CN110742639A - Scanning system configuration method and device, computer equipment and readable storage medium - Google Patents
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Abstract
The application relates to a scanning system configuration method, a scanning system configuration device, computer equipment and a computer readable storage medium, wherein the scanning system configuration method comprises the following steps: acquiring a scanning bed image of a scanning system; identifying the scanning bed image to obtain the type of the scanning bed; configuring configuration item parameters of the scanning system according to the type of the scanning bed. The scanning system configuration method can automatically acquire the type of the scanning bed board, automatically set the configuration item information of the scanning system according to the type of the scanning bed board, and realize the automatic identification of bed board configuration.
Description
Technical Field
The present invention relates to the medical field, and in particular, to a scanning system configuration method, apparatus, computer device, and readable storage medium.
Background
In clinical applications of PET/CT and CT systems, the scanning bed usually uses an arc-shaped bed plate to improve patient comfort. In order to support radiotherapy, a flat bed plate needs to be replaced. And the part of scenes support scientific research purposes, and the bed plate of the scanning bed can be replaced by a bed plate of a small animal. The configuration of the scanning system needs to be adjusted accordingly for different bed plate types.
Conventionally, a user is required to observe the bed board type of a scanning bed, and then configuration item information of a scanning system is manually set according to the bed board type, so that the operation process is complicated.
Disclosure of Invention
The application provides a scanning system configuration method, a scanning system configuration device, computer equipment and a readable storage medium, which can automatically configure configuration item information of a scanning system and realize automatic identification of bed board configuration.
A scanning system configuration method, the method comprising:
acquiring a scanning bed image of a scanning system;
identifying the scanning bed image to obtain the type of the scanning bed;
configuring configuration item parameters of the scanning system according to the type of the scanning bed.
In one embodiment, the acquiring a scanning bed image of a scanning system comprises:
acquiring a scanning bed surface profile image acquired by a camera, and/or
Acquiring an image of the scanning bed positioning sheet obtained by scanning the scanning bed through a scanning system;
and taking the scanning bed surface contour image and/or the scanning bed positioning sheet image as the scanning bed image.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes: a visual mark is arranged on the scanning bed and used for marking the type of the scanning bed;
acquiring visual sign information contained in the scanning bed image;
and determining the type of the scanning bed according to the visual mark information.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes:
acquiring electron density difference information contained in the scanning bed image;
and determining the type of the scanning bed according to the electron density difference information.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes:
and inputting the scanning bed image into a deep learning model to obtain the type of the scanning bed.
In an embodiment, the inputting the scan bed image into a deep learning model, and obtaining the type of the scan bed comprises:
constructing an initial model;
acquiring scanning bed images corresponding to all scanning bed types, and taking all scanning bed types and corresponding scanning bed images as a training set;
and training the initial model according to the training set to obtain a deep learning model.
In one embodiment, the automatically configuring the configuration item parameters of the scanning system according to the type of the scanning bed comprises:
reading a configuration file which is stored in a console and corresponds to the type of the scanning bed, wherein the configuration file comprises various configuration item parameters;
the configuration item parameters at least comprise one of a sinking curve parameter, an interference curve parameter and a registration matrix parameter.
A scanning system configuration apparatus, the apparatus comprising:
the acquisition module is used for acquiring a scanning bed image of the scanning system;
the identification module is used for identifying the scanning bed image to obtain the type of the scanning bed;
and the configuration module is used for configuring configuration item parameters of the scanning system according to the type of the scanning bed.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
The scanning system configuration method, the scanning system configuration device, the computer equipment and the computer readable storage medium provided by the embodiment of the application comprise the steps of obtaining a scanning bed image of a scanning system; identifying the scanning bed image to obtain the type of the scanning bed; configuring configuration item parameters of the scanning system according to the type of the scanning bed. The scanning system configuration method can automatically acquire the type of the scanning bed board, automatically set the configuration item information of the scanning system according to the type of the scanning bed board, and realize the automatic identification of bed board configuration.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a scanning system configuration method according to an embodiment;
FIG. 2 is a schematic view of a visual marker provided in accordance with an embodiment;
FIG. 3 is a flow diagram for training a deep learning model according to an embodiment;
FIG. 4 is a block diagram of an apparatus for configuring a scanning system in one embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth to provide a thorough understanding of the present application, and in the accompanying drawings, preferred embodiments of the present application are set forth. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. This application is capable of embodiments in many different forms than those described herein and those skilled in the art will be able to make similar modifications without departing from the spirit of the application and it is therefore not intended to be limited to the specific embodiments disclosed below.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. In the description of the present application, "a number" means at least one, such as one, two, etc., unless specifically limited otherwise.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Positron Emission Tomography (PET) is an advanced clinical examination imaging technique in the field of nuclear medicine. It is to take certain substances, generally necessary in the metabolism of biological life, such as: glucose, protein, nucleic acid, fatty acid, short-lived radionuclides (such as 18F, 11C, etc.) labeled with a label, when injected into a human body, the radionuclides release positrons during decay, and a positron travels from a few tenths of a millimeter to a few millimeters and encounters an electron to be annihilated, thereby generating a pair of photons with energy of 511KeV in opposite directions. This is a pair of photons captured by a highly sensitive camera and corrected for scatter and random information by a computer. By carrying out the same analysis processing on different positrons, a three-dimensional image of the aggregation condition in a living body can be obtained, thereby achieving the purpose of diagnosis.
