CN115251958A - Exposure abnormity early warning method and device, computer equipment and storage medium - Google Patents

Exposure abnormity early warning method and device, computer equipment and storage medium Download PDF

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CN115251958A
CN115251958A CN202210690863.0A CN202210690863A CN115251958A CN 115251958 A CN115251958 A CN 115251958A CN 202210690863 A CN202210690863 A CN 202210690863A CN 115251958 A CN115251958 A CN 115251958A
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exposure
image
images
scanned
abnormal
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闫浩
刘达林
王雯
赵菲妮
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Our United Corp
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Our United Corp
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/54—Control of apparatus or devices for radiation diagnosis
    • A61B6/542—Control of apparatus or devices for radiation diagnosis involving control of exposure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computerised tomographs
    • A61B6/032—Transmission computed tomography [CT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/40—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment with arrangements for generating radiation specially adapted for radiation diagnosis
    • A61B6/4064—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment with arrangements for generating radiation specially adapted for radiation diagnosis specially adapted for producing a particular type of beam
    • A61B6/4085—Cone-beams
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection

Abstract

The application relates to an exposure abnormity early warning method, an exposure abnormity early warning device, computer equipment and a storage medium, wherein the exposure abnormity early warning method comprises the following steps: acquiring a plurality of predicted images of an object to be scanned, which are generated based on preset exposure parameters; determining the number of abnormal exposure images in each prediction image according to the gray value of each pixel point in each prediction image, an overexposure gray threshold value and an underexposure gray threshold value; and if the number of the exposure abnormal images is larger than a first number threshold, generating prompt information for adjusting the exposure parameters. The doctor is prompted to adjust the exposure parameters in time through the prompt information, so that overexposure or underexposure in the actual scanning process is avoided, and image artifacts caused by overexposure or underexposure are further avoided.

Description

Exposure abnormity early warning method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of medical imaging technologies, and in particular, to an exposure anomaly early warning method, an exposure anomaly early warning apparatus, a computer device, and a storage medium.
Background
Tumor radiotherapy is a local treatment method for treating tumors by utilizing radioactive rays, and at present, in order to achieve the purpose of precise radiotherapy, imaging equipment is usually arranged on radiotherapy equipment. The imaging device is used for positioning the patient before the radiotherapy is carried out or monitoring the position of the patient in real time when the radiotherapy is carried out.
CBCT is a common imaging device used in radiotherapy devices, and CBCT imaging has become an important imaging mode for image-guided radiotherapy. In use, CBCT produces a cone beam of radiation at a relatively low dose from a source X-ray generator, the cone beam of radiation passes through a patient, and a detector is positioned on the other side of the patient opposite the source X-ray generator, the cone beam of radiation passing through the patient being received by the detector to produce a corresponding image. Positioning or real-time position monitoring is performed through real-time images of the patient.
Detectors for CBCT generally use flat panel detectors based on scintillators and large-scale amorphous silicon arrays, which have the advantages of high resolution, high linearity, etc. Because the flat panel detector has the limitation of the dynamic range of the output gray scale, when the scanning dose is higher or the size of the scanned object is smaller, the overexposure phenomenon can occur, so that the artifact is introduced into the reconstructed image; when the scanning dose is too low or the size of the scanned object is too large, an underexposure phenomenon occurs, which causes an artifact of a reconstructed image.
Disclosure of Invention
The embodiment provides an exposure abnormity early warning method, an exposure abnormity early warning device, an electronic device and a storage medium, so as to solve the problem that image artifacts occur due to overexposure or underexposure in the related art.
In a first aspect, in this embodiment, a method for warning an exposure abnormality is provided, including: acquiring a plurality of predicted images of an object to be scanned, which are generated based on preset exposure parameters; determining the number of abnormal exposure images in each prediction image according to the gray value of each pixel point in each prediction image, an overexposure gray threshold value and an underexposure gray threshold value; and if the number of the exposure abnormal images is larger than a first number threshold, generating prompt information for adjusting the exposure parameters.
In one embodiment, the acquiring a plurality of predicted images of the object to be scanned, which are generated based on the preset exposure parameters, includes: acquiring a CT image and preset exposure parameters of an object to be scanned; determining parameter information of an object to be scanned according to the CT image; and generating a plurality of predicted images according to the preset exposure parameters and the parameter information of the object to be scanned.
In one embodiment, the determining the parameter information of the object to be scanned according to the CT image includes: performing multi-angle forward projection on the CT image to obtain a plurality of DRR images; and determining the parameter information of the object to be scanned of each pixel point under the corresponding angle according to the plurality of DRR images.
In one embodiment, the generating a plurality of predicted images according to the preset exposure parameter and the parameter information of the object to be scanned includes: the preset exposure parameters comprise preset tube current and preset tube voltage; the parameter information of the object to be scanned includes an attenuation coefficient integrated value; acquiring a null field image corresponding to the preset tube voltage and a null field tube current corresponding to the null field image; and generating a plurality of predicted images according to the preset tube current, the empty field image and the attenuation coefficient integral value of each pixel point under the same angle DRR image.
