Method, apparatus, storage medium, and CT device for adjusting CT scan parameters

Abstract

The present application discloses a method, apparatus, storage medium, and CT device for adjusting CT scan parameters. Among them, the method for adjusting CT scan parameters includes: inputting target plain film data into a section matching model to determine target section information corresponding to the target plain film data; based on the target section information, determining predicted attenuation values of the position to be scanned of the target object corresponding to the target plain film data at different radiation positions; and adjusting the gear positions of the integrating capacitors at the position to be scanned at different radiation positions based on the predicted attenuation values. The method of the present application can select appropriate gear positions of the integrating capacitors, make the measurement data more accurate, and improve the quality of the image.

Description

Technical Field

[0001] The present application relates to the technical field of medical devices, and particularly relates to a method, device, storage medium, and CT device for adjusting CT scan parameters. Background Art

[0002] Medical imaging refers to the technology and processing process of obtaining internal tissue images of the human body or a part of the human body in a non-invasive manner for medical treatment or medical research. Due to its non-invasive characteristics, it has become an important means for auxiliary diagnosis. Especially for computed tomography (CT) devices, they play a very important role in diagnosing whether there are lesions in the human central nervous system, abdominal organs, and bones and joints, etc.

[0003] The CT device scans a certain part of the scanned object by emitting X-rays, and forms an image according to the intensity of the X-rays received by the detector through photoelectric conversion. During the photoelectric conversion process, the accumulated charge amount is measured by relying on an integrating capacitor. After different-energy X-rays pass through different objects and attenuate, the energies accumulated on the integrating capacitor are different. Even if the same amount of energy is accumulated on the integrating capacitor, different gains of amplification can be performed on the measured energy by setting different sizes of integrating capacitor gears. On the premise that the electronic noise is approximately the same, the higher the amplification factor of the integrating capacitor, the stronger the anti-noise interference energy, but the lower the relative accuracy.

[0004] Therefore, selecting a suitable integrating capacitor gear is a technical problem that those skilled in the art need to solve. Summary of the Invention

[0005] In view of this, the present application provides a method, device, storage medium, and CT device for adjusting CT scan parameters, which can select a suitable integrating capacitor gear to make the measurement data more accurate and improve the quality of the image.

[0006] According to one aspect of the present application, a method for adjusting CT scan parameters is provided, including:

[0007] Inputting target plain film data into a section matching model to determine target section information corresponding to the target plain film data;

[0008] Based on the target section information, obtaining predicted attenuation values of the to-be-scanned position of the target object corresponding to the target plain film data at different radiation positions;

[0009] Based on the predicted attenuation values, adjusting the gears of the integrating capacitor at the to-be-scanned position at different radiation positions.

[0010] Optionally, based on the predicted attenuation values, adjusting the gears of the integrating capacitor at the to-be-scanned position at different radiation positions includes:

[0011] Based on the predicted attenuation value, determine the received value of at least one receiving channel at each wire laying position corresponding to the position to be scanned;

[0012] Based on the received value of at least one receiving channel, adjust the gear position of the integration capacitor of at least one receiving channel at the position to be scanned at different wire laying positions.

[0013] Optionally, based on the received value of at least one receiving channel, adjusting the gear position of the integration capacitor of at least one receiving channel at the position to be scanned at different wire laying positions includes:

[0014] Configure the gear position of the integration capacitor of each receiving channel based on the received value of each receiving channel; or,

[0015] Determine the first received characteristic value of any wire laying position based on the received values of all receiving channels corresponding to any wire laying position; based on the first received characteristic value of any wire laying position, calculate the first integration capacitor gear position; if the number of receiving channels of any wire laying position whose predicted received value after being amplified by the integration capacitor is less than the received threshold meets the first preset range, configure the integration capacitors of all receiving channels corresponding to any wire laying position into a fixed gear position based on the first integration capacitor gear position; or,

[0016] Determine the second received characteristic value of any receiving channel based on the received values of all wire laying positions corresponding to any receiving channel; based on the second received characteristic value of any receiving channel, calculate the second integration capacitor gear position; if the number of wire laying positions corresponding to any receiving channel whose predicted received value after being amplified by the integration capacitor is less than the received threshold meets the second preset range, configure the integration capacitors of all wire laying positions corresponding to any receiving channel into a fixed gear position based on the second integration capacitor gear position.

[0017] Optionally, configuring the gear position of the integration capacitor of each receiving channel based on the received value of each receiving channel includes:

[0018] Determine the received value range of each wire laying position according to the received values of multiple receiving channels;

[0019] Obtain the maximum received value from the received value range;

[0020] Configure the gear position of the integration capacitor of each receiving channel according to the maximum received value.

[0021] Optionally, the first received eigenvalue includes: the average received value, the median received value, or the weighted received value obtained based on the weight relationship, of all received channels corresponding to any wire laying position; the second received eigenvalue includes: the average received value, the median received value, or the weighted received value obtained based on the weight relationship, of all wire laying positions corresponding to any received channel; the first preset range is configured to be less than 95% of the number of all received channels corresponding to any wire laying position; the second preset range is configured to be less than 95% of the number of all wire laying positions corresponding to any received channel.

[0022] Optionally, based on the predicted attenuation value, determine the received value of at least one received channel of each wire laying position among different wire laying positions corresponding to the position to be scanned, using the following formula:

[0023]

[0024] where, I m represents the received value, I o represents the transmitted value, and μD represents the predicted attenuation value.

[0025] Optionally, the method for adjusting CT scan parameters further includes:

[0026] Obtain multiple plain film data and multiple groups of section information of a sample object, where the section information includes tomographic section information or helical section information;

[0027] Perform correlation processing on each row of the plain film image corresponding to the plain film data of each sample object and each section information in each group of section information of the sample object according to the bed code value;

[0028] Divide the multiple plain film data of the sample object into sample plain film data and test plain film data according to a preset ratio, and determine the section information associated with the sample plain film data as sample section information, and determine the section information associated with the test plain film data as test section information;

[0029] Train the neural network model according to the sample plain film data and the sample section information;

[0030] Evaluate the trained neural network model according to the test plain film data and the test section information to obtain an evaluation index;

[0031] If the evaluation index meets the convergence condition of the loss function, confirm the trained neural network model as the section matching model.

[0032] Optionally, evaluating the trained neural network model according to the test image includes:

[0033] Input the test plain film data into the trained neural network model to obtain the section information to be tested;

[0034] Calculate an evaluation index based on the first number of pixels of the effective region where the sample object is located in the test cross-section image of the test cross-section information to be tested and the second number of pixels of the effective region where the sample object is located in the test cross-section image of the test cross-section information associated with the test flat film data.

