Method, apparatus, device and storage medium for generating thoracic aortic wall image

Abstract

The present application discloses a method, device, equipment and storage medium for generating an image of the thoracic aortic wall. The method includes: obtaining imaging data corresponding to a subject to be measured; generating a blurred aliased image of the thoracic aortic wall with respiratory motion artifacts according to the imaging data, and determining respiratory motion information corresponding to the subject to be measured according to the collected self-navigation information; correcting the respiratory motion artifacts of the blurred aliased image of the thoracic aortic wall based on the respiratory motion information to obtain an image of the thoracic aortic wall corresponding to the subject to be measured. Therefore, by using the self-navigation technology, on the premise of fast imaging, the clarity and sharpness of the image of the thoracic aortic wall are improved, thereby providing a technical guarantee for the diagnosis of clinical diseases such as aortic plaque detection and vascular wall stenosis.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a method, apparatus, device, and storage medium for generating images of the thoracic aortic wall. Background Art

[0002] Magnetic Resonance Imaging (MRI) technology is a diagnostic technology based on the principle of nuclear magnetic resonance. It has no ionizing radiation, can image through any cross-section, and can perform three-dimensional reconstruction to obtain the anatomical and functional structures of tissues and organs, thereby providing diagnostic information for lesions. For example, three-dimensional black blood magnetic resonance imaging can non-invasively evaluate aortic atherosclerosis and plaques. However, respiratory motion artifacts generated by the free breathing motion of the human body will affect the imaging quality of the thoracic aortic wall and plaque detection, and the generated images of the thoracic aortic wall are not very accurate and effective.

[0003] Therefore, how to improve the accuracy and effectiveness of images of the thoracic aortic wall has become an urgent problem to be solved. Summary of the Invention

[0004] Embodiments of this application provide a method, apparatus, computer device, and storage medium for generating images of the thoracic aortic wall, which can improve the accuracy and effectiveness of images of the thoracic aortic wall.

[0005] In a first aspect, embodiments of this application provide a method for generating images of the thoracic aortic wall, including:

[0006] Obtaining imaging data corresponding to the object to be measured;

[0007] Generating a blurred aliased image of the thoracic aortic wall with respiratory motion artifacts according to the imaging data, and determining respiratory motion information corresponding to the object to be measured according to the collected self-navigation information;

[0008] Correcting the respiratory motion artifacts of the blurred aliased image of the thoracic aortic wall based on the respiratory motion information to obtain an image of the thoracic aortic wall corresponding to the object to be measured.

[0009] In a second aspect, embodiments of this application further provide an apparatus for generating images of the thoracic aortic wall. The apparatus for generating images of the thoracic aortic wall includes a processor, a memory, and a computer program stored on the memory and executable by the processor. When the computer program is executed by the processor, it implements the method for generating images of the thoracic aortic wall as described above.

[0010] In a third aspect, embodiments of this application further provide a computer device, which includes the apparatus for generating images of the thoracic aortic wall as described above.

[0011] Fourthly, an embodiment of the present application further provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, causes the processor to implement the above-mentioned method for generating a thoracic aortic wall image.

[0012] An embodiment of the present application provides a method, device, equipment and storage medium for generating a thoracic aortic wall image. By acquiring imaging data corresponding to a measured object, generating a blurred and aliased image of the thoracic aortic wall with respiratory motion artifacts according to the imaging data, determining the respiratory motion information corresponding to the measured object according to the collected self-navigation information, and correcting the respiratory motion artifacts of the blurred and aliased image of the thoracic aortic wall based on the respiratory motion information, a thoracic aortic wall image corresponding to the measured object is obtained. Thus, fast imaging of the thoracic aortic wall under free breathing of the measured object is realized, and the generated thoracic aortic wall image is accurate and effective, providing guarantee for aortic atherosclerosis and plaque detection, etc. Description of the Drawings

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0014] Figure 1 is a schematic flowchart of the steps of a method for generating a thoracic aortic wall image provided by an embodiment of the present application;

[0015] Figure 2 is a schematic flowchart of the sub-steps of generating a blurred and aliased image of the thoracic aortic wall with respiratory motion artifacts according to the imaging data provided by an embodiment of the present application;

[0016] Figure 3 is a schematic diagram of the filling trajectories corresponding to three different k-space sampling templates provided by an embodiment of the present application;

