X-ray detector quality detection method, system, storage medium and electronic device

Abstract

This invention discloses a method, system, storage medium, and electronic device for X-ray detector quality inspection. The method includes: acquiring an original brightness field image captured by an X-ray detector, and correcting the original brightness field image to obtain a corrected brightness field image; determining target parameters for the corrected brightness field image; if the target parameters exceed a corresponding threshold range, determining the corrected brightness field image as an abnormal image; otherwise, determining the corrected brightness field image as a normal image; if the corrected brightness field image is an abnormal image, determining the X-ray detector quality is abnormal; otherwise, determining the X-ray detector quality is normal. This invention monitors the production quality of X-ray detectors by detecting the quality of X-ray images captured by the X-ray detector, thereby helping to optimize the research and development of X-ray detectors and improve their manufacturing yield.

Description

Technical Field

[0001] This invention relates to the field of quality inspection technology, and in particular to a method, system, storage medium, and electronic device for quality inspection of X-ray detectors. Background Technology

[0002] With the rapid development of X-ray flat panel detectors in digital radiography (DR) systems, X-ray detectors have been widely used in industrial flaw detection, dental imaging, mammography, and mini C-arm imaging. In the manufacturing process of X-ray detectors, the uniformity of the sensitive area and the degree of coupling are extremely critical processes affecting the image quality of the X-ray detector.

[0003] In conventional manufacturing processes, the intensity of light diffusion is assessed by testing the modulation and demodulation function (MTF) to determine the spatial resolution of the scintillator. MTF evaluation requires a high-precision tungsten edge bulk film and specialized calculation programs, making the imaging process cumbersome and expensive. Another drawback of MTF is that it characterizes a local area around the measurement point. To determine the spatial resolution at every location across the entire image, a bulk film needs to be placed at each location, images captured, and calculations performed, further complicating the process.

[0004] Therefore, there is an urgent need to provide a technical solution that simultaneously satisfies the requirements of speed, convenience, ease of operation, and low cost to address the technical problem of evaluating the distribution of the spatial resolution capability of a scintillator across the entire scintillator surface. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method, system, storage medium, and electronic device for X-ray detector quality inspection.

[0006] The technical solution of the X-ray detector quality detection method of the present invention is as follows:

[0007] The original brightness field image is obtained by using an X-ray detector, and the original brightness field image is corrected to obtain a corrected brightness field image.

[0008] The target parameters of the corrected brightness image are determined. When the target parameters exceed the corresponding threshold range, the corrected brightness image is determined to be an abnormal image; otherwise, the corrected brightness image is determined to be a normal image.

[0009] When the corrected brightness field image is an abnormal image, the X-ray detector is determined to be of abnormal quality; otherwise, the X-ray detector is determined to be of normal quality.

[0010] The beneficial effects of the X-ray detector quality detection method of the present invention are as follows:

[0011] The method of this invention monitors the production quality of X-ray detectors by detecting the quality of X-ray images captured by the X-ray detector, thereby helping to optimize the research and development of X-ray detectors and improve their manufacturing yield.

[0012] Based on the above scheme, the X-ray detector quality detection method of the present invention can be further improved as follows.

[0013] Furthermore, the step of determining the target parameters of the corrected brightness field map includes:

[0014] Based on a preset splitting method, the corrected brightness field image is split into multiple first brightness field images of the same size;

[0015] Obtain and, based on the grayscale value of each pixel in any first bright field image, obtain the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to the first bright field image, until the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to each first bright field image are obtained;

[0016] Determine whether the standard deviation of the grayscale value of any first bright field image is less than a first threshold or whether the signal-to-noise ratio of the grayscale value of the first bright field image is greater than a second threshold, and obtain the judgment result, until the judgment result of each first bright field image is obtained;

[0017] The first bright field image with a judgment result of "yes" is determined as the target bright field image, and the target parameters are determined based on the number of target bright field images and the number of first bright field images.