In one embodiment, the generating a plurality of predicted images according to the preset exposure parameter and the parameter information of the object to be scanned includes: the preset exposure parameters comprise preset tube voltage; the parameter information of the object to be scanned comprises the thickness and the attenuation coefficient of the object to be scanned; obtaining the voltage of the preset tube corresponding empty field image and energy spectrum information corresponding to each pixel point in the empty field image; acquiring energy spectrum information corresponding to each pixel point in each DRR image according to the plurality of DRR images; and obtaining a plurality of predicted images according to the empty field image, the energy spectrum information corresponding to each pixel point in the DRR image with the same angle, the attenuation coefficient and the thickness of the object to be scanned.
In one embodiment, the generating a plurality of predicted images according to the preset exposure parameter and the parameter information of the object to be scanned includes: the preset exposure parameters comprise preset exposure dose; the parameter information of the object to be scanned comprises the thickness of the object to be scanned; acquiring the thickness of an object to be scanned corresponding to each pixel point in each DRR image according to the plurality of DRR images; searching a gray value lookup table according to the position corresponding to each pixel point in each DRR image, the thickness of an object to be scanned and a preset exposure dose to generate a plurality of predicted images; the gray value lookup table comprises the positions of the pixel points, the thickness of an object to be scanned and the corresponding relation between the exposure dose and the gray value.
In one embodiment, generating a plurality of predicted images according to the preset exposure parameter and the parameter information of the object to be scanned comprises: and scanning the object to be scanned at a plurality of angles according to the preset exposure parameters to obtain a plurality of predicted images.
In one embodiment, the determining, according to the gray value of each pixel in each of the prediction images, the overexposure gray threshold value, and the underexposure gray threshold value, the number of the abnormal-exposure images in the prediction image includes: determining the number of abnormal exposure pixels in each prediction image according to the gray value of each pixel in each prediction image, an overexposure gray threshold value and an underexposure gray threshold value; and determining the number of the abnormal exposure images in the prediction images according to the number of the abnormal exposure pixel points in each prediction image and a second number threshold.
In one embodiment, determining the number of the abnormal exposure pixels in each prediction image according to the gray value of each pixel in each prediction image, the overexposure gray threshold value and the underexposure gray threshold value includes: taking the pixel points of which the gray values are greater than the overexposure gray threshold value in each predicted image as overexposure pixel points; taking the pixel points of which the gray values are smaller than the underexposure gray threshold value in each predicted image as underexposure pixel points; and counting the number of over-exposed pixels and the number of under-exposed pixels in each predicted image.
In one embodiment, the determining, according to the number of abnormal-exposure pixels in each of the prediction images and the second number threshold, the number of abnormal-exposure images in the prediction image includes: taking the predicted image with the number of over-exposed pixel points larger than a second number threshold value and/or the number of under-exposed pixel points larger than the second number threshold value as an exposure abnormal image; and counting the number of the abnormal exposure images in the predicted image.
In a second aspect, in this embodiment, there is provided an exposure abnormality warning apparatus, including: the acquisition module is used for acquiring a plurality of predicted images of the object to be scanned, which are generated based on preset exposure parameters; the calculation module is used for determining the number of the abnormal exposure images in the prediction images according to the gray value of each pixel point in each prediction image, the overexposure gray threshold value and the underexposure gray threshold value; and the prompting module is used for generating prompting information for adjusting the exposure parameters if the number of the abnormal exposure images is greater than a first number threshold.
In a third aspect, in the present embodiment, there is provided a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the exposure abnormality warning method according to any one of the above.
In a fourth aspect, in the present embodiment, there is provided a computer-readable storage medium, on which a computer program is stored, wherein the computer program is configured to implement the exposure abnormality warning method according to any one of the above-mentioned methods when executed by a processor.
Compared with the related art, the exposure abnormity early warning method provided in the embodiment generates a plurality of prediction images according to the preset exposure parameters of actual scanning in advance before actual scanning, then counts the gray value of each pixel point in each prediction image, compares the gray value of each pixel point with the overexposure gray threshold value and the underexposure gray threshold value to obtain the number of the exposure abnormity images in the prediction images, and when the number of the exposure abnormity images is greater than the first number threshold value, determines that the preset exposure parameters are unreasonable to set, and generates the prompt information for adjusting the exposure parameters. The doctor is prompted to adjust the exposure parameters in time through the prompt information, so that overexposure or underexposure in the actual scanning process is avoided, and image artifacts caused by overexposure or underexposure are further avoided.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of an exposure anomaly early warning method according to an embodiment;
fig. 2 is a flowchart of a method for generating a predictive picture according to an embodiment;
fig. 3 is a flow diagram of a method of generating a predictive picture in accordance with another embodiment;
fig. 4 is a flow diagram of a method of generating a predictive image according to another embodiment;
fig. 5 is a flowchart of a method of generating a predictive picture according to another embodiment;
FIG. 6 is a flow diagram of a method for determining the number of exposure anomaly images provided in one embodiment;
fig. 7 is a block diagram of an exposure abnormality warning apparatus according to an embodiment;
fig. 8 is a schematic hardware configuration diagram of a computer device according to an embodiment.
Detailed Description
For a clearer understanding of the objects, aspects and advantages of the present application, reference is made to the following description and accompanying drawings.
Unless defined otherwise, technical or scientific terms used herein shall have the same general meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The use of the terms "a" and "an" and "the" and similar referents in the context of this application do not denote a limitation of quantity, either in the singular or the plural. The terms "comprises," "comprising," "has," "having," and any variations thereof, as referred to in this application, are intended to cover non-exclusive inclusions; for example, a process, method, and system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or modules, but may include other steps or modules (elements) not listed or inherent to such process, method, article, or apparatus. Reference throughout this application to "connected," "coupled," and the like is not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference to "a plurality" in this application means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. In general, the character "/" indicates a relationship in which the objects associated before and after are an "or". The terms "first," "second," "third," and the like in this application are used for distinguishing between similar items and not necessarily for describing a particular sequential or chronological order.