[0017] Figure 4 is a schematic flowchart of the steps of obtaining self-navigation information provided by an embodiment of the present application;

[0018] Figure 5 is a schematic flowchart of the sub-steps of determining the respiratory motion information corresponding to the measured object according to the collected self-navigation information provided by an embodiment of the present application;

[0019] Figure 6 is a schematic diagram of a respiratory self-navigation projection provided by an embodiment of the present application;

[0020] Figure 7 It is a schematic diagram of a respiratory motion curve provided by an embodiment of the present application;

[0021] Figure 8 It is a schematic flowchart of a sub-step for correcting the respiratory motion artifact of the blurred and aliased image of the thoracic aortic wall based on the respiratory motion information provided by an embodiment of the present application;

[0022] Figure 9 It is a schematic diagram of dividing the first data corrected for the up and down motion displacement into multiple respiratory phases provided by an embodiment of the present application;

[0023] Figure 10 (a) is a schematic diagram of an image of the thoracic aortic wall generated by using traditional diaphragmatic navigation imaging; Figure 10 (b) is a schematic diagram of an image of the thoracic aortic wall generated by using a method for generating an image of the thoracic aortic wall provided by an embodiment of the present application; Figure 10 (c) is a schematic diagram of an uncorrected image of the thoracic aortic wall;

[0024] Figure 11 It is a schematic block diagram of a device for generating an image of the thoracic aortic wall provided by an embodiment of the present application. Detailed implementation manners

[0025] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0026] The flowchart shown in the accompanying drawings is only an example illustration, and does not necessarily include all contents and operations / steps, nor does it necessarily need to be executed in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged, so the actual execution order may be changed according to the actual situation.

[0027] It should be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0028] It should also be understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0029] The following will describe in detail some embodiments of the present application with reference to the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0030] Currently, there are generally two methods for monitoring respiratory motion: The first method requires a monitoring instrument outside the magnetic resonance scanning system. This method generally requires a belly band to be tied to the object to be measured for respiratory gating. Its operation is complex and the preparation time is long. The binding position and tightness of the belly band will directly affect the detection result. At the same time, this device also requires the object to be measured to perform respiratory training before use, and the object to be measured needs to keep the respiratory amplitude as consistent as possible during the examination. However, this method can only be used for detection and cannot correct respiratory motion. The second method collects additional signals through magnetic resonance and processes these data to extract motion information, which is the so-called navigation information. Then, this navigation information is used to correct respiratory motion artifacts. Diaphragm navigation is to use a specially designed excitation pulse to only excite a small area passing through the diaphragm to monitor respiratory motion. Gradient encoding is performed on this area in the vertical direction, and then respiratory motion information can be collected. Imaging data is retained within a certain range at the end of expiration, and the data at other times is discarded. It can be considered that the retained data is at the same respiratory level. On this basis, the displacement information obtained by diaphragm navigation can also be used to correct the spatial position of imaging with an empirical coefficient value of 0.6 to further improve the effect of motion correction. Diaphragm navigation is limited to respiratory motion, and the acquisition efficiency is only about 30%. For objects to be measured with irregular and unstable respiration, the scanning time of this method will be longer and there is a certain time delay between the acquisition of diaphragm signals and subsequent imaging, so it will also have a certain impact on the correction effect.

[0031] To obtain a thoracic aortic wall image corresponding to the object to be measured that eliminates respiratory motion artifacts, an embodiment of the present application provides a method, device, equipment, and storage medium for generating a thoracic aortic wall image, which is used to improve the accuracy and effectiveness of the thoracic aortic wall image. Among them, this method for generating a thoracic aortic wall image can be applied to a computer device, and this computer device can be a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and other devices.

[0032] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for generating a thoracic aortic wall image provided by an embodiment of the present application.

[0033] As Figure 1 shown, this method for generating a thoracic aortic wall image specifically includes steps S101 to S103.

[0034] S101. Obtain imaging data corresponding to the object to be measured.

[0035] Exemplarily, based on the SPACE sequence, using ECG (electrocardiogram) gating, imaging data acquisition is performed during the systolic phase of the subject's heart, that is, after the R wave, to obtain the imaging data corresponding to the subject and achieve aortic black blood imaging.

