Diffusion tensor imaging method, apparatus, magnetic resonance device, and storage medium

Abstract

The application relates to a diffusion tensor imaging method, a diffusion tensor imaging device, a magnetic resonance equipment and a storage medium. The method comprises the following steps: acquiring a total number of diffusion directions; in the case that a user triggers a sub-direction scanning option based on a scanning setting interface, determining at least one target scanning direction from all scanning directions corresponding to the total number of diffusion directions based on the scanning setting interface; scanning a target object according to the at least one target scanning direction; and reconstructing an image according to scanning data. According to the application, the target object can be scanned according to different scanning directions, and images can be reconstructed according to scanning data obtained by scanning in different scanning directions. The problem of low reconstruction efficiency in the prior art, i.e. image reconstruction according to all scanning data after all scanning is completed, can be avoided. According to the application, the required scanning direction can be selected according to the user demand, instead of scanning all scanning directions in the prior art, so that the demand of different users can be met.

Description

Technical Field

[0001] This application relates to the field of magnetic resonance imaging technology, and in particular to a diffusion tensor imaging method, apparatus, magnetic resonance device, and storage medium. Background Technology

[0002] Magnetic resonance imaging (MRI) is a widely used method for acquiring images, and diffusion tensor imaging (DTI) is an advanced application of MRI. DTI can yield excellent results when imaging the brain. For example, DTI can reveal the neural network connections within the brain. Taking brain imaging as an example, when a tumor is present in the brain, DTI can clearly show how the tumor affects neural cell connections, allowing doctors to prepare accordingly during surgery. Simultaneously, DTI can also reveal subtle abnormal changes in the brain associated with stroke, multiple sclerosis, schizophrenia, and dyslexia. Therefore, DTI is gradually becoming one of the important imaging applications in clinical practice and research.

[0003] Existing diffusion tensor imaging methods, in order to increase the accuracy of diffusion coefficient calculation and achieve better fiber bundle tracking, involve scanning the target object one by one under diffusion gradients in different directions, and then reconstructing the image based on the scan data after all scans are completed. However, with the increasing number of scan directions required for diffusion tensor imaging, the stability requirements of the imaging system are becoming more stringent, and the current imaging workflow suffers from low reconstruction efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a diffusion tensor imaging method, apparatus, magnetic resonance device, and storage medium that can improve the efficiency of image reconstruction in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a diffusion tensor imaging method. The method includes:

[0006] Get the total number of diffusion directions;

[0007] When a directional scanning command is obtained based on the scanning setting interface, at least one target scanning direction is determined from all scanning directions corresponding to the total number of diffusion directions based on the scanning setting interface; the directional scanning command is the command obtained by the user by triggering the directional scanning option based on the scanning setting interface.

[0008] The target object is scanned according to at least one target scanning direction, and the image is reconstructed based on the scan data.

[0009] In one embodiment, scanning a target object according to at least one target scanning direction and reconstructing an image based on the scan data includes:

[0010] The scanning directions of each target are grouped to obtain multiple scanning direction sequences;

[0011] The target object is scanned according to the sequence of each scanning direction to obtain the scan data corresponding to each scanning direction sequence;

[0012] The image is reconstructed based on the scan data corresponding to each scan direction sequence.

[0013] In one embodiment, the scan setting interface includes a scan grouping setting area, which groups the target scan directions to obtain multiple scan direction sequences, including:

[0014] The scanning direction grouping command is obtained based on the grouping setting area; the scanning direction grouping command includes the scanning direction grouping method.

[0015] In one embodiment, scanning a target object according to at least one target scanning direction and reconstructing an image based on the scan data includes:

[0016] Based on the scan setting interface, the current scan direction is determined from the scan directions of each target;

[0017] The target object is scanned based on the current scanning direction to obtain the scan data in the current scanning direction;

[0018] Reconstruct the image based on the scan data from the current scanning direction.

[0019] In one embodiment, the diffusion tensor imaging method further includes:

[0020] Based on the scanning data in the current scanning direction, obtain the scanning system calibration parameters;

[0021] The initial parameters of the scanning system are corrected according to the scanning system calibration parameters.

