquisition and signal filling steps in one embodiment;
FIG. 7 is a flow chart showing steps of a data rearrangement process in one embodiment;
FIG. 8 is a diagram illustrating spatial correspondence and data reordering in one embodiment;
FIG. 9 is a block diagram showing an arrangement of an MRI apparatus in an embodiment;
FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The magnetic resonance imaging method provided by the application can be applied to the application environment as shown in fig. 1. The application environment is a magnetic resonance system, the magnetic resonance system 100 includes a bed 110, an MR scanner 120 and a processor 130, the MR scanner 120 includes a magnet, a radio frequency transmission coil, a gradient coil and a radio frequency receiving coil. The bed body 110 is used for bearing a target object 010, the radio frequency transmitting coil is used for transmitting radio frequency pulses to the target object, and the gradient coil is used for generating a gradient field which can be along a phase encoding direction, a layer selecting direction or a frequency encoding direction and the like; a radio frequency receive coil is used to receive the magnetic resonance signals. In one embodiment, the magnet of the MR scanner 120 may be a permanent magnet or a superconducting magnet, and the radio frequency coils constituting the radio frequency unit may be divided into a body coil and a local coil according to functions. In one embodiment, the radio frequency transmit coil, the radio frequency receive coil may be of the kind of 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 radio frequency transmit coil is configured as a birdcage coil, the local coil is configured as an array coil, and the array coil can be configured in a 4-channel mode, an 8-channel mode, or a 16-channel mode.
The magnetic resonance system 100 further includes a controller 140 and an output device 150, wherein the controller 140 can simultaneously monitor or control the MR scanner 110, the processor 130 and the output device 150. The controller 140 may include one or a combination of a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field-Programmable Gate Array (FPGA), an ARM Processor, 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, and operating state of the MR scanner 110 of the subject, and the like. The output device 150 may be one or a combination of Cathode Ray Tube (CRT) output device, liquid Crystal Display (LCD) output device, organic Light Emitting Display (OLED), plasma output device, and so on.
The magnetic resonance 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.
In one embodiment, the processor 130 may control the MR scanner 120 to perform equal-interval or unequal-interval sampling on the detection object (local part of the target object 010), and control the MR scanner 120 to acquire magnetic resonance signals of the detection object, and perform fourier transform on the magnetic resonance signals to obtain a magnetic resonance image of the detection object.
In one embodiment, as shown in fig. 2, a magnetic resonance imaging method is provided, which is exemplified by the application of the method to the magnetic resonance system in fig. 1, and includes the following steps:
The sampling tracks are spiral lines, and the number of sampling points in each sampling track is larger than a preset number threshold.
The rolling artifact is an artifact commonly found in a magnetic resonance image, and if the data quantity of a k-space central area is increased, a larger frequency sampling range can be obtained, and the artifact caused by the frequency error identification in Fourier transform is removed on the magnetic resonance image. Wherein the acquisition of the helix starts in the central region of k-space, and the sampling trajectory is thus determined as a helix.
Meanwhile, since the sampling trajectory of the non-cartesian k-space is a spiral line and the magnetic resonance image is generated from data in the cartesian k-space, data filled in the non-cartesian k-space also needs to be rearranged to the cartesian k-space. In the data rearrangement process, if the number of sampling data participating in the rearrangement is small, a part of artifacts may remain in the image after the magnetic resonance image is generated. To eliminate the residual artifacts, the number of sample data participating in the rearrangement may be increased. Therefore, in practical applications, the number of sampling points in each sampling track should be greater than the preset number threshold. The preset number threshold may be set according to a scanning parameter such as FOV, resolution, and hardware gradient performance, which is not limited in this disclosure.
In practical applications, the time interval between sampling points needs to satisfy the following nyquist sampling principle, and the number of sampling points in each sampling trace is determined by the total sampling duration and the sampling time interval.
Wherein gamma is hydrogen proton magnetic rotation ratio, FOV is imaging visual field, and 2BW is bandwidth, which is reciprocal of time interval of digital sampling point.
The processor of the magnetic resonance system can obtain a track function corresponding to each sampling track, obtain the number of sampling points in each sampling track, and then determine the sampling tracks according to the track function and the number of the sampling points. Where the sampling trajectory may also be referred to as a leaf in k-space, the trajectory function may be a function including amplitude, phase and time. The embodiments of the present disclosure do not limit this.