[0036] S102. Generate a blurred aliased image of the thoracic aortic wall with respiratory motion artifacts based on the imaging data, and determine the respiratory motion information corresponding to the subject according to the self-navigated information collected.

[0037] Exemplarily, use the Cartesian k-space pattern under the golden angle spiral sampling trajectory to undersample and fill the sampling template to obtain multiple blurred aliased images of the thoracic aortic wall with respiratory motion artifacts.

[0038] In some embodiments, as Figure 2 described, the step S102 may include sub-step S1021.

[0039] S1021. According to a plurality of preset different sampling templates, sequentially fill each portion of the segment imaging data into the different sampling templates to obtain multiple blurred aliased images of the thoracic aortic wall.

[0040] During each cardiac systolic phase, N echoes segment1, segment2, ……, segmentN are collected, that is, the obtained imaging data includes N portions of segment imaging data. According to a plurality of preset different sampling templates Mask1, Mask2, Mask3……, sequentially fill each portion of the segment imaging data into different sampling templates.

[0041] Exemplarily, adopt the Cartesian k-space pattern under the golden angle spiral sampling trajectory to undersample and fill each portion of the segment imaging data into different sampling templates. Under the same sampling template, adjacent segment imaging data satisfy the golden angle spiral trajectory, and the middle calibration line is collected multiple times, completing the undersampling filling of multiple sampling templates. For example, perform 2 - 3 times of undersampling, repeat 2 - 3 times, and each time repeat using different sampling templates for filling, and the filling trajectory of each k-space is not exactly the same. For example, as Figure 3 shown, Figure 3 shows the filling trajectories corresponding to three different k-space sampling templates. Using the Cartesian k-space pattern, compared with the k-space filling of radial sampling, the reconstruction time is less.

[0042] Exemplarily, the respiratory motion information corresponding to the subject includes a respiratory motion curve.

[0043] In some embodiments, as Figure 4As described above, steps S104 to S104 may be included before step S102.

[0044] S104. Collect echo signals corresponding to the imaging data during the systolic phase of the heart of the object to be measured;

[0045] S105. Read and obtain the self-navigating information along the readout gradient direction according to the initially collected echo signals.

[0046] Exemplarily, if N echo segments segment1, segment2,..., segmentN are collected during the systolic phase of the heart of the object to be measured, no phase gradient encoding is performed on the first two initially collected echo signals segment1 and segment2, and the corresponding one-dimensional self-navigating information is only read and obtained along the readout gradient direction.

[0047] In some embodiments, as Figure 5 shown, step S102 may include sub-step S1022 and sub-step S1023.

[0048] S1022. Perform Fourier transform on the self-navigating information to obtain the corresponding respiratory self-navigating projection;

[0049] S1023. Obtain the respiratory motion curve according to the respiratory self-navigating projection.

[0050] Exemplarily, perform one-dimensional Fourier transform on the obtained one-dimensional self-navigating information to obtain the respiratory self-navigating projection, as Figure 6 shown.

[0051] According to the obtained respiratory self-navigating projection, select a reference navigation line, and use the cross-correlation algorithm to obtain the respiratory motion curve that changes with time. For example, as Figure 7 shown.

[0052] S103. Correct the respiratory motion artifacts of the blurred aliased image of the thoracic aortic wall based on the respiratory motion information to obtain the thoracic aortic wall image corresponding to the object to be measured.

[0053] Exemplarily, correct the respiratory motion artifacts of the blurred aliased image of the thoracic aortic wall according to the obtained respiratory motion curve that changes with time.

[0054] In some embodiments, as Figure 8 shown, step S103 may include sub-step S1031 to sub-step S1034.

[0055] S1031. Correct the up and down motion displacement of the image data corresponding to the blurred aliased image of the thoracic aortic wall according to the respiratory motion information to obtain the first data after up and down motion displacement correction.

[0056] According to the Fourier shift theory, the vertical motion displacement generated on the blurred aliased image of the thoracic aortic wall can be corrected by the change of the k-space phase. The correction formula is as follows:

[0057]

[0058] Wherein, is the first data of the k-space after correction, K is the data of the blurred aliased image of the thoracic aortic wall in the k-space obtained before correction, N is the sampling trajectory in the readout gradient direction of the k-space, and T is the vertical motion displacement corresponding to the imaging data.

