Spectrum-based data-based silhouette black blood imaging method, system, equipment and medium

Abstract

The invention belongs to the technical field of medical image processing, and discloses a silhouette black blood imaging method, system, equipment and medium based on spectrum-based data, and the method comprises the steps: collecting a computed tomography image of a human blood vessel to obtain corresponding spectrum-based data, and reconstructing and storing a single-level image and a virtual plain scanning image based on the spectrum-based data; the stored single-level image and the virtual plain-scan image are processed through the image silhouette technology, and black blood imaging is generated through image registration and silhouette merging operation. Spectral base data and the CT silhouette technology are combined, high-density blood flow signals in a blood vessel cavity can be removed, the display definition of the blood vessel wall is improved, and the blood vessel imaging quality is improved. Therefore, a more accurate image basis is provided for clinicians, and recognition and evaluation of vascular wall lesions are facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical image processing, in particular to a silhouette black-blood imaging method, system, device and medium based on spectral basis data. BACKGROUND

[0002] Studies have shown that arterial stenosis is the main cause of ischemic stroke in the population, accounting for about 46.6% of cases. However, traditional vascular imaging techniques, such as CTA (computed tomography angiography), MRA (magnetic resonance angiography), etc., mainly focus on the visualization of the vascular lumen, and cannot comprehensively and accurately assess the pathological features and lesion conditions of the vessel wall. In particular, in atherosclerotic lesions, the size, shape, signal intensity and enhancement characteristics of the plaque are closely related to the vulnerability of the plaque, and these features cannot be obtained by simple lumen imaging. Therefore, relying solely on traditional vascular imaging techniques makes it difficult to provide comprehensive pathological information for the clinic, thereby limiting the accurate diagnosis of the cause and the development of individualized treatment strategies.

[0003] The treatment strategy and prognosis of arterial stenosis usually depend on the type of vessel wall lesions. Different types of vessel wall lesions have significant differences in treatment methods and clinical prognosis, so it is crucial to determine the cause of arterial stenosis before treatment, especially to accurately judge the pathological features of the vessel wall, in order to guide clinical treatment.

[0004] Magnetic resonance black-blood imaging (Black-blood MRI) was initially applied to the evaluation of arterial plaques, which can accurately analyze the morphology and signal characteristics of the arterial wall, and is an ideal non-invasive method for diagnosing arterial wall lesions. Compared with traditional imaging techniques, magnetic resonance black-blood imaging provides higher vessel wall contrast and can directly assess structural changes in the vessel wall. However, there are still certain limitations in the practical application of magnetic resonance black-blood imaging, such as limited spatial resolution, small coverage range of two-dimensional imaging, long time consumption of three-dimensional imaging, and incomplete blood signal suppression, which affect its effectiveness and efficiency in clinical application.

[0005] Therefore, how to provide a silhouette black-blood imaging method, system, device and medium based on spectral basis data is a problem that needs to be solved at present. SUMMARY

[0006] The embodiments of the present application provide a silhouette black-blood imaging method, system, device and medium based on spectral basis data to solve the above technical problems existing in the prior art.

[0007] The following presents a simplified summary of some aspects of the disclosed embodiments in order to provide a basic understanding of such embodiments. This summary is not an extensive overview of the embodiments and is intended neither to identify key / critical elements of the embodiments nor to delineate the scope of the embodiments. Its sole purpose is to present some concepts of the embodiments in a simplified form as a prelude to the more detailed description that is presented later.

[0008] According to a first aspect of embodiments of the present application, a method for spectral-based data shadow black-blood imaging is provided.

[0009] In one embodiment, the method for spectral-based data shadow black-blood imaging comprises:

[0010] acquiring computed tomography images of human blood vessels to obtain corresponding spectral-based data, reconstructing and storing monochromatic images and virtual plain scan images based on the spectral-based data;

[0011] processing the stored monochromatic images and virtual plain scan images using image shadowing technology, and generating black-blood images through image registration and shadowing merging operations.

[0012] In one embodiment, the acquiring computed tomography images of human blood vessels to obtain corresponding spectral-based data, reconstructing and storing monochromatic images and virtual plain scan images based on the spectral-based data comprises:

[0013] integrating spectral data packets into a computed tomography angiography scanning protocol, performing spectral scanning, acquiring computed tomography images of blood vessels of a target patient, and obtaining corresponding spectral-based data;

[0014] in a post-processing workstation, selecting a spectral-based data set of the target patient, and performing batch processing to reconstruct monochromatic images and virtual plain scan images;

[0015] transmitting and saving the generated monochromatic images and virtual plain scan images to a local storage device.