[0018] Furthermore, the target parameter is: target ambiguity rate or target pass rate;

[0019] When the target parameter is the target blur rate, the step of determining the target parameter based on the number of target brightness maps and the number of first brightness maps includes:

[0020] The target blur rate is determined based on the ratio between the number of target bright field images and the number of first bright field images;

[0021] When the target parameter is the target pass rate, the step of determining the target parameter based on the number of target brightness maps and the number of first brightness maps includes:

[0022] The target pass rate is determined based on a preset formula, the number of target brightness maps, and the number of first brightness maps; wherein the preset formula is: y represents the target pass rate, x1 represents the number of target bright field images, and x2 represents the number of first bright field images.

[0023] Furthermore, it also includes:

[0024] Based on the standard deviation of grayscale values ​​and the grayscale signal-to-noise ratio corresponding to each first brightness field map, a target region map corresponding to the corrected brightness field map is constructed;

[0025] When the X-ray detector is determined to be of abnormal quality, an optimization scheme for the X-ray detector is determined based on the distribution of the target brightness field in the target area map.

[0026] Furthermore, the preset splitting method is as follows: the corrected brightness field image is split into a brightness field image composed of K M×N pixels; where K, M and N are all positive integers.

[0027] Furthermore, the step of correcting the original brightness field image to obtain a corrected brightness field image includes:

[0028] The original brightness field image is subjected to bias correction, gain correction and bad pixel correction to obtain the corrected brightness field image.

[0029] The technical solution of the X-ray detector quality inspection system of the present invention is as follows:

[0030] It includes: an acquisition module, a processing module, and a judgment module;

[0031] The acquisition module is used to: acquire the original brightness field image obtained by using an X-ray detector, and correct the original brightness field image to obtain a corrected brightness field image;

[0032] The processing module is used to: determine the target parameters of the corrected brightness image; when the target parameters exceed the corresponding threshold range, determine that the corrected brightness image is an abnormal image; otherwise, determine that the corrected brightness image is a normal image.

[0033] The judgment module is used to: determine that the X-ray detector is of abnormal quality when the corrected brightness field image is an abnormal image; otherwise, determine that the X-ray detector is of normal quality.

[0034] The beneficial effects of the X-ray detector quality inspection system of the present invention are as follows:

[0035] The system of this invention monitors the production quality of X-ray detectors by detecting the quality of X-ray images captured by the X-ray detector, thereby helping to optimize the research and development of X-ray detectors and improve their manufacturing yield.

[0036] Based on the above scheme, the X-ray detector quality inspection system of the present invention can be further improved as follows.

[0037] Furthermore, the processing module is specifically used for:

[0038] Based on a preset splitting method, the corrected brightness field image is split into multiple first brightness field images of the same size;

[0039] Obtain and, based on the grayscale value of each pixel in any first bright field image, obtain the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to the first bright field image, until the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to each first bright field image are obtained;

[0040] Determine whether the standard deviation of the grayscale value of any first bright field image is less than a first threshold or whether the signal-to-noise ratio of the grayscale value of the first bright field image is greater than a second threshold, and obtain the judgment result, until the judgment result of each first bright field image is obtained;

[0041] The first bright field image with a judgment result of "yes" is determined as the target bright field image, and the target parameters are determined based on the number of target bright field images and the number of first bright field images.

[0042] The technical solution of a storage medium according to the present invention is as follows:

[0043] The storage medium stores instructions that, when read by a computer, cause the computer to execute the steps of the X-ray detector quality inspection method of the present invention.

[0044] The technical solution of an electronic device according to the present invention is as follows:

[0045] The invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it causes the computer to perform the steps of the X-ray detector quality detection method of the present invention. Attached Figure Description

[0046] Figure 1 A flowchart illustrating an embodiment of the X-ray detector quality detection method provided by the present invention is shown;

[0047] Figure 2 The flowchart of steps 130 to 140 in an embodiment of the X-ray detector quality detection method provided by the present invention is shown.

