CT (Computed Tomography) image spatial resolution test method, test system and computer readable medium

Abstract

The invention relates to a CT image spatial resolution testing method and system and a computer readable medium. The CT image spatial resolution testing method comprises the following steps: acquiring a CT scanning image of a to-be-tested object, and identifying a gray level change region of the CT scanning image; segmenting the CT scanning image, wherein the number of segmented images is multiple; for all the segmented sub-images, any sub-image is processed by the following steps of: obtaining a frame selection region image for a region of which the spatial resolution needs to be measured, obtaining frame selection region gray scale information, and obtaining the spatial resolution corresponding to the sub-image.

Description

Technical Field

[0001] This application relates to CT image spatial resolution testing methods, testing systems, computer-readable media, and computer program products. Background Technology

[0002] Turbine blades are critical components in aero-engines or gas turbines. Non-destructive testing (NDT) is essential for ensuring the safe operation of these engines. CT inspection, with its high density contrast, sensitivity to density variations and internal defects, and intuitive results, is increasingly being used for detecting internal defects in aero-engine and gas turbine blades.

[0003] Typically, CT scans use metrics such as spatial resolution, density resolution, and noise to evaluate image quality. Among these, spatial resolution is the ability of a CT system to identify and distinguish the smallest defects. It quantitatively represents the minimum distance between two detailed features that can be distinguished, affecting the ability to distinguish defect contour features. It is one of the core metrics of CT images and requires accurate measurement.

[0004] Due to the high density, complex surface, and large penetration thickness of turbine blades, the penetration thickness varies along different paths during CT inspection. Furthermore, the complex internal structure easily generates X-ray scattering, leading to beam hardening and scattering artifacts, which reduce image quality and affect defect detection capabilities. Because of the varying penetration capabilities and the different locations and degrees of scattering influence, the spatial resolution of different regions in the blade CT image varies, affecting the ability to identify defects at different locations on the blade. Therefore, it is necessary to quantitatively characterize the spatial resolution of different regions of the image.

[0005] Standards such as ASTM E1695 and GJB5311 specify methods for testing the spatial resolution of CT systems, including the uniform disk method and the line pair method. The uniform disk method involves testing a homogeneous cylinder of a certain diameter using a CT system under specified scanning parameters. By plotting the grayscale distribution curve of the upper boundary of the CT cross-section image of the cylinder, the line spread function is obtained. Finally, the number of line pairs corresponding to 10% modulation on the MTF function is calculated to characterize the system's spatial resolution.

[0006] However, the inventors discovered that the uniform disk method tests independently, without considering the actual size, internal structure, and scattering effects of the actual part. Therefore, it cannot represent the actual spatial resolution of a real part's CT image, nor can it obtain the spatial resolution of different parts. Similarly, the line-pair card method cannot accurately characterize the true spatial resolution of a real part's CT image.

[0007] Therefore, there is a need in the art for a CT image spatial resolution testing method, testing system, computer-readable medium, or computer program product to solve at least one of the above-mentioned technical problems. Summary of the Invention

[0008] To overcome the shortcomings of existing CT image spatial resolution testing methods, such as the inability to calculate the true spatial resolution of different locations on a blade and the inability to perform in-situ measurements, this paper proposes an in-situ spatial resolution testing method based on image recognition. First, the blade is segmented according to the distribution of grayscale and artifacts in the CT image. Then, the segmented image regions are bounded to obtain grayscale images of the boundaries at different locations. Next, the boundaries are identified using image recognition methods to obtain the grayscale distribution of the image boundaries, and edge response function curves are plotted. Then, the line spread function and MTF curve are obtained through smoothing and noise reduction. Finally, the MTF curve is interpolated to calculate the line logarithm under a 10% modulation index, thus realizing the calculation of the spatial resolution of different regions on the turbine blade CT image. The testing method disclosed in this application is applicable to the spatial resolution testing of different regions in CT images of blades with different configurations. It can obtain spatial resolution without adding extra testing work, greatly improving the accuracy and efficiency of spatial resolution testing. It has the advantages of high detection accuracy, short time, and low cost. Furthermore, it is not only applicable to blades but also to the spatial resolution testing of other complex and precision parts, showing good prospects for widespread application.

