Motion parameter determination method, apparatus, device, storage medium, and program product

Abstract

The application relates to a motion parameter determination method, device, equipment, storage medium and program product. First K-space data of multiple channels of a target part is acquired, a first mass center is determined according to the first K-space data, and a motion parameter of the target part is determined according to the difference between the first mass center and the coordinate origin of an imaging coordinate system. The first K-space data is obtained by collecting the target part through a coil that can move synchronously with the target part, and the first mass center is the mass center of an image determined by the first K-space data of the multiple channels. In the embodiment of the application, since the coil that can move synchronously with the target part is arranged, the motion parameter can be directly determined according to the first mass center and the coordinate origin of the coordinate system after the first mass center is obtained based on the first K-space data of the target part collected by the coil, and the accuracy of the motion parameter determination is improved.

Description

Technical Field

[0001] This application relates to the field of medical imaging technology, and in particular to a method, apparatus, device, storage medium, and program product for determining motion parameters. Background Technology

[0002] During magnetic resonance imaging (MRI), rigid body motion of the patient can cause various artifacts in the MRI images, such as ghosting, blurring, geometric distortion, or reduced signal-to-noise ratio. These artifacts can result in up to 20% of the image data being undiagnostic or yielding poor diagnostic results. This often necessitates repeating part or all of the scan, delaying treatment and increasing costs. Furthermore, if not properly managed, motion can lead to false positives or false negatives. Therefore, rigid body motion correction for MRI is necessary.

[0003] Currently, the main approach is to obtain the projection moments from the patient's K-space data, then obtain the center of mass based on the projection moments, and finally obtain the start time point of at least one rigid body motion based on the center of mass. The rigid body motion time period corresponding to each rigid body motion is determined based on the start time point of each rigid body motion. The K-space data corresponding to the rigid body motion state under different rigid body motion time periods are used to reconstruct the image, and the reconstructed image is registered to obtain motion parameters, which are then used to correct the rigid body motion.

[0004] However, the method described above for obtaining motion parameters suffers from low accuracy. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, device, storage medium, and program product for determining motion parameters that can improve the accuracy of motion parameter determination, in response to the above-mentioned technical problems.

[0006] In a first aspect, this application provides a method for determining motion parameters, including:

[0007] Acquire first K-space data of multiple channels of the target area; the first K-space data is obtained by collecting data of the target area through a coil that moves synchronously with the target area;

[0008] A first quality center is determined based on the first K-space data; the first quality center is the quality center of the image determined by the first K-space data of multiple channels.

[0009] The motion parameters of the target part are determined based on the difference between the first center of mass and the origin of the imaging coordinate system.

[0010] In one embodiment, determining the motion parameters of the target region based on the difference between the first center of mass and the origin of the imaging coordinate system includes:

[0011] The translation parameters of the target part are determined based on the difference between the first center of mass and the origin of the imaging coordinate system.

[0012] The first K-space data is translated and corrected based on the translation parameters to obtain the second K-space data;

[0013] The rotation parameters of the target part are determined based on the second K-space data; the rotation parameters are used to perform rotation correction on the second K-space data; the motion parameters include the translation parameters and the rotation parameters.

[0014] In one embodiment, determining the first mass center based on the first K-space data includes:

[0015] The first center of mass is determined based on the first K-space data and the coefficient matrix; the coefficient matrix is ​​determined based on the spoke directions of the first K-space data.

[0016] In one embodiment, determining the first mass center based on the first K-space data and the coefficient matrix includes:

[0017] The first K-space data is compressed to obtain the third K-space data;

[0018] The first mass center is determined based on the third K-space data and the coefficient matrix.

[0019] In one embodiment, determining the first mass center based on the third K-space data and the coefficient matrix includes:

[0020] The projection matrix of the first mass center is determined based on the third K-space data corresponding to each spoke direction;

[0021] The first mass center is determined by a recursive algorithm based on the projection matrix and the coefficient matrix.

[0022] In one embodiment, determining the rotation parameters of the target region based on the second K-space data includes:

[0023] Based on the second K-space data and the coefficient matrix, a mass center matrix is ​​determined by a recursive algorithm; the mass center matrix includes the second mass center corresponding to the second K-space data of each channel.

[0024] The rotation parameters are determined based on the mass center matrix and the reference mass center matrix; the reference mass center matrix includes the reference mass center corresponding to the second K spatial data of each channel when the target part is in the reference orientation.

[0025] Secondly, this application also provides a motion parameter determination device, comprising:

[0026] The acquisition module is used to acquire first K-space data of multiple channels of the target site of the patient; the first K-space data is acquired by collecting data of the target site through a coil that moves synchronously with the target site;

[0027] The first determining module is used to determine a first quality center based on the first K-space data; the first quality center is the quality center of the image determined by the first K-space data of multiple channels.

[0028] The second determining module is used to determine the motion parameters of the target part based on the difference between the first mass center and the origin of the imaging coordinate system.

[0029] Thirdly, this application also provides a computer device, 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:

[0030] Acquire first K-space data of multiple channels of the target area; the first K-space data is obtained by collecting data of the target area through a coil that moves synchronously with the target area;

[0031] A first quality center is determined based on the first K-space data; the first quality center is the quality center of the image determined by the first K-space data of multiple channels.

[0032] The motion parameters of the target part are determined based on the difference between the first center of mass and the origin of the imaging coordinate system.

[0033] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0034] Acquire first K-space data of multiple channels of the target area; the first K-space data is obtained by collecting data of the target area through a coil that moves synchronously with the target area;

[0035] A first quality center is determined based on the first K-space data; the first quality center is the quality center of the image determined by the first K-space data of multiple channels.

[0036] The motion parameters of the target part are determined based on the difference between the first center of mass and the origin of the imaging coordinate system.

[0037] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0038] Acquire first K-space data of multiple channels of the target area; the first K-space data is obtained by collecting data of the target area through a coil that moves synchronously with the target area;

[0039] A first quality center is determined based on the first K-space data; the first quality center is the quality center of the image determined by the first K-space data of multiple channels.

