Gradient coil optimization design method and device for newborn magnetic resonance imaging system

Abstract

The invention discloses a gradient coil optimization design method and device for a newborn magnetic resonance imaging system, and relates to the technical field of magnetic resonance imaging. The method comprises the following steps: obtaining an inductance matrix and a resistance matrix of each coil in gradient coils by using a target field method; constructing a linear optimization model based on the inductance matrix and the resistance matrix, and calculating a Tikhonov explicit initial solution of the linear optimization model; constructing a nonlinear optimization model based on a first scale factor and a second scale factor representing different weight sensitivities in the Tikhonov explicit initial solution, the relative linearity and the absolute linearity, and calculating a current density expansion coefficient of each coil in the gradient coil according to the nonlinear optimization model; and obtaining a stream function of each coil based on the current density expansion coefficient, and determining a wiring track of each coil according to a contour map corresponding to the stream function. According to the method, the relative linearity and the absolute linearity can be effectively considered, and the performance and the applicability of the gradient coil are improved.

Description

Technical Field

[0001] The present application relates to the field of nuclear magnetic resonance imaging technology, and in particular to a method and device for optimizing the design of gradient coils for a neonatal magnetic resonance imaging system. Background Art

[0002] Magnetic resonance imaging plays an irreplaceable role in neonatal medicine. It can non-invasively identify early brain damage and developmental abnormalities, providing critical imaging evidence for neurological function assessment in high-risk newborns. Compared to traditional high-field superconducting systems, low-field permanent magnet magnetic resonance systems offer advantages such as lightweight, low power consumption, and compactness, making them more suitable for clinical scenarios in neonatal intensive care units. However, their unique structure also poses significant challenges to gradient coil design. For example, the small pole plates significantly reduce the available diameter of the gradient coil, while the narrow physical gap between the pole rings strictly limits the total coil thickness.

[0003] Currently, related technologies use a single indicator of relative linearity or absolute linearity to design gradient coils. However, these two standards may lead to different current density distributions and coil topologies during the optimization process. For example, pursuing high relative linearity will sacrifice overall field strength uniformity, while prioritizing absolute linearity will ignore error control in local key areas, making it difficult to achieve global optimization under multi-physical field constraints in a compact design. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the related art, it is desired to provide a gradient coil optimization design method and device for a neonatal magnetic resonance imaging system, which can take into account both relative linearity and absolute linearity and improve the performance and applicability of the gradient coil.

[0005] In a first aspect, the present application provides a method for optimizing the design of a gradient coil for a neonatal magnetic resonance imaging system. The neonatal magnetic resonance imaging system includes a first structure and a second structure symmetrically distributed above and below a permanent magnet. An imaging area is formed between the first structure and the second structure. Each structure includes a pole plate, a pole ring located at the edge of the pole plate, and an anti-eddy current plate and a gradient coil disposed in a groove of the pole ring. The method for optimizing the design of the gradient coil includes:

[0006] obtaining an inductance matrix and a resistance matrix of each coil in the gradient coil by using a target field method;

[0007] Constructing a linear optimization model based on the inductance matrix and the resistance matrix, and calculating a Tikhonov explicit initial solution of the linear optimization model;

[0008] Constructing a nonlinear optimization model based on the Tikhonov explicit initial solution, a first proportional factor and a second proportional factor representing different weight sensitivities in relative linearity and absolute linearity, and calculating a current density expansion coefficient of each coil in the gradient coil according to the nonlinear optimization model;

[0009] The stream function of each coil is obtained based on the current density expansion coefficient, and the wiring trajectory of each coil is determined according to the contour map corresponding to the stream function.

[0010] Optionally, in some embodiments of the present application, obtaining the inductance matrix and resistance matrix corresponding to the gradient coil by using the target field method includes:

[0011] Discretizing the current density distribution in radial and angular directions on the design plane of the gradient coil, and calculating the magnetic induction intensity at each target point in the imaging area and the magnetic energy storage energy and power loss of the system based on the current density distribution;

[0012] The inductance matrix and the resistance matrix are obtained based on the magnetic energy storage energy and the power loss.