After determining a plurality of sampling tracks in the non-cartesian k-space, a processor of the magnetic resonance system determines a scanning sequence according to the sampling tracks, then controls the MR scanner to scan the detection object according to the scanning sequence, and acquires echo signals. The processor of the magnetic resonance system then fills the echo signals acquired by the MR scanner into non-cartesian k-space. The scanning sequence is not limited in the embodiment of the disclosure, and can be set according to actual conditions.
As shown in fig. 3, the sampling trajectory in the non-cartesian k-space is a spiral line, the sampling trajectory in the cartesian k-space is a straight line, and the non-cartesian k-space and the cartesian k-space have a corresponding relationship. The processor of the magnetic resonance system can rearrange the sampled data in the non-cartesian k-space by using the correspondence, and fill the obtained rearranged data into the cartesian k-space.
As shown in fig. 3, for a filling position in cartesian k-space, rearranged data may be calculated from 8 sampled data in non-cartesian k-space and then filled into the filling position.
And 204, carrying out image reconstruction processing based on the rearrangement data filled in the Cartesian k space to obtain a magnetic resonance image.
And after the Cartesian k space is filled, performing image reconstruction processing on the rearranged data in the Cartesian k space to obtain a magnetic resonance image. For example, the data reconstruction in cartesian k-space may employ a sensitivity encoding (SENSE) reconstruction method, a simultaneous acquisition of spatial harmonics (SMASH) method, a generalized self-calibrating partially parallel acquisition (GRAPPA) method, a machine learning-based reconstruction method, a compressed sensing algorithm, and the like. The image reconstruction mode is not limited in the embodiment of the disclosure, and can be set according to actual conditions.
In the magnetic resonance imaging method, a plurality of sampling trajectories in non-cartesian k-space are determined; carrying out signal acquisition according to a plurality of sampling tracks, and filling the acquired echo signals into a non-Cartesian k space; rearranging the sampled data filled in the non-Cartesian k space into a Cartesian k space; and performing image reconstruction processing based on the rearranged data filled in the Cartesian k space to obtain a magnetic resonance image. In the embodiment of the disclosure, because the sampling trajectory in the non-cartesian k-space is a spiral line, and the collection of the spiral line starts from the center of the k-space, the convolution artifact can be eliminated by the high-density k-space center sampling; in addition, because the number of sampling points in each sampling track is greater than a preset number threshold, the non-Cartesian k space provides enough sampling data for data rearrangement, and then the magnetic resonance image is generated according to the Cartesian k space, so that residual artifacts can be further eliminated, and the quality of the magnetic resonance image is improved.
In one embodiment, as shown in fig. 4, the step of determining a plurality of sampling trajectories in non-cartesian k-space may include:
For each sampling trajectory, the processor of the magnetic resonance system may obtain pre-stored starting point positions, rotational angular velocities, radial run-out velocities, scan durations, and oversampling multiples. Where the starting point location is typically the origin of non-cartesian k-space, the oversampling factor is typically set by default to double. However, the number of sampling points determined by the double oversampling factor cannot satisfy the requirement of avoiding artifact residue. Thus, the processor may obtain the oversampling multiple of the user input. The oversampling factor may be four times, eight times, or higher. The oversampling multiple is not limited in the embodiments of the present disclosure.
And step 302, starting from the starting point position, rotating at a rotation angular velocity and a radial unscrewing velocity to obtain a spiral line.
And after the processor obtains the initial point position, performing Archimedes rotation from the initial point position at a rotation angular velocity and a radial unscrewing velocity to obtain a spiral line. Archimedean spinning is the rotation of a point around a fixed point at a constant angular velocity away from the fixed point and at a constant angular velocity of rotation.
After the processor determines the scanning duration and the oversampling multiple, the number of sampling points in the spiral line is calculated according to the scanning duration and the oversampling multiple, and a sampling track is obtained according to the number of the sampling points.
For example, when the scanning time is t and the oversampling multiple is twice, the number of sampling points in the spiral line is a; under the condition that the oversampling multiple is four times, the number of sampling points in the spiral line is 2a; in the case where the oversampling multiple is eight times, the number of sampling points in the spiral line is 4a. It can be seen that, for each sampling trajectory, under the condition that the scanning duration is fixed, the higher the oversampling multiple is, the smaller the time interval between sampling points is, and the more the number of sampling points in the spiral line is; wherein a is an integer and can take the values of 100, 200, 1000, 10000 and the like.
