Magnetic resonance imaging methods, devices, computer equipment and storage media

Abstract

This application relates to a magnetic resonance imaging method, apparatus, computer device, and storage medium. The method includes: determining multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectories are spirals, and the number of sampling points in each sampling trajectory is greater than a preset threshold; acquiring signals according to the multiple sampling trajectories, and filling the acquired echo signals into the non-Cartesian k-space; rearranging the sampled data filled in the non-Cartesian k-space into the 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. This method can eliminate residual artifacts.

Description

Technical Field

[0001] This application relates to the field of magnetic resonance imaging technology, and in particular to a magnetic resonance imaging method, apparatus, computer equipment, and storage medium. Background Technology

[0002] Magnetic Resonance Imaging (MRI) is one of the most advanced medical imaging methods available today, and it is being used more and more widely in clinical practice and scientific research.

[0003] In Cartesian k-space magnetic resonance sampling, double oversampling is typically used during signal acquisition to eliminate crease artifacts. However, in non-Cartesian k-space magnetic resonance sampling, even with double oversampling, some artifacts remain in the magnetic resonance image. Therefore, eliminating artifacts in non-Cartesian k-space magnetic resonance sampling has become a pressing technical problem. Summary of the Invention

[0004] Therefore, it is necessary to provide a magnetic resonance imaging method, apparatus, computer equipment, and storage medium that can eliminate residual artifacts to address the aforementioned technical problems.

[0005] A magnetic resonance imaging method, the method comprising:

[0006] Determine multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectories are spirals, and the number of sampling points in each sampling trajectory is greater than a preset threshold.

[0007] Signals are acquired according to multiple sampling trajectories, and the acquired echo signals are filled into a non-Cartesian k-space.

[0008] Rearrange the sampled data that are not filled in the Cartesian k-space into the Cartesian k-space;

[0009] Magnetic resonance images are obtained by reconstructing images based on rearranged data filled in Cartesian k-space.

[0010] In one embodiment, determining multiple sampling trajectories in a non-Cartesian k-space includes:

[0011] For each sampling trajectory, obtain the starting point position, rotational angular velocity, radial rotation velocity, scan duration, and oversampling factor;

[0012] Starting from the initial position, a helix is ​​obtained by rotating with a rotational angular velocity and a radial rotational velocity;

[0013] The number of sampling points in the spiral is determined based on the scanning duration and oversampling factor, thus obtaining the sampling trajectory.

[0014] In one embodiment, the above-mentioned signal acquisition according to multiple sampling trajectories, and filling the acquired echo signals into a non-Cartesian k-space, includes:

[0015] For each sampling trajectory, the excitation pulse and the oscillation gradient on each logic axis of the magnetic resonance are determined based on the sampling point position in the sampling trajectory to obtain the scanning sequence;

[0016] The detection object is scanned using a scanning sequence, and the echo signal generated by the detection object is acquired;

[0017] The echo signal is filled into the corresponding sampling points in the non-Cartesian k-space.

[0018] In one embodiment, the scanning sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.

[0019] In one embodiment, the above-described rearrangement of the sampled data filled in the non-Cartesian k-space into the Cartesian k-space includes:

[0020] For each target filling position in the Cartesian k-space, acquire multiple target sampling data in the non-Cartesian k-space corresponding to the target filling position;

[0021] The pre-set data rearrangement algorithm is used to rearrange the data of multiple target samples, and the rearranged data is then filled into the target filling positions.

[0022] In one embodiment, the acquisition of multiple target sampling data in the non-Cartesian k-space corresponding to the target filling position includes:

[0023] The correlation between each sampling point in the non-Cartesian k-space and the target filling position is calculated using a pre-set correlation function;

[0024] Sampling points whose correlation meets the preset conditions are identified as target sampling points corresponding to the target filling position, and the sampling data filled in the target sampling points are identified as target sampling data.

[0025] In one embodiment, the above-mentioned data rearrangement processing of multiple target sampled data using a pre-set data rearrangement algorithm includes:

[0026] Obtain the weight coefficients corresponding to the sampled data of each target;

[0027] The rearranged data is obtained by calculating multiple target sample data using data rearrangement algorithms and weighting coefficients.

[0028] A magnetic resonance imaging device, the device comprising:

[0029] The sampling trajectory determination module is used to determine multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectory is a spiral, and the number of sampling points in each sampling trajectory is greater than a preset threshold.

[0030] The signal filling module is used to acquire signals according to multiple sampling trajectories and fill the acquired echo signals into a non-Cartesian k-space.