[0048] Figure 3 The diagram illustrates the blurred region when the corrected brightness field image is a normal image, according to an embodiment of the X-ray detector quality detection method provided by the present invention.

[0049] Figure 4 The diagram shows the blurred region corresponding to the corrected brightness field image being an abnormal image in an embodiment of the X-ray detector quality detection method provided by the present invention.

[0050] Figure 5 A schematic diagram of an embodiment of the X-ray detector quality inspection system provided by the present invention is shown. Detailed Implementation

[0051] Figure 1 A schematic flowchart of an embodiment of the X-ray detector quality inspection method provided by the present invention is shown. Figure 1 As shown, the method includes the following steps:

[0052] Step 110: Obtain the original brightness field image captured by the X-ray detector, and correct the original brightness field image to obtain the corrected brightness field image.

[0053] In this embodiment, ① the X-ray detector is the X-ray detector that needs to be quality inspected. In this embodiment, capturing a single brightness field image provides the distribution of spatial resolution across the entire image, thereby assessing the degradation of spatial resolution caused by scintillator evaporation or coupling, as well as the scintillator itself, and thus determining the quality of the X-ray detector. ② The original brightness field image is the brightness field image captured using the X-ray detector; the original brightness field image is the uncorrected brightness field image. ③ The corrected brightness field image is the brightness field image after correction.

[0054] It should be noted that when using an X-ray detector for imaging, there is only air between the radiation source and the X-ray detector, with no sample present, and the exposure parameters of the X-ray detector are not limited in any way.

[0055] Step 120: Determine the target parameters of the corrected brightness field image. When the target parameters exceed the corresponding threshold range, the corrected brightness field image is determined to be an abnormal image; otherwise, the corrected brightness field image is determined to be a normal image.

[0056] ① The types of target parameters include, but are not limited to, blur rate, pass rate, and sharpness rate. Therefore, the target parameter is one of the following: target blur rate, blur pass rate, or target sharpness rate. ② The threshold range is a pre-set range of target parameters. This threshold range is set according to requirements and is not limited here.

[0057] Step 130: When the corrected brightness field image is an abnormal image, the X-ray detector is determined to be of abnormal quality; otherwise, the X-ray detector is determined to be of normal quality.

[0058] Specifically, when the corrected brightness field image is determined to be an abnormal image, it is presumed that the X-ray detector is of abnormal quality; when the corrected brightness field image is determined to be a normal image, it is presumed that the X-ray detector is of normal quality.

[0059] Preferably, the step of determining the target parameters of the corrected brightness field map includes:

[0060] Based on a preset splitting method, the corrected brightness field image is split into multiple first brightness field images of the same size.

[0061] Specifically, ① the preset splitting method is: splitting the corrected brightness field image into brightness field images composed of K M×N pixels, where K, M, and N are all positive integers. ② The first brightness field image is: the brightness field image composed of M×N pixels obtained after splitting.

[0062] Obtain the grayscale value of each pixel in any first bright field image, and then obtain the standard deviation of the grayscale value and the grayscale signal-to-noise ratio corresponding to that first bright field image, until the standard deviation of the grayscale value and the grayscale signal-to-noise ratio corresponding to each first bright field image are obtained.

[0063] The process of obtaining the grayscale value of each pixel is existing technology and will not be elaborated here. The process of calculating the standard deviation of grayscale values ​​and the grayscale signal-to-noise ratio (average grayscale value divided by standard deviation of grayscale values) corresponding to the first bright field image based on the grayscale values ​​of each pixel in the first bright field image is existing technology and will not be elaborated here.

[0064] Specifically, the grayscale value of each pixel in any first bright field image is obtained, and the standard deviation of the grayscale value and the grayscale signal-to-noise ratio corresponding to the first bright field image are calculated based on the grayscale value of each pixel in the first bright field image. The above steps are repeated until the standard deviation of the grayscale value and the grayscale signal-to-noise ratio corresponding to each first bright field image are obtained.

