A method for designing a flat gradient coil considering magnetization of pole tips and related apparatus

Abstract

This application discloses a design method and related apparatus for a planar gradient coil considering pole magnetization, relating to the field of magnetic resonance imaging system technology. The method includes constructing a target planar gradient coil model and a target pole model; performing a first processing on the target planar gradient coil model to obtain a target response matrix; obtaining the target magnetic flux density matrix of the imaging region, and iteratively processing the target response matrix, the target planar gradient coil model, and the target pole model based on the target magnetic flux density matrix to obtain a final current basis coefficient vector; and performing a second processing on the final current basis coefficient vector to obtain the number of conductor loops in the target planar gradient coil and the spatial position corresponding to each conductor loop. This application considers the influence of the additional magnetic field generated in the imaging region by the magnetization of the upper and lower poles during the planar gradient coil design stage, thereby designing a planar gradient coil with higher accuracy.

Description

Technical Field

[0001] This application relates to the field of magnetic resonance imaging system technology, and in particular to a design method and related apparatus for a flat gradient coil that takes into account pole magnetization. Background Technology

[0002] Magnetic Resonance Imaging (MRI) systems achieve spatial encoding by superimposing a spatially linearly varying gradient magnetic field onto a main magnetic field. This gradient magnetic field is typically generated by gradient coils, whose design goal is to create a magnetic field distribution within the imaging region that meets predetermined linearity requirements, while also considering engineering constraints such as gradient efficiency, inductance, power consumption, mechanical forces, and manufacturability. Therefore, gradient coil design methods have always been one of the key technologies in MRI systems.

[0003] Existing magnetic resonance imaging (MRI) systems are classified into closed MRI systems and open MRI systems based on whether the space in which the gradient coil is located is enclosed. In closed MRI systems, the gradient coil is typically arranged in a cylindrical space. A linear mapping relationship is established between the coil current distribution and the magnetic field of the imaging region, and the coil current distribution is solved using the least squares method or other optimization algorithms to obtain a gradient coil structure that meets the target magnetic field requirements. Open MRI systems typically use an upper and lower ferromagnetic pole structure (hereinafter referred to as poles) to form the main magnetic field, with an open imaging space between the two poles. The gradient coil is usually a flat plate structure arranged near the poles to generate the required gradient magnetic field in the imaging space. However, since the upper and lower poles are made of ferromagnetic materials with high permeability, when the gradient coil is energized, the resulting gradient magnetic field magnetizes the upper and lower poles. The magnetized poles then generate an additional magnetic field in the imaging region, which alters the spatial distribution of the original gradient magnetic field, thus affecting the gradient linearity and potentially introducing additional higher-order magnetic field distortion.

[0004] To address the aforementioned issues, existing technologies typically employ two main approaches: One approach ignores the magnetization effect of the pole head during gradient coil design, or simplifies the pole head to an air boundary or ideal magnetic boundary, directly employing traditional target field methods or boundary element methods for gradient coil design. While relatively simple to implement, this approach suffers from significant magnetic field errors in practical applications, making it difficult to meet high-precision imaging requirements. The other approach involves introducing an equivalent magnetic source model to approximate the influence of the ferromagnetic pole head on the magnetic field. This involves treating the ferromagnetic pole head as an ideal magnetic boundary and setting an equivalent mirror current or equivalent magnetic source on the other side of the pole head to satisfy the boundary conditions, thus indirectly considering the influence of the pole head structure on the gradient magnetic field distribution. Summary of the Invention

[0005] The purpose of this application is to provide a design method and related apparatus for a planar gradient coil that takes into account the magnetization of the pole head. The additional magnetic field generated in the imaging area after the upper and lower pole heads are magnetized by the planar gradient coil is introduced into the design process of the planar gradient coil, thereby providing a planar gradient coil with higher accuracy and better meeting the design requirements.

[0006] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for designing a flat gradient coil that considers pole magnetization, including: Construct a target flat plate gradient coil model and a target pole head model; the target flat plate gradient coil model includes an upper flat plate gradient coil model and a lower flat plate gradient coil model; the upper flat plate gradient coil model includes multiple current bases and the spatial position of each current base; the lower flat plate gradient coil model includes multiple current bases and the spatial position of each current base; the target pole head model includes an upper pole head model, a lower pole head model, the material corresponding to the upper pole head model, and the material corresponding to the lower pole head model; both the upper pole head model and the lower pole head model are composed of multiple computational units.

[0007] The target flat plate gradient coil model is subjected to a first processing to obtain a target response matrix; each element in the target response matrix corresponds one-to-one with each current base of the target flat plate gradient coil model.

[0008] The target magnetic flux density matrix of the imaging region is obtained, and the target response matrix, the target flat plate gradient coil model, and the target pole head model are iteratively processed based on the target magnetic flux density matrix of the imaging region to obtain the final current basis coefficient vector; the imaging region is a spherical space located between the upper pole head-flat plate gradient coil model and the lower pole head-flat plate gradient coil model; the upper pole head-flat plate gradient coil model includes the upper pole head model and the upper flat plate gradient coil model; the lower pole head-flat plate gradient coil model includes the lower flat plate gradient coil model and the lower pole head model.

[0009] The final current base coefficient vector is processed a second time to obtain the number of conductor loops in the target flat plate gradient coil and the spatial position of each conductor loop.

