Diffusion kurtosis imaging method, device, computer equipment and storage medium

Abstract

The present application relates to a diffusion kurtosis imaging method, apparatus, computer device, storage medium, and computer program product. The method comprises: acquiring a scanned image signal of a scanned object, fitting the scanned image signal using an unconstrained optimization algorithm to obtain elements of a first diffusion tensor and elements of a first kurtosis tensor, determining diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor, and generating a parametric image based on the diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters. Because the unconstrained optimization algorithm requires a shorter computation time and higher computational efficiency, the time required to obtain the elements of the first diffusion tensor and the elements of the first kurtosis tensor using the unconstrained optimization algorithm in this embodiment is shorter, thereby improving the efficiency of generating the parametric image and reducing the computation time required to generate the parametric image.

Description

Technical Field

[0001] The present application relates to the technical field of diffusion kurtosis imaging, and in particular to a diffusion kurtosis imaging method, apparatus, computer equipment and storage medium. Background Art

[0002] With the development of magnetic resonance imaging (MRI) technology, diffusion kurtosis imaging (DKI) has emerged. DKI is widely used in the identification of acute cerebral infarction, other acute brain lesions, and tumors throughout the body. It uses a non-Gaussian diffusion model that is more consistent with the actual diffusion characteristics of water molecules in tissues, and is therefore more suitable for describing changes in tissue microstructure.

[0003] In conventional technology, a constrained optimization algorithm is used to calculate each element of the diffusion tensor and each element of the kurtosis tensor, and each parameter value is obtained according to each element of the diffusion tensor and each element of the kurtosis tensor, and a parameter image is generated based on each parameter value.

[0004] However, the current method of generating parameter images based on constrained optimization solution algorithms has the problem of long calculation time. Summary of the Invention

[0005] Based on this, it is necessary to provide a diffusion kurtosis imaging method, apparatus, computer equipment, computer-readable storage medium and computer program product that can reduce the calculation time required to generate a parameter image in order to address the above technical problems.

[0006] In a first aspect, the present application provides a diffusion kurtosis imaging method. The method comprises:

[0007] Acquiring a scanning image signal of the scanned object;

[0008] Fitting the scanned image signal using an unconstrained optimization algorithm to obtain each element of a first diffusion tensor and each element of a first kurtosis tensor;

[0009] determining a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the first diffusion tensor and each element of the first kurtosis tensor;

[0010] A parametric image is generated based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

[0011] In a second aspect, the present application further provides a diffusion kurtosis imaging device. The device comprises:

[0012] An acquisition module, used for acquiring a scanning image signal of a scanned object;

[0013] a fitting module, configured to fit the scanned image signal using an unconstrained optimization algorithm to obtain each element of a first diffusion tensor and each element of a first kurtosis tensor;

[0014] a determination module, configured to determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the first diffusion tensor and each element of the first kurtosis tensor;

[0015] A generating module is configured to generate a parameter image based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

[0016] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0017] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0018] In a fifth aspect, the present application further provides a computer program product, which includes a computer program that implements the steps of the above method when executed by a processor.

[0019] The aforementioned diffusion kurtosis imaging method, apparatus, computer device, storage medium, and computer program product acquire a scanned image signal of a scanned object and fit the scanned image signal using an unconstrained optimization algorithm to obtain elements of a first diffusion tensor and elements of a first kurtosis tensor. Diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters are determined based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor, and a parametric image is generated based on the diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters. Because the unconstrained optimization algorithm requires relatively short computational time and high computational efficiency, the time required to obtain the elements of the first diffusion tensor and the elements of the first kurtosis tensor using the unconstrained optimization algorithm in this embodiment is relatively short, thereby improving the efficiency of generating the parametric image and reducing the computational time required to generate the parametric image. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 1 is a flow chart of a diffusion kurtosis imaging method provided in an embodiment of the present application;

[0021] Figure 2 This is one of the flow charts of the imaging parameter determination method provided in the embodiment of the present application;

[0022] Figure 3This is one of the flow charts of the method for obtaining each element of the second diffusion tensor provided in an embodiment of the present application;

[0023] Figure 4 This is the second flow chart of the imaging parameter determination method provided in the embodiment of the present application;

[0024] Figure 5 This is a second flow chart of a method for obtaining each element of the second diffusion tensor provided in an embodiment of the present application;

[0025] Figure 6 This is the third flow chart of the imaging parameter determination method provided in the embodiment of the present application;

[0026] Figure 7 This is the fourth flow chart of the imaging parameter determination method provided in the embodiment of the present application;

[0027] Figure 8 1 is a flow chart of a method for obtaining each element of a second diffusion tensor and each element of a second kurtosis tensor provided in an embodiment of the present application;

[0028] Figure 9 1 is a flow chart of a method for determining a kurtosis coefficient provided in an embodiment of the present application;

[0029] Figure 10 This is the fifth flow chart of the imaging parameter determination method provided in the embodiment of the present application;

[0030] Figure 11 is a structural schematic diagram of a diffusion kurtosis imaging device provided in an embodiment of the present application;

[0031] Figure 12 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0033] Reference Figure 1 , Figure 1 : is a flow chart of a diffusion kurtosis imaging method provided in an embodiment of the present application, the method comprising the following steps:

[0034] S101: Acquire a scanned image signal of a scanned object.

[0035] The scanned image signal may include image signals with diffusion intensities in multiple directions, or the scanned image signal may include an image signal without diffusion intensities and image signals with diffusion intensities in multiple directions. Diffusion intensities refer to diffusion intensity b values ​​that are not equal to zero, and non-diffusion intensity refer to diffusion intensity b values ​​that are equal to zero.

[0036] S102 : Fitting the scanned image signal using an unconstrained optimization algorithm to obtain each element of a first diffusion tensor and each element of a first kurtosis tensor.

[0037] For example, based on the model shown in the following formulas (1) to (11), an unconstrained optimization algorithm can be used to fit the scanned image signal to obtain each element of the first diffusion tensor and each element of the first kurtosis tensor.

[0038]

[0039] Diffusion coefficient D(n) in each direction:

[0040] Kurtosis coefficient K(n) in each direction:

[0041] Average diffusion coefficient

[0042] Axial diffusion coefficient AD: Ad=λ1 (5)

[0043] Radial diffusion coefficient RD:

[0044] Diffusion anisotropy coefficient FA:

[0045]

[0046] Average kurtosis coefficient

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] Axial kurtosis coefficient AK:

[0054] Radial kurtosis coefficient RK:

[0055]

[0056] Kurtosis anisotropy coefficient KA:

[0057]

[0058]

[0059] Among them, S (n,b) It represents the intensity of the MR signal scanned when the diffusion intensity b is not equal to 0 in the direction vector n, and S0 represents the intensity of the MR signal scanned when the diffusion intensity b is equal to 0 in the direction vector n.

[0060] n i Represents the component of the direction vector n on the coordinate axis, for example, n1 represents the component of the direction vector n on the x-axis, n2 represents the component of the direction vector n on the y-axis, and n3 represents the component of the direction vector n on the z-axis.

[0061] λ1, λ2, and λ3 represent the first diffusion tensor D ij The maximum, median, and minimum of the three eigenvalues ​​of .

[0062] R ii’ Denotes the first diffusion tensor D ij The i'th element in the i'th eigenvector of .

[0063] K in formula (11) i Denotes the first diffusion tensor D ij The kurtosis coefficient in the direction represented by the i-th eigenvector.

