Model-based rapid magnetic resonance diffusion kurtosis parameter imaging method

Abstract

The invention discloses a rapid magnetic resonance diffusion kurtosis parameter imaging method based on a model, and relates to the technical field of magnetic resonance imaging. The method comprises the following steps: performing signal acquisition on a measured target; establishing a DKI mathematical model; calculating sensitivity information S of the multi-channel coil; performing initial reconstruction on the down-sampling k space data of each b value and each diffusion coding direction to obtain an initial complex image; performing DKI tensor estimation according to the amplitude information of the initial complex image to obtain an initial DKI tensor; extracting phase information P of each b value and each diffusion coding direction; constructing a target function with regularization constraint; solving a DKI tensor enabling the objective function to be minimum; calculating a DKI quantization parameter; and calculating a DKI image according to the DKI tensor obtained by optimization solution and the DKI mathematical model. According to the method, the DKI tensor can be directly reconstructed from the downsampling k space data, two steps of image reconstruction and tensor fitting are not needed, error transmission is avoided, and the DKI parameter imaging quality is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of magnetic resonance imaging, and in particular to a model-based rapid magnetic resonance diffusion kurtosis parameter imaging method. Background Art

[0002] Diffusion kurtosis imaging (DKI) is a new magnetic resonance imaging method developed based on diffusion tensor imaging (DTI). DKI uses the non-Gaussian diffusion characteristics of water molecules for imaging and can non-invasively evaluate the complexity and integrity of the microstructure of biological tissues.

[0003] DKI imaging obtains the corresponding diffusion-weighted image by applying diffusion gradients under different diffusion encoding directions and different diffusion sensitivity factors (called b-values). Then, data fitting is performed based on the mathematical model of the evolution of DKI signals with changes in direction and b-value to obtain the DKI tensor (including the diffusion tensor D and the kurtosis tensor K). The diffusion tensor D is a 3×3 symmetric matrix (with 6 unknowns), and the kurtosis tensor K is a 3×3×3×3 symmetric matrix (with 15 unknowns). By solving the eigenvalues ​​and eigenvectors of these two matrices, we can analyze and obtain the quantitative parameters of DKI, such as fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK). These rich quantitative parameters provide more information for disease prevention, diagnosis, and treatment.

[0004] However, compared with other magnetic resonance imaging techniques, DKI requires a longer scanning time and has a lower image signal-to-noise ratio. In order to reduce the impact of noise, the method of repeated acquisition and averaging is often used in clinical practice, which will significantly increase the scanning time and seriously restrict the widespread application of DKI in clinical practice. Therefore, fast DKI imaging has important research significance.

[0005] At present, the common method for realizing fast DKI parameter imaging is to reconstruct the DKI images of each diffusion coding direction and each b value from the downsampled k-space data, and then use the DKI mathematical model to fit the DKI tensor, and finally calculate the DKI parameters. However, this method will cause error transmission, that is, the error in the reconstruction process will be transmitted to the subsequent fitting steps, resulting in errors in the DKI tensor estimation. Summary of the invention

[0006] In order to overcome the above problems or at least partially solve the above problems, the present invention provides a model-based rapid magnetic resonance diffusion kurtosis parameter imaging method, which can directly reconstruct the DKI tensor from the downsampled k-space data without the need for image reconstruction and tensor fitting, thereby avoiding error propagation and improving the quality of DKI parameter imaging.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] In a first aspect, the present invention provides a model-based rapid magnetic resonance diffusion kurtosis parameter imaging method, comprising the following steps:

[0009] The signal of the target to be measured is collected by multi-channel coils according to the downsampling matrix U at different b values ​​and different diffusion coding directions;

[0010] A DKI mathematical model is established in which the DKI signal intensity evolves with the change of b value and diffusion coding direction. The DKI mathematical model is: Where, m is the DKI image signal strength; g n =[g n1 ,g n2 ,g n3 ] is the diffusion coding direction; g ni , g nj , g nk , g nl is the diffusion coding direction g n , i=1,2,3; j=1,2,3; k=1,2,3; l=1,2,3; m0 is the DKI image signal intensity when b=0; θ is the DKI model tensor, including the diffusion tensor D and the kurtosis tensor K;

[0011] The sensitivity information S of the multi-channel coil is calculated based on the b=0 data;

[0012] Perform initial reconstruction on the downsampled k-space data at each b value and each diffusion coding direction to obtain an initial complex image;

[0013] Model fitting is performed according to the amplitude information of the initial complex image and the DKI mathematical model to obtain the initial DKI tensor;

[0014] Extract the phase information P of each b value and each diffusion encoding direction according to the initial complex image;