[0031] The data rearrangement module is used to rearrange sampled data that is not filled in the Cartesian k-space into the Cartesian k-space;

[0032] The image reconstruction module is used to perform image reconstruction processing based on rearranged data filled in Cartesian k-space to obtain magnetic resonance images.

[0033] In one embodiment, the sampling trajectory determination module is specifically used to obtain the starting point position, rotational angular velocity, radial rotation speed, scan duration and oversampling factor for each sampling trajectory; starting from the starting point position, rotate with rotational angular velocity and radial rotation speed to obtain a spiral; determine the number of sampling points in the spiral according to the scan duration and oversampling factor to obtain the sampling trajectory.

[0034] In one embodiment, the signal filling module is specifically used to determine the excitation pulse and the oscillation gradient on each logic axis of the magnetic resonance according to the sampling point position in the sampling trajectory to obtain a scanning sequence; to scan the detection object using the scanning sequence and to collect the echo signal generated by the detection object; and to fill the echo signal into the corresponding sampling point in the non-Cartesian k space.

[0035] In one embodiment, the scanning sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.

[0036] In one embodiment, the data rearrangement module includes:

[0037] The data acquisition submodule is used to acquire multiple target sampling data in the non-Cartesian k-space corresponding to each target filling position in the Cartesian k-space;

[0038] The rearrangement processing submodule is used to rearrange multiple target sample data using a pre-set data rearrangement algorithm, and then fill the rearranged data into the target filling positions.

[0039] In one embodiment, the data acquisition submodule is specifically used to calculate the correlation degree between each sampling point in the non-Cartesian k-space and the target filling position using a pre-set correlation degree function; to determine the sampling points whose correlation degree meets the preset conditions as target sampling points corresponding to the target filling position; and to determine the sampling data filled in the target sampling points as target sampling data.

[0040] In one embodiment, the aforementioned rearrangement processing submodule is specifically used to obtain the weight coefficients corresponding to each target sample data; and to calculate the rearranged data by using the data rearrangement algorithm and the weight coefficients on multiple target sample data.

[0041] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps:

[0042] Determine multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectories are spirals, and the number of sampling points in each sampling trajectory is greater than a preset threshold.

[0043] Signals are acquired according to multiple sampling trajectories, and the acquired echo signals are filled into a non-Cartesian k-space.

[0044] Rearrange the sampled data that are not filled in the Cartesian k-space into the Cartesian k-space;

[0045] Magnetic resonance images are obtained by reconstructing images based on rearranged data filled in Cartesian k-space.

[0046] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0047] Determine multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectories are spirals, and the number of sampling points in each sampling trajectory is greater than a preset threshold.

[0048] Signals are acquired according to multiple sampling trajectories, and the acquired echo signals are filled into a non-Cartesian k-space.

[0049] Rearrange the sampled data that are not filled in the Cartesian k-space into the Cartesian k-space;

[0050] Magnetic resonance images are obtained by reconstructing images based on rearranged data filled in Cartesian k-space.

[0051] The aforementioned magnetic resonance imaging method, apparatus, computer equipment, and storage medium determine multiple sampling trajectories in a non-Cartesian k-space; acquire signals according to the multiple sampling trajectories, and fill the acquired echo signals into the non-Cartesian k-space; rearrange the sampled data filled in the non-Cartesian k-space into the Cartesian k-space; and perform image reconstruction processing based on the rearranged data filled in the Cartesian k-space to obtain a magnetic resonance image. In this embodiment, since the sampling trajectories in the non-Cartesian k-space are spirals, and the acquisition of multiple spirals all begins at the center of the k-space, the high-density sampling at the center of the k-space can be used to eliminate folding artifacts; furthermore, since the number of sampling points in each sampling trajectory is greater than a preset threshold, the non-Cartesian k-space provides sufficient sampling data for data rearrangement. Thus, generating a magnetic resonance image based on the Cartesian k-space can further eliminate residual artifacts, thereby improving the quality of the magnetic resonance image. Attached Figure Description

[0052] Figure 1 This is a diagram illustrating the application environment of a magnetic resonance imaging method in one embodiment.

[0053] Figure 2 This is a schematic flowchart of a magnetic resonance imaging method in one embodiment;

[0054] Figure 3 This is a schematic diagram of spatial correspondence and data rearrangement in one embodiment;

[0055] Figure 4 This is a flowchart illustrating the steps for determining multiple sampling trajectories in a non-Cartesian k-space in one embodiment.