[0035] According to another aspect of the present application, there is provided an adjustment device for CT scan parameters, including:

[0036] A matching module for inputting target flat film data into a cross-section matching model to determine target cross-section information corresponding to the target flat film data;

[0037] A determination module for determining predicted attenuation values of the position to be scanned of the target object corresponding to the target flat film data at different wire placement positions based on the target cross-section information;

[0038] An adjustment module for adjusting the gear positions of the integrating capacitors at the position to be scanned at different wire placement positions based on the predicted attenuation values.

[0039] Optionally, the determination module is specifically configured to determine received values of at least one receiving channel of each wire placement position corresponding to the position to be scanned based on the predicted attenuation values; the adjustment module is specifically configured to adjust the gear positions of the integrating capacitors of at least one receiving channel at the position to be scanned at different wire placement positions based on the received values of at least one receiving channel.

[0040] Optionally, the adjustment module is specifically configured to configure the gear positions of the integrating capacitors of each receiving channel based on the received value of each receiving channel.

[0041] Optionally, the determination module is specifically configured to determine a first received feature value of any wire placement position based on the received values of all receiving channels corresponding to any wire placement position; calculate a first integrating capacitor gear position based on the first received feature value of any wire placement position; the adjustment module is specifically configured to, if the number of receiving channels of any wire placement position whose estimated received value after being amplified by the integrating capacitor is less than the received threshold meets a first preset range, configure the integrating capacitors of all receiving channels corresponding to any wire placement position into fixed gear positions based on the first integrating capacitor gear position.

[0042] Optionally, the determination module is specifically configured to determine a second received feature value of any receiving channel based on the received values of all wire placement positions corresponding to any receiving channel; calculate a second integrating capacitor gear position based on the second received feature value of any receiving channel; the adjustment module is specifically configured to, if the number of wire placement positions corresponding to any receiving channel whose estimated received value after being amplified by the integrating capacitor is less than the received threshold meets a second preset range, configure the integrating capacitors of all wire placement positions corresponding to any receiving channel into fixed gear positions based on the second integrating capacitor gear position.

[0043] Optionally, a determination module is specifically configured to determine the received value range of each wire laying position according to the received values of multiple receiving channels; obtain the maximum received value from the received value range; an adjustment module is specifically configured to configure the gear of the integration capacitor of each receiving channel according to the maximum received value.

[0044] Optionally, the first received eigenvalue includes: the average received value, the median received value, or the weighted received value obtained based on the weight relationship of all receiving channels corresponding to any wire laying position; the second received eigenvalue includes: the average received value, the median received value, or the weighted received value obtained based on the weight relationship of all wire laying positions corresponding to any receiving channel; the first preset range is configured to be less than 95% of the number of all receiving channels corresponding to any wire laying position; the second preset range is configured to be less than 95% of the number of all wire laying positions corresponding to any receiving channel.

[0045] Optionally, based on the predicted attenuation value, determine the received value of at least one receiving channel of each wire laying position among different wire laying positions corresponding to the position to be scanned, using the following formula:

[0046]

[0047] where, I m represents the received value, I o represents the transmitted value, and μD represents the predicted attenuation value.

[0048] Optionally, the device for adjusting CT scan parameters further includes: an acquisition module, configured to acquire multiple plain film data and multiple groups of section information of a sample object, and the section information includes tomographic section information or helical section information; an association module, configured to perform association processing on each row in the plain film image corresponding to the plain film data of each sample object and each section information in each group of section information of the sample object according to the bed code value; the acquisition module is further configured to divide the multiple plain film data of the sample object into sample plain film data and test plain film data according to a preset ratio, and determine the section information associated with the sample plain film data as sample section information, and determine the section information associated with the test plain film data as test section information; a training module, configured to train a neural network model according to the sample plain film data and the sample section information; evaluate the trained neural network model according to the test plain film data and the test section information to obtain an evaluation index; if the evaluation index meets the convergence condition of the loss function, confirm the trained neural network model as a section matching model.

[0049] Optionally, the training module is further configured to input the test plain film data into the trained neural network model to obtain the cross-section information to be tested; and calculate an evaluation index according to the number of first pixels in the effective area where the sample object is located in the cross-section image of the cross-section information to be tested and the number of second pixels in the effective area where the sample object is located in the cross-section image of the test cross-section information associated with the test plain film data.

[0050] According to another aspect of the present application, there is provided a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the above method for adjusting CT scan parameters are implemented.

[0051] According to yet another aspect of the present application, there is provided a CT device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the program, the steps of the above method for adjusting CT scan parameters are implemented.

[0052] By means of the above technical solution, the plain film data of a large number of sample objects is used as input data, and the sample cross-section information associated with the sample plain film data is used as a label to train a deep learning network, so as to obtain a cross-section matching model based on deep learning. The cross-section matching model is used to match the plain film data of the target object with the possible estimated cross-section information of multiple different parts of the target object. The predicted attenuation values at different wire placement positions are used to configure different gear positions of the integral capacitor for each receiving channel at different wire placement positions. On the one hand, by constructing the relationship between the plain film data and the body type of the target object through deep learning, and establishing a cross-section matching model through a large number of existing sample data, the accuracy of the model is higher, and the accuracy of body type estimation is improved. On the other hand, the attenuation value of each wire placement position can be obtained by predicting the target cross-section information output by the cross-section matching model, so as to know the required integral capacitance value, and finally determine the appropriate integral capacitor gear position for adaptive adjustment. Using the integral capacitor after gear adjustment can improve the accuracy and efficiency of measurement data, and further improve the imaging quality of the CT device.

[0053] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0055] Figure 1Shows one of the flow diagrams of the method for adjusting CT scan parameters provided by the embodiments of the present application;

[0056] Figure 2 Shows another flow diagram of the method for adjusting CT scan parameters provided by the embodiments of the present application;

[0057] Figure 3 Shows the structural block diagram of the device for adjusting CT scan parameters provided by the embodiments of the present application;

[0058] Figure 4 Shows the schematic diagram of the predicted attenuation value at a wire laying position provided by the embodiments of the present application;

[0059] Figure 5 Shows the schematic diagram of the scanning scene of the CT device provided by the embodiments of the present application. Detailed implementation manners

[0060] In the following, the present application will be described in detail with reference to the accompanying drawings and in combination with embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0061] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described by referring to the accompanying drawings below are exemplary and are only used to explain the present application, and cannot be construed as a limitation to the present application.