[0016] In one embodiment, the spectral-based data comprises computed tomography images and spectral results.

[0017] In one embodiment, the in a post-processing workstation, selecting a spectral-based data set of the target patient, and performing batch processing to reconstruct monochromatic images and virtual plain scan images comprises:

[0018] in a post-processing workstation, selecting a spectral-based data set of the target patient corresponding to the obtained spectral-based data;

[0019] using spectral data processing software to switch image sequences of the selected spectral-based data set to obtain image views of different energy levels;

[0020] According to the acquired image view, an image sequence needing to be processed is selected, a batch processing operation is performed to perform image reconstruction on the image sequence, and a single-energy level image and a virtual plain scan image are generated.

[0021] In one embodiment, the parameters of the batch processing include layer thickness, layer spacing, and layer number.

[0022] In one embodiment, the stored single-energy level image and the virtual plain scan image are processed by using an image clipping technique, a black blood imaging is generated by image registration and clipping merging operation, and the black blood imaging includes:

[0023] Based on the stored single-energy level image and the virtual plain scan image, an image sequence needing to be processed by clipping is selected;

[0024] The selected image sequence is imported into a computed tomography browser, an image registration is performed on the selected image sequence according to a Z-axis position by using an image integration tool built in the computed tomography browser;

[0025] The image sequence after the image registration is checked to ensure that the single-energy image and the virtual plain scan image are alternately arranged at the same Z-axis position;

[0026] According to the checking result, a clipping merging mode is selected by using an image clipping tool, the image sequence after the image registration is processed by clipping, and a black blood imaging is generated.

[0027] In one embodiment, the clipping merging mode includes summation, selection of minimum value, and selection of maximum value.

[0028] According to a second aspect of an embodiment of the present application, a clipping black blood imaging system based on spectral base data is provided.

[0029] In one embodiment, the clipping black blood imaging system based on spectral base data includes:

[0030] A data acquisition and image reconstruction module is configured to acquire a computed tomography image of a human blood vessel to obtain corresponding spectral base data, reconstruct a single-energy level image and a virtual plain scan image based on the spectral base data, and store the single-energy level image and the virtual plain scan image;

[0031] A black blood imaging generation module is configured to process the stored single-energy level image and the virtual plain scan image by using an image clipping technique, generate a black blood imaging by image registration and clipping merging operation.

[0032] According to a third aspect of an embodiment of the present application, a computer device is provided.

[0033] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0034] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided.

[0035] In one embodiment, the computer readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above method.

[0036] The technical solutions provided by the embodiments of the present application can include the following beneficial effects:

[0037] The present application can remove the high-density blood flow signal in the blood vessel lumen by combining the spectral-based data with the CT shadow technology, improve the display clarity of the blood vessel wall, and thus provide more accurate image basis for the clinicians, which is helpful for the identification and evaluation of the blood vessel wall lesions.

[0038] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0039] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application.

[0040] Figure 1 is a flow chart of a spectral-based data-based shadow black blood imaging method according to an exemplary embodiment;

[0041] Figure 2 is a schematic block diagram of a spectral-based data-based shadow black blood imaging system according to an exemplary embodiment;

[0042] Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment;

[0043] Figure 4 is an operation flow chart of a spectral-based data-based shadow black blood imaging method according to an exemplary embodiment;

[0044] Figure 5 is a spectral data processing software diagram in a spectral-based data-based shadow black blood imaging method according to an exemplary embodiment;

[0045] Figure 6 is a spectral data processing software diagram in a spectral-based data-based shadow black blood imaging method according to an exemplary embodiment;

[0046] Figure 7 is an image sequence switching diagram in a spectral-based data-based shadow black blood imaging method according to an exemplary embodiment;

[0047] Figure 8 is a batch processing options diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0048] Figure 9 is a batch processing flow diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0049] Figure 10 is a CT browser diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0050] Figure 11 is an image integration tool diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0051] Figure 12 is an image integration tool click diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0052] Figure 13 is an image configuration diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0053] Figure 14 is an image configuration check diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0054] Figure 15 is an image silhouette tool diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0055] Figure 16 is a silhouette merge mode diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0056] Figure 17 is a black-blood imaging effect diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment;

[0057] Figure 18 is a black-blood imaging contrast diagram in a spectral-based data silhouette black-blood imaging method according to an example embodiment. DETAILED DESCRIPTION