[0013] Optionally, in some embodiments of the present application, the inductance matrix L is obtained by the following formula:

[0014]

[0015] Among them, the inductance transformation matrix can be expressed as:

[0016]

[0017] E represents the magnetic energy storage energy, ρ0 represents the minimum radius of the gradient coil, ρ m represents the maximum radius of the gradient coil, a and b represent The subscript value of U q represents the current density expansion coefficient, Q represents the current density expansion order, μ0 represents the vacuum permeability, and U represents the current density expansion coefficient;

[0018]

[0019] β=qc(ρ-ρ0);

[0020]

[0021] ρ represents the polar diameter, represents the polar angle, q represents the subscript value of the current density expansion coefficient, d represents the distance from the center of the central magnetic field region to the design plane.

[0022] Optionally, the inductance transformation matrix in some embodiments of the present application may also be expressed as:

[0023]

[0024] Where F represents the function to be integrated as a whole;

[0025] ε represents a minimum constant.

[0026] Optionally, the linear optimization model in some embodiments of the present application is:

[0027]

[0028] Where D represents the electromagnetic conversion matrix, U represents the current density expansion coefficient, represents the target magnetic field vector, λ1 represents the first weight factor, λ2 represents the second weight factor, L represents the inductance matrix, R represents the resistance matrix, and T represents transpose.

[0029] Optionally, the Tikhonov explicit initial solution in some embodiments of the present application is:

[0030]

[0031] Optionally, the nonlinear optimization model in some embodiments of the present application is:

[0032]

[0033] The constraints are:

[0034]

[0035] U≤max(2×U Opt );

[0036] -U≤max(2×U Opt );

[0037] Wherein, k1 represents the first proportional factor, k2 represents the second proportional factor, D1 is the transformation matrix used to calculate the surface magnetic field of the central magnetic field region, D2 is the transformation matrix used to calculate the surface magnetic field of the shielded region, η represents the minimum constant, represents the magnetic field intensity in the central magnetic field area, Indicates the magnetic field strength on the shielding area.

[0038] Optionally, the stream function in some embodiments of the present application is:

[0039]

[0040] Where ρ represents the polar diameter, represents the polar angle, ρ0 represents the minimum radius of the gradient coil, U q represents the current density expansion coefficient, q represents the subscript value of the current density expansion coefficient, Q represents the current density expansion order, ρ m Indicates the maximum radius of the gradient coil.

[0041] Optionally, in some embodiments of the present application, the stream function value corresponding to each contour line in the contour map is:

[0042] ψ max =ψ min +(i-1 / 2)×A,i∈1,2,...,n;

[0043] Among them, ψ max represents the maximum value of the stream function, ψ min represents the minimum value of the stream function, and n represents the coil

[0044] Number of turns, i represents the serial number corresponding to the number of coil turns, and A represents the coil current.

[0045] In a second aspect, the present application provides a gradient coil optimization design device for a neonatal magnetic resonance imaging system, the neonatal magnetic resonance imaging system comprising a first structure and a second structure symmetrically distributed above and below a permanent magnet, the imaging area being between the first structure and the second structure, each structure comprising a pole plate, a pole ring located at the edge of the pole plate, and an anti-eddy current plate and a gradient coil disposed in a pole ring groove, the gradient coil optimization design device comprising:

[0046] an acquisition module, configured to obtain an inductance matrix and a resistance matrix of each coil in the gradient coil by using a target field method;

[0047] A first building module is used to build a linear optimization model based on the inductance matrix and the resistance matrix, and calculate a Tikhonov explicit initial solution of the linear optimization model;

[0048] a second construction module, configured to construct a nonlinear optimization model based on the Tikhonov explicit initial solution, a first proportional factor and a second proportional factor representing different weight sensitivities in relative linearity and absolute linearity, and calculate a current density expansion coefficient of each coil in the gradient coil according to the nonlinear optimization model;

[0049] A determination module is used to obtain the stream function of each coil based on the current density expansion coefficient, and determine the wiring trajectory of each coil according to the contour map corresponding to the stream function.