[0059] S1032. Perform respiratory phase processing on the first data to obtain second data of multiple different respiratory phases.

[0060] After the preliminary correction of the vertical motion displacement of the respiratory motion artifact, according to the respiratory motion curve, the first data with the corrected vertical motion displacement is divided into multiple respiratory phases. For example, as Figure 9 shown, the first data with the corrected vertical motion displacement is divided into respiratory phases Bin1, Bin2,..., BinN. The first data close to the same respiratory phase are assigned to the same sampling template, and multiple first data at the same k-space position are averaged to obtain second data of multiple different respiratory phases.

[0061] S1033. Use the iterative reconstruction algorithm with regularization constraint to perform image reconstruction on the second data of each respiratory phase to obtain reconstructed images of different respiratory phases.

[0062] For the second data of each respiratory phase, use the iterative reconstruction algorithm with regularization constraint to perform image reconstruction. Exemplarily, the iterative reconstruction algorithm formula is as follows:

[0063]

[0064] Wherein, A represents the MR system matrix, including the Fourier operator, the sampling matrix, etc.; represents the reconstructed image after each respiratory phase division; W n is the weight matrix of the nth respiratory phase (0 < W n < 1), and the specific value of W n can be flexibly designed according to actual needs and is not specifically limited herein. K n is the first data after one-dimensional vertical motion displacement correction and respiratory phase division. The regularization term R(I n) is a penalty term, which can be a constraint on the data space or on the image domain, such as TV regularization constraint, wavelet sparsity constraint, etc. Then, use the corresponding convergence algorithm of fast iteration to solve and obtain the thoracic aortic wall images of each respiratory phase.

[0065] S1034. Register and sum the reconstructed images of different respiratory phases to correct the non-rigid respiratory motion artifacts.

[0066] Exemplarily, select the reconstructed image of any one respiratory phase, perform affine registration with the reconstructed images of other respiratory phases, and sum the reconstructed images of each respiratory phase after registration to complete the correction of non-rigid respiratory motion artifacts. Thus, fast imaging of the thoracic aortic wall under free breathing of the measured object is realized, and the generated thoracic aortic wall images provide guarantee for the detection of aortic atherosclerosis and plaques.

[0067] Compare the thoracic aortic wall images generated by using traditional diaphragmatic navigation imaging with the thoracic aortic wall images generated by using the method for generating thoracic aortic wall images provided in the embodiments of the present invention, as Figure 10 shown, Figure 10 (a) is the thoracic aortic wall image generated by using traditional diaphragmatic navigation imaging, the acquisition window is 1 mm, the acquisition efficiency is 35%, and the acquisition time is 7 minutes; Figure 10 (b) is the thoracic aortic wall image generated by using the method for generating thoracic aortic wall images provided in the embodiments of the present invention, and the acquisition time is 4.5 minutes; Figure 10 (c) is the uncorrected thoracic aortic wall image. From the results, the thoracic aortic wall images generated by using the method for generating thoracic aortic wall images provided in the embodiments of the present invention effectively suppress the respiratory motion artifacts on the premise of improving the sampling efficiency, and the thoracic aortic wall is brighter and the sharpness is clearer.

[0068] The method for generating thoracic aortic wall images provided in the above embodiments, by obtaining the imaging data corresponding to the measured object, generating a blurred aliased image of the thoracic aortic wall with respiratory motion artifacts according to the imaging data, determining the respiratory motion information corresponding to the measured object according to the self-navigation information collected, and correcting the respiratory motion artifacts of the blurred aliased image of the thoracic aortic wall based on the respiratory motion information, to obtain the thoracic aortic wall image corresponding to the measured object. Thus, fast imaging of the thoracic aortic wall under free breathing of the measured object is realized, and the generated thoracic aortic wall images are accurate and effective, providing guarantee for the detection of aortic atherosclerosis and plaques, etc.

[0069] Please refer to Figure 11 , Figure 11 which is a schematic block diagram of a device for generating thoracic aortic wall images provided in an embodiment of the present application.

[0070] As shown Figure 11 in the figure, the thoracic aortic wall image generation device may include a processor, a memory, and a network interface. The processor, the memory, and the network interface are connected through a system bus, such as an I2C (Inter-integrated Circuit) bus.