As shown in fig. 5, in the water model test, the FOV used is 200mm × 200mm, the matrix is 128 × 128, the layer thickness is 5mm, the number of sampling tracks is 16, and the oversampling multiples of the left, middle and right three images are two, four and eight, respectively. As can be seen from fig. 5, the ringing artifacts gradually decrease as the oversampling factor increases.
In the process of determining a plurality of sampling tracks in the non-Cartesian k space, acquiring a starting point position, a rotation angular velocity, a radial spin-out velocity scanning duration and an oversampling multiple for each sampling track; rotating at a rotation angular velocity and a radial unscrewing velocity from the starting point position to obtain a spiral line; and determining the number of sampling points in the spiral line according to the scanning time length and the oversampling multiple to obtain a sampling track. In the embodiment of the present disclosure, in the case that the scanning duration is fixed, the residual artifact may be gradually eliminated with the increase of the oversampling multiple, so as to improve the quality of the magnetic resonance image.
In an embodiment, as shown in fig. 6, the step of acquiring signals according to a plurality of sampling trajectories and filling the acquired echo signals into non-cartesian k-space may include:
After the processor determines the sampling tracks, the positions of the sampling points in the sampling tracks can be determined. For each sampling track, the processor can determine the excitation pulse and the oscillation gradient on each logic axis of magnetic resonance according to the position of the sampling point in the sampling track; and then generating a scanning sequence according to the excitation pulses corresponding to the plurality of sampling tracks and the oscillation gradients on each logic axis of the magnetic resonance.
In one embodiment, the scan sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.
And after the processor obtains the scanning sequence, the MR scanner is controlled to scan the detection object according to the scanning sequence, and echo signals generated by the detection object are collected.
In the case of a fast spin echo sequence, the MR scanner may acquire a plurality of echo signals forming an echo train during each scan. It can be understood that, by using the fast spin echo sequence, the acquisition efficiency of the echo signal can be improved, thereby improving the imaging efficiency of the magnetic resonance image.
And step 403, filling the echo signals into corresponding sampling points of the non-Cartesian k space.
And after the processor acquires the echo signals acquired by the MR scanner, filling the echo signals into corresponding sampling points of a non-Cartesian k space.
In the process of carrying out signal acquisition according to a plurality of sampling tracks and filling the acquired echo signals into a non-Cartesian k space, for each sampling track, determining excitation pulses and oscillation gradients on each logic axis of magnetic resonance according to the position of the sampling point in the sampling track to obtain a scanning sequence; scanning a detection object by using a scanning sequence, and acquiring an echo signal generated by the detection object; the echo signals are filled into corresponding sampling points of non-cartesian k-space. In the embodiment of the disclosure, scanning is performed according to a sampling track, and then the acquired echo signals are filled into a non-cartesian k space, so that a data basis is provided for subsequent image rearrangement and image reconstruction.
In one embodiment, as shown in fig. 7, the step of rearranging the filled sampling data in the non-cartesian k-space into the cartesian k-space may include:
The processor calculates the association degree between each sampling point and the target filling position in the non-Cartesian k space by using a preset association degree function; and determining the sampling points with the association degrees meeting the preset conditions as target sampling points corresponding to the target filling positions, and determining the sampling data filled in the target sampling points as target sampling data.
As shown in fig. 3, there is an association between one filling position in cartesian k-space and each sampling point in non-cartesian k-space. The processor calculates the association degree between each sampling point and a target filling position X in a non-Cartesian k space by using an association degree function, wherein if the association degree between each sampling point and the target filling position X is in accordance with a preset condition, and the association degree between each sampling point 1, 2 \8230, 8 and the target filling position X is not in accordance with the preset condition, the sampling points 1, 2 \8230, 8 are determined as target sampling points corresponding to the target filling position X, and the sampling data filled in the sampling points 1, 2 \8230, 8 are determined as target sampling data.
The correlation degree may be used to represent the distance between the sampling point position and the target filling position, the correlation function may be a Kaiser-Bessel window function, and the preset condition may include that the correlation degree is greater than a preset correlation degree threshold. The relevance, the relevance function and the preset relevance threshold are not limited in the embodiment of the disclosure.