[0065] Determine whether the standard deviation of the grayscale value of any first bright field image is less than a first threshold or whether the signal-to-noise ratio of the grayscale value of the first bright field image is greater than a second threshold, and obtain the judgment result, until the judgment result of each first bright field image is obtained.

[0066] Wherein, ① the first threshold is the threshold corresponding to the standard deviation of grayscale values. ② the second threshold is the threshold corresponding to the signal-to-noise ratio of grayscale values.

[0067] It should be noted that both the first and second thresholds can be set according to requirements, and no restrictions are set here.

[0068] The first bright field image with a judgment result of "yes" is determined as the target bright field image, and the target parameters are determined based on the number of target bright field images and the number of first bright field images.

[0069] The target brightness field image is defined as: the first brightness field image in which the standard deviation of grayscale value is less than the first threshold or the grayscale value signal-to-noise ratio is greater than the second threshold.

[0070] Preferably, the target parameter is: target ambiguity rate or target pass rate.

[0071] It should be noted that the target parameters are not limited to the target fuzziness rate and the target pass rate. Other types of parameters can also be set according to needs, and there are no restrictions here.

[0072] When the target parameter is the target blur rate, the step of determining the target parameter based on the number of target brightness maps and the number of first brightness maps includes:

[0073] The target blur rate is determined based on the ratio between the number of target bright field images and the number of first bright field images.

[0074] Wherein, the target blur rate = the number of target bright field images / the number of first bright field images.

[0075] When the target parameter is the target pass rate, the step of determining the target parameter based on the number of target brightness maps and the number of first brightness maps includes:

[0076] The target pass rate is determined based on a preset formula, the number of target brightness maps, and the number of first brightness maps; wherein the preset formula is: y represents the target pass rate, x1 represents the number of target bright field images, and x2 represents the number of first bright field images.

[0077] Preferably, in an embodiment of the X-ray detector quality inspection method provided by the present invention, such as Figure 2 As shown, it also includes the following steps:

[0078] Step 140: Construct the target region map corresponding to the corrected bright field map based on the standard deviation of gray values ​​and the gray value signal-to-noise ratio corresponding to each first bright field map.

[0079] The target region map is an image constructed based on the standard deviation of grayscale values ​​and the grayscale signal-to-noise ratio corresponding to the first bright field map.

[0080] Specifically, taking the target region image as an example, which corresponds to the target blur rate, the severity of blurring in the bright field image can be observed through this image, allowing for targeted optimization of the X-ray detector through processes or subsequent processing. For instance, assuming the threshold range for the target blur rate is 0-10%, then... Figure 3 The image shows the target region map (target blur rate 6%) when the corrected brightness field map is a normal image. Figure 4 The target region map (target blur rate 28%) is shown when the corrected brightness field map is an anomalous image.

[0081] It should be noted that the process of constructing the target region map is as follows: the location of the target bright field map and the locations of the non-target bright field maps in all the original first bright field maps are marked with different colors; or different colors are marked according to the standard deviation or the grayscale signal-to-noise ratio.

[0082] Step 150: When the X-ray detector is determined to be of abnormal quality, an optimization scheme for the X-ray detector is determined based on the distribution of the target brightness field in the target area map.

[0083] For example, Figure 4 The fuzzy distribution in the target area map is concentrated in the two lower corners, so process analysis and troubleshooting can be carried out on the two lower corners of the X-ray detector.

[0084] Preferably, the step of correcting the original brightness field image to obtain a corrected brightness field image includes:

[0085] The original brightness field image is subjected to bias correction, gain correction and bad pixel correction to obtain the corrected brightness field image.

[0086] It should be noted that the specific processes of bias correction, gain correction and bad pixel correction are all existing technologies, and will not be elaborated on here.

[0087] The technical solution of this embodiment monitors the production quality of X-ray detectors by detecting the quality of X-ray images captured by the X-ray detector, thereby helping to optimize the research and development of X-ray detectors and improve their manufacturing yield.

[0088] Figure 5 A schematic diagram of an embodiment of the X-ray detector quality inspection system provided by the present invention is shown. Figure 5 As shown, the system 200 includes: an acquisition module 210, a processing module 220, and a judgment module 230.