[0050] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0051] An embodiment of the present application provides a gradient coil optimization design method and apparatus for a neonatal magnetic resonance imaging system. The inductance matrix and resistance matrix of each coil in the gradient coil are obtained through a target field method, and a linear optimization model is constructed based on the obtained inductance matrix and resistance matrix. That is, the inductance matrix and resistance matrix are introduced as penalty terms in the calculation process to preliminarily obtain a Tikhonov explicit initial solution. Then, starting from the Tikhonov explicit initial solution, a first proportional factor and a second proportional factor representing different weight sensitivities in relative linearity and absolute linearity are introduced to construct a nonlinear optimization model, and a current density expansion coefficient of each coil in the gradient coil is obtained, effectively taking into account relative linearity and absolute linearity. The current density expansion coefficient is then used to obtain the stream function of each coil, and the wiring trajectory of each coil is determined based on the contour map corresponding to the stream function, thereby improving the performance and applicability of the gradient coil. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0053] Figure 1 A schematic cross-sectional view of a neonatal magnetic resonance imaging system provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the partial structure of a neonatal magnetic resonance imaging system provided in an embodiment of the present application;

[0055] Figure 3 A schematic flow chart of a gradient coil optimization design method for a neonatal magnetic resonance imaging system provided in an embodiment of the present application;

[0056] Figure 4 A schematic plan view of a gradient coil design provided in an embodiment of the present application;

[0057] Figure 5 A graph showing the relationship between resistance weight and X-gradient coil linearity provided in an embodiment of the present application;

[0058] Figure 6 A relationship diagram between the inductance weight and the linearity of the X-gradient coil provided in an embodiment of the present application;

[0059] Figure 7 A relationship diagram between resistance weight and Y gradient coil linearity provided in an embodiment of the present application;

[0060] Figure 8A relationship diagram between the inductance weight and the linearity of the Y gradient coil provided in an embodiment of the present application;

[0061] Figure 9 A diagram showing the relationship between resistance weight and Z gradient coil linearity provided in an embodiment of the present application;

[0062] Figure 10 A relationship diagram between the inductance weight and the linearity of the Z gradient coil provided in an embodiment of the present application;

[0063] Figure 11 A schematic diagram of an X-gradient coil structure provided in an embodiment of the present application;

[0064] Figure 12 A schematic diagram of a Y-gradient coil structure provided in an embodiment of the present application;

[0065] Figure 13 A schematic diagram of the main coil structure of a Z gradient coil provided in an embodiment of the present application;

[0066] Figure 14 A schematic diagram of the shielding coil structure of a Z-gradient coil provided in an embodiment of the present application;

[0067] Figure 15 A structural block diagram of a device for optimizing the design of gradient coils for a neonatal magnetic resonance imaging system provided in an embodiment of the present application;

[0068] Figure 16 A structural block diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0070] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0071] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other. Figures 1 to 16 The gradient coil optimization design method and device of the neonatal magnetic resonance imaging system provided by the embodiments of the present application are described in detail.

[0072] Please refer to Figure 1, which is a schematic cross-sectional structure diagram of a neonatal magnetic resonance imaging system provided in an embodiment of the present application. The 0.23T neonatal magnetic resonance imaging system 10 includes a first structure 102 and a second structure 103 symmetrically distributed along a permanent magnet 101. An imaging area 104 is located between the first structure 102 and the second structure 103. Each structure includes a pole plate, a pole ring located at the edge of the pole plate, and an anti-eddy current plate and a gradient coil arranged in a pole ring groove, that is, the pole plate can be the first pole plate 1021 or the second pole plate 1031, the pole ring can be the first pole ring 1022 or the second pole ring 1032, the anti-eddy current plate can be the first anti-eddy current plate 1023 or the second anti-eddy current plate 1033, and the gradient coil can be the first gradient coil 1024 or the second gradient coil 1034.

[0073] Furthermore, if Figure 2 As shown, it is a schematic diagram of the partial structure of a neonatal magnetic resonance imaging system provided by an embodiment of the present application. That is, the first gradient coil 1024 includes an X-gradient coil, a Y-gradient coil, a Z-gradient coil, and a Z-shielding coil. The height of the X-gradient coil is 122.65mm, the height of the Y-gradient coil is 113.35mm, the height of the Z-gradient coil is 110.25mm, and the height of the Z-shielding coil is 125.75mm. The design radius is 386mm. The final thickness of the partition in the dotted box in the figure is 7.6mm. Among them, the Z-gradient coil adopts a self-shielding design, which can effectively suppress the leakage of magnetic field to adjacent components such as pole rings and anti-eddy current plates. The X-gradient coil and the Y-gradient coil adopt an unshielded design, which can suppress eddy current artifacts through the self-cancellation effect of reverse currents in the same plane, and can meet imaging performance requirements without the need for additional shielding layers.

