CT system, method, and storage medium for flow field structure detection

Abstract

The application discloses a flow field structure non-interference measurement system. The system comprises at least two imaging assemblies, the imaging assembly comprising a ray output structure for relative arrangement and outputting X rays and a detector for receiving the X rays passing through a flow field to be measured to obtain projection data; the at least two imaging assemblies are distributed on at least two groups of edges of a virtual polygon, and each group of edges is distributed oppositely; the imaging assembly is used for obtaining the projection data of the flow field to be measured arranged inside the virtual polygon; and a processor is used for controlling the at least two imaging assemblies to simultaneously perform X ray scanning on the flow field to be measured to obtain at least two groups of projection data, and performing image reconstruction on the at least two groups of projection images to obtain a target image of the flow field to be measured. The embodiment of the application can reduce the interference on dynamic imaging of the measured flow field, and realize instantaneous imaging of the dynamic evolution structure of the multiphase flow field.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of measurement, in particular to a CT system for flow field structure detection. BACKGROUND

[0002] CT is to reconstruct inverse high-dimensional structure information by using multi-angle low-dimensional measurement information, to realize non-destructive structure reconstruction of the measured object in two dimensions / three dimensions or even four dimensions. CT has wide application in medical imaging, industrial non-destructive testing, process dynamic measurement and many other fields.

[0003] For a dynamically evolving measured object, the time-averaged imaging characteristics of CT result in motion artifacts, and existing industrial CT cannot obtain high-quality flow field CT images. Existing medical CT has made important progress in rapid monitoring, and proposed EBCT scheme based on scanning electron beams and five-generation CT scheme with multiple multi-probes, but medical CT is mainly for human body, and the device is large, complex in structure and high in cost, which is not suitable for industrial non-destructive testing.

[0004] In summary, in the field of flow field detection, the existing CT technology at least has the problem of being unable to balance the time resolution, device cost and image quality. SUMMARY

[0005] The present application provides a CT system for flow field structure detection, which solves the problem of the existing CT technology in the field of flow field detection, at least being unable to balance the time resolution, device cost and image quality.

[0006] In one aspect, the present application discloses a CT system for flow field structure detection, comprising:

[0007] At least two imaging assemblies, the imaging assembly comprising a ray output structure for relative arrangement and outputting X-rays and a detector for receiving X-rays passing through the measured flow field to obtain projection data; the at least two imaging assemblies are distributed on at least two groups of edges of a virtual polygon, and each group of edges is distributed oppositely; the imaging assembly is used for obtaining the projection data of the measured flow field arranged inside the virtual polygon;

[0008] A processor is configured to control the at least two imaging assemblies to simultaneously perform X-ray scanning on the measured flow field to obtain at least two groups of projection data, and to perform image reconstruction on the at least two groups of projection images to obtain a target image of the measured flow field.

[0009] In another aspect, the present application discloses a flow field structure determination method, comprising:

[0010] Obtaining at least two groups of projection data of the measured flow field according to any one of the embodiments;

[0011] input the at least two groups of projection data into the trained image processing model to obtain a detection result of the to-be-detected flow field.

[0012] In another aspect, the embodiments of the present application disclose a storage medium containing computer executable instructions, which when executed by a computer processor, are used to perform the flow field structure determination method of any of the embodiments.

[0013] Compared with the prior art, the technical solution provided by the embodiments of the present application can simultaneously obtain at least two groups of projection data of the to-be-detected flow field arranged inside the virtual polygon through at least two groups of imaging components distributed on at least two groups of edges of the virtual polygon and each group of edges is relatively distributed, and the target image of the to-be-detected flow field can be obtained based on the at least two groups of projection data, so that the to-be-detected flow field is simultaneously and multi-point and multi-angle ray detected, and the system has simple structure and low manufacturing cost, and can solve the problem of low time resolution of industrial CT.

[0014] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 is a structural schematic diagram of a CT system for flow field structure detection according to the embodiments of the present application;

[0017] Figure 2 is a control block diagram of a CT system for flow field structure detection according to the embodiments of the present application;

[0018] Figure 3 is a structural schematic diagram of another CT system for flow field structure detection according to the embodiments of the present application;

[0019] Figure 4 is a flow chart of a flow field structure determination method according to the embodiments of the present application. DETAILED DESCRIPTION

[0020] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort should fall within the protection scope of the present application.