[0071] Specifically, the processor may be a micro-control unit (MCU), a central processing unit (CPU), a digital signal processor (DSP), or the like.

[0072] Specifically, the memory may be a Flash chip, a read-only memory (ROM), a magnetic disk, an optical disc, a USB flash drive, or a mobile hard disk, etc.

[0073] The network interface is used for network communication, such as sending the assigned tasks, etc. Those skilled in the art can understand that Figure 11 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the thoracic aortic wall image generation device to which the solution of this application is applied. The specific thoracic aortic wall image generation device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0074] Among them, the processor is used to run the computer program stored in the memory and, when executing the computer program, implement the following steps:

[0075] Obtain the imaging data corresponding to the object to be measured;

[0076] According to the imaging data, generate a blurred aliased image of the thoracic aortic wall with respiratory motion artifacts, and determine the respiratory motion information corresponding to the object to be measured according to the collected self-navigation information;

[0077] Based on the respiratory motion information, correct the respiratory motion artifacts of the blurred aliased image of the thoracic aortic wall to obtain the thoracic aortic wall image corresponding to the object to be measured.

[0078] In some embodiments, the imaging data includes multiple segment imaging data. When the processor implements generating a blurred aliased image of the thoracic aortic wall with respiratory motion artifacts according to the imaging data, it specifically implements:

[0079] According to a plurality of preset different sampling templates, fill each segment imaging data into the different sampling templates in turn to obtain a plurality of the blurred aliased images of the thoracic aortic wall.

[0080] In some embodiments, when the processor implements filling each piece of the segment imaging data into the different sampling templates in sequence, the specific implementation is as follows:

[0081] Using a Cartesian k-space pattern under a golden-angle spiral sampling trajectory, undersample and fill each piece of the segment imaging data into the different sampling templates.

[0082] In some embodiments, the respiratory motion information includes a respiratory motion curve. When the processor implements determining the respiratory motion information corresponding to the object under test according to the collected self-navigated information, the specific implementation is as follows:

[0083] Perform a Fourier transform on the self-navigated information to obtain a corresponding respiratory self-navigated projection;

[0084] Obtain the respiratory motion curve according to the respiratory self-navigated projection.

[0085] In some embodiments, before the processor implements determining the respiratory motion information corresponding to the object under test according to the collected self-navigated information, the specific implementation is as follows:

[0086] Collect the echo signal corresponding to the imaging data during the systolic phase of the heart of the object under test;

[0087] According to the collected initial echo signal, read out along the readout gradient direction to obtain the self-navigated information.

[0088] In some embodiments, when the processor implements correcting the respiratory motion artifact of the blurred aliased image of the thoracic aortic wall based on the respiratory motion information, the specific implementation is as follows:

[0089] According to the respiratory motion information, perform up-and-down motion displacement correction on the image data corresponding to the blurred aliased image of the thoracic aortic wall to obtain first data after up-and-down motion displacement correction;

[0090] Perform respiratory phase processing on the first data to obtain second data of multiple different respiratory phases;

[0091] Use an iterative reconstruction algorithm with regularization constraints to perform image reconstruction on the second data of each respiratory phase to obtain reconstructed images of different respiratory phases;

[0092] Perform registration and summation on the reconstructed images of different respiratory phases to correct non-rigid respiratory motion artifacts.

[0093] In some embodiments, when the processor implements performing registration and summation on the reconstructed images of different respiratory phases to correct non-rigid respiratory motion artifacts, the specific implementation is as follows:

[0094] Select any one of the reconstructed images of a respiratory phase, perform affine registration with the reconstructed images of other respiratory phases, and sum the reconstructed images of each respiratory phase after registration to correct the respiratory motion artifacts.

[0095] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the thoracic aortic wall image generation device described above can refer to the corresponding process in the foregoing embodiment of the thoracic aortic wall image generation method, and will not be repeated here.

[0096] An embodiment of the present application also provides a computer device, which may include the above-mentioned thoracic aortic wall image generation device. The specific working process of this computer device can refer to the corresponding process in the foregoing embodiment of the thoracic aortic wall image generation method, and will not be repeated here.