[0022] In one embodiment, the diffusion tensor imaging method further includes:

[0023] Based on the scan settings interface, obtain at least two preset b values;

[0024] Scanning a target object according to at least one target scanning direction and reconstructing an image based on the scan data includes:

[0025] Under each preset b value, the target object is scanned according to each target scanning direction, and the image is reconstructed based on the scan data.

[0026] In one embodiment, the diffusion tensor imaging method further includes:

[0027] Acquire images corresponding to at least six target scanning directions;

[0028] Fiber bundle tracking is performed using images corresponding to the scanning directions of each target to obtain fiber bundle tracking results.

[0029] Secondly, this application also provides a diffusion tensor imaging device. The device includes:

[0030] The acquisition module is used to obtain the total number of diffusion directions; the total number of diffusion directions is entered by the user based on the diffusion direction total number setting option triggered by the scan setting interface;

[0031] The determination module is used to determine at least one target scanning direction from all scanning directions corresponding to the total number of diffusion directions, based on the scanning setting interface when a sub-directional scanning instruction is obtained based on the scanning setting interface; the sub-directional scanning instruction is the instruction obtained by the user based on the sub-directional scanning option triggered by the scanning setting interface.

[0032] The reconstruction module is used to scan the target object according to at least one target scanning direction and reconstruct the image based on the scan data.

[0033] Thirdly, this application also provides a magnetic resonance imaging (MRI) device. The MRI device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the embodiments of the first aspect described above.

[0034] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the method in any of the embodiments of the first aspect described above.

[0035] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method in any of the embodiments of the first aspect described above.

[0036] The aforementioned diffusion tensor imaging method, apparatus, magnetic resonance device, and storage medium, by acquiring the total number of diffusion directions, and upon receiving a user's instruction to trigger a sub-direction scanning option based on the scan setting interface, determine at least one target scanning direction from all scanning directions corresponding to the total number of diffusion directions. The target object is then scanned according to this at least one target scanning direction, and the image is reconstructed based on the scan data. This allows for scanning of the target object according to different scanning directions and reconstructing images separately based on the scan data from each direction. This avoids the problems of large scan data volume, high reconstruction pressure, and low reconstruction efficiency inherent in existing technologies, where image reconstruction is performed based on all scan data after all scans are completed. Furthermore, it allows users to select the desired scanning direction, rather than requiring scanning in all directions as in existing technologies, thus meeting the needs of different users. Attached Figure Description

[0037] Figure 1 This is a diagram illustrating the application environment of the diffusion tensor imaging method in one embodiment.

[0038] Figure 2 This is a flowchart illustrating a diffusion tensor imaging method in one embodiment;

[0039] Figure 3 This is a schematic diagram of the scanning setting interface in one embodiment;

[0040] Figure 4 This is a schematic flowchart of another embodiment of the diffusion tensor imaging method;

[0041] Figure 5 This is a schematic flowchart of another embodiment of the diffusion tensor imaging method;

[0042] Figure 6 This is a schematic flowchart of another embodiment of the diffusion tensor imaging method;

[0043] Figure 7 This is a schematic flowchart of another embodiment of the diffusion tensor imaging method;

[0044] Figure 8 This is a structural block diagram of a diffusion tensor imaging device in one embodiment;

[0045] Figure 9 This is a diagram of the internal structure of a magnetic resonance device in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] To improve the accuracy of diffusion coefficient calculation and thus achieve better fiber tract tracking, existing diffusion tensor imaging methods scan the object under different diffusion gradients, with increasingly higher image resolution requirements. This places high demands on the stability of the magnetic resonance system, such as field drift and gradient temperature. Excessive field drift or gradient temperature can cause various errors and artifacts in the image, and in extreme cases, lead to scan failure and trigger rescanning. This long-duration, high-resolution scanning also presents challenges for image reconstruction. Not only is the large data volume leading to excessively long reconstruction times and low efficiency, but various post-processing methods are also needed to calibrate the various errors in the acquired data caused by imperfections in hardware and other system conditions, thereby ensuring the accuracy of the final fiber tract tracking.