[0058] The following description and drawings are illustrative of specific embodiments thereof and are not intended to limit the scope of the embodiments. Parts and features of some embodiments can be included or substituted in or for parts and features of other embodiments. The scope of the embodiments encompassed herein includes the whole scope of the claims together with all available equivalents of the claims. In this document, the terms "first", "second", etc. are used merely to distinguish one element from another, and do not require or imply any actual relationship or order between the elements. In fact, the first element can be referred to as the second element, and vice versa. Also, the terms "comprises", "comprising", or any other variations thereof are intended to cover a non-exclusive inclusion, such that a structure, device, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such structure, device, or apparatus. Without further limitation, an element defined by an "includes a" statement does not exclude the presence of additional identical elements in the structure, device, or apparatus that includes the element. Various embodiments are described in progressive stages, each of which focuses on the differences from other embodiments, and the same or similar parts between various embodiments can be referred to each other.

[0059] The terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, as used herein to indicate orientation or positional relationships based on the orientations or positional relationships shown in the drawings, are used only for convenience and are not intended to imply or suggest that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In the description herein, unless otherwise specified and limited, the terms "mount", "connect", "connection" should be understood broadly, for example, can be mechanical connection or electrical connection, can be internal communication of two elements, can be direct connection, or indirect connection through intermediate medium, and the specific meaning of the above terms can be understood by the person skilled in the art according to the specific circumstances.

[0060] In this document, the term "multiple" means two or more, unless otherwise specified.

[0061] In this document, the character " / " represents an "or" relationship between the objects before and after it. For example, A / B means A or B.

[0062] In this document, the term "and / or" is a description of the relationship between the objects, which means that there can be three relationships. For example, A and / or B means that there are three relationships of A or B, or A and B.

[0063] It should be understood that although the steps in the flowchart are shown in a sequential order, the steps need not be performed in the order shown by the arrows. Unless explicitly stated otherwise, the steps can be performed in any order, and the steps need not be performed in the order shown. Moreover, at least some of the steps shown in the figure can include multiple sub-steps or multiple stages, which need not be performed in a same order, and can be performed at different times, and need not be performed sequentially, but can be performed at least partially in parallel or in an interleaved manner.

[0064] The modules in the device or system of the present application can be implemented wholly or partially by software, hardware and combinations thereof. The modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the modules.

[0065] The embodiments in the present application and the features in the embodiments can be combined with each other without conflicts.

[0066] Figure 1 An embodiment of the present application is shown, which is a method for silhouette black-blood imaging based on spectral data.

[0067] In this optional embodiment, the method for silhouette black-blood imaging based on spectral data comprises:

[0068] In step S101, a computed tomography image of a human blood vessel is acquired to obtain corresponding spectral data, and a mono-energetic image and a virtual plain scan image are reconstructed and stored based on the spectral data;

[0069] In step S102, the stored mono-energetic image and virtual plain scan image are processed by using an image silhouette technology, and a black-blood image is generated through image registration and silhouette merging operations.

[0070] In this optional embodiment, the acquisition of the computed tomography image of the human blood vessel to obtain corresponding spectral data, and the reconstruction and storage of the mono-energetic image and the virtual plain scan image based on the spectral data comprise:

[0071] The spectral data packet is integrated into a computed tomography angiography scanning protocol, a spectral scan is performed, a computed tomography image of a blood vessel of a target patient is acquired, and corresponding spectral data is obtained;

[0072] In a post-processing workstation, a spectral data set of the target patient is selected, and batch processing is performed to reconstruct and generate a mono-energetic image and a virtual plain scan image;

[0073] The generated single-energy level images and the virtual plain scan images are transmitted and saved to a local storage device.

[0074] In this optional embodiment, the spectral base data includes computed tomography images and spectral results.

[0075] In this optional embodiment, in the post-processing workstation, the spectral base data set of the target patient is selected, and batch processing is performed to reconstruct the generated single-energy level images and the virtual plain scan images, which includes:

[0076] Based on the acquired spectral base data, the spectral base data set corresponding to the target patient is selected in the post-processing workstation;

[0077] The selected spectral base data set is subjected to image sequence switching using spectral data processing software to obtain image views of different energy levels;

[0078] According to the obtained image views, the image sequence that needs to be processed is selected, and batch processing is performed to reconstruct the image sequence to generate single-energy level images and virtual plain scan images.

[0079] In this optional embodiment, the parameters of the batch processing include layer thickness, layer spacing, and number of layers.