[0056] Figure 5 This is a schematic diagram of magnetic resonance image comparison in one embodiment;

[0057] Figure 6 This is a flowchart illustrating the signal acquisition and signal filling steps in one embodiment;

[0058] Figure 7 This is a flowchart illustrating the data rearrangement process in one embodiment;

[0059] Figure 8 This is a schematic diagram of spatial correspondence and data rearrangement in one embodiment;

[0060] Figure 9 This is a structural block diagram of a magnetic resonance imaging device in one embodiment;

[0061] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0063] The magnetic resonance imaging method provided in this application can be applied to, for example... Figure 1 The application environment shown is a magnetic resonance system (MRS) system 100. The MR system 100 includes a bed 110, an MR scanner 120, and a processor 130. The MR scanner 120 includes a magnet, an RF transmitting coil, a gradient coil, and an RF receiving coil. The bed 110 carries the target object 010. The RF transmitting coil transmits RF pulses to the target object. The gradient coil generates a gradient field, which can be along a phase encoding direction, a slice selection direction, or a frequency encoding direction, etc. The RF receiving coil receives the magnetic resonance signal. In one embodiment, the magnet of the MR scanner 120 can be a permanent magnet or a superconducting magnet. Depending on the function, the RF coils constituting the RF unit can be divided into body coils and local coils. In one embodiment, the RF transmitting coil and the RF receiving coil can be birdcage coils, solenoid coils, saddle coils, Helmholtz coils, array coils, loop coils, etc. In a specific embodiment, the RF transmitting coil is set as a birdcage coil, and the local coil is set as an array coil. The array coil can be set to a 4-channel mode, an 8-channel mode, or a 16-channel mode.

[0064] The magnetic resonance imaging system 100 also includes a controller 140 and an output device 150. 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 several of the following: a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), and an ARM processor.

[0065] The output device 150, such as a display, can display magnetic resonance images of the region of interest. Furthermore, the output device 150 can also display the subject's height, weight, age, imaging location, and the operating status of the MR scanner 110. The output device 150 can be one or a combination of several of the following: cathode ray tube (CRT) output device, liquid crystal display (LCD) output device, organic light-emitting diode (OLED) output device, and plasma output device.

[0066] The magnetic resonance system 100 can be connected to a local area network (LAN), a wide area network (WAN), a public network, a private network, a proprietary network, a public switched telephone network (PSTN), the Internet, a wireless network, a virtual network, or any combination of the above networks.

[0067] In one embodiment, the processor 130 can control the MR scanner 120 to perform equal-interval or non-equal-interval sampling on the detection object (a local part of the target object 010), and control the MR scanner 120 to acquire the magnetic resonance signal of the detection object, and perform Fourier transform on the magnetic resonance signal to obtain the magnetic resonance image of the detection object.

[0068] In one embodiment, such as Figure 2 As shown, a magnetic resonance imaging method is provided, which is applied to... Figure 1 Taking the magnetic resonance system in China as an example, the following steps are included:

[0069] Step 201: Determine multiple sampling trajectories in the non-Cartesian k-space.

[0070] The sampling trajectory is a spiral, and the number of sampling points in each sampling trajectory is greater than a preset threshold.

[0071] Wrinkle artifacts are common artifacts in magnetic resonance imaging (MRI). Increasing the amount of data in the central region of k-space can obtain a larger frequency sampling range, thus removing these artifacts caused by misidentification of frequencies during Fourier transform in MRI images. The helical sampling begins in the central region of k-space; therefore, the sampling trajectory is determined to be a helix.

[0072] Meanwhile, since the sampling trajectory in the non-Cartesian k-space is a spiral, and the magnetic resonance image is generated based on data in the Cartesian k-space, it is necessary to rearrange the data filling the non-Cartesian k-space into the Cartesian k-space. During the data rearrangement process, if the number of sampling data participating in the rearrangement is small, some artifacts will remain in the generated magnetic resonance image. To eliminate the residual artifacts, the number of sampling data participating in the rearrangement can be increased. Therefore, in practical applications, the number of sampling points in each sampling trajectory should be greater than a preset threshold. The preset threshold can be set according to scanning parameters such as FOV, resolution, and hardware gradient performance, and this embodiment does not limit it.

[0073] In practical applications, the time interval between sampling points needs to satisfy the Nyquist sampling principle, where the number of sampling points in each sampling trajectory is determined by the total sampling duration and the sampling time interval.

[0074]

[0075] Where γ is the hydrogen proton magnetogyritude ratio, FOV is the imaging field of view, 2BW is the bandwidth, and is the reciprocal of the time interval between digital sampling points.

[0076] The processor of the magnetic resonance system can acquire the trajectory function corresponding to each sampling trajectory and the number of sampling points in each sampling trajectory. Then, it determines the sampling trajectory based on the trajectory function and the number of sampling points. Here, the sampling trajectory can also be referred to as a leaf in k-space, and the trajectory function can be a function including amplitude, phase, and time. This disclosure does not limit this aspect.