[0015] According to the above downsampling matrix U, downsampling k-space data d, DKI mathematical model, sensitivity information S of the multi-channel coil, and phase information P, an objective function with regularization constraints is constructed, and the expression is as follows:

[0016]

[0017] Where L(θ) represents the function of the DKI tensor θ, n and c represent the index value in the diffusion dimension and the index value of the coil channel respectively, and N DWI Represents the total number of diffusion dimensions, N c represents the total number of coils, F represents Fourier transform, and λ is the weight coefficient of the regularized TV constraint;

[0018] Based on the initial DKI tensor, the above objective function is solved using the optimization algorithm to obtain the final DKI tensor θ;

[0019] Calculate the DKI quantization parameter based on the DKI tensor θ obtained by optimization;

[0020] The DKI image is calculated based on the DKI tensor θ obtained through optimization and the DKI mathematical model.

[0021] The present invention does not need to reconstruct the DKI image, and can directly reconstruct the DKI tensor from the k-space data of the downsampled DKI, thereby avoiding the error introduced when reconstructing the DKI image and causing the DKI tensor fitting error, and improving the reconstruction quality of the DKI tensor.

[0022] Based on the first aspect, further, the initial reconstruction of the downsampled k-space data for each b value and each diffusion coding direction includes the following steps:

[0023] The POCSENSE algorithm is used to perform initial reconstruction of the downsampled k-space data for each b value and each diffusion encoding direction.

[0024] Based on the first aspect, further, the above-mentioned DKI tensor estimation is performed according to the amplitude information of the initial complex image to obtain the initial DKI tensor, which includes the following steps:

[0025] According to the amplitude information of the initial complex image, the constrained weighted least squares fitting method is used to fit the data to obtain the initial DKI tensor.

[0026] Based on the first aspect, further, the above-mentioned extraction of phase information P of each b value and each diffusion coding direction according to the initial complex image includes the following steps:

[0027] According to the initial complex image, the phase information P of each b value and each diffusion coding direction is extracted using the formula P=I / |I|; wherein I is the initial complex image after total variation denoising, and |*| represents the amplitude operation.

[0028] Based on the first aspect, further, the above-mentioned optimization algorithm includes any one of a nonlinear conjugate gradient descent method, a l-BFGS algorithm, and a gradient descent method.

[0029] Based on the first aspect, further, the above-mentioned use of the optimization algorithm to solve the DKI tensor θ that minimizes the above-mentioned objective function includes the following steps:

[0030] The minimum θ of the objective function is determined by taking the derivative of the objective function with respect to the DKI tensor θ as follows:

[0031]

[0032] Based on the first aspect, further, the above-mentioned DKI quantitative parameters include anisotropy fraction FA, mean diffusion coefficient MD, axial diffusion coefficient AD, radial diffusion coefficient RD, kurtosis anisotropy fraction KFA, mean kurtosis coefficient MK, axial kurtosis coefficient AK and radial kurtosis coefficient RK.

[0033] Based on the first aspect, further, the above-mentioned calculation of the DKI image according to the DKI tensor θ obtained by optimization and the DKI mathematical model includes the following steps:

[0034] The DKI image is calculated according to the following formula:

[0035]

[0036] Among them, D ij and K ijkl are the elements of the diffusion tensor D and the kurtosis tensor K, respectively.

[0037] In a second aspect, the present application provides an electronic device, comprising a memory for storing one or more programs; a processor; when the one or more programs are executed by the processor, the method as described in any one of the above-mentioned first aspects is implemented.

[0038] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any method as described in the first aspect above.

[0039] The present invention has at least the following advantages or beneficial effects:

[0040] The present invention provides a model-based rapid magnetic resonance diffusion kurtosis parameter imaging method, which does not require reconstruction of a DKI image and can directly reconstruct a DKI tensor from k-space data of a downsampled DKI, thereby avoiding errors introduced when reconstructing the DKI image and causing DKI tensor fitting errors, and improving the reconstruction quality of the DKI tensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 This is a flow chart of a model-based rapid magnetic resonance diffusion kurtosis parameter imaging method according to an embodiment of the present invention;

[0043] Figure 2 Schematic diagram of complementary down-sampling k-space data in different directions in an embodiment of the present invention;

[0044] Figure 3 b = 1000s / mm 2 DKI image and its local magnified image;

[0045] Figure 4 b = 2000s / mm 2 DKI image and its local magnified image;

[0046] Figure 5 Color-coded FA, MD, MK and their local magnifications;

[0047] Figure 6 A structural block diagram of an electronic device provided by an embodiment of the present invention.