[0010] The iterative process is as follows: Based on the current basis coefficient vector corresponding to the current iteration number, a third processing is performed on the target flat plate gradient coil model and the target pole model to obtain the pole magnetization response matrix. The pole magnetization response matrix is ​​composed of the unit current basis coefficient magnetization response of each computing unit in the target pole model in the imaging region. The current basis coefficient vector is composed of the coefficients corresponding to each current basis of the target flat plate gradient coil model. The unit current basis coefficient magnetization response corresponding to the computing unit is when the current basis coefficient is set to 1. The target flat plate gradient coil model generates a magnetic field at the target pole model and magnetizes the computing unit, resulting in the magnetic induction intensity generated by the magnetized computing unit in the imaging region.

[0011] The target magnetic induction intensity matrix, the target response matrix, and the pole magnetization response matrix are processed in a fourth step to obtain the current basis coefficient vector corresponding to the next iteration number.

[0012] The process involves determining whether the calculated value corresponding to the current iteration number is less than the calculation threshold. If so, the current base coefficient vector corresponding to the next iteration number is determined as the final current base coefficient vector. Otherwise, the current iteration number is incremented by 1, and the current base coefficient vector corresponding to the next iteration number is determined as the current base coefficient vector corresponding to the current iteration number. The process then returns to the step of performing a third processing on the target flat plate gradient coil model and the target pole model based on the current base coefficient vector corresponding to the current iteration number to obtain the pole magnetization response matrix. The calculated value is determined based on the current base coefficient vector corresponding to the current iteration number and the current base coefficient vector corresponding to the next iteration number.

[0013] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the planar gradient coil design method considering pole magnetization as described above.

[0014] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the planar gradient coil design method considering pole magnetization as described above.

[0015] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the planar gradient coil design method considering pole magnetization as described above.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a design method and related apparatus for a planar gradient coil considering pole magnetization. First, a target planar gradient coil model and a target pole model are constructed, and the target planar gradient coil model undergoes a first processing step to obtain a target response matrix. This step pre-establishes the target planar gradient coil model, representing it as a linear combination of several current bases. Second, the target magnetic flux density matrix of the imaging region is obtained, and based on this matrix, the target response matrix, the target planar gradient coil model, and the target pole model are iteratively processed to obtain the final current base coefficient vector. This application fully considers the influence of the magnetization response generated in the imaging region by the energization of the planar gradient coil on the actual magnetic field of the imaging region. Therefore, during the design of the planar gradient coil, the pre-established target planar gradient coil model and the target pole model are iteratively optimized based on the target magnetic flux density matrix of the imaging region to obtain the final current base coefficient vector, i.e., the optimal current base coefficient vector, which facilitates subsequent processing. Finally, the final current base coefficient vector is processed a second time to obtain the number of conductor loops in the target flat plate gradient coil and the spatial position of each conductor loop, thus obtaining the target flat plate gradient coil that meets the design requirements, thereby effectively improving the design accuracy and precision of the flat plate gradient coil design method. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the 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.

[0018] Figure 1 This is an application environment diagram of a planar gradient coil design method considering pole magnetization in one embodiment of this application; Figure 2 A schematic flowchart illustrating a planar gradient coil design method considering pole magnetization, provided as an embodiment of this application; Figure 3(a) is a three-dimensional spatial view of a target flat plate gradient coil model composed of multiple current substrates provided in an embodiment of this application; Figure 3(b) is a top view of a target flat plate gradient coil model composed of multiple current substrates provided in an embodiment of this application; Figure 4 This is a spatial position diagram of each conductor loop in a target flat gradient coil provided in an embodiment of this application; Figure 5(a) is a three-dimensional spatial view of the upper plate gradient coil model, the upper pole model, the lower pole model, and the target area provided in an embodiment of this application; Figure 5(b) is a diagram of the magnetic field strength generated when a current base of a target flat gradient coil is set to a unit coefficient according to an embodiment of this application. Figure 6 This is a schematic diagram of the iterative processing flow described in step 203 of this application, provided as an embodiment of the present application, in which the corresponding steps in step 203 are replaced by steps 501 to 504, and the corresponding steps in step 203 are replaced by steps 601 to 602. Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] In related technologies, the design methods for planar gradient coils typically ignore the magnetization effect of the poles or simplify the poles to air boundaries or ideal magnetic boundaries, thus directly employing traditional target field methods, boundary element methods, and other similar approaches for planar gradient coil design. While these methods are relatively simple to implement, because they do not consider the magnetization response of the poles, the established design model deviates from the actual physical system. Consequently, the designed planar gradient coil may generate significant magnetic field errors in the actual system, making it difficult to meet the requirements of high-precision imaging.

[0022] Specifically, in magnetic resonance imaging systems that include a tip structure, especially in open magnetic resonance imaging systems, the magnetic field generated at the tip by the energized planar gradient coil will cause magnetization of the tip. The magnetized tip will then generate an additional magnetic field in the imaging area, resulting in the actual magnetic field in the imaging area having lower linearity and more higher-order distortions compared to the target magnetic field in the imaging area, thus affecting the use of the open magnetic resonance imaging system.