[0048] Based on this, the diffusion tensor imaging method provided in this application embodiment can be applied to, for example, Figure 1 The application environment shown. Figure 1 This is a schematic diagram illustrating the application environment of the data storage method for magnetic resonance scanning according to an embodiment of this application, such as... Figure 1 As shown. The application environment may include a magnetic resonance scanning device, including a data acquisition device 101, a scanning bed 102, and a host 103. The operator can control the data acquisition device 101 to perform directional scanning on the target object on the scanning bed 102 through the host 103. After receiving the directional command, the device acquires the scanning data in each direction and then reconstructs the image based on the scanning data in each direction.

[0049] In one embodiment, such as Figure 2 As shown, a diffusion tensor imaging method is provided, which is applied to... Figure 1 The magnetic resonance imaging (MRI) device described herein includes the following steps:

[0050] S202, obtain the total number of diffusion directions.

[0051] Specifically, users can base their decisions on, for example... Figure 3 The preset scan settings interface, when selecting the "Custom Diffusion Direction" option, allows you to input the total number of diffusion directions in the preset direction input area, thus obtaining the total number of diffusion directions. Alternatively, users can select the "Sequence Default Gradient Settings" option in the preset scan settings interface to automatically select the default gradient direction table; or they can input the gradient direction table in the preset gradient direction table input area, and the total number of diffusion directions to be scanned will be updated in real time after obtaining the gradient table, thus obtaining the total number of diffusion directions. There is no restriction on this. Optionally, the total number of diffusion scan directions must be greater than or equal to 6.

[0052] S204, when a sub-direction scanning command is obtained based on the scanning setting interface, at least one target scanning direction is determined from all scanning directions corresponding to the total number of diffusion directions based on the scanning setting interface; the sub-direction scanning command is the command obtained by the user based on the sub-direction scanning option triggered by the scanning setting interface.

[0053] Specifically, after determining the total number of diffusion directions, a default scanning direction can be used as the target scanning direction. This default scanning direction can be all acquired diffusion scanning directions, and scanning can be performed sequentially according to their order. Alternatively, after determining the total number of diffusion directions, the user can use a custom scanning direction. This means the user can select a sub-direction scanning option based on the scanning settings interface, and further select the target scanning direction to be scanned. The target scanning direction can be at least one of all diffusion directions. For example, if the total number of acquired diffusion directions is 6, the first direction can be selected as the target scanning direction based on the scanning settings interface.

[0054] S206, Scan the target object according to at least one target scanning direction, and reconstruct the image based on the scan data.

[0055] Specifically, after obtaining the target scanning direction, a corresponding target scanning sequence can be generated based on the target scanning direction. Then, the target object is scanned according to the target scanning sequence to obtain the corresponding scanning data. A preset image reconstruction algorithm can be used to reconstruct the image. The preset image reconstruction algorithm can include, but is not limited to, half-Fourier transform algorithms, compressed sensing, etc. The target object can include animals or human bodies.

[0056] After obtaining the target scan direction, users can further select scan direction grouping options on the scan settings interface. Within a preset selection area, the target scan directions are grouped so that after scanning each group, a preset image reconstruction algorithm is used to reconstruct images of different groups based on the scan data. The number of target scan directions in each group can be the same or different; there is no restriction. Once the target scan direction grouping is determined, magnetic resonance scanning can be performed on the target object according to each group. The scan data obtained from scanning different directions within the target scan direction group can then be used to reconstruct images of different target scan direction groups.

[0057] For example, if a total of 64 diffusion directions are acquired, 56 of them can be selected based on the scan setting interface. These 56 directions can be grouped into sets of 8 consecutive scan directions, or any 8 scan directions can be selected as a group. Furthermore, the number of target scan directions in each group is not limited and is not restricted here. For example, if a total of 6 diffusion directions are acquired, 2 of them can be selected based on the scan setting interface. These 2 target scan directions can be further divided into two groups, or they can be grouped into one group; there is no restriction here.

[0058] Optionally, during scanning in different target scanning directions, the system can detect in real time whether abnormal scanning parameters such as signal-to-noise ratio information and eddy current calibration information are generated. If abnormal scanning parameters are generated, the relevant system parameters and other setting parameters can be adjusted or recalibrated before scanning in the next target scanning direction or before setting a certain target scanning direction to avoid generating abnormal scanning parameters. This can effectively avoid the problem of reduced scanning efficiency caused by having to complete scanning in all directions before locating abnormal parameters. A certain target scanning direction can include any target scanning direction.