[0009] One objective of this application is to provide a method for testing the spatial resolution of CT images.

[0010] One objective of this application is to provide a CT image spatial resolution testing system.

[0011] One object of this application is to provide a computer-readable medium.

[0012] A CT image spatial resolution testing method according to one aspect of this application includes: acquiring a CT scan image of the object to be tested and identifying grayscale variation regions in the CT scan image; segmenting the CT scan image into multiple segments; and for all segmented sub-images, processing any sub-image includes: obtaining a bounding box image of the region for which spatial resolution needs to be measured, obtaining grayscale information of the bounding box region, and obtaining the spatial resolution corresponding to the sub-image.

[0013] In one or more embodiments, the step of obtaining the spatial resolution corresponding to the sub-image includes: obtaining the edge gray-scale distribution curve of the gray-scale information of the corresponding selected area; obtaining the line spread function (LSF) curve based on the edge gray-scale distribution curve; obtaining the MTF curve based on the LSF curve; interpolating the MTF curve to obtain the interpolated MTF curve; and obtaining the spatial resolution corresponding to the sub-image based on the interpolated MTF curve.

[0014] In one or more embodiments, after obtaining the Line Spread Function (LSF) curve, Gaussian filtering is performed to obtain the filtered LSF curve.

[0015] In one or more embodiments, the MTF curve is subjected to cubic spline interpolation.

[0016] In one or more embodiments, based on the interpolated MTF curve, the horizontal coordinate corresponding to 10% of the MTF curve is obtained, the horizontal coordinate under the 10% modulation is converted into a line pair value LP, and the spatial resolution corresponding to the sub-image is obtained by the following formula:

[0017] R = 0.5 * 1 / LP.

[0018] In one or more embodiments, the object under test includes turbine blades of a gas turbine engine.

[0019] In one or more embodiments, gray-scale variation regions in CT cross-sectional images are identified based on gray-scale, noise, and artifact distribution.

[0020] In one or more embodiments, before obtaining the edge grayscale distribution curve of the corresponding selected region grayscale information, the Canny edge detection method is used to identify the image contour boundary.

[0021] A CT image spatial resolution testing system according to another aspect of this application includes: a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement the CT image spatial resolution testing method as described above.

[0022] According to another aspect of this application, a computer-readable medium has a computer program thereon that is executed by a processor to implement the CT image spatial resolution testing method as described above.

[0023] According to another aspect of this application, a computer-readable medium has a computer program thereon that is executed by a processor to implement the CT image spatial resolution testing method as described above. Attached Figure Description

[0024] The above and other features, properties and advantages of this application will become more apparent from the following description taken in conjunction with the accompanying drawings and embodiments, wherein:

[0025] Figure 1 A CT cross-sectional view of a blade in a test method according to one embodiment.

[0026] Figure 2 The test method based on one embodiment Figure 1 A schematic diagram showing the division of image grayscale noise variation regions.

[0027] Figure 3 The test method based on one embodiment Figure 1 A cropped portion of the image.

[0028] Figure 4 The test method based on one embodiment Figure 3 Select the image within the analysis area.

[0029] Figure 5 The test method based on one embodiment Figure 4 The image of the selected area boundary.

[0030] Figure 6 The test method based on one embodiment Figure 5 A schematic diagram of the recognition contour boundary.

[0031] Figure 7 This is a schematic diagram of obtaining the contour normal grayscale distribution of a test method according to one embodiment.

[0032] Figure 8 To obtain the edge grayscale distribution ESF curve.

[0033] Figure 9 To Figure 8 The ESF curve shown has been smoothed.

[0034] Figure 10 To obtain the line spread function (LSF) curve, we need to differentiate the ESF.

[0035] Figure 11 The curve after Gaussian filtering of the Line Spread Function (LSF).

[0036] Figure 12 To calculate the MTF curve.

[0037] Figure 13 This is the interpolated MTF curve.