[0062] Those skilled in the art of the present technology can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "including" used in the specification of the present application means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "joined" to another element, it can be directly connected or joined to other elements, or there may also be intermediate elements. In addition, the "connection" or "joining" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more related listed items.

[0063] Now, exemplary embodiments according to the present application will be described in more detail with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many different forms and should not be construed as being limited only to the embodiments set forth herein. It should be understood that these embodiments are provided so that the disclosure of the present application is thorough and complete, and the concepts of these exemplary embodiments are fully conveyed to those of ordinary skill in the art.

[0064] In this embodiment, a method for adjusting CT scan parameters is provided. As Figure 1 shown, the method includes:

[0065] Step 101, input the target plain film data into the sectional matching model to determine the target sectional information corresponding to the target plain film data;

[0066] Among them, the target plain film data is the data obtained by using X-ray penetration, fluorescence and photosensitive effects in the CT device to scan the target object. The X-ray penetrates the target object, causing the film behind the target object to be exposed to light. The target object can be a human body, or it can refer to other living or inanimate objects such as animals with medical imaging needs. The plain film data can be used to form a plain film image. The higher the tissue density in the target object, the less the X-ray penetrates, the less the exposure, and the darker the image formed. Conversely, a brighter image is formed. According to the observation habit, the black and white of the photo is flipped to become a plain film image.

[0067] In actual application scenarios, the target sectional information includes sectional images and / or sectional data. For different scanning methods of CT devices, the estimated sectional information can be tomographic sectional information or helical sectional information.

[0068] It can be understood that when using a CT device for scanning, the user needs to position the target object differently according to the clinical manifestations and individual characteristics of the target object. After the positioning of the target object is determined, usually the plain film data is scanned first. The plain film data has a positioning function. According to the plain film data, the scanning position and range can be determined, and at least part of the target object is distributed on the plain film data. The scanning part of the target object for tomographic scanning or helical scanning is located through the plain film data. The plain film data can correspond to different parts of the target object, that is, one plain film data corresponds to one part. Of course, one plain film data can also correspond to multiple different parts of the scanning object at the same time. Taking the scanning object as a human body as an example, different parts can be the head, chest, abdomen, limbs, etc.

[0069] In this embodiment, the target plain film data of the target object is input into the cross-section matching model, so as to match the possible estimated cross-section information of multiple different parts of the target object through the cross-section matching model. This not only realizes the determination of the estimated cross-section information by using big data, facilitates the analysis of the body type of the target object, reduces the detection workload of the user, but also improves the accuracy of the estimation of the cross-section image and the work efficiency.

[0070] Further, as Figure 2 shown, as a refinement and extension of the specific implementation manner of the above embodiment, in order to completely illustrate the specific implementation process of this embodiment, before step 101, the method for adjusting the CT scanning parameters further includes:

[0071] Step 201, obtain multiple plain film data and multiple groups of cross-section information of the sample object;

[0072] Among them, the cross-section information of the sample object includes tomographic cross-section information or helical cross-section information. The cross-section information includes cross-section data and cross-section images. The tomographic cross-section information is the information obtained by scanning the scanned object by rotating the radiation source of the CT device. The helical cross-section information is the information obtained by scanning the scanned object while the radiation source of the CT device scans and the scanning bed also moves at the same time.

[0073] It can be understood that each part of the sample object corresponds to a group of cross-section information, and each group of cross-section information includes at least one cross-section information.

[0074] In this embodiment, a large number of clinically scanned plain film data and corresponding tomographic / helical imaging information are collected. The parts scanned by the plain film data and the cross-section information should cover all possible parts of the scanned object. Taking the scanned object as a human body as an example, different parts can be the head, chest, abdomen, limbs, etc. Thus, sufficient data support is provided for subsequent training and testing of the model.

[0075] Step 202, perform an association process on each row in the plain film image corresponding to the plain film data of each sample object and each cross-section information in each group of cross-section information of the sample object according to the bed code value;

[0076] In this embodiment, each row on the flat panel image corresponding to the flat panel data corresponds to a couch code value, and each sectional information in each group of tomography / spiral information also corresponds to a couch code value. There is a corresponding relationship between the couch code value of each row on the flat panel image and the couch code value of each sectional information. For different models of CT devices, this corresponding relationship can be the same or different. Using this corresponding relationship, each row on the flat panel image corresponding to the flat panel data is aligned with the couch code of each sectional information in each group of tomography / spiral sectional information, so as to associate the flat panel data corresponding to each row on the flat panel image with each sectional information one by one. At this time, the flat panel data corresponding to each row on the flat panel image can be associated with a group of sectional information, that is, at least one sectional information, so as to use the sectional image as a label of the flat panel data to train the sectional matching model.

[0077] Specifically, for example, from the minimum value of the starting couch code of the flat panel image and the tomography / spiral image to the maximum value of the ending couch code of the flat panel and tomography / spiral image, at a certain interval, such as the spacing between each row of the flat panel image (1mm or 2mm), a tomography / spiral image can be located.

[0078] Step 203, divide multiple flat panel data of the sample object into sample flat panel data and test flat panel data according to a preset ratio, and determine the sectional information associated with the sample flat panel data as sample sectional information, and determine the sectional information associated with the test flat panel data as test sectional information;

[0079] In this embodiment, the associated multiple flat panel data and multiple groups of sectional information are divided into a training set and a test set according to a preset ratio. The training set is the sample flat panel data and the sample sectional information, and the test set is the test flat panel data and the test sectional information. The sample set is used to train the model, and the test set is used to test the trained model. Thus, it provides sufficient data support for subsequent training and testing of the sectional matching model, so that the output result of the finally obtained sectional matching model can be more in line with the actual sectional information, which helps to improve the accuracy of scanning.

[0080] Among them, the preset ratio can be reasonably set according to the training and testing requirements of the target model. For example, the ratio of the training set to the test set is 9:1 or 8:2.

[0081] Step 204, train the neural network model according to the sample flat panel data and the sample sectional information;

[0082] Specifically, the neural networks (NN) model can be a convolutional neural networks (CNN) model, a deep residual shrinkage network (DRSN) model, a fully connected neural network (FCNN) model, a recurrent neural network (RNN) model, or a long short-term memory (LSTM) model. The embodiments of the present application do not make specific limitations.

[0083] Step 205: According to the test plain film data and the test section information, evaluate the trained neural network model to obtain an evaluation index.