[0021] It should be noted that the terms "first", "second", and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product, or device.

[0022] Embodiments

[0023] Figure 1 An CT system for flow field structure detection is provided in the embodiments of the present application. As shown in Figure 1 and Figure 2 , the CT system for flow field structure detection disclosed in the embodiments can include a processor 13 and at least two imaging assemblies; the imaging assembly includes a ray output structure 11 for opposite arrangement and for outputting X-rays and a detector 12 for receiving X-rays passing through a flow field to be detected to obtain projection data; the at least two imaging assemblies are distributed on at least two groups of edges of a virtual polygon, and each group of edges is oppositely distributed; the imaging assembly is used to obtain projection data of the flow field to be detected 2 arranged on the inside of the virtual polygon; the processor 13 is used to control the at least two imaging assemblies to simultaneously perform X-ray scanning on the flow field to be detected to obtain at least two sets of projection data, and to perform image reconstruction on the at least two sets of projection images to obtain a target image of the flow field to be detected.

[0024] Wherein, the flow field is the spatial distribution of fluid motion at a certain moment.

[0025] As shown in Figure 1 , the imaging assembly includes the oppositely arranged ray output structure 11 and the detector 12, wherein the ray output structure 11 is preferably a linear ray source array, which can use an existing ray source, such as a field emission signal source array; correspondingly, the detector is preferably a linear multi-channel detector.

[0026] In one embodiment, the virtual regular polygon has an even number of sides, and is configured as a virtual square, a virtual regular hexagon, or the like. The virtual square is preferred. The imaging assembly is arranged on at least two groups of sides of the virtual regular polygon, and each group of sides is oppositely distributed, which can simplify the system structure and reduce the data operation amount of the detector in generating projection data. As shown in Figure 1 the upper and lower sides of the virtual square are respectively provided with the ray output structure and the detector, and the left and right sides of the virtual square are respectively provided with the ray output structure and the detector.

[0027] In one embodiment, as shown in Figure 3 the virtual polygon is a virtual regular hexagon. The imaging assembly is arranged on at least two groups of sides of the virtual regular polygon, and each group of sides is oppositely distributed, which can simplify the system structure and reduce the data operation amount of the detector in generating projection data. The upper side and the two right sides of the virtual regular hexagon are respectively provided with the ray output structure, and the bottom side and the two left sides of the virtual regular hexagon are respectively provided with the detector.

[0028] In one embodiment, the system further comprises a base and a regular polygon support arranged on the base, and the ray output structure comprises a strip-shaped fixed plate 111 and at least two ray sources 112 arranged on the strip-shaped fixed plate 111 in a linear distribution. When the shape and size of the regular polygon support are the same as those of the virtual regular polygon, the ray output structure 11 and the detector 12 can be arranged on the desired virtual regular polygon.

[0029] In one embodiment, the system further comprises a base and at least two support arm combinations, and each support arm combination comprises a first support arm and a second support arm. One end of the first support arm is fixedly arranged on the base, and the other end is used for supporting or suspending the ray output structure. One end of the second support arm is fixedly arranged on the base, and the other end is used for supporting or suspending the detector. The first support arm for supporting or suspending the ray output structure is opposite to the second support arm for supporting or suspending the detector. The first support arm and the second support arm are strip-shaped, L-shaped or η-shaped. For example, the virtual regular polygon is a regular quadrilateral, the support arm for supporting the ray output structure or the detector arranged on the lowest side is strip-shaped, the support arm for supporting the detector or the ray output structure arranged on the highest side is η-shaped, and the support arm for supporting the ray output structure or the detector arranged on the left and right sides is L-shaped.