A Computed Tomography (CT) apparatus typically includes a gantry, a couch, and a console for operation by a physician. One side of the frame is provided with a bulb tube, and the side opposite to the bulb tube is provided with a detector. The console is a computer device for controlling scanning, and the computer device is also used for receiving scanning data acquired by the detector, processing and reconstructing the data and finally forming a CT image. When CT is used for scanning, a patient lies on a scanning bed, the scanning bed sends the patient into the aperture of a stand, a bulb tube arranged on the stand emits X rays, the X rays penetrate through the patient and are received by a detector to form scanning data, the scanning data are transmitted to computer equipment, and the computer equipment carries out primary processing and image reconstruction on the scanning data to obtain a CT image.
Fig. 1 is a flowchart of a scan system configuration method according to an embodiment, and as shown in fig. 1, the scan system configuration method includes steps 110 to 130, where:
The scanning system comprises a scanning device positioned in the shielding chamber, the scanning device surrounds to form a scanning cavity, and the scanning cavity is a containing part of an object to be scanned in the scanning process. The scanning system also includes a scanning bed located within the shielded room, and the scanning bed is movable along the scanning chamber. The object to be scanned is laid on the scanning bed, and the object to be scanned is carried by the scanning bed and transferred from the outside of the scanning device to the scanning cavity. Specifically, the scanning bed comprises a bed plate, and an object to be scanned is placed on the bed plate for image scanning. The bed board of the scanning bed can be of various types, and the type of the bed board can be selected according to the actual application scene. Generally, in order to improve the comfort of an object to be scanned, an arc bed plate is generally adopted; in the radiotherapy process, a flat bed plate is needed; and part of using scenes can be replaced by the small animal bed board for scientific research.
Before performing the scan, a scan bed image is first acquired to find the type of the scan bed from the scan bed image.
In one embodiment, the acquiring a scanning bed image of a scanning system comprises:
and acquiring a scanning bed surface contour image acquired by a camera, and taking the scanning bed surface contour image as the scanning bed image.
The camera may be disposed in the scanning chamber, or may be disposed outside the scanning chamber and above the scanning bed, and the specific disposition position of the camera is not limited in this embodiment, as long as it is ensured that an image of the scanning bed of the scanning system can be acquired. Preferably, the camera can be arranged right above the scanning bed, so that the scanning bed surface contour image can be acquired well.
It will be appreciated that when the camera is located within the scanning chamber, the scanning bed is moved from outside the scanning apparatus into the scanning chamber and reaches a predetermined scanning position before it is taken to capture an image of the bed surface profile. When the camera is arranged outside the scanning cavity, the shooting can be started outside the scanning device so as to acquire the scanning bed surface contour image.
The scanning bed surface contour image may be obtained by processing an image shot by a camera, and specifically, the image shot by the camera may be subjected to gray scale processing first, and then a frame is extracted by using an image gradient algorithm to obtain the scanning bed surface contour image.
In one embodiment, the acquiring a scanning bed image of a scanning system comprises:
acquiring an image of the scanning bed positioning sheet obtained by scanning the scanning bed through a scanning system; and taking the scanning bed positioning sheet image as the scanning bed image.
Scanning systems in the present application, including but not limited to scanning devices, may be medical imaging devices, including: positron Emission Tomography (PET), Magnetic Resonance imaging (MR), Computed Tomography (CT), and scanning apparatuses combining these apparatuses.
If the image of the bed spacer is obtained by a positron emission computed tomography system, a certain substance can be injected into the bed, and the image of the aggregation condition in the bed can be obtained by carrying out the same analysis processing on different positrons, so that the image of the bed spacer is obtained.