[0084] Specifically, to calculate the evaluation index of the test plain film data, the following formula is used:

[0085] R(f(z), a) = |f(z) - a|,

[0086] In the formula, z represents the test plain film data, f represents the trained neural network model, a represents the number of valid pixels of the section image in the test section information, and R represents the evaluation index of a single test plain film data.

[0087] It can be understood that if the number of test plain film data in the test set is m, that is, the test plain film data corresponds to each plain film image, and m is a positive integer greater than 1, the average value of the evaluation indexes of the m test plain film data is used as the evaluation index R of the test set mean , as follows:

[0088]

[0089] Specifically, according to the test plain film data and the test section information, the evaluation process of the trained neural network model includes:

[0090] Step 205-1: Input the test plain film data into the trained neural network model to obtain the to-be-tested section information.

[0091] Step 205-2: Calculate the evaluation index according to the number of first pixels in the effective area where the scanned object of the to-be-tested section image in the to-be-tested section information is located and the number of second pixels in the effective area where the scanned object of the test section image in the test section information is located.

[0092] In this embodiment, the sample plain film data and its labeled sample cross-section information are used as input data and labels respectively to train a neural network model, and a trained neural network model is obtained. Then, the test plain film data is input into the trained neural network model, and the training result is evaluated according to the absolute value of the difference between the first pixel number of the cross-section image to be tested output by the trained neural network model and the second pixel number of the test cross-section image associated with the test plain film data. This is to facilitate calculating the loss function based on the evaluation index to determine the accuracy of the trained neural network model. Thus, it can be further determined whether the training of the neural network model meets the user's accuracy requirement, which helps improve the model training efficiency while ensuring that the model meets the accuracy requirement.

[0093] Step 206, if the evaluation index meets the convergence condition of the loss function, confirm the trained neural network model as the cross-section matching model.

[0094] In this embodiment, when the loss function converges, it indicates that the cross-section image matched by the trained neural network model is almost the same as the test cross-section image. At this time, it can be determined that the training of the neural network model is completed, and the neural network model is output as the cross-section matching model. This is to facilitate matching the target plain film data of the target object with the possible estimated cross-section information of multiple different parts of the target object through the cross-section matching model, thereby realizing the estimation of the body shape of the target object through big data, improving the accuracy of body shape estimation, and at the same time, there is no need to add additional reminder detection equipment, which is beneficial to reducing equipment costs.

[0095] In an actual application scenario, the loss function can adopt the Mean Square Error (MSE) function, Mean Absolute Error (MAE) function, Root Mean Square Error (RMSE) function, Mean Squared Log Error function, or Mean Relative Error (MRE) function. Taking the use of the Mean Absolute Error (MAE) as the loss function for neural network model training as an example, the loss function is as follows:

[0096]

[0097] Where L represents the loss function, MAE represents the Mean Absolute Error function, and n represents the number of training samples. When the MAE function drops below 1%, it is determined that the model training accuracy meets the standard, and the target model is output.

[0098] Step 102: Based on the target cross-sectional information, obtain the predicted attenuation values of the target object corresponding to the target radiograph data at the position to be scanned under different beam positions.

[0099] Among them, different parts of the target object have their respective corresponding attenuation coefficients. The attenuation value is the integral value of the attenuation coefficient along the path traveled by the ray, that is, the value obtained after air correction of the value received by the CT device detector. It can be understood that if the target object is a human body, according to the different CT values in different regions of the target cross-sectional information, bone tissue, soft tissue, air, etc. in each cross-sectional image of the target cross-sectional information can be distinguished. During the determination of the predicted attenuation value, the predicted attenuation values corresponding to bone tissue, soft tissue, and air can be weighted and calculated to obtain the predicted attenuation value of the target cross-sectional information.

[0100] Specifically, project the cross-sectional images in the predicted cross-sectional information of the part to be scanned according to the number of beam positions at the full field of view angle to obtain the predicted attenuation value of each beam position among different beam positions.

[0101] Step 103: Based on the predicted attenuation values, adjust the gear positions of the integrating capacitors at the position to be scanned under different beam positions.

[0102] In the embodiment of the present application, the radiograph data of a large number of sample objects are used as input data, and the sample cross-sectional information associated with the sample radiograph data is used as labels to train a deep learning network, obtaining a cross-section matching model based on deep learning. Use this cross-section matching model to match the radiograph data of the target object with the possible predicted cross-sectional information of multiple different parts of the target object. Use the predicted attenuation values of the predicted cross-sectional information under different beam positions to configure different gear positions of the integrating capacitors for each receiving channel at different beam positions. On the one hand, by constructing the relationship between the radiograph data and the body shape of the target object through deep learning, and establishing a cross-section matching model through a large number of existing sample data, the accuracy of the model is higher, improving the accuracy of body shape prediction. On the other hand, by predicting the attenuation value of each beam position through the target cross-sectional information output by the cross-section matching model, the required integrating capacitance can be known, and finally the appropriate gear position of the integrating capacitor is determined for adaptive adjustment, so as to be able to set appropriate scanning parameters for different parts to be scanned of the same target object in a targeted manner, which not only meets the clinical requirements of the reconstructed image but also reduces the damage of X-rays to the target object.

[0103] Further, as a refinement and extension of the specific implementation manner of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, Step 103, that is, based on the predicted attenuation values, adjust the gear positions of the integrating capacitors at the position to be scanned under different beam positions, specifically includes:

[0104] Step 103-1: Based on the predicted attenuation value, determine the received value of at least one receiving channel at each wire laying position corresponding to the position to be scanned.

[0105] Wherein, the received value is the value received by the detector of the CT device.

[0106] In this embodiment, the received values of different receiving channels are calculated respectively through the predicted attenuation values of the respective receiving channels corresponding to each wire laying position. Thus, the integration capacitor gear can be dynamically configured for different receiving channels according to the magnitude of the received value, and the measurement data accuracy can be improved by using the integration capacitor after gear adjustment, thereby improving the imaging quality of the CT machine.

[0107] Exemplarily, when the CT device scans the target object along the Z direction, the predicted attenuation value curve determined by the target section information is as Figure 4 shown.

[0108] Specifically, based on the predicted attenuation value, to determine the received value of at least one receiving channel at each wire laying position corresponding to the position to be scanned, the following formula is used:

[0109]

[0110] Wherein, I m represents the received value of the receiving channel, I o represents the emission value of the radiation source of the X-ray, which is related to the magnitude of the known dose, and μD represents the predicted attenuation value of the target object from the wire laying position to the receiving channel.