[0089] The acquisition module 210 is used to: acquire the original brightness field image obtained by using an X-ray detector, and correct the original brightness field image to obtain a corrected brightness field image;

[0090] The processing module 220 is used to: determine the target parameters of the corrected brightness image; when the target parameters exceed the corresponding threshold range, determine that the corrected brightness image is an abnormal image; otherwise, determine that the corrected brightness image is a normal image.

[0091] The judgment module 230 is used to: determine that the X-ray detector is of abnormal quality when the corrected brightness field image is an abnormal image; otherwise, determine that the X-ray detector is of normal quality.

[0092] Preferably, the processing module 220 is specifically used for:

[0093] Based on a preset splitting method, the corrected brightness field image is split into multiple first brightness field images of the same size;

[0094] Obtain and, based on the grayscale value of each pixel in any first bright field image, obtain the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to the first bright field image, until the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to each first bright field image are obtained;

[0095] Determine whether the standard deviation of the grayscale value of any first bright field image is less than a first threshold or whether the signal-to-noise ratio of the grayscale value of the first bright field image is greater than a second threshold, and obtain the judgment result, until the judgment result of each first bright field image is obtained;

[0096] The first bright field image with a judgment result of "yes" is determined as the target bright field image, and the target parameters are determined based on the number of target bright field images and the number of first bright field images.

[0097] The technical solution of this embodiment monitors the production quality of X-ray detectors by detecting the quality of X-ray images captured by the X-ray detector, thereby helping to optimize the research and development of X-ray detectors and improve their manufacturing yield.

[0098] The parameters and steps for implementing the corresponding functions of each module in the X-ray detector quality inspection system 200 of this embodiment can be referred to the parameters and steps in the embodiments of the X-ray detector quality inspection method above, and will not be repeated here.

[0099] An embodiment of the present invention provides a storage medium, comprising: the storage medium storing instructions, which, when read by a computer, cause the computer to execute steps such as an X-ray detector quality detection method. For details, please refer to the parameters and steps in the embodiments of the X-ray detector quality detection method described above, which will not be repeated here.

[0100] Computer storage media include, for example, USB flash drives and external hard drives.

[0101] An electronic device provided by an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The device is characterized in that when the processor executes the computer program, it causes the computer to perform steps such as an X-ray detector quality detection method. For details, please refer to the parameters and steps in the embodiments of the X-ray detector quality detection method described above, which will not be repeated here.

[0102] Those skilled in the art will know that the present invention can be implemented as a method, system, storage medium, and electronic device.

[0103] Therefore, the present invention can be implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention can also be implemented as a computer program product contained in one or more computer-readable media, which contains computer-readable program code. Any combination of one or more computer-readable media can be used. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. Although embodiments of the invention have been shown and described above, it is to be understood that these embodiments are exemplary and should not be construed as limiting the invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the invention.