[0077] Step 202: Collect signals according to multiple sampling trajectories and fill the collected echo signals into the non-Cartesian k-space.

[0078] After determining multiple sampling trajectories in the non-Cartesian k-space, the processor of the magnetic resonance system first determines the scanning sequence based on the sampling trajectories, and then controls the MR scanner to scan the object to be detected according to the scanning sequence and acquire echo signals. Afterwards, the processor of the magnetic resonance system fills the non-Cartesian k-space with the echo signals acquired by the MR scanner. This embodiment does not limit the scanning sequence and can be set according to actual conditions.

[0079] Step 203: Rearrange the sampled data that is not filled in the Cartesian k-space into the Cartesian k-space.

[0080] like Figure 3As shown, the sampling trajectory in non-Cartesian k-space is a spiral, while the sampling trajectory in Cartesian k-space is a straight line. There is a correspondence between non-Cartesian k-space and Cartesian k-space. The processor of the magnetic resonance system can use this correspondence to rearrange the sampled data in non-Cartesian k-space and fill the rearranged data into Cartesian k-space.

[0081] like Figure 3 As shown, for a filling position in the Cartesian k-space, the rearranged data can be calculated based on 8 sampled data in the non-Cartesian k-space, and then the rearranged data can be filled into the filling position.

[0082] Step 204: Image reconstruction is performed based on the rearranged data filled in the Cartesian k-space to obtain a magnetic resonance image.

[0083] After the Cartesian k-space is filled, image reconstruction processing is performed on the rearranged data in the Cartesian k-space to obtain the magnetic resonance image. Exemplary methods for data reconstruction in the Cartesian k-space include Sensitivity Encoding (SENSE) reconstruction, Simultaneous Spatial Harmonic Acquisition (SMASH) method, Generalized Self-calibrated Partial Parallel Acquisition (GRAPPA) method, machine learning-based reconstruction methods, and compressed sensing algorithms. This disclosure does not limit the image reconstruction method; it can be set according to actual conditions.

[0084] In the aforementioned magnetic resonance imaging method, multiple sampling trajectories in a non-Cartesian k-space are determined; signals are acquired according to the multiple sampling trajectories, and the acquired echo signals are filled into the non-Cartesian k-space; the sampled data filled in the non-Cartesian k-space is rearranged into the Cartesian k-space; and image reconstruction processing is performed based on the rearranged data filled in the Cartesian k-space to obtain a magnetic resonance image. In this embodiment, since the sampling trajectories in the non-Cartesian k-space are spirals, and the acquisition of the spirals all begins at the center of the k-space, high-density sampling at the center of the k-space can eliminate folding artifacts; furthermore, since the number of sampling points in each sampling trajectory is greater than a preset threshold, the non-Cartesian k-space provides sufficient sampling data for data rearrangement. Thus, generating a magnetic resonance image based on the Cartesian k-space can further eliminate residual artifacts, thereby improving the quality of the magnetic resonance image.

[0085] In one embodiment, such as Figure 4 As shown, the steps described above for determining multiple sampling trajectories in a non-Cartesian k-space may include:

[0086] Step 301: For each sampling trajectory, obtain the starting point position, rotational angular velocity, radial rotation speed, scanning duration, and oversampling factor.

[0087] For each sampling trajectory, the processor of the magnetic resonance system can acquire pre-stored starting point position, rotational angular velocity, radial spin-out velocity, scan duration, and oversampling factor. The starting point position is typically the origin of a non-Cartesian k-space, and the oversampling factor is usually set to two by default. However, the number of sampling points determined by a two-fold oversampling factor is insufficient to avoid artifact persistence. Therefore, the processor can acquire an oversampling factor input by the user. This oversampling factor can be four, eight, or higher. This disclosure does not limit the oversampling factor.

[0088] Step 302: Starting from the initial position, rotate with rotational angular velocity and radial rotation velocity to obtain a helix.

[0089] After obtaining the starting position, the processor performs an Archimedean rotation from that position with a rotational angular velocity and a radial rotational velocity, resulting in a helix. An Archimedean rotation is a point moving away from a fixed point at a constant speed while simultaneously rotating around that fixed point with a fixed rotational angular velocity.

[0090] Step 303: Determine the number of sampling points in the spiral based on the scanning duration and oversampling factor to obtain the sampling trajectory.

[0091] After the processor determines the scan duration and oversampling factor, it calculates the number of sampling points in the spiral based on the scan duration and oversampling factor, and obtains the sampling trajectory based on the number of sampling points.