[0048] Description of reference numerals: 101, memory; 102, processor; 103, communication interface. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0052] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0053] Example:

[0054] like Figure 1 As shown, in a first aspect, an embodiment of the present invention provides a model-based rapid magnetic resonance diffusion kurtosis parameter imaging method, comprising the following steps:

[0055] The signal of the target to be measured is collected by a multi-channel coil according to a downsampling matrix U at different b values ​​and different diffusion coding directions; the downsampling matrix U is a binary mask whose values ​​include 0 and 1, 0 represents an unsampled point, and 1 represents a sampled point;

[0056] A DKI mathematical model is established in which the DKI signal intensity evolves with the change of b value and diffusion coding direction. The DKI mathematical model is: Where, m is the DKI image signal strength; g n =[g n1 ,g n2 ,g n3 ] is the diffusion coding direction; g ni , g nj , g nk , g nl (i=1,2,3;j=1,2,3;k=1,2,3;l=1,2,3) is the diffusion coding direction g nelement value; m0 is the DKI image signal intensity when b = 0; θ is the DKI model tensor, including the diffusion tensor D and the kurtosis tensor K, and the expression of θ is: θ = {D 11 ,D 22 ,D 33 ,D 12 ,D 13 ,D 23 ,K 1111 ,K 2222 ,K 3333 ,K 1112 ,K 1113 ,K 1222 ,K 1333 ,K 2223 ,K 2333 ,K 1122 ,K 1133 ,K 2233 ,K 1123 ,K 1223 ,K 1233}, where {D 11 ,D 22 ,D 33 ,D 12 ,D 13 ,D 23} are the six independent elements of the diffusion tensor D, {K 1111 ,K 2222 ,K 3333 ,K 1112 ,K 1113 ,K 1222 ,K 1333 ,K 2223 ,K 2333 ,K 1122 ,K 1133 ,K 2233 ,K 1123 ,K 1223 ,K 1233} are the 15 independent elements of the kurtosis tensor K;

[0057] The sensitivity information S of the multi-channel coil is calculated based on the b=0 data; S=I0 / SOS(I0); wherein I0 represents the b=0 multi-channel image, and SOS represents the square root operation;

[0058] Perform initial reconstruction on the downsampled k-space data at each b value and each diffusion coding direction to obtain an initial complex image;

[0059] Model fitting is performed according to the amplitude information of the initial complex image and the DKI mathematical model to obtain the initial DKI tensor;

[0060] Extract the phase information P of each b value and each diffusion encoding direction according to the initial complex image;

[0061] According to the above downsampling matrix U, downsampling k-space data d, DKI mathematical model, sensitivity information S of the multi-channel coil, and phase information P, an objective function with regularization constraints is constructed, and the expression is as follows:

[0062]

[0063] Where L(θ) represents the function of the DKI tensor θ, n and c represent the index value in the diffusion dimension (including b value and diffusion encoding direction) and the index value of the coil channel, respectively, and N DWI Represents the total number of diffusion dimensions (i.e., the total number of b values ​​and diffusion coding directions), N c represents the total number of coils, F represents Fourier transform; the first term represents the data fidelity term, the second term TV(*) is the total variation (TV) regularization constraint term, and λ is the weight coefficient of the regularized TV constraint;

[0064] Based on the initial DKI tensor, the above objective function is solved using the optimization algorithm to obtain the final DKI tensor θ;

[0065] The DKI quantization parameters are calculated according to the DKI tensor θ obtained by optimization. The DKI quantization parameters include fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK).

[0066] The DKI image is calculated based on the DKI tensor θ obtained through optimization and the DKI mathematical model.

[0067] This embodiment uses multiple excitation DKI raw k-space data collected on a Siemens 3.0T Prisma device. The number of coil channels is 32, the number of excitations is 4, the partial Fourier acquisition factor is 0.75, TR / TE=3000 / 65ms, and the resolution is 1.2×1.2×5mm 3 , FOV is 212×212. Diffusion coding parameters are: 1 b=0s / mm 2 , b=1000s / mm2 There are 32 diffusion coding directions, b = 2000s / mm 2 There are 64 diffusion coding directions.

[0068] By comparing the imaging results of the traditional method and the imaging effect diagram of the present invention, it is further verified that the image reconstruction effect of the present invention is better. Figure 3-Figure 5 As shown, respectively: b = 1000s / mm 2 DKI image and its local enlarged image ( Figure 3 ), in which the POCSENSE method is a traditional method and the MB-DKI method is the method of the present invention; b = 2000s / mm 2 DKI image and its local enlarged image ( Figure 4 ), in which the POCSENSE method is a traditional method and the MB-DKI method is the method of the present invention; color-coded FA, MD, MK and their partial enlarged diagrams ( Figure 5 ), in which the POCSENSE method is a traditional method, NLS is a nonlinear least squares fitting, CWLLS is a constrained linear least squares fitting, and the MB-DKI method is the method of the present invention.