[0074] Please refer to Figure 3 , which is a flow chart of a gradient coil optimization design method for a neonatal magnetic resonance imaging system provided in an embodiment of the present application. The gradient coil optimization design method specifically includes the following steps:

[0075] S101 , using a target field method to obtain an inductance matrix and a resistance matrix of each coil in the gradient coil.

[0076] In some embodiments of the present application, the gradient coil can be designed on a plane (such as Figure 4 The z shown is ±d, d is Figure 2 The z-direction distance from the center of the central magnetic field region (Diameter of Sphere Volume, DSV) to the design plane is shown in the figure. The design planes corresponding to the X gradient coil, Y gradient coil and Z gradient coil are different. The current density distribution is discretized along the radial and angular directions respectively. For example, the radial component J of the current density is expressed by the combination of trigonometric basis functions in the polar coordinate system. ρ and angular components The pole is the center of the central magnetic field region, and we get:

[0077]

[0078] In formula (1), ρ represents the polar diameter, represents the polar angle, ρ0 represents the minimum radius of the gradient coil, and its value is 0; U q represents the current density expansion coefficient, q represents the subscript value of the current density expansion coefficient; Q represents the current density expansion order, which is Q = 4 for the X gradient coil and the Y gradient coil, and Q = 6 for the Z gradient coil; w is used to determine the type of designed gradient coil, w = 0 corresponds to the Z gradient coil, and w = 1 corresponds to the X gradient coil and the Y gradient coil; c depends on the wiring range, and the current density must meet the geometric constraints of the dual-plane gradient coil, that is:

[0079]

[0080] In formula (2), ρ m Indicates the maximum radius of the gradient coil, which is 386mm; Represents the current density.

[0081] Furthermore, based on the Biot-Savart law, the magnetic induction intensity at each target point in the imaging area and the magnetic energy storage energy and power loss of the system are calculated according to the current density distribution, that is, Figure 4 The magnetic field B(r) generated at the target point shown is:

[0082]

[0083] In formula (3), μ0 represents the vacuum magnetic permeability, r represents the spatial coordinates of the target point, and r ′ represents the spatial coordinates of the current element, S represents the area of ​​the current element plane, and J represents the current density at the current element. For the design of a dual-plane gradient coil in polar coordinates, the inductance matrix and resistance matrix can be represented by the magnetic energy storage energy and power loss of the system, and the magnetic energy storage energy E and power loss P are respectively:

[0084]

[0085] In formula (4), r′ + represents the spatial coordinate on the positive plane (z=d), r′ - represents the spatial coordinate on the negative plane (z=-d), δ represents the resistivity of the coil, and t represents the thickness of the coil.

[0086] Furthermore, the inductance matrix and resistance matrix are obtained based on the magnetic energy storage energy and power loss, e.g. U represents the current density expansion coefficient, T represents the transpose, and L represents the inductance matrix. For example R represents the resistance matrix. In order to speed up the calculation, the present application simplifies the calculation method of the inductance matrix L. For example, the magnetic energy storage energy is first converted into polar coordinate form, that is:

[0087]

[0088] In formula (5), q a and q b To make a distinction, both are from 1 to Q value, but they do not change synchronously, and the inductance transformation matrix can be expressed as:

[0089]

[0090] In formula (6), and In expression (7) The subscript value is a or b, for example Points are right Integrate, so first integrate b, then integrate a. In this case, a and b are also the same form of formula. They are classified in different cases to show the difference. a and q b The usage is the same as and Similarly;

[0091]

[0092] β=qc(ρ-ρ0) (8)

[0093]

[0094] In MATLAB, the calculation of the above quadruple integral can be achieved by defining a function handle. However, due to the singularity of formula (6), that is, when ρ a =ρ b ,and When R1=0, the integral will be unsolvable.