[0092] For example, with a scan duration of t and an oversampling factor of 2, the number of sampling points in the spiral is 'a'; with an oversampling factor of 4, the number of sampling points in the spiral is 2a; and with an oversampling factor of 8, the number of sampling points in the spiral is 4a. It can be seen that for each sampling trajectory, with a fixed scan duration, the higher the oversampling factor, the smaller the time interval between sampling points, and the more sampling points in the spiral; where 'a' is an integer, and its value can be 100, 200, 1000, 10000, etc.

[0093] like Figure 5 As shown, in the water model test, the FOV used was 200mm×200mm, the matrix was 128×128, the layer thickness was 5mm, the number of sampling trajectories was 16, and the oversampling factors for the left, center, and right images were two, four, and eight, respectively. Figure 5 It can be seen that the ring artifacts gradually weaken as the oversampling factor increases.

[0094] In the process of determining multiple sampling trajectories in a non-Cartesian k-space, for each sampling trajectory, the starting point position, rotational angular velocity, radial spin-out velocity, scan duration, and oversampling factor are obtained. Starting from the starting point position, a spiral is obtained by rotating with the rotational angular velocity and radial spin-out velocity. The number of sampling points in the spiral is determined according to the scan duration and oversampling factor, thus obtaining the sampling trajectory. In this embodiment of the present disclosure, with a fixed scan duration, as the oversampling factor increases, residual artifacts can be gradually eliminated, thereby improving the quality of the magnetic resonance image.

[0095] In one embodiment, such as Figure 6 As shown, the steps described above for acquiring signals according to multiple sampling trajectories and filling the acquired echo signals into a non-Cartesian k-space can include:

[0096] Step 401: For each sampling trajectory, the excitation pulse and the oscillation gradient on each logic axis of the magnetic resonance are determined according to the sampling point position in the sampling trajectory to obtain the scanning sequence.

[0097] After determining the sampling trajectory, the processor can determine the position of each sampling point in each sampling trajectory. For each sampling trajectory, the processor can determine the excitation pulse and the oscillation gradient on each logic axis of the magnetic resonance based on the position of the sampling point in the sampling trajectory; then, it generates a scan sequence based on the excitation pulse and the oscillation gradient on each logic axis of the magnetic resonance corresponding to multiple sampling trajectories.

[0098] In one embodiment, the scanning sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.

[0099] Step 402: Scan the object to be detected using a scanning sequence and acquire the echo signal generated by the object to be detected.

[0100] After obtaining the scan sequence, the processor controls the MR scanner to scan the object to be detected according to the scan sequence and acquires the echo signal generated by the object to be detected.

[0101] When the scanning sequence is a fast spin echo sequence, the MR scanner can acquire multiple echo signals to form an echo train during each scan. Understandably, using a fast spin echo sequence can improve the acquisition efficiency of echo signals, thereby improving the imaging efficiency of magnetic resonance images.

[0102] Step 403: Fill the corresponding sampling points in the non-Cartesian k-space with the echo signal.

[0103] After the processor acquires the echo signal collected by the MR scanner, it fills the corresponding sampling points in the non-Cartesian k-space with the echo signal.

[0104] In the process of acquiring signals according to multiple sampling trajectories and filling the acquired echo signals into the non-Cartesian k-space, for each sampling trajectory, the excitation pulse and the oscillation gradient on each logical axis of the magnetic resonance are determined based on the sampling point positions in the sampling trajectory to obtain a scanning sequence; the detection object is scanned using the scanning sequence, and the echo signals generated by the detection object are acquired; the echo signals are then filled into the corresponding sampling points in the non-Cartesian k-space. In this embodiment of the present disclosure, scanning is performed according to the sampling trajectory, and then the acquired echo signals are filled into the non-Cartesian k-space, providing data basis for subsequent image rearrangement and image reconstruction.

[0105] In one embodiment, such as Figure 7 As shown, the step of rearranging the sampled data filled in the non-Cartesian k-space into the Cartesian k-space can include:

[0106] Step 501: For each target filling position in the Cartesian k-space, obtain multiple target sampling data in the non-Cartesian k-space corresponding to the target filling position.

[0107] The processor uses a pre-set correlation function to calculate the correlation between each sampling point in the non-Cartesian k-space and the target filling position; the sampling points whose correlation meets the preset conditions are determined as the target sampling points corresponding to the target filling positions, and the sampling data filled in the target sampling points are determined as the target sampling data.

[0108] like Figure 3 As shown, a filling position in the Cartesian k-space is associated with each sampling point in the non-Cartesian k-space. The processor uses a correlation function to calculate the correlation degree between each sampling point in the non-Cartesian k-space and the target filling position X. Among them, the correlation degree between sampling points 1, 2...8 and the target filling position X meets the preset condition, while the correlation degree between other sampling points and the target filling position does not meet the preset condition. Therefore, sampling points 1, 2...8 are determined as target sampling points corresponding to the target filling position X, and the filling sampling data in sampling points 1, 2...8 are determined as target sampling data.