[0069] The present invention does not need to reconstruct the DKI image, and can directly reconstruct the DKI tensor from the k-space data of the downsampled DKI, thereby avoiding the error introduced when reconstructing the DKI image and causing the DKI tensor fitting error, and improving the reconstruction quality of the DKI tensor.

[0070] Based on the first aspect, further, the initial reconstruction of the downsampled k-space data for each b value and each diffusion coding direction includes the following steps:

[0071] The POCSENSE algorithm is used to perform initial reconstruction of the downsampled k-space data for each b value and each diffusion encoding direction.

[0072] Based on the first aspect, further, the above-mentioned model fitting is performed according to the amplitude information of the initial complex image and the DKI mathematical model to obtain the initial DKI tensor, which includes the following steps:

[0073] According to the amplitude information of the initial complex image, the constrained weighted least squares fitting method is used to fit the data to obtain the initial DKI tensor.

[0074] Based on the first aspect, further, the above-mentioned extraction of phase information P of each b value and each diffusion coding direction according to the initial complex image includes the following steps:

[0075] According to the initial complex image, the phase information P of each b value and each diffusion coding direction is extracted using the formula P=I / |I|; wherein I is the initial complex image after total variation denoising, and |*| represents the amplitude operation.

[0076] Based on the first aspect, further, the above-mentioned optimization algorithm can be any one of a nonlinear conjugate gradient descent method, a l-BFGS algorithm, and a gradient descent method.

[0077] Based on the first aspect, further, the above-mentioned use of the optimization algorithm to solve the DKI tensor θ that minimizes the above-mentioned objective function includes the following steps:

[0078] Obtain the derivative of the objective function with respect to the DKI tensor θ:

[0079]

[0080] Based on the first aspect, further, the above-mentioned calculation of DKI quantization parameters FA, MD, AD, RD, KFA, MK, AK and RK according to the DKI tensor θ obtained by optimization includes the following steps:

[0081] FA, MD, AD, RD, KFA, MK, AK and RK are calculated according to the formulas shown in the following table:

[0082]

[0083]

[0084] Among them, λ1,λ2,λ3 are the three eigenvalues ​​obtained by eigenvalue decomposition of the diffusion tensor D (λ1>λ2>λ3), v1,v2,v3 are the three eigenvectors obtained by eigenvalue decomposition of the diffusion tensor D, Σ 2 is the unit sphere, W(g) is the kurtosis along direction g, K is the kurtosis tensor, The expression is I is a symmetric, rank-4 isotropic tensor whose element values ​​are defined as δ ij is the Kronecker delta function (Kroneckerδ), ||*|| F is the Frobenius norm.

[0085] In some embodiments of the present invention, parameters such as FA, MD, and MK can quantitatively reflect changes in tissue microstructure. For example, FA values ​​closer to 0 represent isotropic diffusion, FA values ​​closer to 1 represent anisotropic diffusion, and a decrease in white matter FA values ​​can reflect damage to tissue fiber bundles. A decrease in MD values ​​indicates limited diffusion of water molecules in the tissue, which can reflect an increase in cell density. MK reflects the complexity of the microstructure in the tissue. An increase in MK values ​​indicates an increase in the complexity of the tissue microstructure. The MK value of tumor tissue is often shown to be increased.

[0086] Based on the first aspect, further, the above-mentioned calculation of the DKI image according to the DKI tensor θ obtained by optimization and the DKI mathematical model includes the following steps:

[0087] The DKI image is calculated according to the following formula:

[0088]

[0089] Among them, D ij and K ijkl are the elements of the diffusion tensor D and the kurtosis tensor K, respectively.

[0090] like Figure 6 As shown, in a second aspect, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs and a processor 102. When the one or more programs are executed by the processor 102, any method in the first aspect described above is implemented.

[0091] The memory 101, the processor 102 and the communication interface 103 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules, and the processor 102 executes various functional applications and data processing by executing the software programs and modules stored in the memory 101. The communication interface 103 can be used to communicate signaling or data with other node devices.

[0092] Among them, the memory 101 can be but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0093] The processor 102 may be an integrated circuit chip with signal processing capability. The processor 102 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0094] In the embodiments provided in the present application, it should be understood that the disclosed method and system can also be implemented in other ways. The method and system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the method and system, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0095] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0096] In the third aspect, the embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by the processor 102, a method as in any one of the first aspects described above is implemented. If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk.