[0095] To this end, in some embodiments of the present application, the integration region can be divided into four sub-regions, and the singular point is placed on the integration boundary. Then the inductance transformation matrix can also be expressed as:

[0096]

[0097] In formula (10), F represents the function to be integrated. For example, F can be By replacing the iterative integral with the variable upper limit equivalent, it can be proved that the integral values ​​of these four terms are equivalent. For the first term L1 and the second term L2, the integral domain can be expressed as the union of two sub-regions, which are relative to the straight line Symmetry, that is:

[0098]

[0099] By replacing the two variables in D2, using the symmetry of the function and the characteristic that Jacobian is 1, we can get L1=L2. Similarly, the third and fourth terms are also equal. In addition, since the function is about the line ρ a =ρ b It also has symmetry, namely:

[0100]

[0101] According to the symmetry, the values ​​of the four parts in the formula are equal. In order to further improve the efficiency of numerical integration and avoid the denominator being zero, in the embodiment of the present application

[0102] ε represents a very small constant, and its value is 1E-12.

[0103] S102: construct a linear optimization model based on the inductance matrix and the resistance matrix, and calculate a Tikhonov explicit initial solution of the linear optimization model.

[0104] In some embodiments of the present application, the linear optimization model may be:

[0105]

[0106] In formula (13), D represents the electromagnetic conversion matrix, Represents the target magnetic field vector, λ1 represents the first weight factor, and λ2 represents the second weight factor. This optimization problem belongs to the least squares problem with a quadratic regularization term and has a Tikhonov explicit initial solution, namely:

[0107]

[0108] S103: Construct a nonlinear optimization model based on the Tikhonov explicit initial solution, the first proportional factor and the second proportional factor representing different weight sensitivities in the relative linearity and the absolute linearity, and calculate the current density expansion coefficient of each coil in the gradient coil according to the nonlinear optimization model.

[0109] In some embodiments of the present application, the Tikhonov explicit initial solution is used as a starting point to improve linear performance, and the nonlinear optimization model can be:

[0110]

[0111] The constraints are:

[0112]

[0113] U≤max(2×U Opt )

[0114] -U≤max(2×U Opt ) (15)

[0115] In formula (15), k1 represents the first proportional factor, which can be set to 50, k2 represents the second proportional factor, which can be set to 1, and its value is set according to the experiment; D1 is the transformation matrix used to calculate the surface magnetic field of the central magnetic field area, and D2 is the transformation matrix used to calculate the surface magnetic field of the shielding area; η represents the minimum constant, represents the magnetic field intensity in the central magnetic field area, Indicates the magnetic field strength on the shielding area.

[0116] by Figure 2 Take the corresponding design parameters as an example, Figure 5 The figure shows the effect of the resistance weight on the relative linearity and absolute linearity of the X-gradient coil. The range of λ1 is 1E-10 to 1E-9. Figure 5 It can be seen that the relative linearity is mostly within 5%, and the absolute linearity is all within 5%. Figure 6 The figure shows the effect of the inductance weight on the relative linearity and absolute linearity of the X-gradient coil. The value of λ2 ranges from 1.6E-2 to 2.5E-2. Figure 6 It can be seen that the relative linearity and absolute linearity are both within 5%, and as the weight increases, the relative linearity remains almost unchanged, while the absolute linearity error decreases.

[0117] For example Figure 7 The figure shows the effect of the resistance weight on the relative linearity and absolute linearity of the Y gradient coil, with the value of λ1 ranging from 1E-10 to 1E-9. Figure 7 It can be seen that the relative linearity is mostly within 5%, and the absolute linearity is all within 5%. Figure 8 The figure shows the effect of the inductance weight on the relative linearity and absolute linearity of the Y gradient coil, with the value of λ2 ranging from 1.6E-2 to 2.5E-2. Figure 8 It can be seen that most of the relative linearity is within 5%, all of the absolute linearity is within 5%, and as the weight increases, the errors of both linearity increase.