[0059] In the aforementioned diffusion tensor imaging method, by obtaining the total number of diffusion directions, and upon receiving a user's instruction to trigger a directional scanning option based on the scan setting interface, at least one target scanning direction is determined from all scanning directions corresponding to the total number of diffusion directions. The target object is then scanned according to this target scanning direction, and the image is reconstructed based on the scan data. This method allows for scanning of the target object according to different scanning directions and reconstructing images separately based on the scan data from each direction. This avoids the problems of large scan data volume, high reconstruction pressure, and low reconstruction efficiency inherent in existing technologies, where image reconstruction is performed based on all scan data after all scans are completed. Furthermore, existing technologies require rescanning after correcting problematic directions, further increasing the pressure on scan reconstruction. Therefore, this solution allows users to select the required scanning direction based on their needs, rather than scanning all directions as required by existing technologies. This satisfies the needs of different users and significantly reduces the pressure on scanning and reconstruction to a certain extent, improving efficiency by only scanning and reconstructing the problematic directions. Meanwhile, the system state, including eddies and drift, can be corrected using the scan results from at least one direction. The resulting system correction factor can be used for subsequent scans to improve the accuracy of the scan results.

[0060] The above embodiments have described the diffusion tensor imaging method. Now, an embodiment will be used to further illustrate how image reconstruction is performed based on a determined target scanning direction. In one embodiment, such as... Figure 4As shown, scanning a target object according to at least one target scanning direction and reconstructing an image based on the scan data includes:

[0061] S402, group the target scanning directions to obtain multiple scanning direction sequences;

[0062] S404, Scan the target object according to each scanning direction sequence to obtain the scanning data corresponding to each scanning direction sequence;

[0063] S406, Reconstruct the image based on the scan data corresponding to each scan direction sequence.

[0064] Specifically, after obtaining the target scanning directions, the user can further select the scanning direction grouping option on the scanning settings interface. Within the preset selection area, the user chooses the target scanning directions to be grouped according to their needs. The number of target scanning directions in each group can be the same or different; there is no restriction. Alternatively, after obtaining the target scanning directions, the user can select the random grouping option on the scanning settings interface to randomly group the target scanning directions. After grouping the target scanning directions, a sequence of scanning directions corresponding to multiple groups will be generated in ascending order of the target scanning direction number.

[0065] Once the scan direction sequences are obtained, the small object can be scanned sequentially according to each scan sequence to obtain the scan data corresponding to each scan direction sequence. After obtaining the scan data corresponding to each scan direction sequence, the image can be reconstructed using methods such as half-Fourier transform and compressed sensing.

[0066] In this embodiment, by grouping the target scanning directions, multiple scanning direction sequences are obtained. The target object is scanned according to each scanning direction sequence to obtain the scanning data corresponding to each scanning direction sequence. Based on the scanning data corresponding to each scanning direction sequence, images are reconstructed. This allows for further grouping of the target scanning directions and scanning according to different groups of target scanning directions. Image reconstruction is then performed based on the scanning structure of different target scanning direction groups, resulting in images with different groups. Obviously, this can significantly reduce the amount of data required for image reconstruction based on all scanning data after scanning in all directions in the prior art. Furthermore, it can obtain images reconstructed from scanning data of different direction groups, and can also provide different data bases for subsequent data analysis.

[0067] The above embodiments illustrate how to group the scanning directions of each target. Now, an embodiment further illustrates how to group the scanning directions of each target. In one embodiment, the scanning setting interface includes a scanning grouping setting area, which groups the scanning directions of each target to obtain multiple scanning direction sequences, including:

[0068] The scanning direction grouping command is obtained based on the grouping setting area; the scanning direction grouping command includes the scanning direction grouping method.

[0069] Specifically, users can select a scan grouping method based on the grouping setting area of ​​the scan settings interface and trigger a scan direction grouping command. The scan grouping method can include randomly selecting different target scan directions and assigning them to arbitrary groups according to a default arbitrary division rule, with each group containing the same number of target scan directions. Alternatively, users can arbitrarily select different target scan directions as the same group based on the grouping setting area, with each group containing either the same or different numbers of target scan directions; there are no restrictions on this.