Cone beam projection computed tomography equipment, also known as Conebeam CT, CBCT for short. CBCT may be used alone or in combination with other medical devices, such as Single Photon Emission Computed Tomography (SPECT) devices, or radiotherapy devices. The radiotherapy equipment is a medical equipment for treating tumor, and alpha, beta and gamma rays or X rays, electron rays, proton beams, other particle beams and other rays generated by the equipment are used for irradiating tumor cells so as to achieve the purpose of killing the tumor cells. In order to avoid irradiating the healthy tissue cells around the tumor cells with the radiation while killing the tumor cells, it is necessary that the radiation can irradiate the tumor cells accurately. By assembling the CBCT equipment on the radiotherapy equipment, before the radiotherapy, the patient is positioned by the CBCT to accurately determine the position of the tumor of the patient, so that the rays can be accurately delivered to the tumor area. During the radiation therapy process, the position of the patient is monitored in real time through the CBCT, the patient is prevented from moving in the therapy process, and the accuracy of the ray delivered to the tumor region is further improved.
CBCT generally includes an X-ray generator and a detector disposed in the path of a beam of radiation generated by the X-ray generator. And, the X-ray generator and the detector are respectively disposed at both sides of the object to be scanned. The object to be scanned may be a human body, a phantom, or other substances that can be imaged under X-rays. When a CBCT is in use, first the X-ray generator produces a cone-shaped beam of radiation which passes through the object to be scanned and is received by the detector. The detector transmits the received data to a processor with computing power, and the processor generates a corresponding image according to the data. Wherein the processor may be a computer device.
The detector of the CBCT in the embodiment of the application is a flat panel detector based on a scintillator large-scale amorphous silicon array, and has the advantages of high resolution, high linearity and the like and is widely applied. But since the flat panel detector has a dynamic range limitation of gray scale, the output gray scale of the exemplary 16bit flat panel detector is 65535. Thus, both overexposure and underexposure can affect the quality of the data received by the detector to produce an image. For example, when the scanning dose is too high or the size of the scanned object is small, an overexposure phenomenon occurs, and projection data is truncated by a saturation value, so that an artifact is introduced into a reconstructed image; when the scanning dose is low or the size of the scanned object is large, an underexposure phenomenon occurs, and rays cannot penetrate through the object to reach a detector, so that reconstructed image artifacts are caused.
In order to solve the above problems, an embodiment of the present application provides an exposure anomaly early warning method, in which before a CBCT performs formal scanning on an object to be scanned, virtual simulated scanning is performed on the object to be scanned according to exposure parameters of the formal scanning, so as to obtain a plurality of predicted images; or real pre-scanning is carried out on the object to be scanned through the exposure parameters of formal scanning to obtain a plurality of predicted images. And determining whether the currently used exposure parameter is applicable or not according to the plurality of predicted images, if not, generating prompt information to prompt a doctor to modify the currently used exposure parameter. Therefore, overexposure or underexposure in the actual scanning process is avoided, and image artifacts caused by overexposure or underexposure are further avoided.
In this embodiment, an exposure anomaly early-warning method is provided, and fig. 1 is a flowchart of the exposure anomaly early-warning method provided in one embodiment, and as shown in fig. 1, the flowchart includes the following steps:
step S100, acquiring a plurality of predicted images of the object to be scanned, which are generated based on the preset exposure parameters.
The exposure parameters used in the CBCT formal exposure scanning are analyzed to determine whether the exposure abnormality occurs in the formal scanning by using the exposure parameters. The imaging image of the exposure parameter can be simulated in a computer virtual calculation mode, and a plurality of predicted images are generated for the object to be scanned, so that the exposure parameter is analyzed; the exposure parameters can also be used for pre-scanning through CBCT to generate a plurality of predicted images aiming at the object to be scanned, so as to analyze the exposure parameters. The preset exposure parameters are exposure parameters prepared for CBCT formal exposure scanning. That is, the exposure parameters for detection analysis are required in the embodiments of the present application. And the plurality of predicted images are generated by analyzing the exposure parameters before the CBCT main exposure scanning and are related to the exposure parameters.
And step S200, determining the number of the abnormal exposure images in the prediction images according to the gray value of each pixel point in each prediction image, the overexposure gray threshold value and the underexposure gray threshold value.