As shown in fig. 8, in the case where the oversampling multiple is two times, the number of target sampling points whose association with the target filling position meets the preset condition is small, whereas in fig. 3, in the case where the oversampling multiple is four times or eight times, the number of target sampling points whose association with the target filling position meets the preset condition is increased, and the number of sample data corresponding to the target filling position is increased accordingly.
After acquiring and determining target sampling data corresponding to the target filling position, the processor acquires a weight coefficient corresponding to each target sampling data; and calculating the multiple target sampling data by using a data rearrangement algorithm and the weight coefficient to obtain rearranged data.
For example, if the data rearrangement algorithm is a weighted average method, weighted average calculation is performed on the sample data filled in the sample points 1, 2 \8230; 82308 according to the weight coefficient corresponding to each target sample data, and the average value is determined as the rearranged data. Or, if the data rearrangement algorithm is a weighted summation method, weighted summation calculation is carried out on the sampling data filled in the sampling points 1 and 2 \8230; 8230; 8 according to the weight coefficient corresponding to each target sampling data, and the sum is determined as the rearranged data. The data rearrangement algorithm is not limited in the embodiment of the disclosure, and can be set according to actual conditions.
As can be seen from fig. 3 and 8, after the oversampling factor is increased, the sampling data corresponding to the target filling position is increased, so that the calculated rearrangement data can be more accurate, thereby eliminating the artifact remaining in the magnetic resonance image.
And after the processor obtains the rearranged data, filling the rearranged data into the target filling position.
For example, the processor performs weighted average calculation on the sampling data filled in the sampling points 1, 2 \8230; 8 to obtain an average value, and fills the average value into the target filling position X. Or the processor carries out weighted average calculation on the sampling data filled in the sampling points 1, 2 \8230, 8230and 8 to obtain a sum, and the sum is filled into the target filling position X.
In the above embodiment, for each target filling position of the cartesian k-space, a plurality of target sampling data in the non-cartesian k-space corresponding to the target filling position are obtained; and performing data rearrangement processing on the plurality of target sampling data by using a preset data rearrangement algorithm, and filling the rearranged data obtained by processing into the target filling position. In the embodiment of the disclosure, the relevance function and the data rearrangement algorithm are used for calculating the rearranged data, so that the data conversion process of the non-Cartesian k space and the Cartesian k space is realized, and the magnetic resonance image can be generated. Furthermore, the oversampling multiple is increased, so that the rearranged data can be more accurate, thereby eliminating the artifact remained in the magnetic resonance image and improving the quality of the magnetic resonance image.
It should be understood that, although the steps in the flowcharts of fig. 2 to 8 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2 to 8 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the steps or stages in other steps.
In one embodiment, as shown in fig. 9, there is provided a magnetic resonance imaging apparatus comprising:
a sampling trajectory determination module 601 for determining a plurality of sampling trajectories in non-cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold;
a signal filling module 602, configured to perform signal acquisition according to multiple sampling trajectories, and fill acquired echo signals into a non-cartesian k-space;
a data rearranging module 603, configured to rearrange the sample data filled in the non-cartesian k-space into cartesian k-space;
and an image reconstruction module 604, configured to perform image reconstruction processing based on the rearranged data filled in the cartesian k-space, so as to obtain a magnetic resonance image.
In one embodiment, the sampling trajectory determining module 601 is specifically configured to, for each sampling trajectory, obtain a start point position, a rotation angular velocity, a radial unwinding velocity, a scanning duration, and an oversampling multiple; rotating at a rotation angular velocity and a radial unscrewing velocity from the starting point position to obtain a spiral line; and determining the number of sampling points in the spiral line according to the scanning time length and the oversampling multiple to obtain a sampling track.
In one embodiment, the signal filling module 602 is specifically configured to determine, for each sampling track, an excitation pulse and an oscillation gradient on each logical axis of magnetic resonance according to a sampling point position in the sampling track to obtain a scanning sequence; scanning a detection object by using a scanning sequence, and acquiring an echo signal generated by the detection object; the echo signals are filled into corresponding sampling points of non-cartesian k-space.
In one embodiment, the scan sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.