[0064] v i Denotes the first diffusion tensor D ij The elements in the eigenvector of, for example, v1, v2, v3 represent the first diffusion tensor D ij The three elements in the eigenvector of .

[0065] The first kurtosis tensor W ijkl According to the first diffusion tensor D ij The kurtosis tensor after the rotation.

[0066] It should be noted that the first kurtosis tensor W in the above formula (1) is ijkl It is a symmetric fourth-order tensor with 15 independent parameters. At least 15 nonlinear related equations are required to solve the 15 independent parameters simultaneously. Therefore, images with diffusion intensity in at least 15 directions are required. The first diffusion tensor Dij It is a symmetric second-order tensor, so it has 6 independent parameters, and at least 6 nonlinear related equations are required to solve the 6 independent parameters simultaneously. Therefore, the above formula (1) is used to determine the first diffusion tensor D ij and the first kurtosis tensor W ijkl , it is necessary to collect images with diffusion intensity in at least 15 directions, and at least 22 images with diffusion intensity b values ​​not equal to zero in these at least 15 directions need to be collected. Alternatively, it is necessary to collect one image with a diffusion intensity b value equal to 0 and images with diffusion intensity in at least 15 directions, and at least 21 images with diffusion intensity b values ​​not equal to zero in these at least 15 directions need to be collected.

[0067] S103 : Determine diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor.

[0068] In this step, diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters can be determined based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor. For example, the diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters can be determined using the above formulas (2) to (11). The diffusion tensor imaging parameters can include at least one coefficient among the diffusion coefficient in each direction, the average diffusion coefficient, the axial diffusion coefficient, the radial diffusion coefficient, and the diffusion anisotropy coefficient. The kurtosis tensor imaging parameters can include at least one coefficient among the kurtosis coefficient in each direction, the average kurtosis coefficient, the axial kurtosis coefficient, the radial kurtosis coefficient, and the kurtosis anisotropy coefficient.

[0069] S104 : Generate a parameter image based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

[0070] In this step, a diffusion parameter image may be generated based on the diffusion tensor imaging parameters. For example, an average diffusion parameter image may be generated based on the average diffusion coefficient in the diffusion tensor imaging parameters; or an axial diffusion parameter image may be generated based on the axial diffusion coefficient.

[0071] A kurtosis parameter image can be generated based on the kurtosis tensor imaging parameters. For example, an average kurtosis parameter image can be generated based on the average kurtosis coefficient in the kurtosis tensor imaging parameters; or an axial kurtosis parameter image can be generated based on the axial kurtosis coefficient.

[0072] The diffusion kurtosis imaging method provided in this embodiment obtains a scanned image signal of a scanned object and fits the scanned image signal using an unconstrained optimization algorithm to obtain elements of a first diffusion tensor and elements of a first kurtosis tensor. Diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters are determined based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor, and a parametric image is generated based on the diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters. Because the unconstrained optimization algorithm requires relatively short computational time and high computational efficiency, the time required to obtain the elements of the first diffusion tensor and the first kurtosis tensor using the unconstrained optimization algorithm in this embodiment is relatively short, thereby improving the efficiency of generating the parametric image and reducing the computational time required to generate the parametric image.

[0073] Reference Figure 2 , Figure 2 This is one of the flow charts of the imaging parameter determination method provided in an embodiment of the present application. This embodiment involves a method of determining diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters by only correcting the individual elements of the diffusion tensor. This embodiment involves an optional implementation method of how to determine diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on the individual elements of the first diffusion tensor and the individual elements of the first kurtosis tensor. Based on the above embodiment, the above S103 may include the following steps:

[0074] S201 : Determine a diffusion coefficient in a corresponding direction based on each element of a first diffusion tensor and a direction vector in each direction.

[0075] Based on the elements of the first diffusion tensor and the direction vectors in each direction determined by the unconstrained optimization algorithm, the diffusion coefficient in the corresponding direction can be determined. For example, the diffusion coefficient in the corresponding direction can be obtained by forward calculation using the above formula (2). The average diffusion coefficient, axial diffusion coefficient, radial diffusion coefficient, and diffusion anisotropy coefficient can also be calculated based on the above formulas (4) to (7).

[0076] S202 : Correct the first diffusion tensor according to the diffusion coefficients in each direction to obtain each element of the second diffusion tensor.

[0077] In this step, the first diffusion tensor is corrected according to the diffusion coefficients in each direction to obtain each element of the second diffusion tensor. For example, the elements of the second diffusion tensor are obtained by calculation using the above formula (2).

[0078] S203 : Determine diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on each element of the second diffusion tensor and each element of the first kurtosis tensor.

[0079] In this embodiment, the diffusion coefficient in the corresponding direction is determined based on each element of the first diffusion tensor and the direction vector in each direction, and the first diffusion tensor is corrected according to the diffusion coefficient in each direction to obtain each element of the second diffusion tensor. Then, based on each element of the second diffusion tensor and each element of the first kurtosis tensor, diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters are determined. This allows the first diffusion tensor determined based on the unconstrained optimization algorithm to be corrected, ensuring the accuracy of each element of the obtained second diffusion tensor, thereby improving the accuracy of the determined diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters.

[0080] Reference Figure 3 , Figure 3 This is one of the flow diagrams of a method for obtaining the elements of the second diffusion tensor provided in an embodiment of the present application. This embodiment relates to an optional implementation method for modifying the first diffusion tensor based on the diffusion coefficients in each direction to obtain the elements of the second diffusion tensor. Based on the above embodiment, S202 may include the following steps:

[0081] S301: If there is a diffusion coefficient not within the first preset range among the diffusion coefficients in each direction, correct the diffusion coefficient not within the first preset range to a value within the first preset range.

[0082] Among them, the first preset interval is an interval within [first preset threshold, second preset threshold], the first preset threshold can be equal to zero, or the first preset threshold is equal to other values, the settings of the first preset threshold and the second preset threshold can be determined based on clinical experience, the second preset threshold is greater than the first preset threshold, [first preset threshold, second preset threshold] means greater than or equal to the first preset threshold and less than or equal to the second preset threshold, and the meaning of expressions similar to [first preset threshold, second preset threshold] in this application is similar to the meaning of [first preset threshold, second preset threshold]. If there are diffusion coefficients in the diffusion coefficients of each direction determined based on the unconstrained optimization algorithm that are not within the first preset interval, the diffusion coefficients that are not within the first preset interval are corrected to values ​​within the first preset interval, so as to ensure that the diffusion coefficients in each direction finally obtained are all values ​​within the first preset interval.

[0083] In one embodiment, if any diffusion coefficient in each direction is less than a first preset threshold, the diffusion coefficient less than the first preset threshold can be corrected to be equal to any value within the first preset interval. If any diffusion coefficient in each direction is greater than a second preset threshold, the diffusion coefficient greater than the second preset threshold can be corrected to be equal to any value within the first preset interval.

[0084] For example, if there is a diffusion coefficient less than a first preset threshold value among the diffusion coefficients in each direction, the diffusion coefficient less than the first preset threshold value can be corrected to be equal to the first preset threshold value. If there is a diffusion coefficient greater than a second preset threshold value among the diffusion coefficients in each direction, the diffusion coefficient greater than the second preset threshold value can be corrected to be equal to the second preset threshold value.

[0085] S302 : Correct the first diffusion tensor according to the corrected diffusion coefficient and other diffusion coefficients to obtain elements of the second diffusion tensor, wherein the other diffusion coefficients include diffusion coefficients in each direction that are within a first preset range.