After a plurality of predicted images are obtained, the number of overexposure pixel points and the number of underexposure pixel points in each image are respectively determined aiming at each predicted image, whether the current predicted image is an abnormal exposure image or not is determined according to the number of the overexposure pixel points and the number of the underexposure pixel points, and finally the number of the abnormal exposure images is counted. For example, for a predicted image, firstly, the gray value of each pixel in the predicted image needs to be acquired, then the gray value of each pixel is compared with an overexposure gray threshold and an underexposure gray threshold, and when the number of pixels of which the gray values are greater than the overexposure gray threshold and the underexposure gray threshold is greater than an abnormal pixel threshold, the corresponding abnormal exposure image is taken as an abnormal exposure image. The abnormal pixel point threshold value is the maximum number of pixel points of over-exposed pixel points and under-exposed pixel points allowed to appear in a CBCT image. The abnormal pixel point threshold value can be set according to the requirement in actual use, and the embodiment of the application is not particularly limited. The setting of the overexposure gray level threshold and the underexposure gray level threshold is determined according to performance parameters of the flat panel detector, for example, the flat panel detector used in CBCT is a 16-bit flat panel detector, and the gray level threshold capable of being output by the flat panel detector is 0-65535, at this time, the overexposure threshold may be set to 65535, and the underexposure threshold may be set to 0. The overexposure threshold may also be set to 65500 and the underexposure threshold to 100. The setting of the overexposure gray level threshold and the underexposure gray level threshold is specifically set according to the performance of the detector used in the CBCT and the actual use requirement, and the embodiment of the present application is not particularly limited.
Step S300, if the number of the abnormal exposure images is larger than a first number threshold, prompt information for adjusting the exposure parameters is generated.
Based on the determined number of the exposure abnormal images, if the number of the exposure abnormal images is less than or equal to the first number threshold, it is indicated that the currently used exposure parameters are applicable to the object to be scanned, and the exposure parameters do not need to be adjusted. If the number of the abnormal exposure images is larger than the first number threshold, it indicates that the currently used exposure parameters are not suitable for the object to be scanned, and the exposure parameters need to be adjusted, so that prompt information for adjusting the exposure parameters needs to be generated, and the prompt information is used for prompting a doctor that the exposure parameters need to be adjusted when the current object to be scanned has abnormal exposure. The prompt information can be displayed in a text mode through the display equipment, can be prompted in a sound mode through the voice equipment, and can be prompted in a light mode through the lighting equipment. Only needs to be prompted to the physician, and the prompting mode is not limited in the embodiment of the present application.
In the exposure abnormality early warning method provided in this embodiment, before actual scanning, a plurality of prediction images are generated in advance according to actually scanned preset exposure parameters, then the gray values of each pixel point in each prediction image are counted, the gray values of each pixel point are compared with an overexposure gray threshold and an underexposure gray threshold, the number of exposure abnormality images in the prediction images is obtained, and when the number of exposure abnormality images is greater than a first number threshold, it is determined that the preset exposure parameters are unreasonable in setting, and prompt information for adjusting the exposure parameters is generated. The doctor is prompted to adjust the exposure parameters in time through the prompt information, so that overexposure or underexposure in the actual scanning process is avoided, and image artifacts caused by overexposure or underexposure are further avoided.
As shown in fig. 2, fig. 2 is a flowchart of a method for generating a prediction image according to an embodiment, where the flowchart includes the following steps:
step S110, a CT image of the object to be scanned and a preset exposure parameter are acquired.
For tumor patients, before treatment by radiotherapy equipment, the patient needs to be imaged by a CT equipment to obtain a CT image. The tumor position of the patient is determined through the CT image, the tumor is outlined in the CT image, and a treatment plan is made based on the CT image after the delineation is completed. When the radiotherapy equipment is actually used for tumor treatment, a patient is placed at a proper position according to the CT image and the CBCT image, and finally treatment is carried out according to the formulated treatment plan. Thus, for the object to be scanned, a corresponding CT image already exists before CBCT imaging is performed. At this time, it is only necessary to extract the CT image of the corresponding object to be scanned from the storage medium. The exposure parameters are set as exposure parameters to be used in the CBCT formal exposure scanning. The preset exposure parameters can be directly acquired through an external input device. Or the preset exposure parameters which are predicted to be suitable for the current object to be scanned based on a deep learning algorithm.
And step S120, determining the parameter information of the object to be scanned according to the CT image.
After the CT image is obtained, it is necessary to determine parameter information of the patient from the CT image. In an example, firstly, a CT image is subjected to multi-angle forward projection to obtain a plurality of DRR images; and determining the parameter information of the object to be scanned of each pixel point under the corresponding angle according to the plurality of DRR images. The CT image is a three-dimensional image, and the multi-angle forward projection can be that the CT image is forward projected at the current angle, and then the CT image is forward projected after being rotated for a certain angle according to a certain rotation angle, so that a plurality of DRR images are obtained. After obtaining a plurality of DRR images, determining at least one of the thickness, the attenuation coefficient integral value, the attenuation coefficient and the energy spectrum information of the object to be scanned corresponding to each pixel point position in each DRR image.
Step S130, generating a plurality of predicted images according to the preset exposure parameters and the parameter information of the object to be scanned.
And calculating a predicted image corresponding to each DRR angle based on the obtained parameter information of the object to be scanned and the preset exposure parameters. For example, the obtained DRR images are DRR images at three angles, respectively, so that the obtained parameter information of the object to be scanned, that is, the parameter information corresponding to the three angles, is obtained. That is, each pixel point in the DRR image of the first angle has parameters such as corresponding thickness, attenuation coefficient integral, attenuation coefficient, energy spectrum information, and the like; corresponding parameter information also exists in each pixel point of the DRR image at the second angle and the DRR image at the third angle. And obtaining a predicted image under the corresponding angle based on the parameter information of the object to be scanned corresponding to each pixel point under each angle and the preset exposure parameter. One predicted image can be obtained for each angle, that is, a plurality of predicted images can be obtained. The method for obtaining the predicted image may be a software simulation method, a lookup table establishment method, or a calculation method using a corresponding formula.