[0111] Step 103-2: Based on the received values of at least one receiving channel, adjust the gear of the integration capacitor of at least one receiving channel at the position to be scanned at different wire laying positions.

[0112] Wherein, the integration capacitor usually has multiple gears, each gear corresponding to a different range, and the gain corresponding to each gear, that is, the received value amplification factor, is also different. The larger the integration capacitor gear, the larger the gain and the greater the amplification factor of the received value. If the emission dose is the same, when a certain part of the target object has a large attenuation, the received value is small. To ensure the same received value level, a larger integration capacitor gear is required, and vice versa, a smaller integration capacitor gear is required.

[0113] In this embodiment, the received values of each receiving channel at each wire laying position of the target object are calculated by using the predicted attenuation values of the predicted cross-sectional images at different wire laying positions. Different integral capacitance levels are configured for different receiving channels according to the received values of different receiving channels, so as to ensure that different receiving channels have similar received value levels. Thus, the attenuation value at each wire laying position is predicted through the target cross-sectional information output by the cross-sectional matching model. According to the received values of the receiving channels at different wire laying positions, the required integral capacitance can be obtained, and finally the appropriate integral capacitance level is determined for adaptive adjustment. The matching degree between the integral capacitance level selected by the highly accurate received value and the actual received value is higher. The integral capacitance after level adjustment can improve the accuracy and efficiency of measurement data, and further improve the imaging quality of the CT device.

[0114] Specifically, the received value needs to be digitized when displayed. As an example, the upper limit of the display is N. Due to different level ranges, the received value needs to be amplified by the magnification factor (gain) corresponding to the level when displayed. To ensure that the received values are similar in size and less than N when displayed, the following formula can be used to determine the gain corresponding to the integral level required by the receiving channel, and then the appropriate integral capacitance level is matched for the receiving channel according to the required gain size, achieving the purpose of automatically selecting the appropriate integral capacitance level for different receiving channels and ensuring the optimal image quality.

[0115]

[0116] Wherein, I m represents the received value of the receiving channel, I m is less than N, I o represents the emission value of the radiation source of the X-ray, μD represents the predicted attenuation value of the target object from the wire laying position to the receiving channel, and DMS gain represents the gain corresponding to the integral capacitance level.

[0117] In an actual application scenario, based on the received values of at least one receiving channel, adjusting the levels of the integral capacitance of at least one receiving channel at different wire laying positions of the position to be scanned includes the following methods:

[0118] Method 1: Configure the levels of the integral capacitance of each receiving channel based on the received value of each receiving channel.

[0119] In this embodiment, different integral capacitance levels are set for each receiving channel corresponding to each wire laying position according to the received value of each receiving channel corresponding to each wire laying position. That is, the integral capacitance levels configured for each receiving channel are different as needed, thus ensuring the imaging quality of the CT device to the greatest extent.

[0120] Specifically, configuring the gear positions of the integrating capacitors for each receiving channel based on the received values of each receiving channel includes: determining the received value range of each wire laying position according to the received values of multiple receiving channels; obtaining the maximum received value from the received value range; and configuring the gear positions of the integrating capacitors for each receiving channel according to the maximum received value.

[0121] In this embodiment, during the scanning process of the CT device, the wire laying positions (also known as Views) of the X-ray radiation source continuously change. Each wire laying position corresponds to multiple receiving channels on the detector. Through projection processing, the predicted attenuation values of different receiving channels corresponding to each wire laying position can be obtained. The predicted attenuation values of different receiving channels may be the same or different. Through the predicted attenuation values of different receiving channels, the attenuation curve and the average attenuation value at each wire laying position can be obtained, and then the received value range at each wire laying position can be calculated to know the required integrating capacitor range. On the premise of ensuring that most data does not overflow, in order to ensure that the received values displayed by each receiving channel are similar in size, the maximum received value is selected from the calculated received value range. The gear position of the integrating capacitor of the channel with the largest received value can be set to the minimum gear position or the gear position that meets the upper limit of the received value. Then, taking the receiving channel with the maximum received value as a reference, the gear positions of the required integrating capacitors of all receiving channels are adjusted. Thus, the adaptive selection of the integrating capacitor gear positions is realized, which maximally ensures better image quality and improves efficiency and accuracy.

[0122] For example, the attenuation values of five receiving channels at a wire laying position View i are μD1, μD2, μD3, μD4, and μD5 respectively. The attenuation curves of the above five receiving channels can be fitted using μD1, μD2, μD3, μD4, and μD5. The received values are calculated through the attenuation values of the five receiving channels as: I1, I2, I3, I4, and I5, where I1 is the maximum received value and I4 is the minimum received value. Then, the received value range of the receiving channels at View i can be obtained as [I4, I1].

[0123] Exemplarily, the first gear position corresponds to the magnification factor K1, the second gear position corresponds to the magnification factor K2, and the third gear position corresponds to the magnification factor K3. As an optional implementation, the maximum received value among the received values of different receiving channels can be multiplied by the magnification factors corresponding to different gear positions of the integrating capacitor to obtain multiple products. The gear position corresponding to the product with the value less than the display upper limit N and closest to N among the respective products is used as the gear position to be adjusted. For example, the maximum received value is obtained as I4, and I4 is multiplied by K1, K2, and K3 respectively to obtain the products N1, N2, and N3. Among them, N2 is the closest to N and less than N. Therefore, if the gear position of the integrating capacitor is not the second gear position, the gear position of the integrating capacitor is adjusted to the second gear position.

[0124] Method 2: Determine the first reception eigenvalue of any wire laying position based on the reception values of all reception channels corresponding to the wire laying position; calculate the first integrated capacitor gear based on the first reception eigenvalue of any wire laying position; if the number of reception channels whose estimated reception value is less than the reception threshold after being amplified by the integrated capacitor meets the first preset range, configure the integrated capacitors of all reception channels corresponding to any wire laying position into a fixed gear based on the first integrated capacitor gear.

[0125] Among them, the reception threshold is the maximum data reception amount allowed for the reception channels of the CT device, which can be reasonably set according to the device parameters of the CT device. The first reception eigenvalue can be the average reception value, median reception value, or weighted reception value obtained based on the weight relationship of all reception channels corresponding to any wire laying position. The first preset range is configured to be less than 95% of the number of all reception channels corresponding to any wire laying position, that is, the number of reception channels with an estimated reception value less than the reception threshold is less than 95% of the number of all reception channels corresponding to any wire laying position, and then it can be triggered to set the integrated capacitors of all reception channels corresponding to any wire laying position to the same fixed gear.