[0118] Another example Figure 9 The figure shows the effect of the resistance weight on the relative linearity and absolute linearity of the Z gradient coil, with the value of λ1 ranging from 11E-5 to 20E-5. Figure 9It can be seen that the relative linearity is mostly within 5%, and the absolute linearity is all within 5%. Figure 10 The figure shows the effect of the inductance weight on the relative and absolute linearity of the Z gradient coil, with λ2 values ​​ranging from 0.1 to 1. Figure 10 It can be seen that the relative linearity and absolute linearity are mostly within 5%, and as the weight increases, the errors of both linearities decrease.

[0119] S104 , obtaining a stream function of each coil based on the current density expansion coefficient, and determining a wiring trajectory of each coil according to a contour map corresponding to the stream function.

[0120] In some embodiments of the present application, the current density on the design plane of the gradient coil must satisfy the two-dimensional current continuity equation, namely:

[0121]

[0122] Furthermore, a scalar function ψ is constructed on the coil plane, namely:

[0123]

[0124] Then any coordinate point on the coil plane The stream function at can be expressed as:

[0125]

[0126] Furthermore, by selecting an appropriate number of coil turns n, the wiring trajectory of the coil can be obtained from the contour map corresponding to the stream function, and the stream function value corresponding to each contour line in the contour map is:

[0127] ψ max =ψ min +(i-1 / 2)×A,i∈1,2,...,n (19)

[0128] In formula (19), ψ max represents the maximum value of the stream function, ψ min Indicates the minimum value of the stream function, n represents

[0129] Indicates the number of coil turns, i indicates the number of coil turns, and A indicates the coil current. For example, the number of turns of the X gradient coil can be 18, and the coil structure is as follows Figure 11 As shown, the horizontal axis represents the x-axis and the vertical axis represents the y-axis, both in meters. For example, the number of turns of the Y gradient coil can be 18, and the coil structure is as follows Figure 12 As shown, the horizontal axis represents the y-axis and the vertical axis represents the x-axis, both in meters. For example, the number of turns of the main coil of the Z gradient coil can be 19, and the number of turns of the corresponding shielding coil can be 14. The main coil structure is as follows Figure 13 As shown and the shielding coil structure is as Figure 14 As shown, the horizontal axis represents the x-axis, and the vertical axis represents the y-axis, both in meters. This meets both linearity and engineering requirements. Furthermore, the number of coil turns must be selected to ensure that the coil current is less than the maximum current the system can supply. Furthermore, the current requirements for the main and shield coils of the Z gradient coil are equal, ensuring that they can be powered from a single power supply.

[0130] An embodiment of the present application provides a gradient coil optimization design method for a neonatal magnetic resonance imaging system. The inductance matrix and resistance matrix of each coil in the gradient coil are obtained through a target field method, and a linear optimization model is constructed based on this. That is, the inductance matrix and resistance matrix are introduced as penalty terms in the calculation process to preliminarily obtain a Tikhonov explicit initial solution. Then, using the Tikhonov explicit initial solution as a starting point, a first proportional factor and a second proportional factor representing different weight sensitivities in relative linearity and absolute linearity are introduced to construct a nonlinear optimization model, and a current density expansion coefficient of each coil in the gradient coil is obtained, effectively taking into account relative linearity and absolute linearity. The current density expansion coefficient is then used to obtain the stream function of each coil, and the wiring trajectory of each coil is determined based on the contour map corresponding to the stream function, thereby improving the performance and applicability of the gradient coil.

[0131] Based on the above embodiments, the present application provides a gradient coil optimization design device for a neonatal magnetic resonance imaging system. The neonatal magnetic resonance imaging system includes a first structure 102 and a second structure 103 symmetrically distributed along a permanent magnet 101. An imaging area 104 is located between the first structure 102 and the second structure 103. Each structure includes a pole plate, a pole ring located at the edge of the pole plate, and an anti-eddy current plate and a gradient coil arranged in a groove of the pole ring. The gradient coil optimization design device 20 can be applied to Figures 1 to 14 For the gradient coil optimization design method of the corresponding embodiment, please refer to Figure 15 The gradient coil optimization design device 20 includes:

[0132] An acquisition module 201 is configured to obtain the inductance matrix and resistance matrix of each coil in the gradient coil using a target field method;

[0133] A first building module 202 is configured to build a linear optimization model based on the inductance matrix and the resistance matrix, and calculate a Tikhonov explicit initial solution of the linear optimization model;

[0134] A second construction module 203 is configured to construct a nonlinear optimization model based on the Tikhonov explicit initial solution, the first proportional factor and the second proportional factor representing different weight sensitivities in the relative linearity and the absolute linearity, and calculate the current density expansion coefficient of each coil in the gradient coil according to the nonlinear optimization model;

[0135] The determination module 204 is configured to obtain the stream function of each coil based on the current density expansion coefficient, and determine the wiring trajectory of each coil according to the contour map corresponding to the stream function.