As shown in fig. 3, fig. 3 is a flowchart of a method for generating a prediction image according to another embodiment, where the flowchart includes the following steps:
step S131a, a null field image corresponding to the preset tube voltage and a null field tube current corresponding to the null field image are obtained.
In this embodiment, the preset exposure parameters include a preset tube current and a preset tube voltage; the parameter information of the object to be scanned includes an attenuation coefficient integrated value. The parameter information of the object to be scanned includes an attenuation coefficient integral value of each pixel point under a plurality of angles corresponding to the DRR image. Firstly, according to a preset tube voltage in preset exposure parameters, obtaining a null field image corresponding to the preset tube voltage, wherein the null field image is an image formed by directly receiving generated rays by a detector when a scanning object is not placed in the CBCT. And the empty field image corresponding to the preset tube voltage is an image formed by generating a ray bundle by using the preset tube voltage and directly receiving the ray bundle by a detector. After the null field image is acquired, the corresponding null field tube current at the time the null field image was generated is acquired.
Step S132a, generating a plurality of prediction images according to the preset tube current, the empty field image and the attenuation coefficient integral value of each pixel point under the same angle DRR image.
After the parameters are obtained, calculating the pixel value of each pixel point of the predicted image under the corresponding angle through the following formula, and finally combining the pixel value of each pixel point into the predicted image of the corresponding angle:
g(x，y，θ)＝R*Iair(x，y)*exp[-p(x，y，θ)]
wherein g (x, y, theta) is the gray value of each pixel point under the corresponding angle; r is the ratio of the current of the preset tube to the current of the empty field tube; i isairAnd (x, y) is the gray value of the empty field image, and p (x, y, theta) is the integral value of the attenuation coefficient of each pixel point under the corresponding angle.
According to the formula, a predicted image corresponding to each angle of the DRR image is calculated.
As shown in fig. 4, fig. 4 is a flowchart of a method for generating a prediction image according to another embodiment, where the flowchart includes the following steps:
step S131b, a null field image corresponding to the preset tube voltage and energy spectrum information corresponding to each pixel point in the null field image are obtained.
The preset exposure parameters comprise preset tube current and preset tube voltage; the parameter information of the object to be scanned includes an attenuation coefficient. The parameter information of the object to be scanned includes energy spectrum information of each pixel point under a plurality of angles corresponding to the DRR image. The energy spectrum information is the energy value of all photons received by each pixel point. Firstly, according to a preset tube voltage in a preset exposure parameter, a null field image corresponding to the preset tube voltage is obtained, wherein the null field image is an image formed by directly receiving a generated ray by a detector when a scanning object is not placed in the CBCT. And the empty field image corresponding to the preset tube voltage is an image formed by generating a ray bundle by using the preset tube voltage and directly receiving the ray bundle by a detector. After the null field image is obtained, for each pixel point of the null field image, the energy values of all photons received corresponding to all the pixel points are obtained.
Step S132b, acquiring the energy spectrum information corresponding to each pixel point in each DRR image according to the plurality of DRR images.
After obtaining the plurality of DRR images, the DRR images include, for example, DRR images at three angles. After obtaining the DRR images, acquiring the energy values of all photons received by each pixel point in each DRR image according to each DRR image.
And step S133b, obtaining a plurality of predicted images according to the empty field image, the energy spectrum information corresponding to each pixel point in the DRR image with the same angle, the attenuation coefficient and the thickness of the object to be scanned.
After the parameters are obtained, calculating the pixel value of each pixel point of the predicted image under the corresponding angle through the following formula, and finally combining the pixel value of each pixel point into the predicted image of the corresponding angle:
g(x，y，θ)＝∫I(x，y，E)*exp[-μp(x，y，E)*T(x，y，θ)]dE
wherein g (x, y, theta) is the gray value of each pixel point under the corresponding angle; i (x, y, E) is a null field image and energy spectrum information corresponding to each pixel point in the null field image; mu.sp(x, y, E) is the attenuation coefficient and energy spectrum information corresponding to each pixel point in the DRR image; and T (x, y, theta) is the thickness of the object to be scanned corresponding to each pixel point in the DRR image.
According to the formula, a predicted image corresponding to each angle of the DRR image is calculated.
As shown in fig. 5, fig. 5 is a flowchart of a method for generating a prediction image according to another embodiment, where the flowchart includes the following steps:
step 131c, according to the plurality of DRR images, obtaining the thickness of the object to be scanned corresponding to each pixel point in each DRR image.
After obtaining the plurality of DRR images, the DRR images include, for example, DRR images at three angles. After obtaining the DRR images, the thickness of the object to be scanned corresponding to each pixel point in each DRR image is obtained according to each DRR image.
Step S132c, searching a gray value lookup table to generate a plurality of predicted images according to the position corresponding to each pixel point in each DRR image, the thickness of the object to be scanned and the preset exposure dose; the gray value lookup table comprises the positions of the pixel points, the thickness of an object to be scanned and the corresponding relation between the exposure dose and the gray value.