[0023] To address the aforementioned issues, a relevant solution is to introduce an equivalent magnetic source model to approximate the influence of the pole head on the magnetic field. This involves treating the pole head as an ideal magnetic boundary and setting an equivalent mirror current or equivalent magnetic source on the other side of the pole head to satisfy the boundary conditions, thereby indirectly calculating the influence of the pole head structure on the gradient magnetic field distribution. This method can reflect the influence of the pole head on the magnetic field to a certain extent and avoids establishing a complete and complex numerical field model, thus exhibiting high computational efficiency. However, the above methods are typically based on assumptions such as ideal magnetic boundaries, homogeneous media, or constant permeability, essentially providing an approximate description of the pole head's magnetization behavior. For the pole head structure in practical open magnetic resonance imaging systems, its geometry is often complex, and the ferromagnetic materials of the pole head typically exhibit significant nonlinear BH magnetization characteristics. Different local magnetic field strengths at different locations may lead to different magnetization states in different regions of the pole head. Therefore, approximate methods based on ideal boundaries or fixed permeability assumptions cannot accurately describe the nonlinear and spatially non-uniform magnetization response of the pole head under complex geometric conditions, and still contain significant errors in high-precision gradient coil design.

[0024] In summary, while existing methods can reflect the influence of ferromagnetic pole heads on the magnetic field to some extent, they rely on assumptions such as ideal magnetic boundaries, homogeneous media, or constant permeability. Consequently, significant errors still exist when using these methods to analyze open magnetic resonance imaging (MRI) systems. Therefore, this application proposes a planar gradient coil design method and related apparatus that considers pole head magnetization. This allows the design of the planar gradient coil to account for the additional magnetic field generated in the imaging region by the magnetization of the upper and lower pole heads during the design phase, thereby resulting in a more accurate planar gradient coil design.

[0025] The planar gradient coil design method considering pole magnetization provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send the target magnetic field strength matrix of the imaging area to server 104. Server 104 receives the target magnetic field strength matrix of the imaging area and, for the target magnetic field strength matrix of the imaging area, server 104 can construct a target flat plate gradient coil model and a target pole model. The target flat plate gradient coil model includes an upper flat plate gradient coil model and a lower flat plate gradient coil model. The upper flat plate gradient coil model includes multiple current substrates and the spatial position of each current substrate. The lower flat plate gradient coil model includes multiple current substrates and the spatial position of each current substrate. The target pole model includes... The system comprises an upper pole head model, a lower pole head model, a material corresponding to the upper pole head model, and a material corresponding to the lower pole head model; both the upper and lower pole head models are composed of multiple computational units; a first processing step is performed on the target flat plate gradient coil model to obtain a target response matrix; each element in the target response matrix corresponds one-to-one with each current base of the target flat plate gradient coil model; the target magnetic induction intensity matrix of the imaging region is obtained, and based on the target magnetic induction intensity matrix of the imaging region, the target response matrix, the target flat plate gradient coil model, and the target pole head model are iteratively processed to obtain the final current base. The imaging region is a spherical space located between the upper pole-plate gradient coil model and the lower pole-plate gradient coil model; the upper pole-plate gradient coil model includes the upper pole model and the upper plate gradient coil model; the lower pole-plate gradient coil model includes the lower plate gradient coil model and the lower pole model; the final current base coefficient vector is processed in a second way to obtain the number of conductor loops in the target plate gradient coil and the spatial position corresponding to each conductor loop; the iterative processing is: according to the current base coefficient vector corresponding to the current iteration number, the target plate... The plate gradient coil model and the target pole model undergo a third processing step to obtain the pole magnetization response matrix. The pole magnetization response matrix is ​​composed of the unit current basis coefficient magnetization response of each computing unit in the target pole model in the imaging region. The current basis coefficient vector is composed of the coefficients corresponding to each current basis of the target plate gradient coil model. The unit current basis coefficient magnetization response of the computing unit is when the current basis coefficient is set to 1. The target plate gradient coil model generates a magnetic field at the target pole model and magnetizes the computing unit, resulting in the magnetic induction intensity generated by the magnetized computing unit in the imaging region.The target magnetic induction intensity matrix, the target response matrix, and the pole magnetization response matrix undergo a fourth processing step to obtain the current base coefficient vector corresponding to the next iteration number. It is then determined whether the calculated value corresponding to the current iteration number is less than a calculation threshold. If so, the current base coefficient vector corresponding to the next iteration number is determined as the final current base coefficient vector; otherwise, the current iteration number is incremented by 1, and the current base coefficient vector corresponding to the next iteration number is determined as the current base coefficient vector corresponding to the current iteration number. The process then returns to the step of performing a third processing step on the target planar gradient coil model and the target pole model based on the current base coefficient vector corresponding to the current iteration number to obtain the pole magnetization response matrix. The calculated value is determined based on the current base coefficient vector corresponding to the current iteration number and the current base coefficient vector corresponding to the next iteration number. The server 104 can feed back the number of conductor loops in the obtained target planar gradient coil and the spatial position corresponding to each conductor loop to the terminal 102. Furthermore, in some embodiments, the planar gradient coil design method considering pole magnetization can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly process the target magnetic induction intensity matrix of the imaging region to obtain the number of conductor loops in the target planar gradient coil and the spatial position corresponding to each conductor loop. Alternatively, the server 104 can obtain the target magnetic induction intensity matrix of the imaging region from the data storage system and process it to obtain the number of conductor loops in the target planar gradient coil and the spatial position corresponding to each conductor loop.