[0086] The first diffusion tensor is modified using the above formula (2) to obtain each element of the second diffusion tensor, that is, the diffusion tensor is reversely calculated using formula (2), and the new diffusion tensor obtained is used as the second diffusion tensor.

[0087] In this embodiment, if any of the diffusion coefficients in each direction is outside the first preset range, the diffusion coefficient outside the first preset range is corrected to a value within the first preset range. The first diffusion tensor is then corrected based on the corrected diffusion coefficient and other diffusion coefficients to obtain each element of the second diffusion tensor. In other words, the diffusion coefficients used to correct the first diffusion tensor are all within the first preset range, thereby ensuring the accuracy of the correction of the first diffusion tensor and, consequently, the accuracy of each element of the obtained second diffusion tensor.

[0088] Reference Figure 4 , Figure 4 This is the second flow chart of the imaging parameter determination method provided by the embodiment of the present application. This embodiment involves only correcting the elements of the kurtosis tensor to achieve a method of determining the diffusion tensor imaging parameters and / or the kurtosis tensor imaging parameters. This embodiment involves an optional implementation method of how to determine the diffusion tensor imaging parameters and / or the kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor. Based on the above embodiment, the above S103 may include the following steps:

[0089] S401 : Determine a diffusion coefficient in a corresponding direction based on each element of a first diffusion tensor and a direction vector in each direction.

[0090] S402. Modify the first kurtosis tensor according to the diffusion coefficients in each direction to obtain each element of the second kurtosis tensor.

[0091] S403 : Determine diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the second kurtosis tensor.

[0092] In this embodiment, the diffusion coefficient in the corresponding direction is determined based on each element of the first diffusion tensor and the direction vector in each direction, and the first kurtosis tensor is corrected according to the diffusion coefficient in each direction to obtain each element of the second kurtosis tensor. Then, based on each element of the first diffusion tensor and each element of the second kurtosis tensor, diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters are determined. This allows the first kurtosis tensor determined based on the unconstrained optimization algorithm to be corrected, ensuring the accuracy of each element of the obtained second kurtosis tensor, thereby improving the accuracy of the determined diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters.

[0093] Reference Figure 5 , Figure 5 This is the second flow diagram of the method for obtaining each element of the second diffusion tensor provided in an embodiment of the present application. This embodiment involves an optional implementation method for modifying the first diffusion tensor based on the diffusion coefficients in each direction to obtain each element of the second diffusion tensor. Based on the above embodiment, the above S402 may include the following steps:

[0094] S501: If there is a diffusion coefficient in a second preset interval among the diffusion coefficients in each direction, determine the kurtosis coefficient of the direction corresponding to the diffusion coefficient in the second preset interval according to each element of the first kurtosis tensor.

[0095] The second preset interval is an interval within [the third preset threshold, the fourth preset threshold]. The fourth preset threshold is greater than the third preset threshold.

[0096] In this step, for example, if there is a diffusion coefficient in the second preset interval among the diffusion coefficients in each direction, formula (3) is used to determine the kurtosis coefficient of the direction corresponding to the diffusion coefficient in the second preset interval based on each element of the first kurtosis tensor.

[0097] S502: If there is a diffusion coefficient not located in the second preset interval among the diffusion coefficients in each direction, determine that the kurtosis coefficient of the direction corresponding to the diffusion coefficient not located in the second preset interval is equal to a value within the third preset interval.

[0098] The third preset interval is an interval within [the fifth preset threshold, the sixth preset threshold]. The sixth preset threshold is greater than the fifth preset threshold.

[0099] In one embodiment, if there is a diffusion coefficient not within the second preset interval among the diffusion coefficients in each direction, determining that the kurtosis coefficient of the direction corresponding to the diffusion coefficient not within the second preset interval is equal to a value within the third preset interval can be achieved as follows:

[0100] If there is a diffusion coefficient less than the third preset threshold among the diffusion coefficients in each direction, it can be determined that the kurtosis coefficient of the direction corresponding to the diffusion coefficient less than the third preset threshold is equal to any value within the third preset interval, or, if there is a diffusion coefficient greater than the fourth preset threshold among the diffusion coefficients in each direction, it can be determined that the kurtosis coefficient of the direction corresponding to the diffusion coefficient greater than the fourth preset threshold is equal to any value within the third preset interval.

[0101] For example, if a diffusion coefficient less than a third preset threshold exists among the diffusion coefficients in each direction, it can be determined that the kurtosis coefficient of the direction corresponding to the diffusion coefficient less than the third preset threshold is equal to the fifth preset threshold. If a diffusion coefficient greater than a fourth preset threshold exists among the diffusion coefficients in each direction, it can be determined that the kurtosis coefficient of the direction corresponding to the diffusion coefficient greater than the fourth preset threshold is equal to the sixth preset threshold.

[0102] S503. Modify the first kurtosis tensor according to the kurtosis coefficients in each direction to obtain each element of the second kurtosis tensor.

[0103] The above formula (3) is used to correct the first kurtosis tensor according to the kurtosis coefficients in each direction to obtain the elements of the second diffusion tensor. That is, the kurtosis tensor is recalculated using formula (3), and the new kurtosis tensor is used as the second kurtosis tensor.

[0104] The kurtosis coefficients in each direction in this step may include the kurtosis coefficients in the directions corresponding to the diffusion coefficients in the second preset interval determined in S501 and the kurtosis coefficients in the second preset interval determined in S502.

[0105] In this embodiment, if there is a diffusion coefficient located in the second preset interval among the diffusion coefficients in each direction, the kurtosis coefficient of the direction corresponding to the diffusion coefficient located in the second preset interval is determined according to each element of the first kurtosis tensor; if there is a diffusion coefficient not located in the second preset interval among the diffusion coefficients in each direction, the kurtosis coefficient of the direction corresponding to the diffusion coefficient not located in the second preset interval is determined to be equal to a value within the third preset interval, and then the first kurtosis tensor is corrected according to the kurtosis coefficients in each direction to obtain each element of the second kurtosis tensor, thereby ensuring the accuracy of each element of the obtained second kurtosis tensor.

[0106] Optionally, in the above S501, if there is a diffusion coefficient in the second preset interval among the diffusion coefficients in each direction, after determining the kurtosis coefficient of the direction corresponding to the diffusion coefficient in the second preset interval according to each element of the first kurtosis tensor, the following steps may be further included:

[0107] If the kurtosis coefficients in the directions corresponding to the diffusion coefficients determined to be in the second preset interval include kurtosis coefficients not in the third preset interval, the kurtosis coefficients not in the third preset interval are corrected to values ​​within the third preset interval.

[0108] In this embodiment, if there is a kurtosis coefficient that is not located in the third preset interval among the kurtosis coefficients in the directions corresponding to the diffusion coefficients determined to be located in the second preset interval, the kurtosis coefficient that is not located in the third preset interval is corrected to a value within the third preset interval, which can further ensure that the kurtosis coefficients in each direction finally obtained are all values ​​located within the third preset interval, that is, it is ensured that the kurtosis coefficients in each direction in the above S503 are all kurtosis coefficients of values ​​located within the third preset interval, thereby improving the accuracy of each element of the second kurtosis tensor obtained after correcting the first kurtosis tensor according to the kurtosis coefficients in each direction.