[0038] Figure 14 This is a flowchart illustrating a testing method according to one embodiment.

[0039] Figure 15 This is a flowchart illustrating a testing method according to another embodiment.

[0040] Figure 16 This is a flowchart illustrating a test method according to yet another embodiment.

[0041] Figure 17 This is a schematic block diagram of a test system according to one embodiment. Detailed Implementation

[0042] The following discloses various implementation methods or embodiments of the described subject matter technical solutions. To simplify the disclosure, specific examples of the various elements and arrangements are described below. Of course, these are merely examples and are not intended to limit the scope of protection of this application.

[0043] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0044] It is understood that flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that, depending on the actual situation, the preceding or following operations may not necessarily be performed precisely in sequence. Other operations may be added to these processes, or one or more steps may be removed from them.

[0045] refer to Figure 15 As shown, in some embodiments, the CT image spatial resolution testing method includes the following steps:

[0046] S100. Acquire the CT scan image of the object to be tested and identify the grayscale variation area of ​​the CT scan image.

[0047] S200. Segment the CT scan image into multiple segments.

[0048] S300. For all segmented sub-images, the processing steps for any sub-image include: obtaining a bounding box image for the region whose spatial resolution needs to be measured, obtaining grayscale information of the bounding box region, and obtaining the spatial resolution corresponding to the sub-image.

[0049] In S300, after processing all sub-images to obtain the spatial resolution corresponding to each sub-image, the spatial resolution of different regions of the blade corresponding to each sub-image can be obtained.

[0050] In some embodiments, for S100, the gray-level variation region in the CT cross-sectional image can be identified based on the distribution of gray-level, noise, and artifacts.

[0051] refer to Figure 16 As shown, in some embodiments, the step of S300 in obtaining the spatial resolution corresponding to the sub-image includes:

[0052] S301. Obtain the edge grayscale distribution curve of the corresponding selected area;

[0053] S302. Based on the edge grayscale distribution curve, obtain the line spread function (LSF) curve;

[0054] S303. Obtain the MTF curve based on the LSF curve;

[0055] S304. Interpolate the MTF curve to obtain the interpolated MTF curve;

[0056] S305. Obtain the spatial resolution corresponding to the sub-image based on the interpolated MTF curve.

[0057] In some embodiments, before S301, the following steps may be included: before obtaining the edge grayscale distribution curve of the grayscale information of the corresponding selected area, the Canny edge detection method is used to identify the image contour boundary.

[0058] In some embodiments, for S302, after obtaining the Line Spread Function (LSF) curve, Gaussian filtering is performed to obtain a filtered LSF curve, and in S303, the MTF curve is obtained based on the filtered LSF curve. It can be understood that other filtering methods can also be used, but the steps for using Gaussian filtering are relatively simple, and the filtering result is reliable.

[0059] In some implementations, for S304, the specific method for interpolating the MTF curve can be cubic spline interpolation, which achieves a high degree of smoothness and has relatively simplified calculations. It is understood that other interpolation smoothing methods can also be used.

[0060] In some embodiments, for S305, specifically, based on the interpolated MTF curve, the horizontal coordinate corresponding to 10% of the MTF curve is obtained, the horizontal coordinate under the 10% modulation is converted into a line pair value LP, and the spatial resolution corresponding to the sub-image is obtained through the following formula:

[0061] R = 0.5 * 1 / LP.

[0062] The spatial resolution obtained in this way is more accurate.

[0063] Specifically, you can refer to Figures 1 to 14 As shown, the following will further describe the CT image spatial resolution testing method with a specific blade as an example. The test object is a turbine blade of a gas turbine engine, such as... Figure 14 As shown, the test method includes the following steps.

[0064] 1. Select the turbine blade to be tested, scan the blade, and obtain the CT scan image of the blade.

[0065] 2. Obtain CT cross-sectional images of the blade at different locations. For example... Figure 1 As shown.

[0066] 3. Identify areas of grayscale variation in CT cross-sectional images based on the distribution of grayscale, noise, and artifacts. For example... Figure 2 As shown.