[0040] The motion parameters of the target part are determined based on the difference between the first center of mass and the origin of the imaging coordinate system.

[0041] The aforementioned motion parameter determination method, apparatus, device, storage medium, and program product acquire first K-space data of multiple channels of the target part, determine a first center of mass based on the first K-space data, and determine the motion parameters of the target part based on the difference between the first center of mass and the origin of the imaging coordinate system. The first K-space data is acquired by a coil that moves synchronously with the target part, and the first center of mass is the center of mass of the image determined based on the first K-space data of multiple channels. In this embodiment, because a coil that moves synchronously with the target part is provided, after obtaining the first center of mass from the first K-space data of the target part acquired by the coil, the motion parameters can be directly determined based on the first center of mass and the origin of the coordinate system. This eliminates the need to divide the first K-space data into K-space data corresponding to different rigid body motion states, and to perform image reconstruction and registration on the K-space data corresponding to different rigid body motion states to obtain the motion parameters, thus improving the accuracy of motion parameter determination. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an application environment diagram of the motion parameter determination method in one embodiment;

[0044] Figure 2 This is a flowchart illustrating a method for determining motion parameters in one embodiment;

[0045] Figure 3 This is a flowchart illustrating the motion parameter determination method in another embodiment;

[0046] Figure 4 This is a flowchart illustrating a method for determining the first quality center in one embodiment;

[0047] Figure 5 This is a schematic diagram of a method for determining the first quality center in one embodiment;

[0048] Figure 6 This is a flowchart illustrating the method for determining the first quality center in another embodiment;

[0049] Figure 7 This is a flowchart illustrating a method for determining rotation parameters in one embodiment;

[0050] Figure 8 This is a flowchart illustrating a method for determining the quality center matrix in one embodiment;

[0051] Figure 9 This is a flowchart illustrating a motion-corrected image determination method in one embodiment;

[0052] Figure 10 This is a schematic diagram illustrating the translation estimation error under different amplitudes in one embodiment;

[0053] Figure 11 This is a schematic diagram illustrating the rotation estimation error under different amplitudes in one embodiment;

[0054] Figure 12 This is a schematic diagram of motion correction results for non-voluntary motion in one embodiment.

[0055] Figure 13 This is a schematic diagram of the motion correction results for instantaneous discrete motion in one embodiment;

[0056] Figure 14 This is a schematic diagram of the motion correction results for continuous motion in one embodiment;

[0057] Figure 15 This is a schematic diagram of motion correction results without motion in one embodiment;

[0058] Figure 16 This is a schematic diagram of the motion correction results for non-voluntary motion in another embodiment;

[0059] Figure 17 This is a schematic diagram of the motion correction results for instantaneous discrete motion in another embodiment;

[0060] Figure 18 This is a schematic diagram of motion correction results in continuous motion in another embodiment;

[0061] Figure 19 Here is a box plot before and after motion correction in one embodiment;

[0062] Figure 20 Box plots before and after motion correction in another embodiment;

[0063] Figure 21 This is a structural block diagram of a motion parameter determination device in one embodiment;

[0064] Figure 22 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0065] 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.

[0066] Magnetic resonance imaging (MRI) is highly sensitive to patient motion, which is one of the most challenging problems in clinical practice. Generally, motion can cause various artifacts in images, such as ghosting, blurring, geometric distortion, or reduced signal-to-noise ratio. These effects vary depending on the specific k-space acquisition protocol and the type of motion involved. Studies have shown that motion-free time and the average displacement of the scanned object are highly correlated with brain imaging quality. In clinical MRI, patient motion can result in up to 20% of imaging data being undiagnostic or poorly diagnosed. This often necessitates repeating part or the entire scan, delaying treatment and increasing costs. Furthermore, if not handled properly, motion can lead to false positives or false negatives. Motion during MRI scans is more common in certain populations, including the elderly, children, and patients with certain neurological disorders such as Parkinson's disease and epilepsy. These individuals often require extra attention and care during imaging. In current clinical settings, sedation or anesthesia is often used on young children to ensure successful imaging. However, concerns have been raised about the potential long-term adverse effects of this practice.

[0067] Previous researchers have proposed various methods for determining motion parameters in magnetic resonance imaging (MRI). Navigation echoes involve additional data collection to determine these parameters; the navigation signal is typically located at the beginning or end of certain repetitive cycles. Because of the need to acquire additional data, this method requires additional scan time, thus impacting efficiency.

[0068] Self-navigation techniques directly measure motion parameters in imaging data by repeatedly sampling portions of the K-space. One such technique, called Propeller (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction), acquires rotating Cartesian blocks ("blades") and reconstructs low-resolution images using overlapping center data. Motion information is then obtained by registering these blades. However, in clinical scans, the assumption that there is no motion during the acquisition of each block is often not met, leading to inaccurate determination of motion parameters.

[0069] Another self-navigation technique, called Free Induction Decay Navigator (FIDNavigator), utilizes the fact that the local spatial sensitivity curve of a coil array is sensitive to local signal changes. A model is built based on the fundamental physical principles of the acquisition process to obtain motion parameters. In three-dimensional radial acquisition, the center point in K-space can serve as a self-encoded FID navigation signal, thus eliminating the need for additional scanning. While motion parameters can be estimated for each spoke, this method requires building a forward-facing model of the specific subject using low-resolution calibration data, which may also be affected by potential motion interference.

[0070] Center-of-mass (COM) analysis derived from radial projection matrices leverages the inherent self-navigation capabilities of radial acquisition and shows promise for correcting rigid body motion. Translational and rotational parameters can be estimated by analyzing the first-order moment (COM) and the second-order projection matrix. However, this estimation method is poorly robust to noise and is currently limited to two-dimensional scenes. Projection moment analysis in three-dimensional radial magnetic resonance imaging has demonstrated its ability to detect motion. This technique operates on the assumption that motion occurs discontinuously, interspersed with periods of relative stillness. Under this assumption, this method reconstructs a low-resolution image using a selected subset of data. Motion parameters are then determined through registration, using a method similar to the PROPELLER technique. However, this method has limitations in handling continuous motion. Therefore, this application proposes a method, apparatus, device, storage medium, and program product for determining motion parameters that can solve the problems of the aforementioned techniques.