[0109] Reference Figure 6 , Figure 6 This is the third flow chart of the imaging parameter determination method provided in the embodiment of the present application. This embodiment relates to an optional implementation method for determining diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor. Based on the above embodiment, the above S103 may include the following steps:

[0110] S601 : Determine the kurtosis coefficient of the corresponding direction based on each element of the first kurtosis tensor and the direction vector of each direction.

[0111] S602: If there is a kurtosis coefficient not located in the fourth preset interval among the kurtosis coefficients in each direction, correct the kurtosis coefficient not located in the fourth preset interval to a value within the fourth preset interval.

[0112] The fourth preset interval is an interval within [the seventh preset threshold, the eighth preset threshold]. The eighth preset threshold is greater than the seventh preset threshold.

[0113] In this step, if there is a kurtosis coefficient in each direction that is not within the fourth preset interval, the kurtosis coefficient that is not within the fourth preset interval is corrected to a value within the fourth preset interval, which can be achieved by:

[0114] If there is a kurtosis coefficient less than the seventh preset threshold value among the kurtosis coefficients in each direction, the kurtosis dispersion coefficient less than the seventh preset threshold value may be corrected to any value within the fourth preset interval. Alternatively, if there is a kurtosis coefficient greater than the eighth preset threshold value among the kurtosis coefficients in each direction, the kurtosis dispersion coefficient greater than the eighth preset threshold value may be corrected to any value within the fourth preset interval.

[0115] For example, if there is a kurtosis coefficient less than the seventh preset threshold value among the kurtosis coefficients in each direction, the kurtosis coefficient less than the seventh preset threshold value may be corrected to the seventh preset threshold value. If there is a kurtosis coefficient greater than the eighth preset threshold value among the kurtosis coefficients in each direction, the kurtosis coefficient greater than the eighth preset threshold value may be corrected to the eighth preset threshold value.

[0116] S603. Modify the first kurtosis tensor according to the kurtosis coefficients in each direction to obtain each element of the second kurtosis tensor.

[0117] S604 : Determine diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the second kurtosis tensor.

[0118] In this embodiment, the kurtosis coefficient of the corresponding direction is determined based on each element of the first kurtosis tensor and the direction vector of each direction. If there is a kurtosis coefficient in the kurtosis coefficients of each direction that is not within the fourth preset interval, the kurtosis coefficient that is not within the fourth preset interval is corrected to a value within the fourth preset interval, thereby correcting the first kurtosis tensor to obtain each element of the second kurtosis tensor. Then, based on each element of the first diffusion tensor and each element of the second kurtosis tensor, diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters are determined, thereby improving the accuracy of the determined diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters.

[0119] Reference Figure 7 , Figure 7 This is the fourth flow chart of the imaging parameter determination method provided by the embodiment of the present application. This embodiment involves simultaneously correcting the first diffusion tensor and the first kurtosis tensor to achieve a method of determining diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters. This embodiment specifically involves an optional implementation method of how to determine diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on the various elements of the first diffusion tensor and the various elements of the first kurtosis tensor. Based on the above embodiment, the above S103 may include the following steps:

[0120] S701 : Determine a diffusion coefficient in a corresponding direction based on each element of a first diffusion tensor and a direction vector in each direction.

[0121] S702 : Correct the first diffusion tensor and the first kurtosis tensor respectively according to the diffusion coefficients in each direction to obtain each element of the second diffusion tensor and each element of the second kurtosis tensor.

[0122] S703 : Determine diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on each element of the second diffusion tensor and each element of the second kurtosis tensor.

[0123] In this embodiment, the diffusion coefficient in each direction is determined based on each element of the first diffusion tensor and the direction vector in each direction, and the first diffusion tensor and the first kurtosis tensor are respectively corrected based on the diffusion coefficient in each direction to obtain each element of the second diffusion tensor and each element of the second kurtosis tensor. Diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters are then determined based on each element of the second diffusion tensor and each element of the second kurtosis tensor. Because the first diffusion tensor and the first kurtosis tensor are corrected simultaneously in this embodiment, the accuracy of the determined diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters can be further improved.

[0124] Reference Figure 8 , Figure 8 This is a flow chart illustrating a method for obtaining the elements of the second diffusion tensor and the elements of the second kurtosis tensor, provided in an embodiment of the present application. This embodiment relates to an optional implementation method for correcting the first diffusion tensor and the first kurtosis tensor based on the diffusion coefficients in each direction to obtain the elements of the second diffusion tensor and the elements of the second kurtosis tensor. Based on the above embodiment, S702 may include the following steps:

[0125] S801: If there is a diffusion coefficient not within the fifth preset interval among the diffusion coefficients in each direction, correct the diffusion coefficient not within the fifth preset interval to a value within the fifth preset interval.

[0126] The fifth preset interval is an interval within [a ninth preset threshold value, a tenth preset threshold value]. The tenth preset threshold value is greater than the ninth preset threshold value.

[0127] In one embodiment, if any diffusion coefficient in each direction is less than a ninth preset threshold, the diffusion coefficient less than the ninth preset threshold is corrected to any value within a fifth preset interval. If any diffusion coefficient in each direction is greater than a tenth preset threshold, the diffusion coefficient greater than the tenth preset threshold is corrected to any value within the fifth preset interval.

[0128] For example, if there is a diffusion coefficient less than the ninth preset threshold among the diffusion coefficients in each direction, the diffusion coefficient less than the ninth preset threshold is corrected to the ninth preset threshold; if there is a diffusion coefficient greater than the tenth preset threshold among the diffusion coefficients in each direction, the diffusion coefficient greater than the tenth preset threshold is corrected to the tenth preset threshold.

[0129] S802: Correct the first diffusion tensor according to the target diffusion coefficient to obtain each element of the second diffusion tensor, wherein the target diffusion coefficient includes the corrected diffusion coefficient and the diffusion coefficients in each direction that are within a fifth preset interval.

[0130] The first diffusion tensor is corrected according to the target diffusion coefficient using the above formula (2) to obtain each element of the second diffusion tensor. That is, the diffusion tensor is recalculated using formula (2) and the new diffusion tensor obtained is used as the second diffusion tensor.

[0131] S803. Determine the kurtosis coefficient in the direction corresponding to the target diffusion coefficient according to the target diffusion coefficient.

[0132] S804. Determine each element of the second kurtosis tensor according to the kurtosis coefficient in the direction corresponding to the target diffusion coefficient.

[0133] Using the above formula (3), the first kurtosis tensor is corrected according to the kurtosis coefficient in the direction corresponding to the target diffusion coefficient to obtain the elements of the second diffusion tensor, that is, the kurtosis tensor is recalculated using formula (3), and the new kurtosis tensor is used as the second kurtosis tensor.

[0134] In this embodiment, the first diffusion tensor is corrected according to the target diffusion coefficient to obtain each element of the second diffusion tensor, and the kurtosis coefficient in the direction corresponding to the target diffusion coefficient is determined according to the target diffusion coefficient. Then, each element of the second kurtosis tensor is determined according to the kurtosis coefficient in the direction corresponding to the target diffusion coefficient, thereby ensuring the accuracy of each element of the obtained second diffusion tensor and each element of the second kurtosis tensor.