[0109] The correlation degree can be used to characterize the distance between the sampling point location and the target filling location. The correlation degree function can be a Kaiser-Bessel window function, and the preset condition can include the correlation degree being greater than a preset correlation degree threshold. This disclosure does not limit the correlation degree, the correlation degree function, or the preset correlation degree threshold.

[0110] like Figure 8 As shown, when the oversampling factor is 2x, the number of target sampling points whose correlation with the target filling location meets the preset conditions is relatively small, while... Figure 3In the case of oversampling multiples of four or eight, the number of target sampling points whose correlation with the target filling position meets the preset conditions increases, and the number of sampling data corresponding to the target filling position also increases accordingly.

[0111] Step 502: Use a pre-set data rearrangement algorithm to rearrange the data of multiple target samples, and fill the rearranged data into the target filling positions.

[0112] After the processor obtains the target sampling data corresponding to the target filling position, it obtains the weight coefficients corresponding to each target sampling data; and uses the data rearrangement algorithm and weight coefficients to calculate the rearranged data of multiple target sampling data.

[0113] For example, if the data rearrangement algorithm is a weighted average method, then the sampled data filling sampling points 1, 2...8 are calculated by weighting the sampled data according to the weight coefficients corresponding to each target sampled data, and the average value is determined as the rearranged data. Alternatively, if the data rearrangement algorithm is a weighted sum method, then the sampled data filling sampling points 1, 2...8 are calculated by weighting the sampled data according to the weight coefficients corresponding to each target sampled data, and the sum is determined as the rearranged data. This disclosure does not limit the data rearrangement algorithm; it can be set according to actual conditions.

[0114] Depend on Figure 3 and Figure 8 As can be seen, as the oversampling factor increases, the number of sampled data corresponding to the target filling position increases. In this way, the calculated rearranged data can be more accurate, thereby eliminating residual artifacts in the magnetic resonance image.

[0115] After the processor obtains the rearranged data, it fills the rearranged data into the target filling position.

[0116] For example, the processor calculates the average value by performing a weighted average on the sampled data filled in sampling points 1, 2...8, and then fills the target filling position X with the average value. Alternatively, the processor calculates the sum by performing a weighted average on the sampled data filled in sampling points 1, 2...8, and then fills the sum with the sum of ...

[0117] In the above embodiments, for each target filling position in the Cartesian k-space, multiple target sampling data in the non-Cartesian k-space corresponding to the target filling position are acquired; a pre-set data rearrangement algorithm is used to rearrange the multiple target sampling data, and the rearranged data is then filled into the target filling position. In this embodiment, the rearranged data is calculated using a correlation function and a data rearrangement algorithm, realizing the data conversion process between the non-Cartesian k-space and the Cartesian k-space, thereby generating a magnetic resonance image. Furthermore, by increasing the oversampling factor, the rearranged data can be made more accurate, thereby eliminating residual artifacts in the magnetic resonance image and improving the quality of the magnetic resonance image.

[0118] It should be understood that, although Figures 2 to 8 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figures 2 to 8 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0119] In one embodiment, such as Figure 9 As shown, a magnetic resonance imaging device is provided, comprising:

[0120] The sampling trajectory determination module 601 is used to determine multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectory is a spiral, and the number of sampling points in each sampling trajectory is greater than a preset number threshold.

[0121] The signal filling module 602 is used to acquire signals according to multiple sampling trajectories and fill the acquired echo signals into a non-Cartesian k-space.

[0122] The data rearrangement module 603 is used to rearrange the sampled data that is not filled in the Cartesian k space into the Cartesian k space;

[0123] Image reconstruction module 604 is used to perform image reconstruction processing based on rearranged data filled in Cartesian k-space to obtain magnetic resonance images.

[0124] In one embodiment, the sampling trajectory determination module 601 is specifically used to obtain the starting point position, rotational angular velocity, radial rotation speed, scan duration and oversampling factor for each sampling trajectory; starting from the starting point position, rotate with rotational angular velocity and radial rotation speed to obtain a spiral; determine the number of sampling points in the spiral according to the scan duration and oversampling factor to obtain the sampling trajectory.

[0125] In one embodiment, the signal filling module 602 is specifically used to determine the excitation pulse and the oscillation gradient on each logic axis of the magnetic resonance according to the sampling point position in the sampling trajectory to obtain a scanning sequence; to scan the detection object using the scanning sequence and to collect the echo signal generated by the detection object; and to fill the echo signal into the corresponding sampling point in the non-Cartesian k space.