[0071] The motion parameter determination method provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown includes a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 1As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores data related to motion parameter determination. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a motion parameter determination method. The server can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0072] In one exemplary embodiment, such as Figure 2 As shown, a method for determining motion parameters is provided, which can be applied to... Figure 1 The following explanation uses computer equipment as an example, including the following steps S201 to S203. Wherein:

[0073] S201, acquire the first K-space data of multiple channels of the target part; the first K-space data is acquired by collecting data of the target part through a coil that moves synchronously with the target part.

[0074] Optionally, the target part that may be subject to rigid body motion may include, but is not limited to, the head, limbs, etc.

[0075] Optionally, the first K-space data can be two-dimensional or three-dimensional. That is, the dimension for determining motion parameters is not limited to the three-dimensional radial sampling mode. When using the two-dimensional radial sampling mode, it can also be used to determine motion parameters in a plane.

[0076] When acquiring data from the target area using a coil that moves synchronously with the target area, a radial sampling mode is employed. Compared to the traditional Cartesian sampling mode, radial sampling is an effective method for reducing motion artifacts. This effectiveness is mainly attributed to the high oversampling at the K-space center in radial sampling, which effectively averages out the effects of motion. Therefore, motion artifacts in radial sampling typically manifest as localized blurring, a stark contrast to the ghosting commonly seen in Cartesian sampling. This characteristic makes radial sampling inherently more resistant to object movement during the imaging process.

[0077] Optionally, the scanning parameters of the three-dimensional radial acquisition sequence / two-dimensional radial acquisition sequence can be arbitrarily set according to requirements. Optionally, the imaging modality and contrast used can be arbitrarily set according to application requirements, including but not limited to T1-weighted, T2-weighted, PD-weighted, quantitative susceptibility mapping (QSM), myelin sheath water image (MWI), chemical exchange saturation transfer (CEST), arterial spin labeling (ASL), and magnetic resonance elastography (MRE).

[0078] In this embodiment, one or more coils that can move synchronously with the target part are used to collect data. When the target part moves to a certain position, the coils are used to collect multi-channel first K-space data, and the collected multi-channel first K-space data is sent to a computer device.

[0079] In one possible implementation, after acquiring the first K-space data, the acquired multi-channel first K-space data can be stored in a storage medium, and the computer device can retrieve the first K-space data from the storage medium.

[0080] S202, determine the first quality center based on the first K-space data; the first quality center is the quality center of the image determined by the first K-space data of multiple channels.

[0081] In physics, the center of mass (COM) of an object is defined as the point where the weighted sum of the relative positions of its mass distributions is zero. For a single rigid body, the COM is fixed relative to the object. Therefore, the center of mass c of an image can be calculated by the ratio of its first moment to its zeroth moment, using the following formula:

[0082]

[0083] Where I represents the image, and x is the pixel coordinate in the image.

[0084] In this embodiment, the first mass center can be determined directly based on the first K-space data and the coefficient matrix. The coefficient matrix is ​​determined based on the spoke direction of the first K-space data and can be manually adjusted by different first K-space data acquisition and sorting schemes in 3D radial acquisition.

[0085] In one possible implementation, the projection points of the first mass center in each of the three spoke directions of the first K-space data can be calculated based on the first K-space data in each spoke direction, with each spoke direction corresponding to the x / y / z axes of the imaging coordinate system. The coordinates of the three projection points are then the coordinates of the first mass center. For example, based on the first K-space data in the first spoke direction (corresponding to the x-axis of the imaging coordinate system), the projection point a1 of the first mass center on the x-axis can be calculated, and projection point a1 is the x-coordinate of the first mass center.

[0086] S203, determine the motion parameters of the target part based on the difference between the first mass center and the origin of the imaging coordinate system.

[0087] In this embodiment, the motion parameters may include translation parameters, which are obtained based on the difference between the first center of mass and the origin of the imaging coordinate system.

[0088] In one possible implementation, the motion parameters may include translation and rotation parameters. The translation parameters of the target region are obtained based on the difference between the first center of mass and the origin of the imaging coordinate system. The first K-space data is then translated using these translation parameters to obtain the second K-space data. Further, the second K-space data is analyzed and processed to obtain the rotation parameters of the target region.

[0089] In the aforementioned method for determining motion parameters, first K-space data from multiple channels of the target area is acquired, a first center of mass is determined based on the first K-space data, and motion parameters of the target area are determined based on the difference between the first center of mass and the origin of the imaging coordinate system. The first K-space data is acquired by a coil that moves synchronously with the target area, and the first center of mass is the center of mass of the image determined based on the first K-space data from multiple channels. In this embodiment, because a coil that moves synchronously with the target area is provided, after obtaining the first center of mass from the first K-space data of the target area acquired by the coil, motion parameters can be directly determined based on the first center of mass and the origin of the coordinate system. This eliminates the need to divide the first K-space data into K-space data corresponding to different rigid body motion states, and to perform image reconstruction and registration on the K-space data corresponding to different rigid body motion states to obtain motion parameters, thus improving the accuracy of motion parameter determination.

[0090] Figure 3 This is a flowchart illustrating the motion parameter determination method in another embodiment, as shown below. Figure 3 As shown, this application embodiment relates to a possible implementation of how to determine the motion parameters of a target part based on the difference between the first center of mass and the origin of the imaging coordinate system, including the following steps:

[0091] S301, determine the translation parameters of the target part based on the difference between the first mass center and the origin of the imaging coordinate system.

[0092] In this embodiment, by calculating the difference between the first mass center and the origin of the imaging coordinate system, that is, by aligning the first mass center to the origin, the translation parameters of the target part in that orientation can be obtained.