[0070] In this embodiment, by obtaining the scanning direction grouping instruction based on the grouping setting area, the target scanning direction can be grouped so that the image reconstruction can be performed according to the target scanning direction during subsequent image reconstruction. Image reconstruction is then performed on the scanning data obtained from each group, thereby improving the efficiency of image reconstruction.

[0071] The above embodiments illustrate how to group targets according to their scanning directions in a diffusion tensor imaging method. Now, one embodiment will be used to illustrate another method of scanning a target object according to its scanning direction and reconstructing an image from the scan data. In one embodiment, such as... Figure 5 As shown, scanning a target object according to at least one target scanning direction and reconstructing an image based on the scan data includes:

[0072] S502, based on the scan setting interface, determines the current scan direction from the scan directions of each target;

[0073] S504, Scan the target object based on the current scanning direction to obtain the scanning data in the current scanning direction;

[0074] S506, Reconstruct the image based on the scan data in the current scanning direction.

[0075] Specifically, after obtaining the target scanning direction, the user can input one of the target scanning directions as the current scanning direction in the current scanning direction setting area based on actual user needs, according to the scanning setting interface. A scanning sequence for the current scanning direction is generated based on pre-set protocol information. The target object is then scanned according to the current scanning sequence to obtain scanning data for the current scanning direction. Based on this scanning data, the image corresponding to the current scanning direction can be reconstructed using methods such as half-Fourier transform or compressed sensing.

[0076] In this embodiment, the current scanning direction is determined from each target scanning direction based on the scanning setting interface. The target object is scanned based on the current scanning direction to obtain the scanning data of the current scanning direction. The image is reconstructed based on the scanning data of the current scanning direction. This enables the target object to be scanned and reconstructed for only one scanning direction, and the image information of the target direction can be obtained more accurately.

[0077] The above embodiments illustrate the diffusion tensor imaging method. Before image reconstruction, a diffusion factor can be set. An embodiment will now be described, in which... Figure 6 As shown, the diffusion tensor imaging method also includes:

[0078] S602, based on the scan setting interface, obtains at least two preset b values.

[0079] In this context, the preset b value is the diffusion factor in diffusion tensor imaging. The magnitude of the preset b value affects the diffusion process; the larger the b value, the greater the diffusion and the lower the signal-to-noise ratio. At least one of the two preset b values ​​is b0, meaning the diffusion factor is 0.

[0080] Specifically, users can enter the number of preset b values ​​in the b value setting area on the scan setting interface. After determining the number of preset b values, users can set the specific size of different preset b values ​​to obtain the preset b values.

[0081] S604, scanning a target object according to at least one target scanning direction and reconstructing an image based on the scanning data, includes: scanning the target object according to each target scanning direction under each preset b value, and reconstructing an image based on the scanning data.

[0082] Specifically, with a preset b value set, multiple scanning sequences can be generated based on the preset b value and the target scanning direction. The target object is then scanned according to each scanning sequence to obtain scanning data and reconstruct the image.

[0083] Furthermore, images corresponding to different target scanning directions and different preset b values ​​can be grouped, and fiber bundle tracking can be performed on the images of each group to obtain different fiber bundle tracking results.

[0084] For example, based on each preset b value, the target object is scanned according to each target scanning direction, and the image is reconstructed based on the scan data. Therefore, it is possible to have multiple images reconstructed after scanning with different preset b values ​​for a single target scanning direction.

[0085] In this embodiment, based on the scan setting interface, at least two preset b values ​​are obtained. Under each preset b value, the target object is scanned according to each target scan direction, and the image is reconstructed based on the scan data. Different scan sequences can be generated for different diffusion factors to meet different observation needs.

[0086] The above embodiments illustrate the diffusion tensor imaging method. After obtaining the reconstructed image, fiber tract tracking can be performed on the image. Furthermore, based on the fiber tract tracking results, a precise analysis of the overall scanning results can be conducted. An embodiment will be used to illustrate this method. In one embodiment, such as... Figure 7 As shown, the diffusion tensor imaging method also includes:

[0087] S702, acquire images corresponding to at least six target scanning directions;

[0088] S704 uses the images corresponding to the scanning directions of each target to perform fiber bundle tracking and obtain the fiber bundle tracking results.