[0136] Optionally, in some embodiments of the present application, the acquisition module 201 is specifically configured to discretize the current density distribution in the radial and angular directions on the design plane of the gradient coil, and calculate the magnetic induction intensity at each target point in the imaging area and the magnetic energy storage energy and power loss of the system based on the current density distribution;

[0137] The inductance matrix and resistance matrix are obtained based on the magnetic energy storage energy and power loss.

[0138] It should be noted that, for the description of the same steps and contents in this embodiment as those in other embodiments, reference can be made to the description in other embodiments and will not be repeated here.

[0139] An embodiment of the present application provides a gradient coil optimization design device for a neonatal magnetic resonance imaging system. The inductance matrix and resistance matrix of each coil in the gradient coil are obtained through a target field method, and a linear optimization model is constructed based on the obtained inductance matrix and resistance matrix. That is, the inductance matrix and resistance matrix are introduced as penalty terms in the calculation process to preliminarily obtain a Tikhonov explicit initial solution. Then, using the Tikhonov explicit initial solution as a starting point, a first proportional factor and a second proportional factor representing different weight sensitivities in relative linearity and absolute linearity are introduced to construct a nonlinear optimization model, and a current density expansion coefficient of each coil in the gradient coil is obtained, effectively taking into account relative linearity and absolute linearity. The current density expansion coefficient is then used to obtain the stream function of each coil, and the wiring trajectory of each coil is determined based on the contour map corresponding to the stream function, thereby improving the performance and applicability of the gradient coil.

[0140] Based on the above embodiments, this application embodiment provides a terminal device. Figure 16 The terminal device 30 may include a processor 301 and a memory 302. The memory 302 stores at least one instruction, at least one program, code set or instruction set, which is loaded and executed by the processor 301 to implement Figures 1 to 14 The steps of the gradient coil optimization design method of the corresponding embodiment.

[0141] As another aspect, the present invention provides a computer-readable storage medium for storing program code for executing the aforementioned Figures 1 to 14 Any implementation manner of the gradient coil optimization design method corresponding to the embodiment.

[0142] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. On the other hand, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0144] In addition, the functional modules in the various embodiments of the present application may be integrated into a single processing unit, or each module may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated units may be implemented in the form of hardware or in the form of software functional units. If the integrated units are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium.

[0145] Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for causing a computer device (such as a personal computer, server, or network device) to execute all or part of the steps of the gradient coil optimization design method according to various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0146] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0147] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A gradient coil optimization design method for a neonatal magnetic resonance imaging system, characterized in that: The neonatal magnetic resonance imaging system includes a first structure and a second structure symmetrically distributed along the upper and lower sides of the permanent magnet. An imaging area is formed between the first structure and the second structure. Each structure includes a pole plate, a pole ring located at the edge of the pole plate, and an anti-eddy current plate and a gradient coil arranged in a groove of the pole ring. The gradient coil optimization design method includes: obtaining an inductance matrix and a resistance matrix of each coil in the gradient coil by using a target field method; Constructing a linear optimization model based on the inductance matrix and the resistance matrix, and calculating a Tikhonov explicit initial solution of the linear optimization model; Constructing a nonlinear optimization model based on the Tikhonov explicit initial solution, a first proportional factor and a second proportional factor representing different weight sensitivities in relative linearity and absolute linearity, and calculating a current density expansion coefficient of each coil in the gradient coil according to the nonlinear optimization model; The stream function of each coil is obtained based on the current density expansion coefficient, and the wiring trajectory of each coil is determined according to the contour map corresponding to the stream function.