[0080] In this optional embodiment, the stored single-energy level images and virtual plain scan images are processed using image clipping technology, and black blood imaging is generated through image registration and clipping merging operations, which includes:

[0081] Based on the stored single-energy level images and virtual plain scan images, the image sequence that needs to be subjected to clipping processing is selected;

[0082] The selected image sequence is imported into a computed tomography browser, and an image integration tool built-in is used to perform image registration on the selected image sequence according to the Z-axis position;

[0083] The image sequence after image registration is checked to ensure that the single-energy images and the virtual plain scan images are arranged alternately at the same Z-axis position;

[0084] According to the checking result, a clipping merging mode is selected using an image clipping tool to perform clipping processing on the image sequence after image registration to generate black blood imaging.

[0085] In this optional embodiment, the clipping merging mode includes summation, selection of minimum value, and selection of maximum value.

[0086] It should be noted that, as shown in Figure 4 In specific embodiments, the spectral clipping black blood imaging method based on spectral base data includes:

[0087] Step 1: Obtain the spectral base data of human blood vessel CT enhancement, i.e., the SBI data package.

[0088] SBI stands for Spectral Base Images. This special raw data sequence can be used to reconstruct various spectral results in spectral applications. The SBI package contains 12 major categories of spectral results and can be viewed and processed simultaneously on the host computer, post-processing workstation, and PACS. The SBI package contains conventional CT images and various spectral results, including: Virtual Monolevel Images (MonoE), Virtual Non-Nuclear Scan Images (VNC), Iodine No Water Images, Iodine Density Images, Z Effective Atomic Number Images, Iodine Removed Images, Uric Acid Removed Images, Uric Acid Images, and Contrast-Enhanced Structures, among 12 other major categories. These can be directly accessed to reconstruct multi-parameter spectral images. Figure 5 As shown, by adding a spectral data packet to the standard CTA scanning protocol, SBI data can be automatically generated upon completion of the scan.

[0089] Step 2: Using the SBI data packet, reconstruct a set of single-level images and a set of virtual flat scan images.

[0090] 1. In the patient directory, select the SBI data package under the patient; in the upper left corner of the Philips post-processing workstation, select and enter Spectral CTViewer (SBI data package browsing and processing software), such as... Figure 6 As shown.

[0091] 2. Click on the sequence name Conventional (HU) to switch to Mono E (HU) (Equiv.toconventional CT), as shown below. Figure 7 As shown; selecting Series will bring up a corresponding drop-down menu. Select the Batch option, as shown. Figure 8 As shown; perform thick-layer batch processing of images to ensure that the layer thickness, interlayer spacing, and number of layers in the reconstructed spectral data are completely consistent, such as... Figure 9 As shown; after completing the image batch processing, the batch processing needs to be transferred to Local (the name of the local memory) for storage; the virtual scan image can be reconstructed by repeating the above steps.

[0092] Step 3: Using silhouette technology, load the two sequences that need to be subtracted into CTviewer, such as... Figure 9As shown; need to integrate two sequences together in 2D first, as shown Figure 10 As shown; click the integration tool, as shown Figure 11 As shown; in the pop-up settings dialog box, select Mix series (Zpos.), as shown Figure 12 As shown; click OK to confirm integration, observe the integrated sequence, find that each single energy level image with the same Z-axis position is arranged together with each virtual plain scan image, and when dynamically browsing, it is equivalent to the plain scan image and the enhanced image appearing alternately, as shown Figure 13 As shown; after completing the above sequence integration and dynamic browsing inspection without error, click the subtraction tool to select each two images of the integrated sequence for merging, as shown Figure 14

[0093] Among them, there are three ways to merge:

[0094] (1) Sum: generally used to generate subtraction images;

[0095] (2) Take the minimum value: generally used to generate minimum density images;

[0096] (3) Take the maximum value: generally used to generate maximum density images;

[0097] Select the first one: sum (Sum), set the weight and bias of the two sequences, as shown Figure 15 As shown, this realizes the subtraction of the images, and since this subtraction method can be perfectly matched in spatial alignment, it is called a "rigid algorithm", and its biggest advantage is high accuracy and only needs to be based on one scan to reduce the radiation dose by 50%; As shown Figure 16 When the weights are selected as-0.5 and 1.0 respectively (modify the weight index according to the patient image to achieve the best black blood imaging), the blood vessel wall imaging will be further highlighted, as shown Figure 17 As shown.

[0098] In addition, since there are some pain points in magnetic resonance black blood imaging: long scanning time, large blood vessel pulsation artifacts, uneven blood signal suppression, etc., the spectral CT image generated by the present application achieves the same effect as magnetic resonance black blood imaging, as shown Figure 18 As shown; compared with magnetic resonance, spectral CT shadow black blood imaging has shorter time, more uniform blood suppression (blue arrow), smaller blood vessel pulsation artifacts, and the same ability to display plaque distribution (red arrow) and vessel wall lesion properties (blue arrow) as magnetic resonance.

