A Model-Based Fast Magnetic Resonance Diffusion Kurtosis Parameter Imaging Method

Abstract

The present invention discloses a model-based rapid magnetic resonance diffusion kurtosis parameter imaging method, which relates to the field of magnetic resonance imaging technology. The method includes: acquiring signals from a target; establishing a DKI mathematical model; calculating sensitivity information S of a multi-channel coil; performing initial reconstruction on downsampled k-space data for each b-value and each diffusion coding direction to obtain an initial complex image; estimating a DKI tensor based on the amplitude information of the initial complex image to obtain an initial DKI tensor; extracting phase information P for each b-value and each diffusion coding direction; constructing an objective function with regularization constraints; solving the DKI tensor that minimizes the objective function; calculating DKI quantization parameters; and calculating a DKI image based on the DKI tensor obtained through optimization and the DKI mathematical model. The present invention can directly reconstruct a DKI tensor from downsampled k-space data without the two steps of image reconstruction and tensor fitting, thus avoiding error propagation and improving the quality of DKI parameter imaging.

Description

Technical Field

[0001] The present 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 novel magnetic resonance imaging method developed based on diffusion tensor imaging (DTI). DKI utilizes the non-Gaussian diffusion properties of water molecules for imaging, enabling non-invasive assessment of the complexity and integrity of biological tissue microstructures.

[0003] DKI imaging obtains the corresponding diffusion-weighted image by applying a diffusion gradient under different diffusion encoding directions and different diffusion sensitivity factors (called b-values). Then, the data is fitted based on a mathematical model that shows how the DKI signal evolves with 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 six 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, quantitative parameters of DKI can be analyzed, 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 to other MRI techniques, DKI requires longer scan times and produces lower signal-to-noise ratios. To mitigate the effects of noise, repeated acquisitions and averaging are often used in clinical practice. This significantly increases scan time and severely limits the widespread clinical application of DKI. Therefore, rapid DKI imaging is of great research significance.

[0005] Currently, a common method for achieving fast DKI parametric imaging is to reconstruct DKI images for each diffusion encoding direction and each b-value from the downsampled k-space data. This is then fitted using the DKI mathematical model to determine the DKI tensor, and finally, the DKI parameters are calculated. However, this method can cause error propagation: errors in the reconstruction process are propagated to subsequent fitting steps, leading to inaccurate 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, avoiding error propagation and thus 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 is collected by multi-channel coils at different b values and different diffusion coding directions according to the downsampling matrix U;

[0010] A DKI mathematical model is established in which the DKI signal intensity evolves with the 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, which includes 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 encoding direction to obtain an initial complex image;

[0013] The initial DKI tensor is obtained by fitting the model based on the amplitude information of the initial complex image and the DKI mathematical model;

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

[0015] Based on the above downsampling matrix U, downsampled 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, which is expressed 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, N DWI Indicates 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 optimization algorithm is used to solve the above objective function 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 require reconstruction of the DKI image and can directly reconstruct the DKI tensor from the downsampled DKI k-space data, 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 based on the amplitude information of the initial complex image to obtain the initial DKI tensor 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 and 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] The phase information P of each b value and each diffusion coding direction is extracted from the initial complex image using the formula P = I / |I|, where 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 the nonlinear conjugate gradient descent method, the l-BFGS algorithm, and the 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 derivative of the objective function with respect to the DKI tensor θ is obtained as follows to determine the minimum θ of the objective function:

[0031]

[0032] Based on the first aspect, further, 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.

[0033] Based on the first aspect, further, the above-mentioned calculation of the DKI image based on 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; and when the one or more programs are executed by the processor, the method of 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 the method as described in any one of the first aspects 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 DKI images and can directly reconstruct the DKI tensor from the k-space data of downsampled DKI. This avoids the errors introduced when reconstructing the DKI image and causes DKI tensor fitting errors, thereby 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 following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any 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 according to 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 This is a structural block diagram of an electronic device provided by an embodiment of the present invention.

[0048] Description of the accompanying drawings: 101, memory; 102, processor; 103, communication interface. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only 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 herein 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 as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall 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, it does not need to be further defined or explained in subsequent drawings.