In one embodiment, the data reordering module 603 includes:
the data acquisition sub-module is used for acquiring a plurality of target sampling data in a non-Cartesian k space corresponding to each target filling position of the Cartesian k space;
and the rearrangement processing submodule is used for carrying out data rearrangement processing on a plurality of target sampling data by utilizing a preset data rearrangement algorithm and filling the rearranged data obtained by processing into a target filling position.
In one embodiment, the data obtaining sub-module is specifically configured to calculate a correlation between each sampling point and a target filling position in a non-cartesian k-space by using a preset correlation function; and determining the sampling point with the relevance conforming to the preset condition as a target sampling point corresponding to the target filling position, and determining the sampling data filled in the target sampling point as target sampling data.
In one embodiment, the rearrangement processing sub-module is specifically configured to obtain a weight coefficient corresponding to each target sample data; and calculating the multiple target sampling data by using a data rearrangement algorithm and the weight coefficient to obtain rearranged data.
For specific limitations of the magnetic resonance imaging apparatus, reference may be made to the above limitations of the magnetic resonance imaging method, which are not described in detail here. The modules in the magnetic resonance imaging apparatus can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a magnetic resonance imaging method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
determining a plurality of sampling trajectories in non-cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold;
carrying out signal acquisition according to a plurality of sampling tracks, and filling the acquired echo signals into a non-Cartesian k space;
rearranging the sampled data filled in the non-Cartesian k space into a Cartesian k space;
and performing image reconstruction processing based on the rearrangement data filled in the Cartesian k space to obtain a magnetic resonance image.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
for each sampling track, acquiring a starting point position, a rotation angular velocity, a radial screw-out velocity, a scanning duration and an oversampling multiple;
rotating at a rotation angular velocity and a radial unscrewing velocity from the starting point position to obtain a spiral line;
and determining the number of sampling points in the spiral line according to the scanning time length and the oversampling multiple to obtain a sampling track.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
for each sampling track, determining excitation pulses and oscillation gradients on each logic axis of magnetic resonance according to the positions of the sampling points in the sampling tracks to obtain a scanning sequence;
scanning a detection object by using a scanning sequence, and collecting an echo signal generated by the detection object;
and filling the echo signals into corresponding sampling points of the non-Cartesian k-space.
In one embodiment, the scan sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
for each target filling position of the Cartesian k-space, acquiring a plurality of target sampling data in the non-Cartesian k-space corresponding to the target filling position;
and performing data rearrangement processing on the plurality of target sampling data by using a preset data rearrangement algorithm, and filling the rearranged data obtained by processing into the target filling position.
In one embodiment, the processor when executing the computer program further performs the steps of:
calculating the relevance between each sampling point and the target filling position in the non-Cartesian k space by utilizing a preset relevance function;
and determining the sampling point with the relevance conforming to the preset condition as a target sampling point corresponding to the target filling position, and determining the sampling data filled in the target sampling point as target sampling data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring a weight coefficient corresponding to each target sampling data;
and calculating the multiple target sampling data by using a data rearrangement algorithm and the weight coefficient to obtain rearranged data.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, performs the steps of:
determining a plurality of sampling trajectories in non-cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold;
carrying out signal acquisition according to a plurality of sampling tracks, and filling the acquired echo signals into a non-Cartesian k space;
rearranging the sampled data filled in the non-cartesian k-space into cartesian k-space;
and performing image reconstruction processing based on the rearrangement data filled in the Cartesian k space to obtain a magnetic resonance image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
for each sampling track, acquiring a starting point position, a rotation angular velocity, a radial screw-out velocity, a scanning time length and an oversampling multiple;
rotating at a rotation angular velocity and a radial unscrewing velocity from the starting point position to obtain a spiral line;
and determining the number of sampling points in the spiral line according to the scanning time length and the oversampling multiple to obtain a sampling track.
In one embodiment, the computer program when executed by the processor further performs the steps of:
for each sampling track, determining excitation pulses and oscillation gradients on each logic axis of magnetic resonance according to the position of the sampling point in the sampling track to obtain a scanning sequence;
scanning a detection object by using a scanning sequence, and collecting an echo signal generated by the detection object;
and filling the echo signals into corresponding sampling points of the non-Cartesian k-space.
In one embodiment, the scan sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.