[0135] Reference Figure 9 , Figure 9 : This is a flow chart of a method for determining a kurtosis coefficient provided in an embodiment of the present application. This embodiment relates to an optional implementation method for determining the kurtosis coefficient in the direction corresponding to the target diffusion coefficient based on the target diffusion coefficient. Based on the above embodiment, the above S803 may include the following steps:

[0136] S901: If the target diffusion coefficient is within a fifth preset interval, determine the kurtosis coefficient of the direction corresponding to the target diffusion coefficient according to each element of the first kurtosis tensor.

[0137] In this step, if the target diffusion coefficient is not within the fifth preset interval, the above formula (3) can be used to determine the kurtosis coefficient of the direction corresponding to the target diffusion coefficient according to each element of the first kurtosis tensor.

[0138] S902: If the target diffusion coefficient is not within the fifth preset interval, determine whether the kurtosis coefficient in the direction corresponding to the target diffusion coefficient is equal to a value within a sixth preset interval.

[0139] The sixth preset interval is an interval within [the eleventh preset threshold value, the twelfth preset threshold value]. The twelfth preset threshold value is greater than the eleventh preset threshold value.

[0140] In this step, if the target diffusion coefficient is not within the fifth preset interval, determining that the kurtosis coefficient in the direction corresponding to the target diffusion coefficient is equal to a value within the sixth preset interval can be achieved by:

[0141] If the target diffusion coefficient is less than the ninth preset threshold, it can be determined that the kurtosis coefficient in the direction corresponding to the diffusion coefficient less than the ninth preset threshold is equal to any value within the sixth preset interval. If the target diffusion coefficient is greater than the tenth preset threshold, it can be determined that the kurtosis coefficient in the direction corresponding to the diffusion coefficient greater than the tenth preset threshold is equal to any value within the sixth preset interval.

[0142] For example, if the target diffusion coefficient is less than the ninth preset threshold, the kurtosis coefficient of the direction corresponding to the diffusion coefficient less than the ninth preset threshold can be determined to be equal to the eleventh preset threshold. If the target diffusion coefficient is greater than the tenth preset threshold, the kurtosis coefficient of the direction corresponding to the diffusion coefficient greater than the tenth preset threshold can be determined to be equal to the twelfth preset threshold.

[0143] In this embodiment, if the target diffusion coefficient is located in the fifth preset interval, the kurtosis coefficient of the direction corresponding to the target diffusion coefficient is determined according to each element of the first kurtosis tensor; if the target diffusion coefficient is not located in the fifth preset interval, the kurtosis coefficient of the direction corresponding to the target diffusion coefficient is determined to be equal to the value within the sixth preset interval, thereby improving the accuracy of the final kurtosis coefficient, and further improving the accuracy of the kurtosis tensor obtained based on the final kurtosis coefficient.

[0144] Optionally, after the above-mentioned S901, in which the kurtosis coefficient of the direction corresponding to the target diffusion coefficient is determined according to each element of the first kurtosis tensor, the following steps may be further included:

[0145] If there is a kurtosis coefficient not located in the sixth preset interval among the kurtosis coefficients in the direction corresponding to the determined target diffusion coefficient, the kurtosis coefficient not located in the sixth preset interval is corrected to a value within the sixth preset interval.

[0146] In this embodiment, if there is a kurtosis coefficient in the direction corresponding to the determined target diffusion coefficient that is not within the sixth preset interval, the kurtosis coefficient that is not within the sixth preset interval is corrected to a value within the sixth preset interval, thereby correcting the calculated kurtosis coefficient, thereby further ensuring that the final kurtosis coefficient is a value within the sixth preset interval, thereby ensuring the accuracy of the second kurtosis tensor determined based on the final kurtosis coefficient and the accuracy of the kurtosis tensor imaging parameters determined based on the second kurtosis tensor.

[0147] Reference Figure 10 , Figure 10This is the fifth flow chart of the imaging parameter determination method provided in the embodiment of the present application. This embodiment relates to an optional implementation method of how to determine the diffusion tensor imaging parameters and / or the kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor. Based on the above embodiment, the above S103 may include the following steps:

[0148] S1001 : Determine a diffusion coefficient in a corresponding direction based on each element of a first diffusion tensor and a direction vector in each direction.

[0149] The diffusion coefficient of the corresponding direction is determined based on each element of the first diffusion tensor and the direction vector of each direction. For example, the diffusion coefficient of the corresponding direction is determined by forward calculation using the above formula (2).

[0150] S1002 : Correct the first diffusion tensor according to the diffusion coefficients in each direction to obtain each element of the second diffusion tensor.

[0151] The first diffusion tensor is modified according to the diffusion coefficients in each direction to obtain the elements of the second diffusion tensor. For example, the above formula (2) is used for reverse calculation to obtain the elements of the second diffusion tensor.

[0152] The first diffusion tensor is corrected according to the diffusion coefficients in each direction to obtain each element of the second diffusion tensor. This can be achieved by the method provided in the above embodiment, which will not be described in detail here.

[0153] S1003 . Determine the kurtosis coefficient of the corresponding direction based on each element of the first kurtosis tensor and the direction vector of each direction.

[0154] The kurtosis coefficient of the corresponding direction is determined based on each element of the first kurtosis tensor and the direction vector of each direction. For example, the kurtosis coefficient of the corresponding direction is determined by forward calculation using the above formula (3).

[0155] S1004. Modify the first kurtosis tensor according to the kurtosis coefficients in each direction to obtain each element of the second kurtosis tensor.

[0156] The first kurtosis tensor is modified according to the diffusion coefficients in each direction to obtain the elements of the second kurtosis tensor. For example, the above formula (3) is used for reverse calculation to obtain the elements of the second kurtosis tensor.

[0157] S1005 : Determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the second diffusion tensor and each element of the second kurtosis tensor.

[0158] In this embodiment, the first diffusion tensor is corrected according to the diffusion coefficient in each direction to obtain each element of the second diffusion tensor, and the first kurtosis tensor is corrected according to the kurtosis coefficient in each direction to obtain each element of the second kurtosis tensor. Then, based on each element of the second diffusion tensor and each element of the second kurtosis tensor, diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters are determined, thereby ensuring the accuracy of the determined diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters.

[0159] Optionally, the above-mentioned S102, using an unconstrained optimization algorithm to fit the scanned image signal to obtain each element of the first diffusion tensor and each element of the first kurtosis tensor, can be implemented as follows:

[0160] Based on the scanning image signal, an unconstrained optimization algorithm is used to solve the multi-directional diffusion kurtosis imaging model to obtain each element of the first diffusion tensor and each element of the first kurtosis tensor.

[0161] The multi-directional diffusion kurtosis imaging model in this step is, for example, the model shown in the above formula (1). Based on the scanned image signal, at least 21 equations based on formula (1) are constructed, and the at least 21 equations are jointly solved to obtain the elements of the first diffusion tensor and the elements of the first kurtosis tensor.

[0162] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0163] Based on the same inventive concept, embodiments of the present application further provide a diffusion kurtosis imaging device for implementing the aforementioned diffusion kurtosis imaging method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more diffusion kurtosis imaging device embodiments provided below can be found in the above-described limitations of the diffusion kurtosis imaging method and will not be further elaborated here.