[0126] In one embodiment, the scanning sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.

[0127] In one embodiment, the data rearrangement module 603 includes:

[0128] The data acquisition submodule is used to acquire multiple target sampling data in the non-Cartesian k-space corresponding to each target filling position in the Cartesian k-space;

[0129] The rearrangement processing submodule is used to rearrange multiple target sample data using a pre-set data rearrangement algorithm, and then fill the rearranged data into the target filling positions.

[0130] In one embodiment, the data acquisition submodule is specifically used to calculate the correlation degree between each sampling point in the non-Cartesian k-space and the target filling position using a pre-set correlation degree function; to determine the sampling points whose correlation degree meets the preset conditions as target sampling points corresponding to the target filling position; and to determine the sampling data filled in the target sampling points as target sampling data.

[0131] In one embodiment, the aforementioned rearrangement processing submodule is specifically used to obtain the weight coefficients corresponding to each target sample data; and to calculate the rearranged data by using the data rearrangement algorithm and the weight coefficients on multiple target sample data.

[0132] Specific limitations regarding the magnetic resonance imaging (MRI) apparatus can be found in the limitations of the MRI method described above, and will not be repeated here. Each module in the aforementioned MRI apparatus can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0133] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a magnetic resonance imaging method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0134] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0135] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0136] Determine multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectories are spirals, and the number of sampling points in each sampling trajectory is greater than a preset threshold.

[0137] Signals are acquired according to multiple sampling trajectories, and the acquired echo signals are filled into a non-Cartesian k-space.

[0138] Rearrange the sampled data that are not filled in the Cartesian k-space into the Cartesian k-space;

[0139] Magnetic resonance images are obtained by reconstructing images based on rearranged data filled in Cartesian k-space.

[0140] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0141] For each sampling trajectory, obtain the starting point position, rotational angular velocity, radial rotation velocity, scan duration, and oversampling factor;

[0142] Starting from the initial position, a helix is ​​obtained by rotating with a rotational angular velocity and a radial rotational velocity;

[0143] The number of sampling points in the spiral is determined based on the scanning duration and oversampling factor, thus obtaining the sampling trajectory.

[0144] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0145] For each sampling trajectory, the excitation pulse and the oscillation gradient on each logic axis of the magnetic resonance are determined based on the sampling point position in the sampling trajectory to obtain the scanning sequence;

[0146] The detection object is scanned using a scanning sequence, and the echo signal generated by the detection object is acquired;

[0147] The echo signal is filled into the corresponding sampling points in the non-Cartesian k-space.

[0148] In one embodiment, the scanning sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.

[0149] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0150] For each target filling position in the Cartesian k-space, acquire multiple target sampling data in the non-Cartesian k-space corresponding to the target filling position;

[0151] The pre-set data rearrangement algorithm is used to rearrange the data of multiple target samples, and the rearranged data is then filled into the target filling positions.

[0152] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0153] The correlation between each sampling point in the non-Cartesian k-space and the target filling position is calculated using a pre-set correlation function;

[0154] Sampling points whose correlation meets the preset conditions are identified as target sampling points corresponding to the target filling position, and the sampling data filled in the target sampling points are identified as target sampling data.

[0155] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0156] Obtain the weight coefficients corresponding to the sampled data of each target;

[0157] The rearranged data is obtained by calculating multiple target sample data using data rearrangement algorithms and weighting coefficients.

[0158] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0159] Determine multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectories are spirals, and the number of sampling points in each sampling trajectory is greater than a preset threshold.

[0160] Signals are acquired according to multiple sampling trajectories, and the acquired echo signals are filled into a non-Cartesian k-space.

[0161] Rearrange the sampled data that are not filled in the Cartesian k-space into the Cartesian k-space;

[0162] Magnetic resonance images are obtained by reconstructing images based on rearranged data filled in Cartesian k-space.

[0163] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0164] For each sampling trajectory, obtain the starting point position, rotational angular velocity, radial rotation velocity, scan duration, and oversampling factor;

[0165] Starting from the initial position, a helix is ​​obtained by rotating with a rotational angular velocity and a radial rotational velocity;

[0166] The number of sampling points in the spiral is determined based on the scanning duration and oversampling factor, thus obtaining the sampling trajectory.

[0167] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0168] For each sampling trajectory, the excitation pulse and the oscillation gradient on each logic axis of the magnetic resonance are determined based on the sampling point position in the sampling trajectory to obtain the scanning sequence;

[0169] The detection object is scanned using a scanning sequence, and the echo signal generated by the detection object is acquired;

[0170] The echo signal is filled into the corresponding sampling points in the non-Cartesian k-space.