[0093] S302, perform translation correction on the first K-space data based on the translation parameters to obtain the second K-space data.

[0094] In this embodiment, translational motion correction can be performed by applying translation parameters to the first K-space data. For example, if the first center of mass is (-2, 3, 5) and the origin of the imaging coordinate system is (0, 0, 0), then the translation parameters are (-2, 3, 5). The phase of the first K-space data is adjusted using the translation parameters, thereby adjusting the first center of mass to the origin of the coordinate system, and thus obtaining the second K-space data.

[0095] S303, determine the rotation parameters of the target part based on the second K-space data; the rotation parameters are used to perform rotation correction on the second K-space data; the motion parameters include translation parameters and rotation parameters.

[0096] In this embodiment, the second K-space data is multi-channel K-space data. The target projection matrix can be obtained from the second K-space data. The target projection matrix includes the projections of the second mass centers corresponding to the second K-space data in each channel onto each spoke direction. A recursive algorithm determines the mass center matrix corresponding to the second K-space data based on the target projection matrix and the coefficient matrix. The rotation parameters of the target part are then determined based on the mass center matrix and the reference mass center matrix at the reference orientation. The mass center matrix includes the second mass centers corresponding to the second K-space data in each channel, and the reference mass center matrix includes the reference mass centers corresponding to the second K-space data in each channel when the target part is at the reference orientation.

[0097] In one possible implementation, after obtaining the target projection matrix, the second K-space data quality center matrix is ​​determined based on the target projection matrix and the coefficient matrix by inverting the coefficient matrix. The rotation parameters of the target part are then determined based on the quality center matrix and the reference quality center matrix under the reference orientation.

[0098] In related technologies, the acquired K-space data is divided into K-space data corresponding to different rigid body motion states. Reconstruction is then performed based on the K-space data corresponding to these rigid body motion states to obtain a reconstructed image, and motion parameters are obtained from the reconstructed image. Because image reconstruction is required, this method needs to acquire a large amount of K-space data, resulting in slow updates of motion parameters and failing to meet real-time requirements. In contrast, this application only requires a small amount of initial K-space data to obtain motion parameters, which can be updated in real-time, allowing for real-time tracking of the target body's motion parameters. Furthermore, in related technologies, motion parameters cannot be obtained during the acquisition of two K-space data sets, and the long interval between these acquisitions leads to blurred reconstructed images and artifacts.

[0099] In this embodiment, the translation parameters of the patient's target area are determined based on the difference between the first center of mass and the origin of the imaging coordinate system. The first K-space data is then translated using these parameters to obtain second K-space data. Finally, the rotation parameters of the target area are determined based on the second K-space data. Alternatively, the translation parameters can be directly determined based on the first center of mass and the origin of the coordinate system. The first K-space data is then translated using these parameters, and the rotation parameters are determined based on the second K-space data obtained after translation correction. The rotation correction of the second K-space data can then be completed based on these rotation parameters. In other words, this application does not require dividing the first K-space data into K-space data corresponding to different rigid body motion states, nor does it require image reconstruction of the K-space data corresponding to different rigid body motion states to obtain motion parameters. This avoids the problem of undersampling of the K-space data corresponding to different rigid body motion states due to the division of the first K-space data, resulting in low-quality reconstructed images and inaccurate motion parameters, thus improving the accuracy of motion parameter determination.

[0100] In one embodiment, determining the first center of mass based on the first K-space data includes: determining the first center of mass based on the first K-space data and the coefficient matrix; the coefficient matrix is ​​determined based on the spoke directions of the first K-space data.

[0101] In this embodiment, the left singular vector of the first K-space data is extracted, and the left singular vector corresponding to the largest singular value is used as a compression matrix. This matrix is ​​multiplied with the first K-space data to obtain the third K-space data. Based on the third K-space data and the coefficient matrix, the first mass center corresponding to the third K-space data is determined.

[0102] In one possible implementation, the first K-space data and coefficient matrix can be used directly. The coefficient matrix is ​​determined based on the spoke orientation of the first K-space data and is manually adjusted using different first K-space data acquisition and sorting schemes in 3D radial acquisition. Since the first K-space data includes multiple channels, candidate mass centers corresponding to the first K-space data in each channel are determined, and the first mass center is determined based on each candidate mass center.

[0103] In this embodiment, the first mass center is determined based on the first K-space data and the coefficient matrix. The first mass center can be obtained without reconstructing the first K-space data, and can be obtained in real time, thus improving the efficiency of determining the first mass center.

[0104] Figure 4 This is a flowchart illustrating a method for determining the first quality center in one embodiment, as shown below. Figure 4 As shown, this application embodiment relates to a possible implementation of how to determine a first mass center based on first K-space data and a coefficient matrix, including the following steps:

[0105] S401, compress the first K-space data to obtain the third K-space data.

[0106] In this embodiment, the compression processing of the first K-space data can be performed by principal component analysis of the first K-space data, extracting the main features in the first K-space data through linear transformation, using the left singular vector corresponding to the largest singular value as the compression matrix, and multiplying it with the first K-space data to obtain the third K-space data. That is, the third K-space data is the K-space data of a single channel.

[0107] Optionally, partial least squares regression and uniform manifold approximate projection algorithms can be used to compress the first K-space data to obtain the third K-space data.

[0108] It should be noted that the compressed third K-space data is only used for translation correction and will not be used for subsequent rotation correction or image reconstruction.

[0109] S402, determine the first mass center based on the third K-space data and the coefficient matrix.

[0110] The coefficient matrix V plays a crucial role in noise resistance during the determination of the first center of mass and can be manually adjusted through different first K-space data acquisition sorting schemes in three-dimensional radial acquisition. Generally, a uniform acquisition scheme provides good noise resistance. Therefore, in practical applications, pseudo-random acquisition modes such as spiral phyllotaxis and 2D golden means can be used. Taking the 2D golden means as an example, a spoke direction can be generated using the following azimuth angle α and polar angle β.

[0111]

[0112]

[0113] Where n is the spoke direction number, It is the two-dimensional golden ratio.