Claims

1. A method of X-ray detector quality testing, characterized in that, include: The original brightness field image is obtained by using an X-ray detector, and the original brightness field image is corrected to obtain a corrected brightness field image. The target parameters of the corrected brightness image are determined. When the target parameters exceed the corresponding threshold range, the corrected brightness image is determined to be an abnormal image; otherwise, the corrected brightness image is determined to be a normal image. When the corrected brightness field image is an abnormal image, the X-ray detector is determined to be of abnormal quality; otherwise, the X-ray detector is determined to be of normal quality. The step of determining the target parameters of the corrected brightness field map includes: Based on a preset splitting method, the corrected brightness field image is split into multiple first brightness field images of the same size; the preset splitting method is: the corrected brightness field image is split into K brightness field images composed of M×N pixels; where K, M and N are all positive integers; Obtain and, based on the grayscale value of each pixel in any first bright field image, obtain the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to the first bright field image, until the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to each first bright field image are obtained; Determine whether the standard deviation of the grayscale value of any first bright field image is less than a first threshold or whether the signal-to-noise ratio of the grayscale value of the first bright field image is greater than a second threshold, and obtain the judgment result, until the judgment result of each first bright field image is obtained; The first bright field image with a judgment result of "yes" is determined as the target bright field image, and the target parameters are determined based on the number of target bright field images and the number of first bright field images; The target parameter is: target fuzziness rate or target pass rate; When the target parameter is the target blur rate, the step of determining the target parameter based on the number of target bright field images and the number of first bright field images includes: determining the target blur rate based on the ratio between the number of target bright field images and the number of first bright field images; When the target parameter is the target pass rate, the step of determining the target parameter based on the number of target brightness images and the number of first brightness images includes: determining the target pass rate based on a preset formula, the number of target brightness images, and the number of first brightness images; wherein, the preset formula is: y represents the target pass rate. The number of target bright field images, The number of the first bright field images; It also includes: constructing a target region map corresponding to the corrected bright field map based on the standard deviation of the gray value and the gray value signal-to-noise ratio corresponding to each first bright field map; when the X-ray detector is determined to be of abnormal quality, determining an optimization scheme for the X-ray detector based on the distribution of the target bright field map in the target region map.

2. The X-ray detector quality inspection method according to claim 1, characterized in that, The step of correcting the original brightness field image to obtain a corrected brightness field image includes: The original brightness field image is subjected to bias correction, gain correction and bad pixel correction to obtain the corrected brightness field image.

3. An X-ray detector quality detection system, characterized in that include: Acquisition module, processing module, and judgment module; The acquisition module is used to: acquire the original brightness field image obtained by using an X-ray detector, and correct the original brightness field image to obtain a corrected brightness field image; The processing module is used to: determine the target parameters of the corrected brightness image; when the target parameters exceed the corresponding threshold range, determine that the corrected brightness image is an abnormal image; otherwise, determine that the corrected brightness image is a normal image. The judgment module is used to: determine that the X-ray detector is of abnormal quality when the corrected brightness field image is an abnormal image; otherwise, determine that the X-ray detector is of normal quality. The processing module is specifically used for: Based on a preset splitting method, the corrected brightness field image is split into multiple first brightness field images of the same size; the preset splitting method is: the corrected brightness field image is split into K brightness field images composed of M×N pixels; where K, M and N are all positive integers; Obtain and, based on the grayscale value of each pixel in any first bright field image, obtain the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to the first bright field image, until the standard deviation of grayscale value and the grayscale signal-to-noise ratio corresponding to each first bright field image are obtained; Determine whether the standard deviation of the grayscale value of any first bright field image is less than a first threshold or whether the signal-to-noise ratio of the grayscale value of the first bright field image is greater than a second threshold, and obtain the judgment result, until the judgment result of each first bright field image is obtained; The first bright field image with a judgment result of "yes" is determined as the target bright field image, and the target parameters are determined based on the number of target bright field images and the number of first bright field images; The target parameter is: target fuzziness rate or target pass rate; When the target parameter is the target blur rate, the processing module is specifically used to: determine the target blur rate based on the ratio between the number of target bright field images and the number of first bright field images; When the target parameter is the target pass rate, the processing module is specifically configured to: determine the target pass rate based on a preset formula, a number of target highlight field maps, and a number of first highlight field maps; wherein the preset formula is: ; y is the target pass rate, is the number of target highlight field maps, is the number of first highlight field maps. It also includes: a construction module; the construction module is used to: construct a target region map corresponding to the corrected bright field map based on the standard deviation of the gray value and the gray value signal-to-noise ratio corresponding to each first bright field map; when the X-ray detector is determined to be of abnormal quality, determine the optimization scheme of the X-ray detector based on the distribution of the target bright field map in the target region map.

4. A storage medium, characterized by The storage medium stores instructions that, when read by a computer, cause the computer to execute the X-ray detector quality inspection method as described in claim 1 or 2.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the computer to perform the X-ray detector quality detection method as described in claim 1 or 2.

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