[0126] In this embodiment, the first reception eigenvalue of any wire laying position is determined based on the reception values of all reception channels corresponding to the wire laying position. A first integrated capacitor gear is calculated using this first reception eigenvalue. It is detected whether the estimated reception value detected after amplifying each reception channel value corresponding to any wire laying position using this first integrated capacitor gear is less than the reception threshold. If the number of reception channels with an estimated reception value less than the reception threshold meets the first preset range, it indicates that most reception channels will not have data overflow after being amplified by the first integrated capacitor gear. Then, the integrated capacitors of all reception channels corresponding to any wire laying position are set to the fixed first integrated capacitor gear. Thus, on the premise of ensuring that most data does not overflow, the capacitor gears of all reception channels corresponding to a wire laying position are unified, simplifying the adjustment steps of the integrated capacitor gear, which is beneficial to reducing the system operation amount and improving the adjustment efficiency of the integrated capacitor gear.

[0127] Method 3: Determine the second reception eigenvalue of any reception channel based on the reception values of all wire laying positions corresponding to the reception channel; calculate the second integrated capacitor gear based on the second reception eigenvalue of any reception channel; if the number of wire laying positions corresponding to any reception channel whose estimated reception value is less than the reception threshold after being amplified by the integrated capacitor meets the second preset range, configure the integrated capacitors of all wire laying positions corresponding to any reception channel into a fixed gear based on the second integrated capacitor gear.

[0128] Among them, the second received eigenvalue can be the average received value, the median received value, or the weighted received value obtained based on the weight relationship, corresponding to all wire laying positions of any receiving channel. The second preset range is configured to be less than 95% of the number of all wire laying positions corresponding to any receiving channel. Then, when the number of wire laying positions where the predicted received value is less than the received threshold is less than 95% of all wire laying positions of the part to be scanned of the target object, it can be triggered to set all wire laying positions to the same fixed gear.

[0129] In this embodiment, the second received eigenvalue of all wire laying positions is determined respectively based on the received values of all wire laying positions corresponding to any receiving channel. A second integral capacitance gear is calculated using this second received eigenvalue. It is detected whether the predicted received value obtained by amplifying the received channel values of all wire laying positions corresponding to any receiving channel using this second integral capacitance gear is less than the received threshold. If the number of wire laying positions where the predicted received value is less than the received threshold meets the second preset range, that is, most wire laying positions will not have data overflow after being amplified by the second integral capacitance gear, then the integral capacitances of all wire laying positions corresponding to any receiving channel of the target object are set to the fixed second integral capacitance gear, that is, the same integral capacitance gear can be used when scanning different parts of the target object. Thus, on the premise of ensuring that most data does not overflow, most wire laying positions or most channels are set to a fixed gear, simplifying the adjustment steps of the integral capacitance gear, which is beneficial to reducing the system operation amount and improving the adjustment efficiency of the integral capacitance gear.

[0130] Furthermore, as Figure 3 shown, as a specific implementation of the above CT scan parameter adjustment method, an embodiment of the present application provides a CT scan parameter adjustment device 300. The CT scan parameter adjustment device 300 includes: a matching module 301, a determination module 302, and an adjustment module 303.

[0131] Among them, the matching module 301 is used to input the target plain film data into the section matching model to determine the target section information corresponding to the target plain film data; the determination module 302 is used to determine the predicted attenuation values of the positions to be scanned of the target object corresponding to the target plain film data at different wire laying positions based on the target section information; the adjustment module 303 is used to adjust the gears of the integral capacitances of the positions to be scanned at different wire laying positions based on the predicted attenuation values.

[0132] In this embodiment, a large number of plain film data of sample objects are used as input data, and sample cross-section information associated with the sample plain film data is used as labels to train a deep learning network, thereby obtaining a cross-section matching model based on deep learning. The cross-section matching model is used to match possible estimated cross-section information of multiple different parts of the target object for the plain film data of the target object. The predicted attenuation values at different wire laying positions are used to configure different integral capacitor gears for each receiving channel at different wire laying positions. On the one hand, the relationship between the plain film data and the body shape of the target object is constructed through deep learning, and a cross-section matching model is established through a large amount of existing sample data, making the model more accurate and improving the accuracy of body shape estimation. On the other hand, the attenuation value of each wire laying position can be obtained by predicting the target cross-section information output by the cross-section matching model, and the required integral capacitance can be known. Finally, an appropriate integral capacitor gear is determined for adaptive adjustment. Using the integral capacitor after gear adjustment can improve the accuracy and efficiency of measurement data, and thus improve the imaging quality of the CT device.

[0133] Further, the determining module 302 is specifically configured to determine the received value of at least one receiving channel of each wire laying position corresponding to the position to be scanned based on the predicted attenuation value; the adjusting module 303 is specifically configured to adjust the integral capacitor gear of at least one receiving channel at different wire laying positions of the position to be scanned based on the received value of at least one receiving channel.

[0134] Further, the adjusting module 303 is specifically configured to configure the integral capacitor gear of each receiving channel based on the received value of each receiving channel.

[0135] Further, the determining module 302 is specifically configured to determine a first received feature value of any wire laying position based on the received values of all receiving channels corresponding to the any wire laying position; calculate a first integral capacitor gear based on the first received feature value of the any wire laying position; the adjusting module 303 is specifically configured to, if the number of receiving channels of any wire laying position whose estimated received value after integral capacitor amplification is less than the received threshold meets a first preset range, configure the integral capacitors of all receiving channels corresponding to the any wire laying position into a fixed gear based on the first integral capacitor gear.

[0136] Optionally, the determining module 302 is specifically configured to determine a second received feature value of any receiving channel based on the received values of all wire laying positions corresponding to the any receiving channel; calculate a second integral capacitor gear based on the second received feature value of the any receiving channel; the adjusting module 303 is specifically configured to, if the number of wire laying positions corresponding to any receiving channel whose estimated received value after integral capacitor amplification is less than the received threshold meets a second preset range, configure the integral capacitors of all wire laying positions corresponding to the any receiving channel into a fixed gear based on the second integral capacitor gear.

[0137] Further, a determination module 302 is configured to determine a received value range for each wire laying position according to received values of multiple receiving channels; obtain a maximum received value from the received value range; an adjustment module 303 is configured to configure a gear position of an integrating capacitor for each receiving channel according to the maximum received value.