[0114] In this embodiment, a spoke direction is determined based on an azimuth angle and a polar angle. By setting multiple spoke directions, the coefficient matrix V can be obtained. The projection matrix can be determined based on the third K-space data of each spoke direction. Therefore, based on the projection matrix and the coefficient matrix, the first mass center is determined through a recursive algorithm. The first mass center is the mass center of the image reconstructed based on the third K-space data.

[0115] In one possible implementation, the projection matrix can be determined based on the third K-space data of each spoke direction. Then, based on the projection matrix and the coefficient matrix, the first center of mass can be determined by inverting the coefficient matrix. Combined with... Figure 5 As shown, assuming that the first K-space data of the target part is collected from two positions respectively, and there is both translation and rotation between the two positions, the first mass center 1 is calculated based on the first K-space data of position 1, and the first mass center 2 is calculated based on the first K-space data of position 2.

[0116] In this embodiment, the first K-space data is compressed to obtain third K-space data. Based on the third K-space data and the coefficient matrix, the first mass center corresponding to the third K-space data is determined. This embodiment reduces the amount of data in the first K-space data by compressing it and then using it to determine the first mass center, thereby improving the efficiency of determining the first mass center.

[0117] Figure 6 This is a flowchart illustrating the method for determining the first quality center in another embodiment, as shown below. Figure 6 As shown, this application embodiment relates to how to determine the first mass center based on third K-space data and a coefficient matrix, including the following steps:

[0118] S601, determine the projection matrix of the first mass center based on the third K-space data corresponding to each spoke direction.

[0119] In this embodiment, the projection matrix p can be obtained using the following formula: Where I is the first, r is the radius vector along the spoke direction, and v = (v x ,v y ,v z ) T The unit vector representing the spoke direction, i.e., a spoke direction determined by an azimuth angle and a polar angle; R is the image Raydon transform along the spoke direction; s is the third K-space data corresponding to each spoke direction; F... r -1 It is a one-dimensional inverse Fourier transform in the spoke direction.

[0120] S602, the first mass center is determined by a recursive algorithm based on the projection matrix and the coefficient matrix.

[0121] Since the projection matrix p, the coefficient matrix V, and the first center of mass c can be represented by the following formula:

[0122]

[0123] In this embodiment, the first mass center can be determined by a recursive algorithm. Optionally, the recursive algorithm can be the recursive least squares algorithm (VFF-RLS), the Kalman filter algorithm, etc. This application embodiment does not limit this.

[0124] When using a recursive algorithm to determine the first center of mass, for the direction of the nth spoke, we can obtain v. n =(v n,x ,v n,y ,v n,z ) T and p n =(p n,1 ,p n,2 ,…,p n,i A new one. The mass center can be estimated based on the direction of the (n-1)th spoke. However, in the above solution process, since the third K-space data has only one channel, channel i is 1. After the calculation is completed, the obtained first mass center can be low-pass filtered to reduce noise and improve smoothness.

[0125] For example, when recursively calculating the first mass center using the VFF-RLS algorithm, the initialization parameters can be set according to the specific application. Table 1 below shows a specific implementation of a VFF-RLS program, where Qn is the estimation error variance matrix calculated in the nth step, Cn is the mass center matrix calculated in the nth step, en is the prior error calculated in the nth step, vn is the system noise calculated in the nth step, σ2 is the corresponding signal energy, γ is a threshold parameter related to the system noise level, λn is the forgetting factor calculated in the nth step, λmax is the maximum possible forgetting factor, ξ is a very small number to prevent division by zero, and α and β are weighting parameters in noise estimation. Let qn be the projection matrix of step n, which is predicted based on the results of step n-1. qn and Kn are intermediate variables in the calculation.

[0126] Table 1

[0127]

[0128] In this embodiment, the projection matrix of the first mass center is determined based on the third K-space data corresponding to each spoke direction. Then, based on the projection matrix and the coefficient matrix, a recursive algorithm is used to determine the first mass center. By introducing a recursive algorithm to calculate the first mass center, the inversion of the coefficient matrix is ​​avoided, and the first mass center of each channel can be recursively estimated, thereby achieving high convergence speed and high estimation accuracy for the first mass center.

[0129] Figure 7 This is a flowchart illustrating a method for determining rotation parameters in one embodiment, as shown below. Figure 7 As shown, this application embodiment relates to how to determine the rotation parameters of a target part based on second K-space data, including the following steps:

[0130] S701, based on the second K-space data and the coefficient matrix, the mass center matrix is ​​determined by a recursive algorithm; the mass center matrix includes the second mass center corresponding to the second K-space data under each channel.

[0131] In this embodiment, the second K-space data consists of K-space data from multiple channels. Based on the second K-space data, the target projection matrix P can be obtained. Then, based on the target projection matrix and the coefficient matrix V, the center of mass matrix C is determined using a recursive algorithm.

[0132]

[0133] Where i is the channel number, and C includes the second quality center of the image corresponding to the second K spatial data under different channels. Since each channel has a specific and unique sensitivity, the second quality centers corresponding to the second K spatial data under each channel are linearly independent.

[0134] For the specific implementation method, please refer to S602 above. Since matrix inversion is avoided, the calculation time in this embodiment can be reduced from more than 30 seconds to 0.2 seconds with 9 channels and 65,000 spoke directions. The calculation time of the entire motion parameter determination process running on a single core is only 24 seconds.

[0135] Combination Figure 8 As shown, based on the above Figure 5 The translation parameters for each position are obtained by using the first mass center at two locations. Based on these translation parameters, the first K-space data at each position is translated and corrected to obtain the corresponding second K-space data. For any given position, the second K-space data consists of K-space data from multiple channels (only four channels are shown in the figure for illustration). The mass center matrix is ​​determined based on the second K-space data of each channel and the coefficient matrix. In other words, the mass center matrix at each position includes the second mass centers corresponding to the second K-space data from multiple channels.