[0030] The processor controls at least two X-ray output structures to simultaneously output X-rays to a flow field under test placed inside a virtual polygon, and controls at least two corresponding detectors to receive the X-rays passing through the flow field under test, thereby generating at least two sets of projection data. Specifically, the X-ray output structures are linear X-ray source arrays, and the processor controls the X-ray sources in each linear X-ray source array to output X-rays sequentially based on a set timing sequence. Optionally, the processor controls the X-ray sources in each linear X-ray source array to output X-rays sequentially based on a set timing sequence and a set trigger direction, where the set trigger direction includes from the left to the right side of the linear X-ray source array, or from the right to the left side of the linear X-ray source array.

[0031] like Figure 1 As shown, linear ray source arrays are arranged on the top and right sides of the virtual square, and detectors 12 are arranged on the bottom and left sides of the virtual square. The linear ray source array includes multiple ray sources 112. As can be seen from the figure, the ray beams output by the first and second ray sources from the left in the linear ray source array located on the top side of the virtual square are spatially related to the flow field to be measured located inside the virtual square.

[0032] In one embodiment, the flow velocity of the flow field to be measured is obtained, and the speed at which the linear ray source array is sequentially triggered is determined based on the flow velocity, wherein the flow velocity is positively correlated with the speed. Specifically, the faster the flow velocity, the faster the linear ray source array is sequentially triggered, and the slower the flow velocity, the slower the linear ray source array is sequentially triggered.

[0033] The processor is also used to perform image reconstruction on the at least two sets of projection data to obtain a target image of the flow field under test. It is understood that, since the imaging component includes at least two sets of edges disposed on the virtual regular polygon, and each set of edges is relatively distributed, image reconstruction based on the at least two sets of projection data can obtain a target image. This target image can be a cross-sectional image of the flow field under test at different detection times, or it can be a three-dimensional fluid motion image of the flow field under test passing through the plane containing the virtual regular polygon during the imaging time. The specific composition and motion information of the flow field under test can be determined through this three-dimensional fluid motion image.

[0034] In one embodiment, the processor also has a built-in trained image processing model. The processor inputs the at least two sets of projection data into the trained image processing model to obtain the detection result of the flow field under test.

[0035] The detection result is either the name identifier of the flow field to be tested or a three-dimensional fluid motion image of the flow field to be tested passing through the plane containing the virtual regular polygon during the imaging time.

[0036] It can be understood that when the detection result is the name identification of the to-be-detected flow field, the corresponding trained image processing model is a neural network model trained based on a set number of projection data or target images carrying name labels. When the detection result is the three-dimensional fluid motion image of the to-be-detected flow field, the corresponding trained image processing model is a neural network model trained based on a set number of projection data and three-dimensional fluid motion images corresponding to the set number of projection data.

[0037] The system further comprises a memory for storing the projection data generated by the detectors in the at least two imaging assemblies. It can be understood that the projection data generated by any detector is stored in a first set position of the memory, and the processor reads the corresponding projection data from the set position and performs image reconstruction on the projection data to obtain the target image.

[0038] Compared with the prior art, the technical scheme provided by the embodiment of the application can simultaneously obtain at least two sets of projection data of the to-be-detected flow field arranged inside the virtual regular polygon through at least two imaging assemblies distributed on at least two groups of edges of the virtual regular polygon and opposite to each group of edges, and can obtain the target image of the to-be-detected flow field based on the at least two sets of projection data, thereby achieving the technical effect of simultaneously performing multi-point and multi-angle ray detection on the to-be-detected flow field, and the system has a simple structure and low manufacturing cost, and can meet the on-site high spatiotemporal resolution non-interference detection of the flow field.

[0039] Figure 4 A flowchart of a flow field structure determination method provided by the embodiment of the application. The method can be executed by the processor of the CT system, can be executed by a server connected to the CT system, or can be executed by any electronic device. As shown in the figure, the method comprises the following steps. Figure 4

[0040] S110, obtaining at least two sets of projection data of the to-be-detected flow field according to the foregoing embodiment.

[0041] If the current processor is communicatively connected to the CT system according to the foregoing embodiment, all projection data of the to-be-detected flow field in the memory of the CT system according to the foregoing embodiment is read and stored in a second set position of the local memory. If the current processor is not communicatively connected to the CT system according to the foregoing embodiment, all projection data of the to-be-detected flow field stored in the memory of the CT system according to the foregoing embodiment is copied to a third set position of the local memory through other means.