[0099] Figure 2 An embodiment of the spectral-based data shadow black blood imaging system of the present application is shown.

[0100] ​In the optional embodiment, the system is a shadow black-blood imaging system based on spectral basis data, and the system comprises:

[0101] The data acquisition and image reconstruction module 201 is configured to acquire a computed tomography image of a human blood vessel to obtain corresponding spectral basis data, reconstruct a single energy level image and a virtual plain scan image based on the spectral basis data, and store the single energy level image and the virtual plain scan image.

[0102] The black-blood imaging generation module 202 is configured to process the stored single energy level image and virtual plain scan image by using an image shadowing technology, generate black-blood imaging through image registration and shadowing merging operations.

[0103] In one embodiment, a computer device, which can be a server, has an internal structure as shown in Figure 3 The computer device comprises a processor, a memory and a network interface connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises 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 running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store static information and dynamic information data. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the steps in the above method embodiments.

[0104] Those skilled in the art can understand that Figure 3 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. A specific computer device can comprise more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0105] In addition, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0106] In addition, the present application also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0107] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in each embodiment of the present 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 or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0108] The present application is not limited to the structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is only limited by the appended claims.

Claims

1. A method of silhouetted black-blood imaging based on spectral basis data, characterized in that, The method comprises: Collecting computer tomography images of human blood vessels to obtain corresponding spectral base data, reconstructing and storing single-energy level images and virtual plain scan images based on the spectral base data; Using image clipping technology to process the stored single-energy level images and virtual plain scan images, generating black blood imaging through image registration and clipping merging operations.

2. The spectral-based data based silhouetted black-blood imaging method of claim 1, wherein, The collecting computer tomography images of human blood vessels to obtain corresponding spectral base data, reconstructing and storing single-energy level images and virtual plain scan images comprises: Integrating the spectral data packet into a computer tomography angiography scanning protocol, performing spectral scanning, collecting computer tomography images of blood vessels of a target patient, and obtaining corresponding spectral base data; In a post-processing workstation, selecting a spectral base data set of the target patient and performing batch processing to reconstruct and generate single-energy level images and virtual plain scan images; Transferring and saving the generated single-energy level images and virtual plain scan images to a local storage device.

3. The spectral -based data based silhouetted black-blood imaging method of claim 2, wherein, The spectral base data comprises computer tomography images and spectral results.

4. The spectral -based data based silhouetted black-blood imaging method of claim 3, wherein, The selecting a spectral base data set of the target patient in the post-processing workstation and performing batch processing to reconstruct and generate single-energy level images and virtual plain scan images comprises: In the post-processing workstation, selecting a spectral base data set corresponding to the target patient based on the obtained spectral base data; Using spectral data processing software to switch image sequences of the selected spectral base data set to obtain image views of different energy levels; According to the obtained image views, selecting image sequences to be processed, performing batch processing to reconstruct the image sequences, and generating single-energy level images and virtual plain scan images.

5. The spectral -based data based silhouetted black-blood imaging method of claim 4, wherein, The parameters of the batch processing comprise layer thickness, layer spacing, and layer number.

6. The spectral -based data based silhouetted black-blood imaging method of claim 1, wherein, The using image clipping technology to process the stored single-energy level images and virtual plain scan images, generating black blood imaging through image registration and clipping merging operations comprises: Based on the stored single-energy level images and virtual plain scan images, selecting image sequences to be processed for clipping; Importing the selected image sequences into a computer tomography browser, using a built-in image integration tool to perform image registration on the selected image sequences according to Z-axis positions; Checking the image sequences after image registration to ensure that the single-energy level images and the virtual plain scan images are alternately arranged at the same Z-axis positions; According to the checking result, using an image clipping tool to select a clipping merging mode, performing clipping processing on the image sequences after image registration to generate black blood imaging.

7. The spectral -based data based silhouetted black-blood imaging method of claim 6, wherein, The clipping merging mode comprises summation, selecting a minimum value, and selecting a maximum value.

8. A system for silhouetted black-blood imaging based on spectral basis data, characterized in that, The system comprises: A data acquisition and image reconstruction module for collecting computer tomography images of human blood vessels to obtain corresponding spectral base data, reconstructing and storing single-energy level images and virtual plain scan images based on the spectral base data; A black blood imaging generation module for using image clipping technology to process the stored single-energy level images and virtual plain scan images, generating black blood imaging through image registration and clipping merging operations. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, which is executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

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