If the scanning bed positioning sheet image is obtained through the computed tomography system, the bulb tube arranged on the rack can be controlled to emit X rays, the X rays penetrate through the scanning bed and are received by the detector to form scanning data, the scanning data are transmitted to the computer equipment, and the computer equipment processes the scanning data to obtain the scanning bed positioning sheet image. Specifically, when the operator issues an acquisition command, the scanning system responds to the acquisition command and automatically adjusts the corresponding components of the scanning system to a preset position (e.g., the X-ray tube is adjusted to the 12 o ' clock position, or the 3 o ' clock position, or the 9 o ' clock position) to acquire the image of the bed spacer.
It should be noted that the scanning system for obtaining the image of the bed locator card may be the same system as the current scanning system, or may be executed by another system, and the embodiment of the scanning system for obtaining the image of the bed locator card is not limited.
And 120, identifying the scanning bed image to obtain the type of the scanning bed.
In one embodiment, as shown in fig. 2, the scanning bed is provided with a visual mark for identifying the type of the scanning bed. The bed board of each scanning bed carries a corresponding visual mark, and the visual mark uniquely identifies the type of the bed board. And obtaining the type of the scanning bed by obtaining the visible mark information contained in the scanning bed image. The visual marks can be patterns, letters and numbers, the specific form is not limited, the visual marks can be obtained by opening the scanning bed, and the structure of the scanning bed can be changed by the visual marks obtained by opening the scanning bed.
It will be appreciated that when the type of bed is determined by identifying visible markings provided on the bed, the bed surface profile image and the bed spacer image may be captured or scanned only for areas where visible markings are present.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes:
acquiring electron density difference information contained in the scanning bed image;
and determining the type of the scanning bed according to the electron density difference information.
Electron density (electron density), also known as electron beam density, refers to the density of the material scattering the electron beam, and in particular, refers to the attenuation coefficient of the beam. CT scanning is a technique of reconstructing a tomographic image by combining a computer with X-rays and using the difference in attenuation coefficients of the X-rays in different regions of an object to be scanned to make the radiation signals received by a detector different. For the scanning bed positioning sheet image, the structure of the scanning bed is different due to different types of the scanning bed, for example, the structure of a flat bed and an arc bed is different. In addition, in order to distinguish the type of the scanning bed more obviously, a visible mark can be arranged on the bed board of the scanning bed, and the structure of the scanning bed can be changed through the visible mark, for example, a hollow structure is arranged. According to the method and the device, the X-ray penetrates through the scanning bed, and the ray attenuation coefficients corresponding to different scanning bed types are different, so that the type of the scanning bed can be determined according to the ray attenuation coefficient distribution difference in the scanning bed locating piece image.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes:
and inputting the scanning bed image into a deep learning model to obtain the type of the scanning bed.
Specifically, after the scanning bed images are input into the deep learning model, the scanning bed images are classified through the deep learning model, and the scanning bed type corresponding to each type of scanning bed image is matched.
In an embodiment, the inputting the scan bed image into a deep learning model, and obtaining the type of the scan bed comprises:
and constructing an initial model. After the initial model is built, the initial model needs to be trained. The number of training samples is not limited in this embodiment, and the greater the number of training samples is, the stronger the robustness of the deep learning model obtained by training is.
And acquiring scanning bed images corresponding to all scanning bed types, and taking all scanning bed types and corresponding scanning bed images as a training set.
And training the initial model according to the training set to obtain a deep learning model.
Specifically, the scanning bed images in the training set are firstly input into the initial model, and the output first scanning bed type is obtained. And if the first scanning bed type is not consistent with the target scanning bed type corresponding to the scanning bed type, adjusting the parameters of the initial model to enable the first scanning bed type output by the deep learning model to be consistent with the target scanning bed type. And when any scanning bed image is input, the target scanning bed type corresponding to the scanning bed image can be obtained, and then the current initial model is determined to be a trained deep learning model. The scanning bed type corresponding to the scanning bed image can be output according to the input scanning bed image through the trained deep learning model.
In one embodiment, the automatically configuring the configuration item parameters of the scanning system according to the type of the scanning bed comprises:
reading a configuration file which is stored in a console and corresponds to the type of the scanning bed, wherein the configuration file comprises various configuration item parameters;
the configuration item parameters at least comprise one of a sinking curve parameter, an interference curve parameter and a registration matrix parameter.