[0042] S120, inputting the at least two sets of projection data into a trained image processing model to obtain a detection result of the to-be-detected flow field.

[0043] The at least two sets of projection data are input into the trained image processing model to obtain a detection result of the to-be-detected flow field.​

[0044] wherein the detection result is a name label of the flow field to be detected or a three-dimensional flow motion image of the flow field to be detected passing through the plane where the virtual regular polygon is located within an imaging time. For example, a three-dimensional flow motion image of the flow passing through the plane where the virtual regular polygon is located within 30 seconds.

[0045] It can be understood that when the detection result is the name label of the flow field to be detected, the corresponding trained image processing model is a neural network model trained based on a set number of projection data or target images carrying name labels. When the detection result is the three-dimensional flow motion image of the flow field to be detected, the corresponding trained image processing model is a neural network model trained based on a set number of projection data and three-dimensional flow motion images reconstructed based on the set number of projection data.

[0046] Compared with the prior art, the technical solution provided by the embodiment of the present application can obtain the detection result of the flow field to be detected simply and efficiently by inputting at least two groups of projection data of the flow field to be detected acquired at multiple points and multiple angles into the trained image processing model to obtain the flow field detection result.

[0047] In some embodiments, a flow field structure determination method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the processor, one or more steps of the flow field structure determination method described above can be performed. Alternatively, in other embodiments, the processor can be configured to perform a flow field structure determination method by any other appropriate means, such as by means of firmware.

[0048] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), system on a chip systems (SOC), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0049] Computer programs for implementing the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, and partially on a remote machine or entirely on a remote machine or server.

[0050] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0051] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0052] The systems and techniques described herein can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0053] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0054] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in series, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0055] The specific implementation described above does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A CT system for detecting flow field structures, characterized in that, include: At least two imaging components are provided, each comprising a ray output structure for outputting X-rays and a detector for receiving X-rays passing through the flow field to be measured to obtain projection data; the at least two imaging components are distributed on at least two sets of sides of a virtual polygon, with each set of sides being distributed relative to each other; the imaging components are used to acquire projection data of the flow field to be measured disposed inside the virtual polygon. The processor is configured to control the at least two imaging components to simultaneously perform X-ray scanning on the flow field under test to obtain at least two sets of projection data, and to perform image reconstruction on the at least two sets of projection images to obtain a target image of the flow field under test. The radiation output structure is a linear radiation source array, and the detector is a multi-channel linear detector array; The processor is also configured to control the X-ray sources in each of the linear X-ray source arrays to output X-rays sequentially based on a set timing sequence. The processor is also configured to control the X-ray sources in each of the linear X-ray source arrays to output X-rays sequentially based on a set timing sequence and a set trigger direction. The set trigger direction includes from the left side to the right side of the linear X-ray source array, or from the right side to the left side of the linear X-ray source array. The triggering speed of the linear ray source array is positively correlated with the flow velocity of the flow field to be measured; The at least two sets of projection data or the target image corresponding to the at least two sets of projection data are input into the trained image processing model to obtain the detection result; The detection result is the name identifier of the flow field to be tested, or a three-dimensional fluid motion image of the flow field to be tested passing through the plane of the virtual polygon during the imaging time. The virtual polygon is a virtual square; The virtual square is provided with the ray output structure and the detector on its top and bottom sides, respectively; The target image is a cross-sectional image of the flow field under test at different detection times, or a three-dimensional fluid motion image of the flow field under test passing through the plane of the virtual regular polygon during the imaging time.

2. The system according to claim 1, characterized in that, include: Base; A polygonal bracket is mounted on the base; The radiation output structure includes a strip-shaped fixing plate disposed on a set side of the polygonal bracket and at least two radiation sources disposed on the strip-shaped fixing plate in a linear arrangement; The detector is positioned on the opposite side of each of the sides containing the ray output structures in the polygonal bracket.

3. A method for determining flow field structure, characterized in that, include: Obtain at least two sets of projection data obtained by the CT system according to any one of claims 1-2; The at least two sets of projection data are input into the trained image processing model to obtain the detection results of the flow field to be tested.

4. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the flow field structure determination method as described in claim 3.

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