[0164] In one embodiment, Figure 11 As shown, Figure 111 is a schematic structural diagram of a diffusion kurtosis imaging device provided in an embodiment of the present application. The device 1100 includes the following modules:

[0165] An acquisition module 1101 is used to acquire a scanned image signal of a scanned object;

[0166] A fitting module 1102 is configured to fit the scanned image signal using an unconstrained optimization algorithm to obtain elements of a first diffusion tensor and elements of a first kurtosis tensor;

[0167] a determination module 1103, configured to determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the first diffusion tensor and each element of the first kurtosis tensor;

[0168] The generating module 1104 is configured to generate a parameter image based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

[0169] The diffusion kurtosis imaging device provided in this embodiment acquires a scanned image signal of a scanned object and fits the scanned image signal using an unconstrained optimization algorithm to obtain elements of a first diffusion tensor and elements of a first kurtosis tensor. Based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor, diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters are determined, and a parametric image is generated based on the diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters. Because the unconstrained optimization algorithm requires relatively short computational time and high computational efficiency, the time required to obtain the elements of the first diffusion tensor and the first kurtosis tensor using the unconstrained optimization algorithm in this embodiment is relatively short, thereby improving the efficiency of generating the parametric image and reducing the computational time required to generate the parametric image.

[0170] In one embodiment, the determining module 1103 includes:

[0171] a first determining unit, configured to determine a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction;

[0172] a first correction unit, configured to correct the first diffusion tensor according to the diffusion coefficients in the directions to obtain elements of a second diffusion tensor;

[0173] The second determining unit is configured to determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the second diffusion tensor and each element of the first kurtosis tensor.

[0174] In one embodiment, the first correction unit is specifically configured to correct, if any diffusion coefficient in each of the directions is not within a first preset interval, the diffusion coefficient not within the first preset interval to a value within the first preset interval, wherein the first preset interval is an interval within [a first preset threshold value, a second preset threshold value]; and correct the first diffusion tensor based on the corrected diffusion coefficient and other diffusion coefficients to obtain elements of the second diffusion tensor, wherein the other diffusion coefficients include the diffusion coefficients in each of the directions that are within the first preset interval.

[0175] In one embodiment, the determining module 1103 includes:

[0176] a third determining unit, configured to determine a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction;

[0177] a third correction unit, configured to correct the first kurtosis tensor according to the diffusion coefficients in the directions to obtain elements of a second kurtosis tensor;

[0178] The fourth determining unit is configured to determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the first diffusion tensor and each element of the second kurtosis tensor.

[0179] In one embodiment, the third correction unit is specifically configured to determine, if there is a diffusion coefficient in a second preset interval among the diffusion coefficients in each of the directions, the kurtosis coefficient of the direction corresponding to the diffusion coefficient in the second preset interval based on each element of the first kurtosis tensor; wherein the second preset interval is an interval within [the third preset threshold value, the fourth preset threshold value];

[0180] If there is a diffusion coefficient not located in the second preset interval among the diffusion coefficients in each of the directions, determining that the kurtosis coefficient of the direction corresponding to the diffusion coefficient not located in the second preset interval is equal to a value within a third preset interval, where the third preset interval is an interval within [fifth preset threshold value, sixth preset threshold value];

[0181] The first kurtosis tensor is modified according to the kurtosis coefficients in each of the directions to obtain each element of the second kurtosis tensor.

[0182] In one embodiment, the third correction unit is further used to correct the kurtosis coefficient that is not located in the third preset interval to a value within the third preset interval if there is a kurtosis coefficient that is not located in the third preset interval among the kurtosis coefficients in the direction corresponding to the diffusion coefficients located in the second preset interval.

[0183] In one embodiment, the determining module 1103 includes:

[0184] a fifth determining unit, configured to determine a kurtosis coefficient in a corresponding direction based on each element of the first kurtosis tensor and a direction vector in each direction;

[0185] a fifth correction unit, configured to correct, if any of the kurtosis coefficients in the directions is not within a fourth preset interval, the kurtosis coefficient not within the fourth preset interval to a value within the fourth preset interval, wherein the fourth preset interval is an interval within [the seventh preset threshold value, the eighth preset threshold value];

[0186] a sixth correction unit, configured to correct the first kurtosis tensor according to the kurtosis coefficients in each of the directions to obtain each element of a second kurtosis tensor;

[0187] A sixth determining unit is configured to determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the first diffusion tensor and each element of the second kurtosis tensor.

[0188] In one embodiment, the determining module 1103 includes:

[0189] a seventh determining unit, configured to determine a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction;

[0190] a seventh correction unit, configured to correct the first diffusion tensor and the first kurtosis tensor respectively according to the diffusion coefficients in the directions, to obtain elements of the second diffusion tensor and elements of the second kurtosis tensor;

[0191] An eighth determining unit is configured to determine the diffusion tensor imaging parameter and the kurtosis tensor imaging parameter based on each element of the second diffusion tensor and each element of the second kurtosis tensor.

[0192] In one embodiment, the seventh correction unit includes:

[0193] a first correction subunit, configured to, if any diffusion coefficient in each of the directions is not within a fifth preset interval, correct the diffusion coefficient not within the fifth preset interval to a value within the fifth preset interval, wherein the fifth preset interval is an interval within [a ninth preset threshold value, a tenth preset threshold value];

[0194] a second correction subunit, configured to correct the first diffusion tensor according to a target diffusion coefficient to obtain elements of a second diffusion tensor, wherein the target diffusion coefficient includes the corrected diffusion coefficient and the diffusion coefficients in each of the directions that are within the fifth preset interval;

[0195] A first determining subunit is configured to determine a kurtosis coefficient in a direction corresponding to the target diffusion coefficient according to the target diffusion coefficient;

[0196] The second determining subunit is configured to determine each element of the second kurtosis tensor according to the kurtosis coefficient in the direction corresponding to the target diffusion coefficient.

[0197] In one embodiment, the first determination subunit is specifically used to determine the kurtosis coefficient of the direction corresponding to the target diffusion coefficient based on each element of the first kurtosis tensor if the target diffusion coefficient is located in the fifth preset interval; if the target diffusion coefficient is not located in the fifth preset interval, determine that the kurtosis coefficient of the direction corresponding to the target diffusion coefficient is equal to a value within the sixth preset interval, wherein the sixth preset interval is an interval within [the eleventh preset threshold, the twelfth preset threshold].

[0198] In one embodiment, the first determination subunit is further configured to correct the kurtosis coefficient that is not within the sixth preset interval to a value within the sixth preset interval if there is a kurtosis coefficient in the direction corresponding to the determined target diffusion coefficient that is not within the sixth preset interval.

[0199] In one embodiment, the determining module 1103 includes:

[0200] a ninth determining unit, configured to determine a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction;

[0201] a ninth correction unit, configured to correct the first diffusion tensor according to the diffusion coefficients in the directions to obtain elements of a second diffusion tensor;

[0202] a tenth determining unit, configured to determine a kurtosis coefficient in a corresponding direction based on each element of the first kurtosis tensor and a direction vector of each direction;

[0203] a tenth correction unit, configured to correct the first kurtosis tensor according to the kurtosis coefficients in each of the directions to obtain each element of a second kurtosis tensor;

[0204] An eleventh determining unit is configured to determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the second diffusion tensor and each element of the second kurtosis tensor.

[0205] In one embodiment, the fitting module is specifically configured to solve a multi-directional diffusion kurtosis imaging model based on the scanned image signal using an unconstrained optimization algorithm to obtain each element of the first diffusion tensor and each element of the first kurtosis tensor.

[0206] In one embodiment, the scanned image signal includes image signals with diffuse intensities in multiple directions.

[0207] Each module in the aforementioned diffusion kurtosis imaging device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0208] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 12 As shown, Figure 12 The figure is a diagram of the internal structure of a computer device in one embodiment. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store scanned image signal data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements a diffusion kurtosis imaging method.