[0026] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0027] In one exemplary embodiment, such as Figure 2 As shown, a design method for a planar gradient coil considering pole magnetization is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. The method meets the design requirements, which include: the magnetic field generated by the planar gradient coil in the imaging area, and the additional magnetic field generated by the upper and lower poles magnetized after the planar gradient coil is energized in the imaging area; the actual magnetic induction intensity matrix corresponding to the actual magnetic field formed by these two magnetic fields is equal to the target magnetic induction intensity matrix.

[0028] In this embodiment of the application, the method is applied to Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 204.

[0029] Step 201: Construct the target flat plate gradient coil model and the target pole model; the target flat plate gradient coil model includes an upper flat plate gradient coil model and a lower flat plate gradient coil model; the upper flat plate gradient coil model includes multiple current bases and the spatial position of each current base; the lower flat plate gradient coil model includes multiple current bases and the spatial position of each current base; the target pole model includes an upper pole model, a lower pole model, the material corresponding to the upper pole model, and the material corresponding to the lower pole model; both the upper pole model and the lower pole model are composed of multiple computational units.

[0030] Step 202: Perform a first processing on the target flat plate gradient coil model to obtain a target response matrix; each element in the target response matrix corresponds one-to-one with each current base of the target flat plate gradient coil model.

[0031] Step 203: Obtain the target magnetic flux density matrix of the imaging region, and iteratively process the target response matrix, the target flat plate gradient coil model, and the target pole head model based on the target magnetic flux density matrix of the imaging region to obtain the final current basis coefficient vector; the imaging region is a spherical space located between the upper pole head-flat plate gradient coil model and the lower pole head-flat plate gradient coil model; the upper pole head-flat plate gradient coil model includes the upper pole head model and the upper flat plate gradient coil model; the lower pole head-flat plate gradient coil model includes the lower flat plate gradient coil model and the lower pole head model.

[0032] The iterative process is as follows: Based on the current base coefficient vector corresponding to the current iteration number, a third processing is performed on the target flat plate gradient coil model and the target pole model to obtain the pole magnetization response matrix. The pole magnetization response matrix is ​​composed of the unit current magnetization response of each computing unit in the target pole model in the imaging region. The current base coefficient vector is composed of the current values ​​corresponding to each current base of the target flat plate gradient coil model. The unit current magnetization response corresponding to the computing unit is the magnetic induction intensity generated by the magnetized computing unit in the imaging region after a unit current is passed through the current base and a magnetic field is generated at the target pole model to magnetize the computing unit.

[0033] The target magnetic induction intensity matrix, the target response matrix, and the pole magnetization response matrix are processed in a fourth step to obtain the current basis coefficient vector corresponding to the next iteration number.

[0034] The process involves determining whether the calculated value corresponding to the current iteration number is less than the calculation threshold. If so, the current base coefficient vector corresponding to the next iteration number is determined as the final current base coefficient vector. Otherwise, the current iteration number is incremented by 1, and the current base coefficient vector corresponding to the next iteration number is determined as the current base coefficient vector corresponding to the current iteration number. The process then returns to the step of performing a third processing on the target flat plate gradient coil model and the target pole model based on the current base coefficient vector corresponding to the current iteration number to obtain the pole magnetization response matrix. The calculated value is determined based on the current base coefficient vector corresponding to the current iteration number and the current base coefficient vector corresponding to the next iteration number.

[0035] Step 204: Perform a second processing on the final current base coefficient vector to obtain the number of conductor loops in the target flat plate gradient coil and the spatial position corresponding to each conductor loop.

[0036] In step 201, the target flat plate gradient coil model can be represented as a linear combination of several current bases, such as a stream function base or a discrete current element, as shown in Figures 3(a) and 3(b). Let the current base coefficient vector be... ,in, For the first Current base coefficients corresponding to each current base , The number of current substrates is given. In Figures 3(a) and 3(b), DSV represents the imaging region, and f in Figure 3(b) represents a current substrate in the target flat plate gradient coil model.

[0037] The magnetic field generated in the imaging region by the gradient coil on the target plate can be expressed as: , The magnetic field strength generated by the target flat plate gradient coil in the imaging region; The target response matrix; This is the vector of current base coefficients.

[0038] To improve computational efficiency, this application establishes the pole magnetization response matrix through pre-calculation. Specifically, after applying a unit current to each current substrate, the magnetic field distribution including the pole structure is simulated using finite element software, thereby obtaining the additional magnetic field generated by pole magnetization. The additional magnetic fields corresponding to each current substrate are combined to form the pole magnetization response matrix. The additional magnetic field generated by the magnetization of the pole head is Then the total magnetic field in the imaging region is .

[0039] By performing steps 201 to 204 above, the gradient coil current is represented as a linear combination of multiple current bases, and the magnetizing additional magnetic field generated by each current base under the condition of the pole tip is calculated. By combining the magnetizing additional magnetic fields corresponding to each current base, a pole tip magnetization response matrix between the coil current and the additional magnetic field in the imaging region is established. In this way, the pole tip magnetization effect can be represented as a pole tip magnetization response matrix related to the current bases, and superimposed with the target response matrix generated by the planar gradient coil in the imaging region to form a total magnetic field response matrix, thereby uniformly describing the coil magnetic field and the pole tip magnetization effect in the gradient coil design process.