[0138] Further, the first received eigenvalue includes: an average received value, a median received value, or a weighted received value obtained based on a weight relationship of all receiving channels corresponding to any wire laying position; the second received eigenvalue includes: an average received value, a median received value, or a weighted received value obtained based on a weight relationship of all wire laying positions corresponding to any receiving channel; the first preset range is configured to be less than 95% of the number of all receiving channels corresponding to any wire laying position; the second preset range is configured to be less than 95% of the number of all wire laying positions corresponding to any receiving channel.

[0139] Further, based on the predicted attenuation value, determine the received value of at least one receiving channel of each wire laying position among different wire laying positions corresponding to the to-be-scanned position, using the following formula:

[0140]

[0141] where, I m represents the received value, I o represents the transmitted value, and μD represents the predicted attenuation value.

[0142] Further, the CT scan parameter adjustment device 300 further includes: an acquisition module (not shown in the figure), the acquisition module is configured to acquire multiple plain film data and multiple groups of sectional information of a sample object, and the sectional information includes tomographic sectional information or helical sectional information; a correlation module (not shown in the figure), the correlation module is configured to perform correlation processing on each row in the plain film image corresponding to the plain film data of each sample object and each sectional information in each group of sectional information of the sample object according to the bed code value; the acquisition module is further configured to divide the multiple plain film data of the sample object into sample plain film data and test plain film data according to a preset ratio, and determine the sectional information associated with the sample plain film data as sample sectional information, and determine the sectional information associated with the test plain film data as test sectional information; a training module (not shown in the figure), the training module is configured to train a neural network model according to the sample plain film data and the sample sectional information; perform an evaluation process on the trained neural network model according to the test plain film data and the test sectional information to obtain an evaluation index; if the evaluation index meets the convergence condition of the loss function, confirm the trained neural network model as a sectional matching model.

[0143] Further, the training module is further configured to input the test plain film data into the trained neural network model to obtain the cross-section information to be tested; and calculate an evaluation index according to the first pixel number of the effective area where the sample object is located in the cross-section image of the cross-section information to be tested and the second pixel number of the effective area where the sample object is located in the cross-section image of the cross-section information associated with the test plain film data.

[0144] For the specific definition of the CT scan parameter adjustment device, reference can be made to the definition of the CT scan parameter adjustment method in the foregoing text, which will not be elaborated herein. Each module in the above CT scan parameter adjustment device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the CT device in the form of hardware or be independent of it, or can be stored in the memory in the CT device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0145] Based on the above as Figure 1 shown in the method, correspondingly, an embodiment of the present application further provides a readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the CT scan parameter adjustment method as Figure 1 shown above.

[0146] Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a CT device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present application.

[0147] Based on the above as Figure 1 shown in the method, and Figure 3 shown in the virtual device embodiment, for the purpose of achieving the above object, an embodiment of the present application further provides a CT device, which includes a storage medium and a processor; the storage medium is used for storing a computer program; the processor is used for executing the computer program to implement the CT scan parameter adjustment method as Figure 1 shown above.

[0148] Specifically, as Figure 5 shown, the CT device may include a scanning gantry 510, a scanning bed 520, a console 530, etc.

[0149] The scanning gantry 510 is mainly used to generate X-rays within the scanning aperture, collect data, and transmit the collected data to the console 530. The scanning gantry 510 may include a radiation source 511 and a detector 512. The radiation source 511 is used to generate X-rays during the CT scan. The radiation source 511 forms a radiation beam 5114 through an aperture 5112. The radiation beam 5114 can pass through a target object 400 located at the center of the scanning gantry 510. After passing through the target object 400, the radiation beam 5114 impinges on the detector 512. The detector 512 is arranged at a position on the scanning gantry 510 opposite to the radiation source 511. The detector 512 is divided into multiple layers in the Z direction and multiple receiving channels in the X direction. Thus, the detector 512 is an array-like detection device composed of multiple detection units. The detection units in the same layer are arranged in the X direction, and the detection units in the same receiving channel are arranged in the Z direction.

[0150] The scanning table 520 is a tool that cooperates with the scanning gantry 510 to complete the scanning task. It is used to support the scanning object 400, position and control the target object 400, and control the up and down movement of the scanning table and its entry and exit from the scanning aperture. Generally, the movement direction of the scanning table 520 is the Z-axis direction.

[0151] The console 530 may include an input panel through which the user can input relevant information. The console 530 may include a processor, a memory, and a display. Computer instructions and data are stored in the memory. The processor can read the computer program instructions from the memory into the memory for execution to implement the CT scan parameter adjustment method in the embodiments of the present application.

[0152] It should be noted that the console 530 may further include at least one of the following: a touch panel, an emergency stop button, a speaker, data and subject information input, scan parameter setting, image reconstruction and display, a tape drive, and photographic control, etc. Among them, some or all of the processing functions in the console 530 can be set on the scanning gantry 510, and some or all of the processing functions on the scanning gantry 510 can also be set on the console 530. When all the processing functions in the console 530 are set on the scanning gantry 510 or all the processing functions on the scanning gantry 510 are set on the console 530, the console 530 and the scanning gantry 510 can be integrated together. That is to say, the CT scan parameter adjustment method in the embodiments of the present invention can be executed by the console 530, or by the scanning gantry 510, or by the cooperation of the console 530 and the scanning gantry 510. For example, the console 530 and the scanning gantry 510 are used as two parts of a distributed system.

[0153] When in use Figure 5When the CT device shown scans the target object 400, the scanning bed 520 controls the target object 400 to enter the scanning aperture. The scanning gantry 510 moves around the target object 400 along the Z-axis as the scanning bed 520 moves, and the X-ray starts to scan the target object 400. Further, during the spiral scan of the CT device, the radiation source 511, the aperture 5112, and the detector 512 rotate around the rotation axis. During the rotation, the wire release position (also known as View) of the radiation source 511 changes continuously. Each wire release position corresponds to multiple receiving channels on the detector 512.

[0154] It can be understood that the console 530 includes the adjustment device for the CT scan parameters provided in the above embodiment.

[0155] Those skilled in the art can understand that the structure of a CT device provided in this embodiment does not limit the CT device, and it may include more or fewer components, or combine certain components, or have different component arrangements.

[0156] The storage medium may also include an operating system and a network communication module. The operating system is a program for managing and storing the hardware and software resources of the CT device, supporting the information processing program and the operation of other software and / or programs. The network communication module is used to implement the communication between the components inside the storage medium, as well as the communication between the storage medium and other hardware and software in the entity device.