[0209] Those skilled in the art will understand that Figure 12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0210] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0211] Acquiring a scanned image signal of a scanned object; fitting the scanned image signal using an unconstrained optimization algorithm to obtain elements of a first diffusion tensor and elements of a first kurtosis tensor; determining diffusion tensor imaging parameters and / or kurtosis tensor imaging parameters based on the elements of the first diffusion tensor and the elements of the first kurtosis tensor; and generating a parametric image based on the diffusion tensor imaging parameters and / or the kurtosis tensor imaging parameters.

[0212] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0213] Determining a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction; modifying the first diffusion tensor according to the diffusion coefficient in each direction to obtain each element of a second diffusion tensor; and determining a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the second diffusion tensor and each element of the first kurtosis tensor.

[0214] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0215] If there is a diffusion coefficient in each of the diffusion coefficients in the directions that is not within the first preset interval, the diffusion coefficient that is not within the first preset interval is corrected to a value within the first preset interval, where the first preset interval is an interval within [a first preset threshold value, a second preset threshold value]. The first diffusion tensor is corrected based on the corrected diffusion coefficient and other diffusion coefficients to obtain each element of the second diffusion tensor, where the other diffusion coefficients include the diffusion coefficient in each of the diffusion coefficients in the directions that is within the first preset interval.

[0216] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0217] Determining a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction; correcting the first kurtosis tensor according to the diffusion coefficient in each direction to obtain each element of a second kurtosis tensor; and determining a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the first diffusion tensor and each element of the second kurtosis tensor.

[0218] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0219] If there is a diffusion coefficient located in the second preset interval among the diffusion coefficients in each of the directions, the kurtosis coefficient of the direction corresponding to the diffusion coefficient located in the second preset interval is determined based on the various elements of the first kurtosis tensor; wherein, the second preset interval is an interval located within [third preset threshold, fourth preset threshold]; if there is a diffusion coefficient not located in the second preset interval among the diffusion coefficients in each of the directions, the kurtosis coefficient of the direction corresponding to the diffusion coefficient not located in the second preset interval is determined to be equal to a value within the third preset interval, and the third preset interval is an interval located within [fifth preset threshold, sixth preset threshold]; according to the kurtosis coefficient of each of the directions, the first kurtosis tensor is corrected to obtain the various elements of the second kurtosis tensor.

[0220] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0221] If the kurtosis coefficients in the directions corresponding to the diffusion coefficients in the second preset interval determined include kurtosis coefficients that are not in the third preset interval, the kurtosis coefficients that are not in the third preset interval are corrected to values ​​within the third preset interval.

[0222] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0223] Based on each element of the first kurtosis tensor and the direction vector of each direction, the kurtosis coefficient of the corresponding direction is determined; if there is a kurtosis coefficient in the kurtosis coefficients of each direction that is not within the fourth preset interval, the kurtosis coefficient that is not within the fourth preset interval is corrected to a value within the fourth preset interval, wherein the fourth preset interval is an interval within [seventh preset threshold, eighth preset threshold]; according to the kurtosis coefficient of each direction, the first kurtosis tensor is corrected to obtain each element of the second kurtosis tensor; based on each element of the first diffusion tensor and each element of the second kurtosis tensor, a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter is determined.

[0224] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0225] Determine a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction; modify the first diffusion tensor and the first kurtosis tensor according to the diffusion coefficient in each direction to obtain each element of a second diffusion tensor and each element of a second kurtosis tensor; and determine the diffusion tensor imaging parameter and the kurtosis tensor imaging parameter based on each element of the second diffusion tensor and each element of the second kurtosis tensor.

[0226] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0227] If there is a diffusion coefficient in the diffusion coefficients in each of the directions that is not within the fifth preset interval, the diffusion coefficient that is not within the fifth preset interval is corrected to a value within the fifth preset interval, wherein the fifth preset interval is an interval within [a ninth preset threshold value, a tenth preset threshold value]. According to the target diffusion coefficient, the first diffusion tensor is corrected to obtain each element of the second diffusion tensor, wherein the target diffusion coefficient includes the corrected diffusion coefficient and the diffusion coefficient in each of the diffusion coefficients in the directions that is within the fifth preset interval. According to the target diffusion coefficient, the kurtosis coefficient of the direction corresponding to the target diffusion coefficient is determined; and according to the kurtosis coefficient of the direction corresponding to the target diffusion coefficient, each element of the second kurtosis tensor is determined.

[0228] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0229] If the target diffusion coefficient is within the fifth preset interval, the kurtosis coefficient in the direction corresponding to the target diffusion coefficient is determined based on the elements of the first kurtosis tensor; if the target diffusion coefficient is not within the fifth preset interval, the kurtosis coefficient in the direction corresponding to the target diffusion coefficient is determined to be equal to a value within the sixth preset interval, wherein the sixth preset interval is an interval within [the eleventh preset threshold, the twelfth preset threshold].

[0230] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0231] If there is a kurtosis coefficient not located in the sixth preset interval among the determined kurtosis coefficients in the direction corresponding to the target diffusion coefficient, the kurtosis coefficient not located in the sixth preset interval is corrected to a value within the sixth preset interval.

[0232] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0233] Determine the diffusion coefficient of the corresponding direction based on each element of the first diffusion tensor and the direction vector of each direction; modify the first diffusion tensor according to the diffusion coefficient of each direction to obtain each element of the second diffusion tensor; determine the kurtosis coefficient of the corresponding direction based on each element of the first kurtosis tensor and the direction vector of each direction; modify the first kurtosis tensor according to the kurtosis coefficient of each direction to obtain each element of the second kurtosis tensor; and determine the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter based on each element of the second diffusion tensor and each element of the second kurtosis tensor.

[0234] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0235] Based on the scanned image signal, an unconstrained optimization algorithm is used to solve a multi-directional diffusion kurtosis imaging model to obtain each element of the first diffusion tensor and each element of the first kurtosis tensor.

[0236] In one embodiment, the scanned image signal includes image signals with diffuse intensities in multiple directions.

[0237] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the diffusion kurtosis imaging method provided in the above embodiments are implemented.

[0238] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps of the diffusion kurtosis imaging method provided in the above embodiments are implemented.

[0239] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0240] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0241] 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.

[0242] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A diffusion kurtosis imaging method, characterized in that: The method comprises: Acquiring a scanning image signal of the scanned object; Fitting the scanned image signal using an unconstrained optimization algorithm to obtain each element of a first diffusion tensor and each element of a first kurtosis tensor; Based on each element of the first diffusion tensor, the first diffusion tensor is modified to obtain each element of a second diffusion tensor; based on each element of the second diffusion tensor and each element of the first kurtosis tensor, a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter is determined; A parametric image is generated based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

2. The method according to claim 1, characterized in that The step of correcting the first diffusion tensor based on the elements of the first diffusion tensor to obtain the elements of the second diffusion tensor includes: determining a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction; The first diffusion tensor is corrected according to the diffusion coefficients in each of the directions to obtain each element of the second diffusion tensor.

3. The method according to claim 2, characterized in that The step of correcting the first diffusion tensor according to the diffusion coefficients in each of the directions to obtain each element of the second diffusion tensor includes: If there is a diffusion coefficient in each of the diffusion coefficients in each direction that is not within the first preset interval, correcting the diffusion coefficient that is not within the first preset interval to a value within the first preset interval, wherein the first preset interval is an interval within [first preset threshold value, second preset threshold value]; The first diffusion tensor is corrected according to the corrected diffusion coefficient and other diffusion coefficients to obtain each element of the second diffusion tensor, wherein the other diffusion coefficients include the diffusion coefficients in each of the directions that are within the first preset range.