[0040] The embodiments of this application can iteratively optimize the pre-constructed target flat plate gradient coil model and target pole model based on the target magnetic induction intensity matrix of the imaging area, thereby obtaining the number of conductor loops in the target flat plate gradient coil that meets the design requirements and the spatial position of each conductor loop.

[0041] In another exemplary embodiment of this application, the calculation threshold in step 203 can be 1%.

[0042] In another exemplary embodiment of this application, in order to make the planar gradient coil design method considering pole magnetization provided in this application more universally applicable, the target response matrix in step 202 above is the planar gradient coil response matrix, or the target response matrix in step 202 above includes the planar gradient coil response matrix, the planar gradient coil inductance matrix, and the planar gradient coil resistance matrix.

[0043] When the target response matrix in step 202 above is the response matrix of a flat plate gradient coil, the first processing in step 202 above is replaced by the following step 3011: Step 3011: Apply the current corresponding to a current base coefficient of 1 to each current base of the target planar gradient coil model, and obtain the planar gradient coil response matrix by analytical method; the planar gradient coil response matrix is ​​composed of the magnetization response of each current base coefficient of the target planar gradient coil model; the magnetization response of each current base coefficient of the current base is the magnetic induction intensity generated in the imaging region when the current base coefficient of the current base is 1.

[0044] When the target response matrix in step 202 above includes the response matrix of the planar gradient coil, the inductance matrix of the planar gradient coil, and the resistance matrix of the planar gradient coil, the first processing in step 202 above is replaced by the following steps 3021 to 3023: Step 3021: Apply the current corresponding to the current base coefficient of 1 to each current base of the target flat plate gradient coil model, and obtain the flat plate gradient coil response matrix by analytical method.

[0045] Step 3022: Calculate the inductance matrix of the planar gradient coil based on the number of current substrates and the spatial position of each current substrate in the target planar gradient coil model.

[0046] Step 3023: Calculate the resistance matrix of the plate gradient coil based on the number of current substrates in the target plate gradient coil model and the spatial position of each current substrate.

[0047] In another exemplary embodiment of this application, a conductor generation method based on stream function contour lines is employed to discretize a continuous current distribution into several conductor loops. First, the stream function distribution on the surface of the flat plate coil is calculated based on the final current basis coefficient vector obtained from the optimization solution. Then, several contour lines are selected on the stream function distribution to determine the contour intervals. Each selected contour line corresponds to a closed conductor path. By selecting appropriate contour intervals, the stream function distribution on the surface of the flat plate coil can be discretized into several conductor loops. After determining the conductor loops, the current direction and current amplitude of each conductor are determined according to the design current magnitude. The conductor paths are then smoothed and discretized based on actual manufacturing requirements to obtain the number of conductor loops in the target flat plate gradient coil and the spatial position corresponding to each conductor loop.

[0048] Specifically, step 204 above can be replaced by the following steps 401 to 402: Step 401: Calculate the stream function corresponding to the current base coefficient of the flat gradient coil based on the final current base coefficient vector.

[0049] Step 402: Obtain the design current value, and based on the stream function corresponding to the current basis coefficient of the flat plate gradient coil and the design current value, use the stream function discretization method to obtain the number of conductor loops in the target flat plate gradient coil and the spatial position corresponding to each conductor loop.

[0050] The transverse cross-sectional view of the spatial position of each conductor loop in the target flat gradient coil obtained after the above processing is shown below. Figure 4 As shown.

[0051] In another exemplary embodiment of this application, since the ferromagnetic material has nonlinear BH magnetization characteristics, its permeability varies with the magnetic field strength. Therefore, in the specific implementation, it is necessary to first obtain the BH curve of the pole material and divide it into several magnetic field intervals. Subsequently, the pole is discretized into multiple computational units, and each computational unit is assigned an initial equivalent permeability. Based on this permeability distribution, a pole magnetization response matrix corresponding to the additional magnetic field generated in the imaging region after pole magnetization is established. In each iteration, the gradient coil current distribution is first solved based on the current magnetization response matrix. After obtaining the current basis coefficient vector, the magnetic field strength at each discrete computational unit inside the pole is further calculated to obtain a new pole magnetization response matrix M. Then, the current basis coefficient vector is re-solved based on the pole magnetization response matrix corresponding to the next iteration number. Repeat the above process until the convergence condition is met. and The difference is less than 1%.

[0052] Specifically, the step 203 above, "based on the current base coefficient vector corresponding to the current iteration number, performs a third processing on the target flat plate gradient coil model and the target pole model to obtain the pole magnetization response matrix," can be replaced by the following steps 501 to 504: Step 501: Based on the material corresponding to the upper pole model in the target pole model, extract the corresponding BH curve from the BH curve library and determine it as the first BH curve; the BH curve library includes a variety of pole materials and the BH curve corresponding to each pole material; the BH curve is the curve showing the relationship between magnetic induction intensity and magnetic field intensity.

[0053] Step 502: Based on the material of the lower electrode model in the target electrode model, extract the corresponding BH curve from the BH curve library and determine it as the second BH curve.

[0054] Step 503: Based on the current base coefficient vector corresponding to the current iteration number, determine the corresponding current base coefficient for each current base in the target flat plate gradient coil model, so as to magnetize the target pole head model and obtain the upper pole head magnetic field strength matrix and the lower pole head magnetic field strength matrix.