[0089] Specifically, after scanning multiple target directions and acquiring images corresponding to different target scanning directions, the user can set images for at least six target scanning directions for fiber bundle tracking based on a preset scanning interface. Then, different fiber bundle tracking results can be obtained by weighting the images from each target scanning direction. This allows the user to analyze the overall scanning results based on the different fiber bundle tracking results and select the image with the best result as the final image. The images corresponding to at least six target scanning directions can be arbitrarily selected from all images corresponding to all target scanning directions.

[0090] Optionally, fiber bundle tracking can be performed using images from each target scanning direction and images without a diffusion factor. The images without a diffusion factor are those reconstructed with a preset b-value of 0. During fiber bundle tracking, the images from each target scanning direction are compared with the images without a diffusion factor to obtain the parameters for fiber bundle tracking, thereby obtaining the fiber bundle tracking result.

[0091] Furthermore, users can group the reconstructed images based on a preset scanning interface. They can choose random grouping or custom grouping. The number of images in each group can be the same or different. For different image groups, if the number of directions in each group is greater than or equal to 6, fiber bundle tracking can be performed separately, or the data can be combined with the data from other groups to obtain fiber bundle tracking results.

[0092] In this embodiment, by acquiring images corresponding to multiple target scanning directions, fiber bundle tracking is performed using at least six diffusion-weighted images combined with images without diffusion applied. This allows for consideration of various combinations of target scanning directions, facilitating the user to select the optimal fiber bundle tracking result for precise localization of abnormal areas in the target object. Furthermore, since fiber bundle tracking has high requirements for gradient table settings, the grouped and direction-specific scanning method can quickly locate problems in each direction. The results of fiber bundle tracking can be compared by combining different directions, thereby optimizing the gradient direction settings. This facilitates the setting, selection, and optimization of fiber bundle tracking directions.

[0093] To facilitate understanding by those skilled in the art, the diffusion tensor imaging method is further illustrated by an embodiment, which includes:

[0094] S802, obtain the total number of diffusion directions.

[0095] S804, based on the scan settings interface, obtains at least two preset b values.

[0096] S806, when a sub-direction scanning command is obtained based on the scanning setting interface, at least one target scanning direction is determined from all scanning directions corresponding to the total number of diffusion directions based on the scanning setting interface; the sub-direction scanning command is the command obtained by the user based on the sub-direction scanning option triggered by the scanning setting interface.

[0097] S808, obtains scanning direction grouping instructions based on the grouping setting area; the scanning direction grouping instructions include scanning direction grouping methods, grouping each target scanning direction to obtain multiple scanning direction sequences.

[0098] S810, under each preset b value, scans the target object according to each scanning direction sequence to obtain the scanning data corresponding to each scanning direction sequence.

[0099] S812 reconstructs images based on the scan data corresponding to each scan direction sequence.

[0100] S814, acquire images corresponding to at least six target scanning directions.

[0101] S816 uses the images corresponding to the scanning directions of each target to perform fiber bundle tracking and obtain the fiber bundle tracking results.

[0102] In this embodiment, by obtaining the total number of diffusion directions, when the user triggers the sub-direction scanning option based on the scanning setting interface, at least one target scanning direction is determined from all scanning directions corresponding to the total number of diffusion directions based on the scanning setting interface. The target object is scanned according to at least one target scanning direction, and the image is reconstructed based on the scanning data. This allows the target object to be scanned according to different scanning directions, and the image can be reconstructed separately based on the scanning data after scanning in different scanning directions. This avoids the problem of low reconstruction efficiency in the prior art, where image reconstruction is performed based on all scanning data after all scanning is completed. Furthermore, the required scanning direction can be selected according to the user's needs, instead of having to scan in all scanning directions as in the prior art, thus meeting the needs of different users.

[0103] To facilitate understanding by those skilled in the art, the diffusion tensor imaging method is further illustrated by an embodiment, which includes:

[0104] S902, obtain the total number of diffusion directions.

[0105] S904, based on the scan setting interface, obtains at least two preset b values.

[0106] S906, when a sub-direction scanning command is obtained based on the scanning setting interface, at least one target scanning direction is determined from all scanning directions corresponding to the total number of diffusion directions based on the scanning setting interface; the sub-direction scanning command is the command obtained by the user based on the sub-direction scanning option triggered by the scanning setting interface.