Before the embodiment of the application is used, the gray value lookup table needs to be constructed firstly. For example, different exposure doses can be set in advance through the CBCT apparatus to scan phantoms with different thicknesses to obtain corresponding images, and then the gray values at different positions in the images are obtained. Therefore, the corresponding relation among the positions of the pixel points, the thickness of the object to be scanned and the exposure dose and the gray value is established. And storing the established gray value lookup table as a background configuration file. After the thickness of the object to be scanned corresponding to each pixel point in each DRR image is obtained, acquiring a preset exposure dose to be used in formal exposure scanning of the CBCT; and searching a gray value lookup table according to the position corresponding to each pixel point in each DRR image, the thickness of the object to be scanned and the preset exposure dose to obtain the pixel gray value of each pixel point position at the corresponding angle. And finally, combining the pixel values of all the pixel points into a predicted image of a corresponding angle.
In one embodiment, the object to be scanned may be scanned at multiple angles according to the preset exposure parameter, so as to obtain multiple predicted images. That is, before the CBCT main exposure scan, a plurality of angles may be selected, and the pre-scan may be performed on the object to be scanned at the plurality of angles by using the preset exposure parameters, so as to obtain the predicted images at the plurality of angles.
In one embodiment, since the human body section can be approximately regarded as an ellipse, the images in the ranges of 0-180 degrees and 180-360 degrees are approximate. That is, the angles of the plurality of predicted images need only be within 0-180 °, or 180-360 °. The calculation process can be simplified and the calculation process can be simplified, the calculation amount is reduced.
As shown in fig. 6, fig. 6 is a flowchart of a method for determining the number of abnormal exposure images according to an embodiment, where the flowchart includes the following steps:
step S210, determining the number of the abnormal exposure pixels in each prediction image according to the gray value of each pixel in each prediction image, the overexposure gray threshold value and the underexposure gray threshold value.
After a plurality of prediction images are obtained, the gray value of each pixel point in each prediction image is extracted according to the plurality of prediction images, and then the gray value of each pixel point is compared with an overexposure gray threshold value and an underexposure gray threshold value respectively to determine the number of the exposure abnormal pixel points in each prediction image. The pixel points with abnormal exposure are the pixel points of which the gray value is greater than the overexposure gray threshold value and the pixel points of which the gray value is less than the underexposure gray threshold value. Illustratively, the pixel points with the gray value of the pixel points in each predicted image being greater than the overexposure gray threshold are taken as overexposure pixel points; taking the pixel points of which the gray values of the pixel points in each predicted image are smaller than the underexposure gray threshold value as underexposure pixel points; and counting the number of over-exposed pixels and the number of under-exposed pixels in each predicted image.
Step S220, determining the number of the abnormal exposure images in the prediction images according to the number of the abnormal exposure pixel points in each prediction image and the second number threshold.
After the number of the abnormal exposure pixels is determined, whether the corresponding predicted image is the abnormal exposure image needs to be determined according to the number of the abnormal exposure pixels in each image and a second number threshold, and finally, the number of the abnormal exposure images needs to be counted. Illustratively, the predicted image with the number of the overexposure pixels larger than the second number threshold is taken as an exposure abnormal image. That is, only the number of overexposed pixels in one predicted image is counted, and if the number of overexposed pixels is greater than the second number threshold, the corresponding predicted image is an exposure abnormal image. The predicted image with the number of the underexposed pixel points larger than the second number threshold value can also be used as an exposure abnormal image. That is, only the number of underexposed pixels in one predicted image is counted, and if the number of underexposed pixels is greater than the second number threshold, the corresponding predicted image is an exposure abnormal image. The number of over-exposed pixel points in the predicted image and the number of under-exposed pixel points in the predicted image can be counted; if the number of the over-exposed pixel points is larger than the second number threshold, the corresponding predicted image is an exposure abnormal image; if the number of the underexposed pixel points is larger than the second number threshold value, the corresponding predicted image is an exposure abnormal image; and if the sum of the number of the over-exposed pixel points and the number of the under-exposed pixel points is greater than a second number threshold, the corresponding predicted image is an exposure abnormal image. And finally counting the number of the abnormal exposure images in the predicted image.
The exposure abnormity early warning method can avoid the phenomenon of abnormal exposure and further avoid introducing reconstructed image artifacts. Firstly, the output gray scale of each pixel of a detector flat plate is predicted before CBCT scanning can be carried out through a CT image of an object to be scanned, and when the number of the pixel points exceeding a set gray scale range exceeds an allowable value, the system carries out exposure parameter adjustment prompting so as to enable a doctor to carry out adjustment on the exposure parameters. By prompting the abnormal exposure parameters, the exposure parameters can be timely adjusted, the abnormal exposure phenomenon can be better avoided, and the introduction of reconstructed image artifacts is further avoided.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The embodiment further provides an exposure abnormality warning device, which is used for implementing the above embodiments and preferred embodiments, and the description of the device is omitted. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 7 is a block diagram of a structure of an exposure abnormality warning apparatus according to an embodiment, as shown in fig. 7, the apparatus includes: an acquisition module 100, a calculation module 200, and a prompt module 300.
The acquiring module 100 is configured to acquire a plurality of predicted images of the object to be scanned, which are generated based on preset exposure parameters.
The calculating module 200 is configured to determine the number of the abnormal exposure images in each of the prediction images according to the gray value of each pixel in each of the prediction images, the overexposure gray threshold value, and the underexposure gray threshold value.
The prompt module 300 is configured to generate prompt information for adjusting the exposure parameters if the number of the abnormal exposure images is greater than a first number threshold.
The acquisition module 100 includes: the device comprises an acquisition unit, a parameter information calculation unit and a prediction image generation unit.
And the acquisition unit is used for acquiring the CT image of the object to be scanned and the preset exposure parameters.