[0055] Step 504: Calculate the magnetization response matrix of the pole head based on the upper pole head magnetic field strength matrix, the first BH curve, the lower pole head magnetic field strength matrix, and the second BH curve.

[0056] If the current iteration number is 0, and the current base coefficient vector corresponding to the current iteration number is a unit current base coefficient vector, then the operation corresponding to step 503 is: to pass a unit current into each current base in the target flat plate gradient coil model, magnetize the target pole head model, and obtain the upper pole head magnetic field strength matrix and the lower pole head magnetic field strength matrix. Further, the pole head magnetization response matrix obtained in step 504 can be expressed as... , The value represents the unit current magnetization response of the j-th computational unit in the imaging region, where j = 1, 2, 3…T. The number of computational units in the target pole head model is shown in Figures 5(a) and 5(b).

[0057] In Figure 5(a), f represents a current substrate in the target flat plate gradient coil model.

[0058] In Figure 5(b), a1 represents the upper pole model, a2 represents the lower pole model, b represents the upper plate gradient coil model, c represents the imaging region, and d represents a current substrate in the upper plate gradient coil model.

[0059] In another exemplary embodiment of this application, the step 203 above, "performing a fourth process on the target magnetic induction intensity matrix, the target response matrix, and the pole magnetization response matrix to obtain the current basis coefficient vector corresponding to the next iteration number," can be replaced by the following steps 601-602: Step 601: Obtain the objective function for optimization.

[0060] Step 602: Based on the optimization objective function, the target magnetic induction intensity matrix, the target response matrix, and the pole magnetization response matrix, the current basis coefficient vector corresponding to the next iteration number is obtained using the least squares algorithm.

[0061] In another exemplary embodiment of this application, when the target response matrix is ​​the response matrix of a flat plate gradient coil, the optimization objective function in step 601 above is the first optimization objective function.

[0062] The first optimization objective function is: ; in, The target magnetic flux density matrix, The response matrix of the plate gradient coil is... The current iteration number The corresponding pole magnetization response matrix, For the next iteration number The corresponding current base coefficient vector.

[0063] In another exemplary embodiment of this application, when inductance or power consumption constraints are added according to engineering requirements, and the target response matrix includes a planar gradient coil response matrix, a planar gradient coil inductance matrix, and a planar gradient coil resistance matrix, the optimization objective function in step 601 above is the second optimization objective function.

[0064] The second optimization objective function is: ; in, The target magnetic flux density matrix, The response matrix of the plate gradient coil is... The current iteration number The corresponding pole magnetization response matrix, For the next iteration number The corresponding current base coefficient vector, For the inductance matrix of a flat gradient coil, The resistance matrix of the plate gradient coil. These are the weighting coefficients of the inductance matrix of the flat plate gradient coil. These are the weighting coefficients of the resistance matrix of the flat plate gradient coil. For the next iteration number The vector obtained by transposing the corresponding current base coefficient vector.

[0065] When the corresponding step in step 203 is replaced by steps 501 to 504, and the corresponding step in step 203 is replaced by steps 601 to 602, the iterative processing flow described in step 203 of this application is as follows: Figure 6 As shown.

[0066] As can be seen from the above embodiments, the planar gradient coil design method considering pole magnetization provided in this application has the following technical effects: Because the ferromagnetic material of the pole head has nonlinear BH magnetization characteristics, its permeability changes with the magnetic field strength, therefore the pole head magnetization response matrix is ​​not fixed. This application provides an iterative update method for the pole head magnetization response matrix, dynamically reflecting changes in the pole head magnetization state during the design of the target flat gradient coil. Specifically, firstly, the BH curve corresponding to the pole head material is obtained and piecewise linearized. The equivalent permeability is used to describe the magnetization characteristics of the pole head material in different magnetic field ranges. Then, the pole head structure is discretized into multiple computational units, and each computational unit is assigned an initial equivalent permeability. In each iteration, the magnetic field strength of each computational unit inside the pole head is calculated using the current basis coefficient vector corresponding to the current iteration number. Then, based on the magnetic field interval in which each computational unit is located, the corresponding equivalent permeability is updated through a pre-segmented linearized BH curve. Based on the updated permeability distribution, the pole head magnetization response matrix corresponding to the current iteration number is obtained by searching the pre-calculated magnetization response matrix library or by interpolation. Finally, based on the pole head magnetization response matrix corresponding to the current iteration number, the current basis coefficient vector corresponding to the next iteration number is solved again, and the above process is repeated until the convergence condition is met, thereby realizing the iterative modeling of the nonlinear magnetization effect of the pole head.

[0067] It is also important to emphasize that in open magnetic resonance imaging systems with ferromagnetic pole tips, energizing the planar gradient coil magnetizes the upper and lower pole tips. This magnetization then generates an additional magnetic field in the imaging region. Therefore, the actual magnetic field in the imaging region is determined by the combined effect of the coil magnetic field generated by the planar gradient coil and the additional magnetic field generated by the pole tip magnetization. Existing methods often neglect the pole tip magnetization effect, resulting in a discrepancy between the actual magnetic field obtained and that in practical applications. This can easily lead to a decrease in the gradient linearity of the planar gradient coil and increase higher-order distortion.