[0171] In one embodiment, the scanning sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.

[0172] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0173] For each target filling position in the Cartesian k-space, acquire multiple target sampling data in the non-Cartesian k-space corresponding to the target filling position;

[0174] The pre-set data rearrangement algorithm is used to rearrange the data of multiple target samples, and the rearranged data is then filled into the target filling positions.

[0175] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0176] The correlation between each sampling point in the non-Cartesian k-space and the target filling position is calculated using a pre-set correlation function;

[0177] Sampling points whose correlation meets the preset conditions are identified as target sampling points corresponding to the target filling position, and the sampling data filled in the target sampling points are identified as target sampling data.

[0178] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0179] Obtain the weight coefficients corresponding to the sampled data of each target;

[0180] The rearranged data is obtained by calculating multiple target sample data using data rearrangement algorithms and weighting coefficients.

[0181] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0182] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A magnetic resonance imaging method, characterized in that, The method includes: Determine multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectories are spirals, and the number of sampling points in each sampling trajectory is greater than a preset threshold. Signals are acquired according to the multiple sampling trajectories, and the acquired echo signals are filled into the non-Cartesian k-space; The sampled data filled in the non-Cartesian k-space is rearranged into the Cartesian k-space; Image reconstruction processing is performed based on the rearranged data filled in the Cartesian k-space to obtain a magnetic resonance image; The determination of multiple sampling trajectories in the non-Cartesian k-space includes: For each of the sampling trajectories, the starting point position, rotational angular velocity, radial rotation velocity, scan duration, and oversampling factor are obtained; Starting from the starting point, a helix is ​​obtained by rotating with the angular velocity and the radial rotation velocity; The number of sampling points in the spiral is determined based on the scanning duration and the oversampling factor to obtain the sampling trajectory.

2. The method according to claim 1, characterized in that, The step of acquiring signals according to the multiple sampling trajectories and filling the acquired echo signals into the non-Cartesian k-space includes: For each of the sampling trajectories, the excitation pulse and the oscillation gradient on each logic axis of the magnetic resonance are determined according to the sampling point positions in the sampling trajectory to obtain the scanning sequence; The detection object is scanned using the scanning sequence, and the echo signal generated by the detection object is acquired; The echo signal is filled into the corresponding sampling points in the non-Cartesian k-space.

3. The method according to claim 2, characterized in that, The scanning sequence includes at least one of a fast spin echo sequence, a gradient echo sequence, and a spin echo sequence.

4. The method according to claim 1, characterized in that, The step of rearranging the sampled data filled in the non-Cartesian k-space into the Cartesian k-space includes: For each target filling position in the Cartesian k-space, acquire multiple target sampling data in the non-Cartesian k-space corresponding to the target filling position; The multiple target sampled data are rearranged using a pre-set data rearrangement algorithm, and the rearranged data is then filled into the target filling positions.

5. The method according to claim 4, characterized in that, The step of acquiring multiple target sampling data in the non-Cartesian k-space corresponding to the target filling position includes: The correlation between each sampling point in the non-Cartesian k-space and the target filling position is calculated using a pre-set correlation function; Sampling points whose correlation meets the preset conditions are determined as target sampling points corresponding to the target filling position, and the sampling data filled in the target sampling points are determined as the target sampling data.

6. The method according to claim 5, characterized in that, The step of rearranging the data of the multiple target samples using a pre-set data rearrangement algorithm includes: Obtain the weight coefficients corresponding to each of the target sampled data; The rearranged data is obtained by calculating the multiple target sampled data using the data rearrangement algorithm and the weighting coefficients.

7. A magnetic resonance imaging device, characterized in that, The device comprises: A sampling trajectory determination module is used to determine multiple sampling trajectories in a non-Cartesian k-space; the sampling trajectory is a spiral, and the number of sampling points in each sampling trajectory is greater than a preset threshold. The signal filling module is used to acquire signals according to the multiple sampling trajectories and fill the acquired echo signals into the non-Cartesian k-space; The data rearrangement module is used to rearrange the sampled data filled in the non-Cartesian k-space into the Cartesian k-space; The image reconstruction module is used to perform image reconstruction processing based on the rearranged data filled in the Cartesian k-space to obtain a magnetic resonance image; Specifically, the sampling trajectory determination module is used to obtain the starting point position, rotational angular velocity, radial rotation speed, scan duration, and oversampling factor for each sampling trajectory; starting from the starting point position, a spiral is obtained by rotating with the rotational angular velocity and the radial rotation speed; and the number of sampling points in the spiral is determined according to the scan duration and the oversampling factor to obtain the sampling trajectory.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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