4. A diffusion kurtosis imaging method, characterized in that: The method comprises: Acquiring a scanning image signal of the scanned object; Fitting the scanned image signal using an unconstrained optimization algorithm to obtain each element of a first diffusion tensor and each element of a first kurtosis tensor; Based on each element of the first diffusion tensor, the first kurtosis tensor is modified to obtain each element of a second kurtosis tensor; based on each element of the first diffusion tensor and each element of the second kurtosis tensor, a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter is determined; A parametric image is generated based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

5. The method according to claim 4, characterized in that The modifying the first kurtosis tensor based on each element of the first diffusion tensor to obtain each element of the second kurtosis tensor includes: determining a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction; The first kurtosis tensor is corrected according to the diffusion coefficients in each of the directions to obtain each element of the second kurtosis tensor.

6. The method according to claim 5, characterized in that The first kurtosis tensor is modified according to the diffusion coefficient in each direction to obtain each element of the second kurtosis tensor, including: If there is a diffusion coefficient located in a second preset interval among the diffusion coefficients in each of the directions, determining the kurtosis coefficient of the direction corresponding to the diffusion coefficient located in the second preset interval according to each element of the first kurtosis tensor; wherein the second preset interval is an interval located within [the third preset threshold value, the fourth preset threshold value]; If there is a diffusion coefficient not located in the second preset interval among the diffusion coefficients in each of the directions, determining that the kurtosis coefficient of the direction corresponding to the diffusion coefficient not located in the second preset interval is equal to a value within a third preset interval, where the third preset interval is an interval within [fifth preset threshold value, sixth preset threshold value]; The first kurtosis tensor is modified according to the kurtosis coefficients in each of the directions to obtain each element of the second kurtosis tensor.

7. The method according to claim 6, characterized in that After determining the kurtosis coefficient in the direction corresponding to the diffusion coefficient in the second preset interval according to each element of the first kurtosis tensor, the method further includes: If the kurtosis coefficients in the directions corresponding to the diffusion coefficients in the second preset interval determined include kurtosis coefficients that are not in the third preset interval, the kurtosis coefficients that are not in the third preset interval are corrected to values ​​within the third preset interval.

8. A diffusion kurtosis imaging method, characterized in that: The method comprises: Acquiring a scanning image signal of the scanned object; Fitting the scanned image signal using an unconstrained optimization algorithm to obtain each element of a first diffusion tensor and each element of a first kurtosis tensor; Based on each element of the first diffusion tensor, modify the first diffusion tensor and the first kurtosis tensor respectively to obtain each element of a second diffusion tensor and each element of a second kurtosis tensor; based on each element of the second diffusion tensor and each element of the second kurtosis tensor, determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter; A parametric image is generated based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

9. The method according to claim 8, characterized in that The first diffusion tensor and the first kurtosis tensor are respectively corrected based on each element of the first diffusion tensor to obtain each element of the second diffusion tensor and each element of the second kurtosis tensor, including: determining a diffusion coefficient in a corresponding direction based on each element of the first diffusion tensor and a direction vector in each direction; The first diffusion tensor and the first kurtosis tensor are respectively corrected according to the diffusion coefficients in the directions to obtain elements of the second diffusion tensor and elements of the second kurtosis tensor.

10. The method according to claim 9, characterized in that The first diffusion tensor and the first kurtosis tensor are respectively corrected according to the diffusion coefficients in the directions to obtain the elements of the second diffusion tensor and the elements of the second kurtosis tensor, including: If there is a diffusion coefficient in each of the diffusion coefficients in each direction that is not within the fifth preset interval, correcting the diffusion coefficient that is not within the fifth preset interval to a value within the fifth preset interval, wherein the fifth preset interval is an interval within [a ninth preset threshold value, a tenth preset threshold value]; Correcting the first diffusion tensor according to a target diffusion coefficient to obtain elements of a second diffusion tensor, wherein the target diffusion coefficient includes the corrected diffusion coefficient and a diffusion coefficient in each of the directions that is within the fifth preset interval; determining a kurtosis coefficient in a direction corresponding to the target diffusion coefficient according to the target diffusion coefficient; Each element of the second kurtosis tensor is determined according to the kurtosis coefficient in the direction corresponding to the target diffusion coefficient.

11. The method according to claim 10, characterized in that Determining the kurtosis coefficient in the direction corresponding to the target diffusion coefficient according to the target diffusion coefficient includes: If the target diffusion coefficient is within the fifth preset interval, determining the kurtosis coefficient of the direction corresponding to the target diffusion coefficient according to each element of the first kurtosis tensor; If the target diffusion coefficient is not within the fifth preset interval, it is determined that the kurtosis coefficient in the direction corresponding to the target diffusion coefficient is equal to a value within a sixth preset interval, wherein the sixth preset interval is an interval within [the eleventh preset threshold, the twelfth preset threshold].

12. The method according to claim 11, characterized in that After determining the kurtosis coefficient of the direction corresponding to the target diffusion coefficient according to each element of the first kurtosis tensor, the method further includes: If there is a kurtosis coefficient not located in the sixth preset interval among the determined kurtosis coefficients in the direction corresponding to the target diffusion coefficient, the kurtosis coefficient not located in the sixth preset interval is corrected to a value within the sixth preset interval.

13. A diffusion kurtosis imaging device, characterized in that: The device comprises: An acquisition module, used for acquiring a scanning image signal of a scanned object; a fitting module, configured to fit the scanned image signal using an unconstrained optimization algorithm to obtain each element of a first diffusion tensor and each element of a first kurtosis tensor; a determination module, configured to modify the first diffusion tensor based on each element of the first diffusion tensor to obtain each element of a second diffusion tensor; and determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the second diffusion tensor and each element of the first kurtosis tensor; A generating module is configured to generate a parameter image based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

14. A diffusion kurtosis imaging device, characterized in that The device comprises: An acquisition module, used for acquiring a scanning image signal of a scanned object; a fitting module, configured to fit the scanned image signal using an unconstrained optimization algorithm to obtain each element of a first diffusion tensor and each element of a first kurtosis tensor; a determination module, configured to modify the first kurtosis tensor based on each element of the first diffusion tensor to obtain each element of a second kurtosis tensor; and determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the first diffusion tensor and each element of the second kurtosis tensor; A generating module is configured to generate a parameter image based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

15. A diffusion kurtosis imaging device, characterized in that: The device comprises: An acquisition module, used for acquiring a scanning image signal of a scanned object; a fitting module, configured to fit the scanned image signal using an unconstrained optimization algorithm to obtain each element of a first diffusion tensor and each element of a first kurtosis tensor; a determination module, configured to modify the first diffusion tensor and the first kurtosis tensor based on each element of the first diffusion tensor to obtain each element of a second diffusion tensor and each element of a second kurtosis tensor; and determine a diffusion tensor imaging parameter and / or a kurtosis tensor imaging parameter based on each element of the second diffusion tensor and each element of the second kurtosis tensor; A generating module is configured to generate a parameter image based on the diffusion tensor imaging parameter and / or the kurtosis tensor imaging parameter.

16. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 12 are implemented.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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