[0067] 4. Segment the image, where the number of segments is N, such as... Figure 2 As shown, it is divided into 18 regions, namely region 1, region 2, region 3, region 4, region 5, region 6, region 7, region 8, region 9, region 10, region 11, region 12, region 13, region 14, region 15, region 16, region 17, and region 18.

[0068] 5. Obtain the segmented sub-images, where the j-th sub-image is denoted as IMGj, such as... Figure 3 As shown, to extract Figure 2 Take region 14 as an example.

[0069] 6. In the sub-image IMGj, select the area where you want to measure the spatial resolution, such as... Figure 4 As shown, for Figure 3 Select the area 14 that has been cut off.

[0070] 7. Generate an image of the selected area, such as... Figure 5 As shown.

[0071] 8. Extract the grayscale information of the selected area. For example... Figure 6 As shown.

[0072] 9. Use the Canny edge detection method to identify image contour boundaries. For example... Figure 7 As shown.

[0073] 10. Obtain the edge grayscale distribution curve. For example... Figure 8 As shown.

[0074] 11. Smooth the edge grayscale curve to obtain the smoothed edge grayscale distribution ESF curve. For example... Figure 9 As shown.

[0075] 12. Perform differential calculations on the edge grayscale distribution curve to obtain the Line Spread Function (LSF) curve. For example... Figure 10 As shown.

[0076] 13. Apply a Gaussian filter to the LSF curve to obtain the filtered LSF curve. For example... Figure 11 As shown.

[0077] 14. Calculate the MTF function and plot the MTF curve. For example... Figure 12 As shown.

[0078] 15. Interpolate the MTF curve. Here, cubic spline interpolation is selected for calculation. The calculation result is as follows: Figure 13 As shown.

[0079] 16. Obtain the x-axis corresponding to 10% on the MTF curve. For example... Figure 13 As shown.

[0080] 17. Convert the x-axis under the above 10% modulation into line pair values ​​LP.

[0081] 18. Obtain the spatial resolution of the sub-image IMGi, R = 0.5 * 1 / LP.

[0082] 19. Repeat the above steps to obtain the spatial resolution of the corresponding locations on different sub-maps. Record the measurement results in Table 1.

[0083] 20. Complete the calculation of the spatial resolution of CT images of different regions of the leaf.

[0084] Table 1 Spatial resolution of different regions of the blade

[0085]

[0086]

[0087] It is understood that the testing methods described above are not limited to manual operation and judgment or calculation. They can also be used to automatically obtain the spatial resolution of CT images through artificial intelligence by combining image processing algorithms and other methods. There are no limitations on these methods.

[0088] It is understandable that, for reference Figure 17 As shown, another aspect of this application also provides a CT image spatial resolution testing system 100, including a memory 101 and a processor 102. The memory 101 is used to store instructions executable by the processor; the processor 102 is used to execute the instructions to implement the CT image spatial resolution testing method described in the above embodiments.

[0089] It should be noted that the aforementioned memory, processor, and database are not limited to a specific memory, processor, or database. For example, in some cases, the memory and processor can have a distributed structure. For instance, it can include memory and processor located at the test equipment end and the backend cloud end, respectively, with the test equipment end and the backend cloud end jointly implementing the aforementioned test method. Furthermore, in embodiments employing a distributed structure, the specific execution terminal for each step can be adjusted according to actual circumstances, and the specific implementation scheme of each step on a particular terminal should not limit the scope of protection of this application.

[0090] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the test method as described in the above embodiments. For details, please refer to the description above, which will not be repeated here.

[0091] Another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the testing methods described in the above embodiments.

[0092] In addition, it is understood that the aforementioned computer-readable storage medium may also be in the form of a system, that is, including multiple computer-readable storage sub-media, so as to implement the steps of the test method described above through multiple computer-readable storage media.