And the parameter information calculation unit is used for determining the parameter information of the object to be scanned according to the CT image.
And the predicted image generating unit is used for generating a plurality of predicted images according to the preset exposure parameters and the parameter information of the object to be scanned.
The parameter information calculation unit is also used for carrying out multi-angle forward projection on the CT image to obtain a plurality of DRR images; and determining the parameter information of the object to be scanned of each pixel point under the corresponding angle according to the plurality of DRR images.
The preset exposure parameters comprise preset tube current and preset tube voltage; the parameter information of the object to be scanned includes an attenuation coefficient integrated value; the predicted image generating unit is further used for acquiring a null field image corresponding to the preset tube voltage and a null field tube current corresponding to the null field image; and generating a plurality of predicted images according to the preset tube current, the empty field image and the attenuation coefficient integral value of each pixel point under the same angle DRR image.
The preset exposure parameters comprise preset tube voltage; the parameter information of the object to be scanned comprises the thickness and the attenuation coefficient of the object to be scanned; the predicted image generating unit is further used for acquiring a null field image corresponding to the preset tube voltage and energy spectrum information corresponding to each pixel point in the null field image; acquiring energy spectrum information corresponding to each pixel point in each DRR image according to the plurality of DRR images; and obtaining a plurality of predicted images according to the empty field image, the energy spectrum information corresponding to each pixel point in the DRR image with the same angle, the attenuation coefficient and the thickness of the object to be scanned.
The preset exposure parameters comprise preset exposure dose; the parameter information of the object to be scanned comprises the thickness of the object to be scanned; the predicted image generating unit is also used for acquiring the thickness of the object to be scanned corresponding to each pixel point in each DRR image according to the plurality of DRR images; searching a gray value lookup table according to the position corresponding to each pixel point in each DRR image, the thickness of an object to be scanned and a preset exposure dose to generate a plurality of predicted images; the gray value lookup table comprises the positions of the pixel points, the thickness of an object to be scanned and the corresponding relation between the exposure dose and the gray value.
And the predicted image generating unit is also used for scanning the object to be scanned at a plurality of angles according to the preset exposure parameters to obtain a plurality of predicted images.
The calculating module 200 is further configured to determine the number of exposure abnormal pixels in each prediction image according to the gray value of each pixel in each prediction image, the overexposure gray threshold value and the underexposure gray threshold value; and determining the number of the abnormal exposure images in the prediction images according to the number of the abnormal exposure pixel points in each prediction image and a second number threshold.
The calculating module 200 is further configured to use the pixel points with the gray values larger than the overexposure gray threshold value in each predicted image as overexposure pixel points; taking the pixel points of which the gray values are smaller than the underexposure gray threshold value in each predicted image as underexposure pixel points; and counting the number of over-exposed pixels and the number of under-exposed pixels in each predicted image.
The calculating module 200 is further configured to use the predicted image with the number of over-exposed pixels greater than the second number threshold and/or the number of under-exposed pixels greater than the second number threshold as an exposure abnormal image; and counting the number of the abnormal exposure images in the predicted image.
It should be noted that the above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
In addition, the exposure anomaly early warning method described in conjunction with fig. 1 in the embodiment of the present application may be implemented by a computer device. Fig. 8 is a schematic hardware configuration diagram of a computer device according to an embodiment.
The computer device may comprise a processor 81 and a memory 82 in which computer program instructions are stored.
Specifically, the processor 81 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
Memory 82 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 82 may include a Hard Disk Drive (Hard Disk Drive, abbreviated to HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 82 may include removable or non-removable (or fixed) media, where appropriate. The memory 82 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 82 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, memory 82 includes Read-Only Memory (ROM) and Random Access Memory (RAM). The ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (earrom), or FLASH Memory (FLASH), or a combination of two or more of these, where appropriate. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended data output Dynamic Random-Access Memory (EDODRAM), a Synchronous Dynamic Random-Access Memory (SDRAM), and the like.
The memory 82 may be used to store or cache various data files that need to be processed and/or used for communication, as well as possible computer program instructions executed by the processor 81.
The processor 81 reads and executes the computer program instructions stored in the memory 82 to implement any one of the exposure abnormality warning methods in the above-described embodiments.
In some of these embodiments, the computer device may also include a communication interface 83 and a bus 80. As shown in fig. 8, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.
The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
Bus 80 includes hardware, software, or both coupling the components of the computer device to each other. Bus 80 includes, but is not limited to, at least one of the following: data Bus (Data Bus), address Bus (Address Bus), control Bus (Control Bus), expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example and not limitation, bus 80 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a vlslave Bus, a Video Bus, or a combination of two or more of these suitable electronic buses. Bus 80 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
The computer device may execute the exposure abnormality early warning method in the embodiment of the present application based on the acquired computer program, thereby implementing the exposure abnormality early warning method described with reference to fig. 1.
In addition, in combination with the exposure anomaly early warning method in the above embodiments, the embodiments of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the above-described exposure anomaly early warning methods.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (13)

1. An exposure abnormity early warning method is characterized by comprising the following steps:
acquiring a plurality of predicted images of an object to be scanned, which are generated based on preset exposure parameters;
determining the number of abnormal exposure images in each prediction image according to the gray value of each pixel point in each prediction image, an overexposure gray threshold value and an underexposure gray threshold value;
and if the number of the abnormal exposure images is larger than a first number threshold, generating prompt information for adjusting exposure parameters.