[0136] S702, determine the rotation parameters based on the center of mass matrix and the reference center of mass matrix; the reference center of mass matrix includes the second reference center of mass corresponding to the second K-space data of each channel when the target part is in the reference orientation.

[0137] Optionally, the reference orientation can be any orientation, such as having the head facing upwards or turning the head to the left.

[0138] In this embodiment, the rotation parameter is obtained by taking the rotation matrix B that minimizes the value of the above formula according to min||C1-BC2||. Here, the mass center matrix C1 is calculated from the first K-space data collected after the target part has been rotated, and the reference mass center matrix C2 is calculated from the K-space data collected at the reference orientation.

[0139] In this embodiment, the mass center matrix is ​​determined by a recursive algorithm based on the second K-space data and the coefficient matrix. The rotation parameters are then determined based on the mass center matrix and the reference mass center matrix. This embodiment directly determines the rotation parameters based on the second mass center corresponding to the second K-space data in each channel. It does not require dividing the second K-space data into multiple K-space data, nor does it require image reconstruction of the divided K-space data to obtain the rotation parameters, thus improving the accuracy of the rotation parameter determination.

[0140] In one embodiment, determining the rotation parameters based on the center of mass matrix and the reference center of mass matrix may include the following steps: obtaining the product of the center of mass matrix and the transpose of the reference center of mass matrix; performing singular value decomposition on the product to obtain the decomposition result; and determining the rotation parameters based on the decomposition result and the transformation matrix.

[0141] In this embodiment, when the coil is fixed relative to the target part, the rotation of the target part will cause a corresponding rotation of the center of mass. This allows the rotation parameters to be directly estimated based on the multi-channel second center of mass. By obtaining the product between the center of mass matrix and the transpose of the reference center of mass matrix, and performing singular value decomposition on the product, the left singular matrix U, the diagonal matrix D, and the right singular matrix G can be obtained, which can be expressed by the following formula:

[0142]

[0143] Based on the left singular matrix U, right singular matrix G, and transformation matrix S from the decomposition results, the rotation parameters are determined. The purpose of the transformation matrix S is to ensure that the matrix obtained based on the left and right singular matrices is necessarily a rotation matrix B. The rotation parameters, determined based on the decomposition results and the transformation matrix, can be expressed as:

[0144] B = USGT

[0145] in, When the number of channels is greater than or equal to 3, the rotation parameters can be uniquely determined. By aligning each orientation with a certain reference orientation, the rotation parameters corresponding to each orientation can be obtained. During the reconstruction of the nonuniform fast fourier transform (NUFFT), the trajectory of the second K-space data of the multi-channel after translation correction will also be rotated accordingly.

[0146] In this embodiment, the product of the center of mass matrix and the transpose of the reference center of mass matrix is ​​obtained, and singular value decomposition (SVD) is performed on the product to obtain the decomposition result. Based on the decomposition result and the transformation matrix, the rotation parameters are determined. Since SVD is a global optimization method, it considers the overall information of all data in the center of mass matrix and the reference center of mass matrix, making it less susceptible to noise and ill-conditioned conditions, thus ensuring more reliable rotation parameters. Furthermore, SVD requires no iteration or optimization, improving the efficiency of rotation parameter determination.

[0147] In one embodiment, the method further includes: performing rotation correction on the second K-space data based on rotation parameters to obtain target K-space data; and reconstructing the target K-space data to obtain a motion-corrected image.

[0148] In this embodiment, combined with Figure 9 As shown, the first K-space data obtained from motion destruction is compressed to obtain the third K-space data of one channel. The first mass center in each spoke direction is calculated by a recursive algorithm and the third K-space data to obtain the final first mass center. The translation parameter is obtained based on the first mass center and the origin of the imaging coordinate system. The translation parameter is used to perform translation correction on the first K-space data to obtain the second K-space data.

[0149] The second mass center in each channel under the direction of each spoke is calculated by a recursive algorithm and the second K-space data to obtain the mass center matrix. Then, the mass center matrix and the reference mass center matrix under the reference orientation are solved based on singular value decomposition to obtain the rotation parameters. Based on the rotation parameters, the rotation trajectory of the second K-space data is adjusted to obtain the target K-space data.

[0150] An image reconstruction algorithm is used to reconstruct the target K-space data to obtain a motion-corrected image. Different image reconstruction algorithms can be used during image reconstruction to obtain higher-quality motion-corrected images.

[0151] In this embodiment, rotation correction is performed on the second K-space data based on rotation parameters to obtain target K-space data. The target K-space data is then reconstructed to obtain a motion-corrected image. Since the target K-space data in this embodiment is obtained by correcting translation and rotation parameters directly obtained from the center of mass, the motion parameters of the target K-space data are determined more accurately, thereby improving the imaging effect of the motion-corrected image.

[0152] In one embodiment, to verify the accuracy and precision of the motion parameter determination method provided in this application, a series of simulations of rigid body motion with different amplitudes were performed. The maximum modulus was 20 mm / 10°. Using the coil sensitivity map measured in the actual scan, a non-uniform discrete Fourier transform of the acquired image in a 2D golden section sampling mode was performed to obtain multi-channel initial K-space data. Motion was simulated by manually rotating the trajectory or introducing a linear phase shift into the acquired initial K-space data to obtain the first K-space data, with each motion amplitude simulating 500 spoke directions.

[0153] Figure 10 This is a schematic diagram illustrating the translation estimation error under different amplitudes in one embodiment. Figure 11 This is a schematic diagram illustrating the rotation estimation error under different amplitudes in one embodiment. When the translation amplitude is 20 mm, the error is 0.39 ± 0.02 mm. In rotation estimation, the estimation error is 0.17 ± 0.02° when the rotation amplitude is 9°. Simulation results show that the average estimation error of translation and rotation provided in this application is only 2%, exhibiting high accuracy. Furthermore, high accuracy is achieved across all simulation ranges.