Specifically, configuration files corresponding to the scanning bed types may be pre-stored in a console of the host, and each configuration file may carry identification information for uniquely identifying the scanning bed type corresponding thereto. After the type of the scanning bed is identified, the configuration file corresponding to the type of the scanning bed and stored in the console is automatically read according to the identification information, and then the configuration item information of the scanning system is configured according to various configuration item parameters in the configuration file, so that the automatic configuration of the scanning system is realized.
The scanning system configuration method provided by the embodiment of the application comprises the steps of obtaining a scanning bed image of a scanning system; identifying the scanning bed image to obtain the type of the scanning bed; configuring configuration item parameters of the scanning system according to the type of the scanning bed. The scanning system configuration method can automatically acquire the type of the scanning bed board, automatically set the configuration item information of the scanning system according to the type of the scanning bed board, and realize the automatic identification of bed board configuration.
It should be understood that although the steps in the flowcharts of fig. 1 and 3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1 and 3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 4, there is provided a scanning system configuration apparatus including: an acquisition module 410, an identification module 420, and a configuration module 430, wherein:
an obtaining module 410, configured to obtain a scanning bed image of a scanning system;
the identification module 420 is configured to identify the scanning bed image to obtain a type of the scanning bed;
a configuration module 430, configured to configure configuration item parameters of the scanning system according to the type of the scanning bed.
In one embodiment, the acquisition module 410 is used to acquire the scanning bed surface contour image acquired by the camera, and/or
Acquiring an image of the scanning bed positioning sheet obtained by scanning the scanning bed through a scanning system;
and taking the scanning bed surface contour image and/or the scanning bed positioning sheet image as the scanning bed image.
In one embodiment, a visual mark is arranged on the scanning bed and used for identifying the type of the scanning bed; the identification module 420 is used for acquiring visible mark information contained in the scanning bed image;
and determining the type of the scanning bed according to the visual mark information.
In one embodiment, the identification module 420 is configured to obtain electron density difference information included in the scan bed image;
and determining the type of the scanning bed according to the electron density difference information.
In one embodiment, the recognition module 420 is configured to input the scan bed image into a deep learning model, and obtain the type of the scan bed.
In an embodiment, the scanning system configuration device further includes a deep learning model building module, configured to build an initial model before inputting the scanning bed image into the deep learning model to obtain the type of the scanning bed;
acquiring scanning bed images corresponding to all scanning bed types, and taking all scanning bed types and corresponding scanning bed images as a training set;
and training the initial model according to the training set to obtain a deep learning model.
In one embodiment, the configuration module 430 is configured to read a configuration file stored in the console and corresponding to the type of the scanning bed, where the configuration file includes configuration parameters;
the configuration item parameters at least comprise one of a sinking curve parameter, an interference curve parameter and a registration matrix parameter.
The scanning system configuration device provided by the embodiment of the application comprises an acquisition module 410, an identification module 420 and a configuration module 430, wherein the acquisition module 410 is used for acquiring a scanning bed image of a scanning system; identifying the scanning bed image through an identification module 420 to obtain the type of the scanning bed; the configuration module 430 configures configuration item parameters of the scanning system according to the type of the scanning bed. The scanning system configuration device provided by the application can automatically acquire the type of the scanning bed board, and automatically set the configuration item information of the scanning system according to the type of the scanning bed board, so that the automatic identification of bed board configuration is realized.
For specific limitations of the scanning system configuration apparatus, reference may be made to the above limitations of the scanning system configuration method, which are not described herein again. The respective modules in the scanning system configuration apparatus described above may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a scanning system configuration method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring a scanning bed image of a scanning system;
identifying the scanning bed image to obtain the type of the scanning bed;
configuring configuration item parameters of the scanning system according to the type of the scanning bed.
In one embodiment, the acquiring a scanning bed image of a scanning system comprises:
acquiring a scanning bed surface profile image acquired by a camera, and/or
Acquiring an image of the scanning bed positioning sheet obtained by scanning the scanning bed through a scanning system;
and taking the scanning bed surface contour image and/or the scanning bed positioning sheet image as the scanning bed image.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes: a visual mark is arranged on the scanning bed and used for marking the type of the scanning bed;
acquiring visual sign information contained in the scanning bed image;
and determining the type of the scanning bed according to the visual mark information.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes:
acquiring electron density difference information contained in the scanning bed image;
and determining the type of the scanning bed according to the electron density difference information.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes:
and inputting the scanning bed image into a deep learning model to obtain the type of the scanning bed.