[0107] S908 determines the current scanning direction from the scanning directions of each target based on the scanning setting interface.

[0108] S910, under each preset b value, scan the target object based on the current scanning direction to obtain the scan data of the current scanning direction.

[0109] S912, reconstructs the image based on the scanning data in the current scanning direction.

[0110] S914, acquire images corresponding to at least six target scanning directions under different preset b values.

[0111] S916 uses the images corresponding to the scanning directions of each target to perform fiber bundle tracking and obtain fiber bundle tracking results.

[0112] In this embodiment, by obtaining the total number of diffusion directions, when the user triggers the sub-direction scanning option based on the scanning setting interface, at least one target scanning direction is determined from all scanning directions corresponding to the total number of diffusion directions based on the scanning setting interface. The target object is scanned according to at least one target scanning direction, and the image is reconstructed based on the scanning data. This allows the target object to be scanned according to different scanning directions, and the image can be reconstructed separately based on the scanning data after scanning in different scanning directions. This avoids the problem of low reconstruction efficiency in the prior art, where image reconstruction is performed based on all scanning data after all scanning is completed. Furthermore, the required scanning direction can be selected according to the user's needs, instead of having to scan in all scanning directions as in the prior art, thus meeting the needs of different users.

[0113] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0114] Based on the same inventive concept, this application also provides a diffusion tensor imaging apparatus for implementing the diffusion tensor imaging method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more of the diffusion tensor imaging apparatus embodiments provided below can be found in the limitations of the diffusion tensor imaging method described above, and will not be repeated here.

[0115] In one embodiment, such as Figure 8 As shown, a diffusion tensor imaging device is provided, comprising:

[0116] Module 801 is used to obtain the total number of diffusion directions;

[0117] The determination module 802 is used to determine at least one target scanning direction from all scanning directions corresponding to the total number of diffusion directions when a sub-directional scanning instruction is obtained based on the scanning setting interface; the sub-directional scanning instruction is the instruction obtained by the user based on the sub-directional scanning option triggered by the scanning setting interface.

[0118] The reconstruction module 803 is used to scan the target object according to at least one target scanning direction and reconstruct the image based on the scan data.

[0119] In this embodiment, the acquisition module acquires the total number of diffusion directions, and the determination module, upon receiving an instruction from the user triggering the direction-specific scanning option based on the scanning setting interface, determines at least one target scanning direction from all scanning directions corresponding to the total number of diffusion directions. The reconstruction module scans the target object according to at least one target scanning direction and reconstructs the image based on the scan data. This allows for scanning the target object according to different scanning directions and reconstructing the image separately based on the scan data after scanning in different directions. This avoids the problem of low reconstruction efficiency in the prior art, where image reconstruction is performed based on all scan data after all scans are completed. Furthermore, it allows users to select the required scanning direction according to their needs, rather than having to scan in all directions as in the prior art, thus meeting the needs of different users.

[0120] In one embodiment, the reconstruction module includes:

[0121] Grouping units are used to group the target scanning directions to obtain multiple scanning direction sequences;

[0122] The first scanning unit is used to scan the target object according to each scanning direction sequence to obtain the scanning data corresponding to each scanning direction sequence;

[0123] The first reconstruction unit is used to reconstruct images based on the scan data corresponding to each scan direction sequence.

[0124] In one embodiment, the scan setting interface includes a scan group setting area, and the first grouping unit is specifically used to obtain a scan direction grouping instruction based on the group setting area; the scan direction grouping instruction includes a scan direction grouping method.

[0125] In one embodiment, the reconstruction module includes:

[0126] The determining unit is used to determine the current scanning direction from each target scanning direction based on the scanning setting interface;

[0127] The second grouping unit is used to scan the target object based on the current scanning direction to obtain the scanning data of the current scanning direction;

[0128] The second reconstruction unit is used to reconstruct the image based on the scan data in the current scanning direction.

[0129] In one embodiment, the diffusion tensor imaging device further includes:

[0130] The acquisition module is used to acquire the scanning system calibration parameters based on the scanning data in the current scanning direction;

[0131] The calibration module is used to calibrate the initial parameters of the scanning system according to the calibration parameters of the scanning system.