[0154] To evaluate the effectiveness of the proposed method in reducing motion artifacts, first-space data of various simulated motion patterns along six degrees of freedom (3 translations: Tx, Ty, Tz; 3 rotations: Rx, Ry, Rz) were generated and analyzed. The generated imaging data matrix was 240x240x240 in size, with 65,000 spokes and 12 channels. Generally, common motion patterns in clinical scans can be categorized into three types: small-range involuntary motion, instantaneous discrete motion, and continuous motion. Therefore, the following three simulation experiments were conducted:

[0155] Non-autonomous motion: Tx / Ty / Tz linearly increases from 0 mm to 3 mm, and Rx / Ry / Rz linearly increases from 0° to 3°; Instantaneous discrete motion: Ten motion states of equal duration were simulated. In each state, Tx / Ty / Tz and Rx / Ry / Rz were randomly selected from [-10 mm, 10 mm] and [-5°, 5°], respectively, which are common motion amplitudes in actual scanning; Continuous motion: Sinusoidal periodic motion with a frequency of 0.1 Hz was simulated. The amplitudes of Tx / Ty / Tz and Rx / Ry / Rz were randomly selected from [-10 mm, 10 mm] and [-5°, 5°], respectively.

[0156] During motion detection, three channels adjacent to the neck were excluded because the neck may not move rigidly with the head, which would affect the estimation of head motion. The channel sensitivity map was generated directly from the motion-damped first K-space data and the motion-corrected target K-space data. For comparison, the motion-damped image and the motion-corrected image were registered onto the motion-free image.

[0157] Figure 12-14 The simulation results of motion correction for the first K-space data are displayed. Figure 12 This is a schematic diagram of the motion correction results for non-voluntary motion in one embodiment. Figure 13 This is a schematic diagram of the motion correction results for instantaneous discrete motion in one embodiment. Figure 14 This is a schematic diagram of the motion correction results for continuous motion in one embodiment. Figure 12-14 The image shows a motion-free image, a motion-damaged image (reconstructed based on the first K-space data), and a motion-corrected image (reconstructed based on the target K-space data). It also displays difference images between the motion-free and motion-damaged images (the image below the motion-damaged image) and between the motion-free and motion-corrected images (the image below the motion-corrected image), all displayed at 5x intensity. It can be seen that for small-scale involuntary motion, motion artifacts are almost completely eliminated. In simulations of instantaneous discrete and continuous motion, only residual artifacts are visible at the image edges.

[0158] In one embodiment, the ability of the method provided in this application to eliminate motion artifacts in actual scanning was experimentally verified. Five healthy subjects (male, age (25.1±1.7) years) were scanned using a 12-channel flexible head coil (acupuncture coil). The coil was mounted on the subject's head to ensure no relative movement between the coil and the head. Written informed consent was obtained from each subject before the scan. A multidimensional golden section sampling mode was used, employing a three-dimensional radial full-echo acquisition of a spoiled gradient echo sequence. The MRI scan parameters were: FOV = 240×240×240 mm3, matrix size = 240x240x240, flip angle = 17°, TR = 7.1 ms, TE = 3 ms, bandwidth = 250 Hz / pixel, number of spokes = 65000. The total scan time was 7.7 minutes.

[0159] Before entering the scanning room, subjects were instructed to perform the required movement patterns to generate first-order K-space data for motion disruption. The following three scans were performed: subjects were instructed to remain still during the scan; subjects were instructed to perform a sudden head-shaking motion every minute, remaining still between adjacent movements, with the first movement able to begin at any time; subjects were instructed to perform a smooth, continuous back-and-forth head-shaking motion every minute. Once the scan began, subjects were allowed to move.

[0160] During motion detection, three channels adjacent to the neck were excluded. The channel sensitivity map was generated directly from the first K-space data of the motion-damaged image and the target K-space data obtained through motion correction. The motion-free image was obtained by motion correction of the first scan. For comparison, the motion-damaged image and the motion-corrected image were registered onto the motion-free image. For both the motion-damaged image and the motion-corrected image, the normalized root-mean-square error (nRMSE) and structural similarity (SSIM) index were calculated relative to the motion-free image.

[0161] Figure 15 This is a schematic diagram of motion correction results in one embodiment where the subject is asked to remain still. Figure 15 In the experiment, the subjects remained almost completely still, yet the motion-corrected images were still slightly better in quality than the original images, as shown by the arrows in the magnified images. This demonstrates the subvoxel motion correction capability of our method. Figure 16 This is a schematic diagram of the motion correction results for non-voluntary motion in another embodiment. Figure 16During the experiment, the subjects performed some small-range involuntary movements. Due to the averaging effect of the three-dimensional radial acquisition, no obvious motion artifacts were observed. As indicated by the arrows in the magnified image, the reduction in blur is clearly visible in the motion-corrected image compared to the original image.

[0162] Figure 17 This is a schematic diagram of the motion correction results for instantaneous discrete motion in another embodiment. Several motion states can be observed in the motion-damped image, which greatly affects the image quality. Motion artifacts in the motion-corrected image are significantly reduced, and there is no obvious image blurring. Figure 18 This is a schematic diagram of motion correction results in continuous motion according to another embodiment. Through motion correction, the image quality is well restored.

[0163] Figure 19 Here are box plots before and after motion correction in one embodiment. Figure 20 Here are box plots before and after motion correction in another embodiment, wherein, Figure 19 For nRMSE box plot, Figure 20 The image is a box plot of the SSIM. As can be seen from the nRMSE and SSIM indices, the image quality of all five volunteers was significantly improved after motion correction. Figure 19 and 20 As shown, for instantaneous motion, nRMSE decreased from 3.92±0.69% to 1.66±0.21%, and SSIM increased from 0.59±0.06 to 0.88±0.02. For continuous motion, nRMSE decreased from 3.25±0.51% to 1.31±0.19%, and SSIM increased from 0.63±0.09 to 0.89±0.02.