[0052] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes 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 limitations, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[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 multi-channel coil acquires signals from the target under test according to the downsampling matrix U at different b values and different diffusion coding directions. The downsampling matrix U is a binary mask whose values contain 0 and 1, where 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 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 6 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); where 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 encoding direction to obtain an initial complex image;

[0059] The initial DKI tensor is obtained by fitting the model based on the amplitude information of the initial complex image and the DKI mathematical model;

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

[0061] Based on the above downsampling matrix U, downsampled 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, which is expressed 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 the b value and the diffusion encoding direction) and the index value of the coil channel, respectively, and N DWI Indicates 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 optimization algorithm is used to solve the above objective function to obtain the final DKI tensor θ;

[0065] The DKI quantization parameters are calculated based on the DKI tensor θ obtained through 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 example uses multi-shot DKI raw k-space data acquired 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 magnification ( Figure 3 ), in which the POCSENSE method is a traditional method and the MB-DKI method is a method of the present invention; b = 2000 s / mm 2 DKI image and its local magnification ( 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 local enlarged images ( Figure 5 ), in which the POCSENSE method is a traditional method, NLS is a nonlinear least squares fitting method, CWLLS is a constrained linear least squares fitting method, and the MB-DKI method is a method of the present invention.

[0069] The present invention does not require reconstruction of the DKI image and can directly reconstruct the DKI tensor from the downsampled DKI k-space data, 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 based on 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 and 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] The phase information P of each b value and each diffusion coding direction is extracted from the initial complex image using the formula P = I / |I|, where 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 the nonlinear conjugate gradient descent method, the l-BFGS algorithm, and the 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 calculation of the DKI quantization parameters FA, MD, AD, RD, KFA, MK, AK and RK based on the DKI tensor θ obtained by optimization includes the following steps:

[0081] Calculate FA, MD, AD, RD, KFA, MK, AK, and RK 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 indicate isotropic diffusion, while FA values closer to 1 indicate anisotropic diffusion. Decreased FA values in white matter can reflect damaged fiber bundles. Decreased MD values indicate restricted water diffusion within the tissue, reflecting increased cell density. MK reflects the complexity of the tissue microstructure; increased MK values indicate increased tissue microstructural complexity. Tumor tissue often exhibits elevated MK values.

[0086] Based on the first aspect, further, the above-mentioned calculation of the DKI image based on 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 as described in the first aspect above is implemented.

[0091] The system also includes a communication interface 103. The memory 101, processor 102, and communication interface 103 are electrically connected to each other directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via 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 the software programs and modules stored in the memory 101 to perform various functional applications and data processing. 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 (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0093] The processor 102 may be an integrated circuit chip with signal processing capabilities. 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 this application, it should be understood that the disclosed methods and systems can also be implemented in other ways. The method and system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the methods and systems, methods, and computer program products 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 portion of code, and the module, program segment, or a portion of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that 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 flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

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

[0096] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements a method as described in any one of the first aspects above when executed by the processor 102. 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 this 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, which is stored in a storage medium and includes several instructions for enabling 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), a random access memory (RAM), a magnetic disk or an optical disk.

[0097] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection 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 characteristics of the present application. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the present application is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed 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 is collected by multi-channel coils at different b values and different diffusion coding directions according to the downsampling matrix U; A DKI mathematical model is established in which the DKI signal intensity evolves with the 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, which includes 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 encoding direction to obtain an initial complex image; The model is fitted based on the amplitude information of the initial complex image and the DKI mathematical model to obtain the initial DKI tensor; Extracting phase information P for each b value and each diffusion coding direction according to the initial complex image, including: extracting phase information P for each b value and each diffusion coding direction according to the formula P=I / |I| according to the initial complex image; wherein I is the initial complex image after total variation denoising, and |*| represents an amplitude operation; Based on the above downsampling matrix U, downsampled 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, which is expressed 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, N DWI Indicates 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 optimization algorithm is used to solve the above objective function 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. The method for rapid magnetic resonance diffusion kurtosis parameter imaging based on a model 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 rapid magnetic resonance diffusion kurtosis parameter imaging based on a model according to claim 1, characterized in that: The step of performing model fitting based on the amplitude information of the initial complex image and the DKI mathematical model to obtain the initial DKI tensor includes 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 and 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 optimization algorithm includes any one of a nonlinear conjugate gradient descent method, an l-BFGS algorithm, and a gradient descent method.

5. 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 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:

6. 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.

7. The method for model-based rapid magnetic resonance diffusion kurtosis parameter imaging according to claim 1, characterized in that: Calculating the DKI image based on the optimized θ and DKI mathematical model includes 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.

8. 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 7 is implemented.

9. 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 7 is implemented.

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