[0068] Furthermore, due to the nonlinear BH magnetization characteristics of ferromagnetic materials, their permeability varies with the local magnetic field strength, thus the pole magnetization response is not a constant. If a fixed permeability or ideal boundary assumption is used, it is difficult to accurately describe the true magnetization behavior of the pole under different operating conditions, which in turn affects the reliability of the gradient coil design results. This application linearizes the BH curve piecewise and dynamically updates the equivalent permeability based on the local magnetic field strength of each discrete unit of the pole during the iteration process, and then updates the magnetization response matrix accordingly. Therefore, it can more accurately reflect the nonlinear magnetization process of the pole, thereby improving the ability to describe complex magnetization effects.

[0069] Furthermore, while rebuilding a complete finite element model and solving for the pole magnetization response at each step of gradient coil optimization can achieve high accuracy, it also leads to high computational cost and long optimization cycles, which is detrimental to multi-parameter gradient coil design. To address this issue, this application adopts a current-based magnetization response matrix construction method and updates the magnetization response matrix through a pre-calculated magnetization response matrix library and table lookup or interpolation methods. This transforms the previously repetitive large-scale field calculations into a matrix combination and iterative update process, thus significantly reducing the computational complexity of the design and improving the efficiency of gradient coil optimization.

[0070] This application also provides an application scenario in which the above-described planar gradient coil design method considering pole magnetization is applied. Specifically, the planar gradient coil design method considering pole magnetization provided in this application embodiment can be applied in the design scenario of an open magnetic resonance imaging system. The design scenario of an open magnetic resonance imaging system includes a data acquisition stage, a design stage, and a results acceptance stage. The target magnetic induction intensity matrix of the imaging area enters the design stage from the data acquisition stage. After modeling and iterative optimization, the number of conductor loops in the target planar gradient coil and the spatial position corresponding to each conductor loop are obtained, and then the results are accepted in the downstream stage. The planar gradient coil design method considering pole magnetization provided in this application embodiment belongs to the design stage.

[0071] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 7 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, 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 operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data on a planar gradient coil design method considering pole magnetization. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a planar gradient coil design method considering pole magnetization.

[0072] Those skilled in the art will understand that Figure 7The 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.

[0073] 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 in the above-described method embodiments.

[0074] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0075] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0076] 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. Moreover, the collection, use and processing of the relevant data are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

[0077] Those skilled in the art will understand that all or part of the processes in 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. When executed, the computer program can include the processes of the embodiments described above. 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).

[0078] 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.

[0079] 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.

[0080] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A design method for a planar gradient coil considering pole magnetization, characterized in that, The method for designing a flat gradient coil includes: Construct a target flat plate gradient coil model and a target pole head model; the target flat plate gradient coil model includes an upper flat plate gradient coil model and a lower flat plate gradient coil model; the upper flat plate gradient coil model includes multiple current bases and the spatial position of each current base; the lower flat plate gradient coil model includes multiple current bases and the spatial position of each current base; the target pole head model includes an upper pole head model, a lower pole head model, the material corresponding to the upper pole head model, and the material corresponding to the lower pole head model; both the upper pole head model and the lower pole head model are composed of multiple computational units; The target flat plate gradient coil model is subjected to a first processing to obtain a target response matrix; each element in the target response matrix corresponds one-to-one with each current base of the target flat plate gradient coil model. The target magnetic flux density matrix of the imaging region is obtained, and based on the target magnetic flux density matrix of the imaging region, the target response matrix, the target flat plate gradient coil model, and the target pole head model are iteratively processed to obtain the final current basis coefficient vector; the imaging region is a spherical space located between the upper pole head-flat plate gradient coil model and the lower pole head-flat plate gradient coil model; the upper pole head-flat plate gradient coil model includes the upper pole head model and the upper flat plate gradient coil model; the lower pole head-flat plate gradient coil model includes the lower flat plate gradient coil model and the lower pole head model; The final current base coefficient vector is processed a second time to obtain the number of conductor loops in the target flat plate gradient coil and the spatial position of each conductor loop. The iterative process is as follows: Based on the current basis coefficient vector corresponding to the current iteration number, a third processing is performed on the target plate gradient coil model and the target pole model to obtain the pole magnetization response matrix. The pole magnetization response matrix is ​​composed of the unit current basis coefficient magnetization response of each computing unit in the target pole model in the imaging region. The current basis coefficient vector is composed of the coefficients corresponding to each current basis of the target plate gradient coil model. The unit current basis coefficient magnetization response corresponding to the computing unit is when the current basis coefficient is set to 1. The target plate gradient coil model generates a magnetic field at the target pole model and magnetizes the computing unit, resulting in the magnetic induction intensity generated by the magnetized computing unit in the imaging region. A fourth processing step is performed on the target magnetic induction intensity matrix, the target response matrix, and the pole magnetization response matrix to obtain the current basis coefficient vector corresponding to the next iteration number; The process involves determining whether the calculated value corresponding to the current iteration number is less than the calculation threshold. If so, the current base coefficient vector corresponding to the next iteration number is determined as the final current base coefficient vector. Otherwise, the current iteration number is incremented by 1, and the current base coefficient vector corresponding to the next iteration number is determined as the current base coefficient vector corresponding to the current iteration number. The process then returns to the step of performing a third processing on the target flat plate gradient coil model and the target pole model based on the current base coefficient vector corresponding to the current iteration number to obtain the pole magnetization response matrix. The calculated value is determined based on the current base coefficient vector corresponding to the current iteration number and the current base coefficient vector corresponding to the next iteration number.