[0097] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0098] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A model-based rapid magnetic resonance diffusion kurtosis parameter imaging method, characterized in that: The following steps are involved: The signal of the target to be measured is collected by multi-channel coils according to the downsampling matrix U at different b values ​​and different diffusion coding directions; A DKI mathematical model is established in which the DKI signal intensity evolves with the change of b value and diffusion coding direction. The DKI mathematical model is: Where, m is the DKI image signal strength; g n =[g n1 ,g n2 ,g n3 ] is the diffusion coding direction; g ni , g nj , g nk , g nl is the diffusion coding direction g n , i=1,2,3; j=1,2,3; k=1,2,3; l=1,2,3; m0 is the DKI image signal intensity when b=0; θ is the DKI model tensor, including the diffusion tensor D and the kurtosis tensor K; The sensitivity information S of the multi-channel coil is calculated based on the b=0 data; Perform initial reconstruction on the downsampled k-space data at each b value and each diffusion coding direction to obtain an initial complex image; Model fitting is performed according to the amplitude information of the initial complex image and the DKI mathematical model to obtain the initial DKI tensor; Extract the phase information P of each b value and each diffusion encoding direction according to the initial complex image; According to the above downsampling matrix U, downsampling k-space data d, DKI mathematical model, sensitivity information S of the multi-channel coil, and phase information P, an objective function with regularization constraints is constructed, and the expression is as follows: Where L(θ) represents the function of the DKI tensor θ, n and c represent the index value in the diffusion dimension and the index value of the coil channel respectively, and N DWI Represents the total number of diffusion dimensions, N c represents the total number of coils, F represents Fourier transform, and λ is the weight coefficient of the regularized TV constraint; Based on the initial DKI tensor, the above objective function is solved using the optimization algorithm to obtain the final DKI tensor θ; Calculate the DKI quantization parameter based on the DKI tensor θ obtained by optimization; The DKI image is calculated based on the DKI tensor θ obtained through optimization and the DKI mathematical model.

2. A model-based rapid magnetic resonance diffusion kurtosis parameter imaging method according to claim 1, characterized in that: The initial reconstruction of the downsampled k-space data for each b value and each diffusion encoding direction comprises the following steps: The POCSENSE algorithm is used to perform initial reconstruction of the downsampled k-space data for each b value and each diffusion encoding direction.

3. The method for model-based rapid magnetic resonance diffusion kurtosis parameter imaging according to claim 1, characterized in that: The step of performing model fitting according to the amplitude information of the initial complex image and the DKI mathematical model to obtain the initial DKI tensor comprises the following steps: According to the amplitude information of the initial complex image, the constrained weighted least squares fitting method is used to fit the data to obtain the initial DKI tensor.

4. The method for model-based rapid magnetic resonance diffusion kurtosis parameter imaging according to claim 1, characterized in that: The step of extracting the phase information P of each b value and each diffusion coding direction according to the initial complex image comprises the following steps: According to the initial complex image, the phase information P of each b value and each diffusion coding direction is extracted using the formula P=I / |I|; wherein I is the initial complex image after total variation denoising, and |*| represents the amplitude operation.

5. The method for model-based rapid magnetic resonance diffusion kurtosis parameter imaging according to claim 1, characterized in that: The optimization algorithm includes any one of a nonlinear conjugate gradient descent method, a l-BFGS algorithm, and a gradient descent method.

6. The method for model-based rapid magnetic resonance diffusion kurtosis parameter imaging according to claim 1, characterized in that: The method of using an optimization algorithm to solve θ that minimizes the above objective function includes the following steps: The derivative of the objective function with respect to the DKI tensor θ is obtained using the following formula to determine the minimum θ of the objective function:

7. The method for model-based rapid magnetic resonance diffusion kurtosis parameter imaging according to claim 1, characterized in that: The DKI quantitative parameters include anisotropy fraction FA, mean diffusion coefficient MD, axial diffusion coefficient AD, radial diffusion coefficient RD, kurtosis anisotropy fraction KFA, mean kurtosis coefficient MK, axial kurtosis coefficient AK and radial kurtosis coefficient RK.

8. The method for model-based rapid magnetic resonance diffusion kurtosis parameter imaging according to claim 1, characterized in that: The step of calculating the DKI image based on the θ obtained by optimization and the DKI mathematical model comprises the following steps: The DKI image is calculated according to the following formula: Among them, D ij and K ijkl are the elements of the diffusion tensor D and the kurtosis tensor K, respectively.

9. An electronic device, characterized in that: include: A memory for storing one or more programs; processor; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

Images