[0132] In one embodiment, the diffusion tensor imaging device further includes:

[0133] The first acquisition module is used to acquire at least two preset b values ​​based on the scan setting interface;

[0134] The scanning and reconstruction module is used to scan the target object according to each target scanning direction under each preset b value, and reconstruct the image based on the scanning data.

[0135] In one embodiment, the diffusion tensor imaging device further includes:

[0136] The second acquisition module is used to acquire images corresponding to at least 6 target scanning directions;

[0137] The tracking module is used to track fiber bundles using images from each target scanning direction to obtain fiber bundle tracking results.

[0138] Each module in the aforementioned diffusion tensor imaging device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0139] In one embodiment, a magnetic resonance imaging (MRI) device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the magnetic resonance imaging (MRI) device includes a processor, memory, and network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores scan data from different scanning directions. The network interface communicates with external terminals via a network. When the computer program is executed by the processor, it implements a diffusion tensor imaging method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the device's casing, or an external keyboard, touchpad, or mouse.

[0140] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0141] In one embodiment, a magnetic resonance device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0142] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0143] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0144] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0145] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0146] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A diffusion tensor imaging method, characterized in that, The method includes: Get the total number of diffusion directions; When a directional scanning instruction is obtained based on the scanning setting interface, at least one target scanning direction is determined from all scanning directions corresponding to the total number of diffusion directions based on the scanning setting interface; the directional scanning instruction is the instruction obtained by the user by triggering the directional scanning option based on the scanning setting interface. The target object is scanned according to the at least one target scanning direction, and the image is reconstructed based on the scan data; The step of scanning the target object according to the at least one target scanning direction and reconstructing the image based on the scan data includes: The scanning directions of each target are grouped to obtain multiple scanning direction sequences; The target object is scanned according to each of the scan direction sequences to obtain scan data corresponding to each scan direction sequence; The images are reconstructed based on the scan data corresponding to each scan direction sequence.

2. The method according to claim 1, characterized in that, The scan setting interface includes a scan grouping setting area, wherein the target scan directions are grouped to obtain multiple scan direction sequences, including: The scanning direction grouping instruction is obtained based on the grouping setting area; the scanning direction grouping instruction includes the scanning direction grouping method.

3. The method according to claim 1, characterized in that, The step of scanning the target object according to the at least one target scanning direction and reconstructing the image based on the scan data includes: Based on the scan setting interface, the current scan direction is determined from each of the target scan directions; The target object is scanned based on the current scanning direction to obtain scan data in the current scanning direction; The image is reconstructed based on the scan data from the current scan direction.

4. The method according to claim 3, characterized in that, The method further includes: Based on the scanning data in the current scanning direction, obtain the scanning system correction parameters; The initial parameters of the scanning system are corrected according to the scanning system correction parameters.

5. The method according to claim 1, characterized in that, The method further includes: Based on the scan settings interface, obtain at least two preset b values; Scanning the target object according to the at least one target scanning direction, and reconstructing the image based on the scan data, includes: Under each of the preset b values, the target object is scanned according to each of the target scanning directions, and the image is reconstructed based on the scan data.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Acquire images corresponding to at least six of the target scanning directions; Fiber bundle tracking is performed using the images corresponding to the scanning directions of each target to obtain fiber bundle tracking results.

7. A diffusion tensor imaging device, characterized in that, The device includes: The acquisition module is used to obtain the total number of diffusion directions; The determination module is used to determine at least one target scanning direction from all scanning directions corresponding to the total number of diffusion directions, based on the scanning setting interface when a sub-directional scanning instruction is obtained based on the scanning setting interface; the sub-directional scanning instruction is the instruction obtained by the user based on the sub-directional scanning option triggered by the scanning setting interface. The reconstruction module is used to scan the target object according to the at least one target scanning direction and reconstruct the image based on the scan data; The reconstruction module includes: A grouping unit is used to group the target scanning directions into multiple scanning direction sequences. The first scanning unit is used to scan the target object according to each of the scanning direction sequences to obtain scanning data corresponding to each of the scanning direction sequences; The first reconstruction unit is used to reconstruct the image based on the scan data corresponding to each scan direction sequence.

8. A magnetic resonance imaging (MRI) device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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