[0093] In summary, the beneficial effects of the testing methods, testing systems, and computer-readable media described in the above embodiments include, but are not limited to, overcoming the shortcomings of existing CT image spatial resolution testing methods, such as the inability to calculate the true spatial resolution of different locations on the blade and the inability to perform in-situ measurements. This enables the measurement of spatial resolution of different parts of the blade in CT images and characterizes the defect identification capability of different regions of the blade. Specifically, the blade is first segmented based on the distribution of grayscale and artifacts in the CT image. Then, the segmented image regions are bounded to obtain grayscale images of the boundaries at different locations. Next, the boundaries are identified using image recognition methods to obtain the grayscale distribution of the image boundaries, and edge response function curves are plotted. Then, the line spread function and MTF curve are obtained through smoothing and noise reduction. Finally, the MTF curve is interpolated to calculate the line logarithm under a 10% modulation index, thus realizing the calculation of spatial resolution of different regions on the turbine blade CT image. The embodiments described are applicable to the spatial resolution testing of different regions of CT images of blades with different configurations. Spatial resolution can be obtained without adding extra testing work, greatly improving the accuracy of spatial resolution testing and increasing detection efficiency. It has the advantages of high detection accuracy, short testing time, and low cost, and is also applicable to the spatial resolution testing of other complex and precision parts, showing great potential for widespread application.

[0094] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0095] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0096] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or via a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0097] While this application discloses preferred embodiments as described above, it is not intended to limit the scope of this application. Any changes and modifications can be made by those skilled in the art without departing from the spirit and scope of this application. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application, without departing from the content of the technical solution of this application, fall within the protection scope defined by the claims of this application.

Claims

1. A method of testing spatial resolution of a CT image, characterized by, The method comprises the following steps: Obtaining a CT scan image of a to-be-tested object, and identifying a gray scale change region in the CT scan image; Segmenting the CT scan image, and obtaining a plurality of segmented images; For all segmented sub-images, the processing steps of any sub-image comprise the following steps:

2. The method of testing spatial resolution of a CT image according to claim 1, wherein, Obtaining a framed region image of a region to be measured for spatial resolution, obtaining gray scale information of the framed region, and obtaining spatial resolution corresponding to the sub-image. The step of obtaining spatial resolution corresponding to the sub-image comprises the following steps: Obtaining an edge gray scale distribution curve corresponding to the gray scale information of the framed region; Obtaining a line spread function (LSF) curve based on the edge gray scale distribution curve; Obtaining an MTF curve based on the LSF curve; Performing interpolation on the MTF curve to obtain an interpolated MTF curve; 3. The method of testing spatial resolution of a CT image according to claim 2, wherein, Obtaining spatial resolution corresponding to the sub-image based on the interpolated MTF curve.

4. The method of testing spatial resolution of a CT image according to claim 2, wherein, After the LSF curve is obtained, a filtering process is performed to obtain a filtered LSF curve.

5. The method of testing spatial resolution of a CT image according to claim 2, wherein, The MTF curve is subjected to cubic spline interpolation. Based on the interpolated MTF curve, a horizontal coordinate corresponding to 10% on the MTF curve is obtained, the horizontal coordinate at 10% modulation is converted into a line pair value (LP), and the spatial resolution corresponding to the sub-image is obtained through the following formula:

6. The method of testing spatial resolution of a CT image according to claim 1, wherein, R = 0.5 * 1 / LP.

7. The method of testing spatial resolution of a CT image according to claim 1, wherein, The to-be-tested object comprises a turbine blade of a gas turbine engine.

8. The method of testing spatial resolution of a CT image according to any one of claims 1 to 7, wherein, The gray scale change region in the CT cross-sectional image is identified according to the distribution of gray scale, noise and artifacts.

9. A CT image spatial resolution test system characterized by, Before the edge gray scale distribution curve corresponding to the gray scale information of the framed region is obtained, a canny edge detection method is used to identify the contour boundary of the image. The method comprises the following steps: A memory for storing instructions executable by a processor; 10. A computer readable medium having a computer program thereon, characterized in that, A processor for executing the instructions to implement the CT image spatial resolution test method according to any one of claims 1 to 8. The program is executed by the processor to implement the CT image spatial resolution test method according to any one of claims 1 to 8.

11. A computer program product comprising a computer program which, when executed by a processor, implements the CT image spatial resolution test method according to any one of claims 1 to 8.

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