In an embodiment, the inputting the scan bed image into a deep learning model, and obtaining the type of the scan bed comprises:
constructing an initial model;
acquiring scanning bed images corresponding to all scanning bed types, and taking all scanning bed types and corresponding scanning bed images as a training set;
and training the initial model according to the training set to obtain a deep learning model.
In one embodiment, the automatically configuring the configuration item parameters of the scanning system according to the type of the scanning bed comprises:
reading a configuration file which is stored in a console and corresponds to the type of the scanning bed, wherein the configuration file comprises various configuration item parameters;
the configuration item parameters at least comprise one of a sinking curve parameter, an interference curve parameter and a registration matrix parameter.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a scanning bed image of a scanning system;
identifying the scanning bed image to obtain the type of the scanning bed;
configuring configuration item parameters of the scanning system according to the type of the scanning bed.
In one embodiment, the acquiring a scanning bed image of a scanning system comprises:
acquiring a scanning bed surface profile image acquired by a camera, and/or
Acquiring an image of the scanning bed positioning sheet obtained by scanning the scanning bed through a scanning system;
and taking the scanning bed surface contour image and/or the scanning bed positioning sheet image as the scanning bed image.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes: a visual mark is arranged on the scanning bed and used for marking the type of the scanning bed;
acquiring visual sign information contained in the scanning bed image;
and determining the type of the scanning bed according to the visual mark information.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes:
acquiring electron density difference information contained in the scanning bed image;
and determining the type of the scanning bed according to the electron density difference information.
In an embodiment, the identifying the scan bed image to obtain the type of the scan bed includes:
and inputting the scanning bed image into a deep learning model to obtain the type of the scanning bed.
In an embodiment, the inputting the scan bed image into a deep learning model, and obtaining the type of the scan bed comprises:
constructing an initial model;
acquiring scanning bed images corresponding to all scanning bed types, and taking all scanning bed types and corresponding scanning bed images as a training set;
and training the initial model according to the training set to obtain a deep learning model.
In one embodiment, the automatically configuring the configuration item parameters of the scanning system according to the type of the scanning bed comprises:
reading a configuration file which is stored in a console and corresponds to the type of the scanning bed, wherein the configuration file comprises various configuration item parameters;
the configuration item parameters at least comprise one of a sinking curve parameter, an interference curve parameter and a registration matrix parameter.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
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 invention, 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 inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of scanning system configuration, the method comprising:
acquiring a scanning bed image of a scanning system;
identifying the scanning bed image to obtain the type of the scanning bed;
configuring configuration item parameters of the scanning system according to the type of the scanning bed.
2. The method of claim 1, wherein acquiring a scanning bed image of a scanning system comprises:
acquiring a scanning bed surface profile image acquired by a camera, and/or
Acquiring a scanning bed positioning sheet image obtained by scanning the scanning bed through a scanning system;
and taking the scanning bed surface contour image and/or the scanning bed positioning sheet image as the scanning bed image.
3. The method of any of claims 1 or 2, wherein the identifying the bed image to obtain the type of the bed comprises: a visual mark is arranged on the scanning bed and used for marking the type of the scanning bed;
acquiring visual sign information contained in the scanning bed image;
and determining the type of the scanning bed according to the visual mark information.
4. The method of any of claims 1 or 2, wherein the identifying the bed image to obtain the type of the bed comprises:
acquiring electron density difference information contained in the scanning bed image;
and determining the type of the scanning bed according to the electron density difference information.
5. The method of any of claims 1 or 2, wherein the identifying the bed image to obtain the type of the bed comprises:
and inputting the scanning bed image into a deep learning model to obtain the type of the scanning bed.
6. The method of claim 5, wherein the inputting the scan bed image into a deep learning model, the obtaining the type of the scan bed previously comprises:
constructing an initial model;
acquiring scanning bed images corresponding to all scanning bed types, and taking all scanning bed types and corresponding scanning bed images as a training set;
and training the initial model according to the training set to obtain a deep learning model.
7. The method of claim 1, wherein automatically configuring configuration item parameters of the scanning system according to the type of the scanning bed comprises:
reading a configuration file which is stored in a console and corresponds to the type of the scanning bed, wherein the configuration file comprises various configuration item parameters;
the configuration item parameters at least comprise one of a sinking curve parameter, an interference curve parameter and a registration matrix parameter.
8. A scanning system configuration apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring a scanning bed image of the scanning system;
the identification module is used for identifying the scanning bed image to obtain the type of the scanning bed;
and the configuration module is used for configuring configuration item parameters of the scanning system according to the type of the scanning bed.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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