2. The abnormal exposure warning method according to claim 1, wherein the obtaining of a plurality of predicted images of the object to be scanned, which are generated based on the preset exposure parameters, comprises:
acquiring a CT image and preset exposure parameters of an object to be scanned;
determining parameter information of an object to be scanned according to the CT image;
and generating a plurality of predicted images according to the preset exposure parameters and the parameter information of the object to be scanned.
3. The method for early warning of exposure abnormality according to claim 2, wherein the determining the parameter information of the object to be scanned according to the CT image includes:
performing multi-angle forward projection on the CT image to obtain a plurality of DRR images;
and determining the parameter information of the object to be scanned of each pixel point under the corresponding angle according to the plurality of DRR images.
4. The method for early warning of abnormal exposure according to claim 3, wherein the generating a plurality of predicted images according to the preset exposure parameters and the parameter information of the object to be scanned comprises: the preset exposure parameters comprise preset tube current and preset tube voltage; the parameter information of the object to be scanned includes an attenuation coefficient integrated value;
acquiring a null field image corresponding to the preset tube voltage and a null field tube current corresponding to the null field image;
and generating a plurality of predicted images according to the preset tube current, the empty field image and the attenuation coefficient integral value of each pixel point under the same angle DRR image.
5. The method for early warning of abnormal exposure according to claim 3, wherein the generating a plurality of predicted images according to the preset exposure parameters and the parameter information of the object to be scanned comprises: the preset exposure parameters comprise preset tube voltage; the parameter information of the object to be scanned comprises the thickness and the attenuation coefficient of the object to be scanned;
acquiring a null field image corresponding to the preset tube voltage and energy spectrum information corresponding to each pixel point in the null field image;
acquiring energy spectrum information corresponding to each pixel point in each DRR image according to the plurality of DRR images;
and obtaining a plurality of predicted images according to the empty field image, the energy spectrum information corresponding to each pixel point in the DRR image with the same angle, the attenuation coefficient and the thickness of the object to be scanned.
6. The method for early warning of abnormal exposure according to claim 3, wherein the generating a plurality of predicted images according to the preset exposure parameters and the parameter information of the object to be scanned comprises: the preset exposure parameters comprise preset exposure dose; the parameter information of the object to be scanned comprises the thickness of the object to be scanned;
acquiring the thickness of an object to be scanned corresponding to each pixel point in each DRR image according to the plurality of DRR images;
searching a gray value lookup table according to the position corresponding to each pixel point in each DRR image, the thickness of an object to be scanned and a preset exposure dose to generate a plurality of predicted images; the gray value lookup table comprises the positions of the pixel points, the thickness of an object to be scanned and the corresponding relation between the exposure dose and the gray value.
7. The method for early warning of abnormal exposure according to claim 2, wherein generating a plurality of predicted images according to the preset exposure parameters and the parameter information of the object to be scanned comprises:
and scanning the object to be scanned at a plurality of angles according to the preset exposure parameters to obtain a plurality of predicted images.
8. The method for early warning of abnormal exposure according to claim 2, wherein the determining the number of abnormal exposure images in the prediction images according to the gray value of each pixel in each prediction image, the overexposure gray threshold value and the underexposure gray threshold value comprises:
determining the number of abnormal exposure pixels in each prediction image according to the gray value of each pixel in each prediction image, an overexposure gray threshold value and an underexposure gray threshold value;
and determining the number of the abnormal exposure images in the prediction images according to the number of the abnormal exposure pixel points in each prediction image and a second number threshold.
9. The abnormal exposure early warning method according to claim 8, wherein the determining the number of the abnormal exposure pixels in each predicted image according to the gray value of each pixel in each predicted image, the overexposure gray threshold value and the underexposure gray threshold value comprises:
taking the pixel points of which the gray values are greater than the overexposure gray threshold value in each predicted image as overexposure pixel points;
taking the pixel points of which the gray values are smaller than the underexposure gray threshold value in each predicted image as underexposure pixel points;
and counting the number of over-exposed pixels and the number of under-exposed pixels in each predicted image.
10. The method of claim 9, wherein the determining the number of the abnormal-exposure images in the predictive image according to the number of the abnormal-exposure pixels in each predictive image and a second number threshold comprises:
taking the predicted image with the number of over-exposed pixel points larger than a second number threshold value and/or the number of under-exposed pixel points larger than the second number threshold value as an exposure abnormal image;
and counting the number of the abnormal exposure images in the predicted image.
11. An exposure abnormality warning device, characterized by comprising:
the acquisition module is used for acquiring a plurality of predicted images of the object to be scanned, which are generated based on preset exposure parameters;
the calculation module is used for determining the number of the abnormal exposure images in the prediction images according to the gray value of each pixel point in each prediction image, the overexposure gray threshold value and the underexposure gray threshold value;
and the prompting module is used for generating prompting information for adjusting the exposure parameters if the number of the abnormal exposure images is greater than a first number threshold.
12. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the exposure abnormality warning method according to any one of claims 1 to 10 when executing the computer program.
13. A computer-readable storage medium on which a computer program is stored, the program realizing the exposure abnormality warning method according to any one of claims 1 to 10 when executed by a processor.
CN202210690863.0A 2022-06-17 2022-06-17 Exposure abnormity early warning method and device, computer equipment and storage medium Pending CN115251958A (en)

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