2. The gradient coil optimization design method according to claim 1, characterized in that: The method of obtaining the inductance matrix and the resistance matrix of each coil in the gradient coil by using the target field method includes: Discretizing the current density distribution in radial and angular directions on the design plane of the gradient coil, and calculating the magnetic induction intensity at each target point in the imaging area and the magnetic energy storage energy and power loss of the system based on the current density distribution; The inductance matrix and the resistance matrix are obtained based on the magnetic energy storage energy and the power loss.

3. The gradient coil optimization design method according to claim 2, characterized in that: The inductance matrix L is obtained by the following formula: Among them, the inductance transformation matrix can be expressed as: E represents the magnetic energy storage energy, ρ0 represents the minimum radius of the gradient coil, ρ m represents the maximum radius of the gradient coil, a and b represent The subscript value of U q represents the current density expansion coefficient, Q represents the current density expansion order, μ0 represents the vacuum permeability, and U represents the current density expansion coefficient; β=qc(ρ-ρ0); ρ represents the polar diameter, represents the polar angle, q represents the subscript value of the current density expansion coefficient, d represents the distance from the center of the central magnetic field region to the design plane.

4. The gradient coil optimization design method according to claim 3, characterized in that: The inductance transformation matrix can also be expressed as: Where F represents the function to be integrated as a whole; ε represents a minimum constant.

5. The gradient coil optimization design method according to any one of claims 1 to 4, characterized in that: The linear optimization model is: Where D represents the electromagnetic conversion matrix, U represents the current density expansion coefficient, represents the target magnetic field vector, λ1 represents the first weight factor, λ2 represents the second weight factor, L represents the inductance matrix, R represents the resistance matrix, and T represents transpose.

6. The gradient coil optimization design method according to claim 5, characterized in that: The Tikhonov explicit initial solution is:

7. The gradient coil optimization design method according to claim 6, characterized in that: The nonlinear optimization model is: The constraints are: U≤max(2×U Opt ); -U≤max(2×U Opt ); Wherein, k1 represents the first proportional factor, k2 represents the second proportional factor, D1 is the transformation matrix used to calculate the surface magnetic field of the central magnetic field region, D2 is the transformation matrix used to calculate the surface magnetic field of the shielded region, η represents the minimum constant, represents the magnetic field intensity in the central magnetic field area, Indicates the magnetic field strength on the shielding area.

8. The gradient coil optimization design method according to claim 5, characterized in that: The stream function is: Where ρ represents the polar diameter, represents the polar angle, ρ0 represents the minimum radius of the gradient coil, U q represents the current density expansion coefficient, q represents the subscript value of the current density expansion coefficient, Q represents the current density expansion order, ρ m Indicates the maximum radius of the gradient coil.

9. The gradient coil optimization design method according to claim 8, characterized in that: The stream function value corresponding to each contour line in the contour map is: ψ max =ψ min +(i-1 / 2)×A,i∈1,2,...,n; Among them, ψ max represents the maximum value of the stream function, ψ min represents the minimum value of the stream function, n represents the number of coil turns, i represents the serial number corresponding to the number of coil turns, and A represents the coil current.

10. A device for optimizing the design of gradient coils for a neonatal magnetic resonance imaging system, characterized in that: The neonatal magnetic resonance imaging system includes a first structure and a second structure symmetrically distributed along the upper and lower sides of the permanent magnet. An imaging area is formed between the first structure and the second structure. Each structure includes a pole plate, a pole ring located at the edge of the pole plate, and an anti-eddy current plate and a gradient coil arranged in a groove of the pole ring. The gradient coil optimization design device includes: an acquisition module, configured to obtain an inductance matrix and a resistance matrix of each coil in the gradient coil by using a target field method; A first building module is used to build a linear optimization model based on the inductance matrix and the resistance matrix, and calculate a Tikhonov explicit initial solution of the linear optimization model; a second construction module, configured to construct a nonlinear optimization model based on the Tikhonov explicit initial solution, a first proportional factor and a second proportional factor representing different weight sensitivities in relative linearity and absolute linearity, and calculate a current density expansion coefficient of each coil in the gradient coil according to the nonlinear optimization model; A determination module is used to obtain the stream function of each coil based on the current density expansion coefficient, and determine the wiring trajectory of each coil according to the contour map corresponding to the stream function.

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