In one embodiment, the computer program when executed by the processor further performs the steps of:
for each target fill position of a Cartesian k-space, obtaining a plurality of target sample data in a non-Cartesian k-space corresponding to the target fill position;
and performing data rearrangement processing on the plurality of target sampling data by using a preset data rearrangement algorithm, and filling the rearranged data obtained by processing into the target filling position.
In one embodiment, the computer program when executed by the processor further performs the steps of:
calculating the relevance between each sampling point and the target filling position in the non-Cartesian k space by utilizing a preset relevance function;
and determining the sampling point with the relevance conforming to the preset condition as a target sampling point corresponding to the target filling position, and determining the sampling data filled in the target sampling point as target sampling data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a weight coefficient corresponding to each target sampling data;
and calculating the multiple target sampling data by using a data rearrangement algorithm and the weight coefficient to obtain rearranged data.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.
Claims (10)
1. A magnetic resonance imaging method, characterized in that the method comprises:
determining a plurality of sampling trajectories in non-cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold;
acquiring signals according to the plurality of sampling tracks, and filling the acquired echo signals into the non-Cartesian k space;
rearranging the sampled data populated in the non-Cartesian k-space into Cartesian k-space;
and performing image reconstruction processing based on the rearranged data filled in the Cartesian k space to obtain a magnetic resonance image.
2. The method of claim 1, wherein determining a plurality of sampling trajectories in non-cartesian k-space comprises:
for each sampling track, acquiring a starting point position, a rotation angular velocity, a radial screw-out velocity, a scanning duration and an oversampling multiple;
rotating at the rotation angular velocity and the radial unscrewing velocity from the starting point position to obtain a spiral line;
and determining the number of sampling points in the spiral line according to the scanning duration and the oversampling multiple to obtain the sampling track.
3. The method of claim 1, wherein said acquiring signals according to said plurality of sampling trajectories and filling acquired echo signals into said non-cartesian k-space comprises:
for each sampling track, determining excitation pulses and oscillation gradients on each logic axis of magnetic resonance according to the positions of the sampling points in the sampling tracks to obtain a scanning sequence;
scanning a detection object by using the scanning sequence, and collecting the echo signal generated by the detection object;
filling the echo signals into corresponding sampling points of the non-Cartesian k-space.
4. The method of claim 3, wherein the scan sequence comprises at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.
5. The method of claim 1, wherein the rearranging the populated sample data in the non-cartesian k-space into cartesian k-space comprises:
for each target fill position of the cartesian k-space, obtaining a plurality of target sample data in the non-cartesian k-space corresponding to the target fill position;
and carrying out data rearrangement processing on the plurality of target sampling data by utilizing a preset data rearrangement algorithm, and filling the rearranged data obtained by processing into the target filling position.
6. The method of claim 5, wherein the acquiring a plurality of target sample data in the non-Cartesian k-space corresponding to the target fill position comprises:
calculating the association degree between each sampling point in the non-Cartesian k space and the target filling position by utilizing a preset association degree function;
and determining the sampling points with the association degree meeting the preset conditions as target sampling points corresponding to the target filling positions, and determining the sampling data filled in the target sampling points as the target sampling data.
7. The method according to claim 6, wherein the performing a data rearrangement process on the plurality of target sample data by using a preset data rearrangement algorithm comprises:
acquiring a weight coefficient corresponding to each target sampling data;
and calculating the plurality of target sampling data by using the data rearrangement algorithm and the weight coefficient to obtain the rearranged data.
8. A magnetic resonance imaging apparatus, characterized in that the apparatus comprises:
a sampling trajectory determination module for determining a plurality of sampling trajectories in non-Cartesian k-space; the sampling tracks are spiral lines, and the number of sampling points in each sampling track is greater than a preset number threshold;
the signal filling module is used for acquiring signals according to the plurality of sampling tracks and filling the acquired echo signals into the non-Cartesian k space;
a data rearrangement module for rearranging the sampling data filled in the non-Cartesian k space into a Cartesian k space;
and the image reconstruction module is used for carrying out image reconstruction processing on the basis of the rearranged data filled in the Cartesian k space to obtain a magnetic resonance image.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN116228913B (en) * | 2023-05-06 | 2023-08-22 | 杭州师范大学 | Processing method and device for magnetic resonance image data and storage medium |
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