[0164] In this application, the effectiveness of the proposed method for motion correction was verified through both simulation and experiment. Furthermore, it achieved excellent results for involuntary motion, instantaneous discrete motion, and continuous motion, significantly reducing motion artifacts and greatly improving image quality. It can be seen that by acquiring the first K-space data of the target part through a coil that moves synchronously with the target part, and obtaining the first center of mass based on the first K-space data, the motion parameters are directly determined based on the first center of mass and the origin of the coordinate system. This significantly improves the accuracy of motion parameter determination, allowing for higher precision correction of the rigid body motion of the target part based on these motion parameters.

[0165] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0166] Based on the same inventive concept, this application also provides a motion parameter determination device for implementing the motion parameter determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more motion parameter determination device embodiments provided below can be found in the limitations of the motion parameter determination method described above, and will not be repeated here.

[0167] In one exemplary embodiment, such as Figure 21 As shown, a motion parameter determination device is provided, comprising: an acquisition module 11, a first determination module 12, and a second determination module 13, wherein:

[0168] The acquisition module 11 is used to acquire first K-space data of multiple channels of the target part; the first K-space data is acquired by collecting data of the target part through a coil that moves synchronously with the target part.

[0169] The first determining module 12 is used to determine a first quality center based on the first K-space data; the first quality center is the quality center of the image determined by the first K-space data of multiple channels.

[0170] The second determining module 13 is used to determine the motion parameters of the target part based on the difference between the first mass center and the origin of the imaging coordinate system.

[0171] In one embodiment, the second determining module includes:

[0172] The first determining unit is used to determine the translation parameters of the target part based on the difference between the first mass center and the origin of the imaging coordinate system.

[0173] The first correction unit is used to perform translation correction on the first K-space data based on translation parameters to obtain the second K-space data;

[0174] The second determining unit is used to determine the rotation parameters of the target part based on the second K-space data; the rotation parameters are used to perform rotation correction on the second K-space data; the motion parameters include translation parameters and rotation parameters.

[0175] In one embodiment, the first determining module includes:

[0176] The third determining unit is used to determine the first center of mass based on the first K-space data and the coefficient matrix; the coefficient matrix is ​​determined based on the spoke directions of the first K-space data.

[0177] In one embodiment, the third determining unit is further configured to compress the first K-space data to obtain third K-space data; and determine the first mass center based on the third K-space data and the coefficient matrix.

[0178] In one embodiment, the third determining unit is further configured to determine the projection matrix of the first mass center based on the third K-space data corresponding to each spoke direction; and to determine the first mass center by a recursive algorithm based on the projection matrix and the coefficient matrix.

[0179] In one embodiment, the second determining unit is further configured to determine a mass center matrix using a recursive algorithm based on the second K-space data and the coefficient matrix; the mass center matrix includes the second mass center corresponding to the second K-space data under each channel; and to determine rotation parameters based on the mass center matrix and a reference mass center matrix; the reference mass center matrix includes the reference mass center corresponding to the second K-space data under each channel when the target location is in a reference orientation.

[0180] Each module in the aforementioned motion parameter determination device 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.

[0181] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 22As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. 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 in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a motion parameter determination method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0182] Those skilled in the art will understand that Figure 22 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.

[0183] In one exemplary 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 implement the steps of any of the above method embodiments.

[0184] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above method embodiments.

[0185] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the above method embodiments.

[0186] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0187] 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 above methods. Any references to memory, 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, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. 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). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0188] 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.

[0189] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. 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 application should be determined by the appended claims.

Claims

1. A method for determining motion parameters, characterized in that, The method includes: Acquire first K-space data of multiple channels of the target area; the first K-space data is obtained by collecting data of the target area through a coil that moves synchronously with the target area; A first quality center is determined based on the first K-space data; the first quality center is the quality center of the image determined by the first K-space data of multiple channels. Based on the difference between the first center of mass and the origin of the imaging coordinate system, the translation parameters of the target part are determined; the first K-space data is translated and corrected based on the translation parameters to obtain second K-space data; the rotation parameters of the target part are determined based on the second K-space data; the rotation parameters are used to perform rotation correction on the second K-space data; the motion parameters include the translation parameters and the rotation parameters.

2. The method according to claim 1, characterized in that, The step of determining the first mass center based on the first K-space data includes: The first center of mass is determined based on the first K-space data and the coefficient matrix; the coefficient matrix is ​​determined based on the spoke directions of the first K-space data.

3. The method according to claim 2, characterized in that, Determining the first mass center based on the first K-space data and the coefficient matrix includes: The first K-space data is compressed to obtain the third K-space data; The first mass center is determined based on the third K-space data and the coefficient matrix.

4. The method according to claim 3, characterized in that, Determining the first mass center based on the third K-space data and the coefficient matrix includes: The projection matrix of the first mass center is determined based on the third K-space data corresponding to each spoke direction; The first mass center is determined by a recursive algorithm based on the projection matrix and the coefficient matrix.

5. The method according to claim 1, characterized in that, Determining the rotation parameters of the target location based on the second K-space data includes: Based on the second K-space data and the coefficient matrix, a mass center matrix is ​​determined by a recursive algorithm; the mass center matrix includes the second mass center corresponding to the second K-space data of each channel. The rotation parameters are determined based on the mass center matrix and the reference mass center matrix; the reference mass center matrix includes the reference mass center corresponding to the second K spatial data of each channel when the target part is in the reference orientation.

6. A motion parameter determining device, characterized in that, The device includes: The acquisition module is used to acquire first K-space data of multiple channels of the target part; the first K-space data is acquired by collecting data of the target part through a coil that moves synchronously with the target part. The first determining module is used to determine a first quality center based on the first K-space data; the first quality center is the quality center of the image determined by the first K-space data of multiple channels. The second determining module is used to determine the translation parameters of the target part based on the difference between the first center of mass and the origin of the imaging coordinate system; to perform translation correction on the first K-space data based on the translation parameters to obtain second K-space data; and to determine the rotation parameters of the target part based on the second K-space data. The rotation parameters are used to perform rotation correction on the second K-space data. The motion parameters include the translation parameters and the rotation parameters.

7. 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 5.

8. 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 5.

9. A computer program product, comprising a computer program, 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 5.

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