[0097] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions, and the processor executes the program instructions to implement the steps of the thoracic aortic wall image generation method provided in the foregoing embodiment. For example, when this computer program is loaded by the processor, the following steps can be executed:

[0098] Obtain imaging data corresponding to the object to be measured;

[0099] Generate a blurred and aliased image of the thoracic aortic wall with respiratory motion artifacts according to the imaging data, and determine the respiratory motion information corresponding to the object to be measured according to the collected self-navigation information;

[0100] Based on the respiratory motion information, correct the respiratory motion artifacts of the blurred and aliased image of the thoracic aortic wall to obtain the thoracic aortic wall image corresponding to the object to be measured.

[0101] For the specific implementation of each of the above operations, reference can be made to the previous embodiments, and details will not be repeated here.

[0102] Among them, the computer-readable storage medium may be an internal storage unit of the computer device in the foregoing embodiment, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0103] Since the computer program stored in the computer-readable storage medium can execute any one of the thoracic aortic wall image generation methods provided by the embodiments of the present application, the beneficial effects achievable by any one of the thoracic aortic wall image generation methods provided by the embodiments of the present application can be realized. For details, refer to the previous embodiments and will not be elaborated herein.

[0104] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments. The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for generating an image of the thoracic aortic wall, characterized in that, Including: Obtaining imaging data corresponding to an object to be measured; Generating a blurred aliased image of the thoracic aortic wall with respiratory motion artifacts based on the imaging data, and determining respiratory motion information corresponding to the object to be measured according to the collected self-navigated information; Correcting the respiratory motion artifacts of the blurred aliased image of the thoracic aortic wall based on the respiratory motion information to obtain a thoracic aortic wall image corresponding to the object to be measured; Wherein, correcting the respiratory motion artifacts of the blurred aliased image of the thoracic aortic wall based on the respiratory motion information includes: Performing up-and-down motion displacement correction on the image data corresponding to the blurred aliased image of the thoracic aortic wall according to the respiratory motion information to obtain first data after up-and-down motion displacement correction; Performing phase-based respiration processing on the first data to obtain second data of multiple different respiratory phases; Performing image reconstruction on the second data of each respiratory phase by using an iterative reconstruction algorithm with regularization constraint to obtain reconstructed images of different respiratory phases; Performing registration and summation on the reconstructed images of different respiratory phases to correct non-rigid respiratory motion artifacts.

2. The method according to claim 1, characterized in that, The imaging data includes multiple segment imaging data. Generating a blurred aliased image of the thoracic aortic wall with respiratory motion artifacts based on the imaging data includes: Sequentially filling each segment imaging data into the different sampling templates according to a plurality of preset different sampling templates to obtain multiple blurred aliased images of the thoracic aortic wall.

3. The method according to claim 2, wherein The sequentially filling each segment imaging data into the different sampling templates includes: Under-sampling and filling each segment imaging data into the different sampling templates by using a Cartesian k-space mode with a golden angle spiral sampling trajectory.

4. The method according to claim 1, wherein The respiratory motion information includes a respiratory motion curve. Determining the respiratory motion information corresponding to the object to be measured according to the collected self-navigated information includes: Performing Fourier transform on the self-navigated information to obtain a corresponding respiratory self-navigated projection; Obtaining the respiratory motion curve according to the respiratory self-navigated projection.

5. The method according to claim 1, wherein Before determining the respiratory motion information corresponding to the object to be measured according to the collected self-navigated information, it includes: Collecting echo signals corresponding to the imaging data during the cardiac systolic phase of the object to be measured; Reading out the self-navigated information along the readout gradient direction according to the collected initial echo signals.

6. The method according to claim 1, characterized in that, Performing registration and summation on the reconstructed images of different respiratory phases to correct non-rigid respiratory motion artifacts includes: Selecting the reconstructed image of any one respiratory phase, performing affine registration with the reconstructed images of other respiratory phases, and summing the reconstructed images of each respiratory phase after registration to correct the respiratory motion artifacts.

7. An image generation device for the thoracic aortic wall, characterized in that, The thoracic aortic wall image generation device includes a processor, a memory, and a computer program stored on the memory and executable by the processor. When the computer program is executed by the processor, it implements the thoracic aortic wall image generation method according to any one of claims 1 to 6.

8. A computer device, characterized in that, The computer device includes the thoracic aortic wall image generation device according to claim 7.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the processor is caused to implement the method for generating an image of the thoracic aortic wall according to any one of claims 1 to 6.

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