[0157] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware. Input the target plain film data into the section matching model to determine the target section information corresponding to the target plain film data; based on the target section information, obtain the predicted attenuation values of the position to be scanned of the target object corresponding to the target plain film data at different wire release positions; based on the predicted attenuation values, adjust the gear positions of the integration capacitors at different wire release positions of the position to be scanned. In one aspect of the embodiments of the present application, by constructing the relationship between the plain film data and the body shape of the target object through deep learning, and establishing a section matching model through a large number of existing sample data, the accuracy of the model is higher, and the accuracy of body shape estimation is improved. On the other hand, by predicting the attenuation value of each wire release position through the target section information output by the section matching model, the required integration capacitance can be obtained, and finally the appropriate integration capacitance gear position is determined for adaptive adjustment. Using the integration capacitor after gear adjustment can improve the accuracy and efficiency of measurement data, and thus improve the imaging quality of the CT device.

[0158] Those skilled in the art can understand that the attached drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the attached drawings are not necessarily essential for implementing the present application. Those skilled in the art can understand that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be correspondingly changed and located in one or more devices different from the present implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0159] The above serial numbers of the present application are only for description and do not represent the advantages or disadvantages of the implementation scenarios. The above-disclosed are only several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present application.

Claims

1. A method for adjusting CT scan parameters, characterized in that, The method includes: Inputting the target plain film data into a cross-section matching model to determine the target cross-section information corresponding to the target plain film data, where the cross-section matching model is trained based on multiple plain film data and multiple groups of cross-section information of sample objects; Based on the target cross-section information, determining the predicted attenuation values of the position to be scanned of the target object corresponding to the target plain film data at different wire laying positions; Based on the predicted attenuation values, adjusting the gear positions of the integrating capacitors at the position to be scanned at different wire laying positions.

2. The method for adjusting CT scan parameters according to claim 1, characterized in that, The adjusting the gear positions of the integrating capacitors at the position to be scanned at different wire laying positions based on the predicted attenuation values includes: Based on the predicted attenuation values, determining the received values of at least one receiving channel at each wire laying position corresponding to the position to be scanned; Based on the received values of the at least one receiving channel, adjusting the gear positions of the integrating capacitors of the at least one receiving channel at the position to be scanned at different wire laying positions.

3. The method for adjusting CT scan parameters according to claim 2, wherein, The adjusting the gear positions of the integrating capacitors of the at least one receiving channel at the position to be scanned at different wire laying positions based on the received values of the at least one receiving channel includes: Configuring the gear positions of the integrating capacitors of each receiving channel based on the received value of each receiving channel; or, Determining a first received feature value of any one wire laying position based on the received values of all receiving channels corresponding to the any one wire laying position; calculating a first integrating capacitor gear position based on the first received feature value of the any one wire laying position; if the number of receiving channels of the any one wire laying position whose estimated received value after being amplified by the integrating capacitor is less than the received threshold meets a first preset range, configuring the integrating capacitors of all receiving channels corresponding to the any one wire laying position into a fixed gear position based on the first integrating capacitor gear position; or, Determining a second received feature value of any one receiving channel based on the received values of all wire laying positions corresponding to the any one receiving channel; calculating a second integrating capacitor gear position based on the second received feature value of the any one receiving channel; if the number of wire laying positions corresponding to the any one receiving channel whose estimated received value after being amplified by the integrating capacitor is less than the received threshold meets a second preset range, configuring the integrating capacitors of all wire laying positions corresponding to the any one receiving channel into a fixed gear position based on the second integrating capacitor gear position.

4. The method for adjusting CT scan parameters according to claim 3, characterized in that, The configuring the gear positions of the integrating capacitors of each receiving channel based on the received value of each receiving channel includes: Determining the received value range of each wire laying position according to the received value of each receiving channel; Obtaining the maximum received value from the received value range; Configuring the gear positions of the integrating capacitors of each receiving channel according to the maximum received value.

5. The method for adjusting CT scanning parameters according to claim 3, characterized in that The first received feature value includes: the average received value, the median received value or the weighted received value obtained based on a weight relationship of all receiving channels corresponding to the any one wire laying position; The second received feature value includes: the average received value, the median received value or the weighted received value obtained based on a weight relationship of all wire laying positions corresponding to the any one receiving channel; The first preset range is configured to be less than 95% of the number of all receiving channels corresponding to any wire laying position; The second preset range is configured to be less than 95% of the number of all wire laying positions corresponding to any receiving channel.

6. The method for adjusting CT scan parameters according to any one of claims 2 to 4, wherein Based on the predicted attenuation value, to determine the received value of at least one receiving channel of each wire laying position among different wire laying positions corresponding to the position to be scanned, the following formula is adopted: ， Among them, I m represents the received value, I o represents the transmitted value, μD represents the predicted attenuation value.

7. The method for adjusting CT scan parameters according to claim 1, characterized in that, The method further includes: Obtaining a plurality of plain film data and multiple groups of sectional information of a sample object, where the sectional information includes tomographic sectional information or helical sectional information; Performing correlation processing on each row of the plain film image corresponding to the plain film data of each sample object and each sectional information in each group of sectional information of the sample object according to the bed code value; Dividing the plurality of plain film data of the sample object into sample plain film data and test plain film data according to a preset ratio, and determining the sectional information associated with the sample plain film data as sample sectional information, and determining the sectional information associated with the test plain film data as test sectional information; Training a neural network model according to the sample plain film data and the sample sectional information; Performing an evaluation process on the trained neural network model according to the test plain film data and the test sectional information to obtain an evaluation index; If the evaluation index meets the convergence condition of the loss function, confirming the trained neural network model as the sectional matching model.

8. An adjustment device for CT scan parameters, characterized in that, The device includes: A matching module, configured to input target plain film data into a sectional matching model to determine target sectional information corresponding to the target plain film data, where the sectional matching model is trained based on a plurality of plain film data and multiple groups of sectional information of a sample object; A determining module, configured to determine a predicted attenuation value of a position to be scanned of a target object corresponding to the target plain film data at different wire laying positions based on the target sectional information; An adjusting module, based on the predicted attenuation value, adjusts the gear positions of the integrating capacitors at different wire laying positions of the position to be scanned.

9. A readable storage medium, on which a program or instructions are stored, characterized in that, When the program or instruction is executed by a processor, the steps of the method for adjusting CT scan parameters according to any one of claims 1 to 7 are implemented.

10. A CT device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the program, the method for adjusting CT scan parameters according to any one of claims 1 to 7 is implemented.

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