2. The planar gradient coil design method considering pole magnetization according to claim 1, characterized in that, The target response matrix is ​​a planar gradient coil response matrix, or the target response matrix includes a planar gradient coil response matrix, a planar gradient coil inductance matrix, and a planar gradient coil resistance matrix. The target flat plate gradient coil model undergoes a first processing step to obtain the target response matrix, specifically including: When the target response matrix is ​​the response matrix of a flat plate gradient coil, the first process is as follows: A current corresponding to a current base coefficient of 1 is applied to each current base of the target planar gradient coil model, and the planar gradient coil response matrix is ​​obtained by analytical method. The planar gradient coil response matrix is ​​composed of the magnetization response of each current base coefficient of the target planar gradient coil model. The magnetization response of each current base coefficient is the magnetic induction intensity generated in the imaging region when the current base coefficient of the current base coefficient is 1. When the target response matrix includes the planar gradient coil response matrix, the planar gradient coil inductance matrix, and the planar gradient coil resistance matrix, the first process is as follows: Apply the current corresponding to the current base coefficient of 1 to each current base of the target flat plate gradient coil model, and obtain the flat plate gradient coil response matrix by analytical method; The inductance matrix of the plate gradient coil is calculated based on the number of current bases and the spatial position of each current base in the target plate gradient coil model. The resistance matrix of the plate gradient coil is calculated based on the number of current bases in the target plate gradient coil model and the spatial position of each current base.

3. The planar gradient coil design method considering pole magnetization according to claim 1, characterized in that, The final current base coefficient vector is subjected to a second processing to obtain the number of conductor loops in the target flat plate gradient coil and the spatial position corresponding to each conductor loop, specifically including: Based on the final current basis coefficient vector, the flow function corresponding to the current basis coefficient of the flat gradient coil is calculated; The design current value is obtained, and based on the flow function corresponding to the current basis coefficient of the flat plate gradient coil and the design current value, the number of conductor loops in the target flat plate gradient coil and the spatial position of each conductor loop are obtained by using the flow function discretization method.

4. The planar gradient coil design method considering pole magnetization according to claim 1, characterized in that, Based on the current base coefficient vector corresponding to the current iteration number, a third processing step is performed on the target flat plate gradient coil model and the target pole model to obtain the pole magnetization response matrix, specifically including: Based on the material corresponding to the upper pole model in the target pole model, the corresponding BH curve is extracted from the BH curve library and determined as the first BH curve; the BH curve library includes a variety of pole materials and the BH curve corresponding to each pole material; the BH curve is the curve showing the relationship between magnetic induction intensity and magnetic field intensity. Based on the material corresponding to the lower pole model in the target pole model, the corresponding BH curve is extracted from the BH curve library and determined as the second BH curve. Based on the current base coefficient vector corresponding to the current iteration number, determine the corresponding current base coefficient for each current base in the target flat plate gradient coil model, so as to magnetize the target pole head model and obtain the upper pole head magnetic field strength matrix and the lower pole head magnetic field strength matrix. The magnetization response matrix of the pole head is calculated based on the upper pole head magnetic field strength matrix, the first BH curve, the lower pole head magnetic field strength matrix, and the second BH curve.

5. The planar gradient coil design method considering pole magnetization according to claim 1, characterized in that, The target magnetic flux density matrix, the target response matrix, and the pole magnetization response matrix are subjected to a fourth processing step to obtain the current basis coefficient vector corresponding to the next iteration number, specifically including: Obtain the objective function for optimization; Based on the optimization objective function, the target magnetic induction intensity matrix, the target response matrix, and the pole magnetization response matrix, the current basis coefficient vector corresponding to the next iteration number is obtained using the least squares algorithm.

6. The planar gradient coil design method considering pole magnetization according to claim 5, characterized in that, When the target response matrix is ​​the response matrix of a flat plate gradient coil, the optimization objective function is: ； in, The target magnetic flux density matrix, The response matrix of the plate gradient coil is... The current iteration number The corresponding pole magnetization response matrix, For the next iteration number The corresponding current base coefficient vector.

7. The planar gradient coil design method considering pole magnetization according to claim 5, characterized in that, When the target response matrix includes the response matrix of the planar gradient coil, the inductance matrix of the planar gradient coil, and the resistance matrix of the planar gradient coil, the optimization objective function is: ； in, The target magnetic flux density matrix, The response matrix of the plate gradient coil is... The current iteration number The corresponding pole magnetization response matrix, For the next iteration number The corresponding current base coefficient vector, For the inductance matrix of a flat gradient coil, The resistance matrix of the plate gradient coil. These are the weighting coefficients of the inductance matrix of the flat plate gradient coil. These are the weighting coefficients of the resistance matrix of the flat plate gradient coil. For the next iteration number The vector obtained by transposing the corresponding current base coefficient vector.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the planar gradient coil design method considering pole magnetization as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the planar gradient coil design method considering pole magnetization as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the planar gradient coil design method considering pole magnetization as described in any one of claims 1-7.

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