C-shaped arm imaging equipment and method and device for generating configuration scheme of C-shaped arm imaging equipment

Abstract

The invention relates to the technical field of C-shaped arm imaging device surgery, and discloses a method and device for generating a C-shaped arm imaging device configuration scheme and a C-shaped arm imaging device.The method comprises the steps that the current position of a doctor is obtained; inputting the current position of the doctor and the position of the region of interest of the target patient into a configuration information recommendation model to obtain recommended configuration information of the C-shaped arm imaging equipment; wherein under the recommended configuration information, the image quality of the obtained region of interest of the target patient meets the quality requirement, and the radiation dose received by the doctor and the target patient is relatively low. Reasoning is carried out based on the configuration information recommendation model, a set of better operation scheme can be generated, and therefore the radiation dose of patients and doctors can be reduced to the maximum extent on the premise that the image quality is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of C-arm imaging equipment surgery, for example, to a C-arm imaging equipment and a method and apparatus for generating a configuration scheme thereof. Background Art

[0002] C-arm imaging equipment, such as DSA (Digital Subtraction Angiography), is an imaging device used to diagnose and treat vascular diseases. It injects contrast agents into blood vessels and takes X-ray images in real time, subtracting background images to highlight the vascular structure. This technology is widely used in the diagnosis and interventional treatment of cardiovascular, cerebrovascular, peripheral vascular and other diseases. During surgery using C-arm imaging equipment, doctors and patients will be exposed to X-ray radiation. Long surgical operations and frequent exposure may result in higher radiation doses, posing potential risks to the health of doctors and patients. Although higher radiation doses can obtain clearer images, they will increase radiation hazards; while reducing radiation doses may lead to reduced image quality, affecting the accuracy and safety of surgical operations.

[0003] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in the related art:

[0004] It is difficult to achieve accurate optimization of equipment layout in related technologies, and it is impossible to fully consider the comprehensive balance between radiation dose and image quality.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0007] The embodiments of the present disclosure provide a C-arm imaging device and a method and apparatus for generating a configuration scheme thereof, so as to reduce the radiation dose of doctors and patients while ensuring image quality.

[0008] In some embodiments, a method for generating a configuration plan for a C-arm imaging device includes: obtaining a doctor's current position; inputting the doctor's current position and the position of a target patient's region of interest into a configuration information recommendation model to obtain recommended configuration information for the C-arm imaging device; wherein, under the recommended configuration information, the image quality of the target patient's region of interest obtained meets the quality requirements, and the radiation dose received by the doctor and the target patient is low.

[0009] Optionally, the C-arm imaging device configuration information includes parameters selected from the following parameters: detector position, gantry position and exposure time.

[0010] Optionally, the configuration information recommendation model is trained by obtaining radiation dose data received by simulated doctors and simulated patients and image quality data of acquired images under different C-arm imaging device configuration information in a simulated surgical environment.

[0011] Optionally, the radiation dose data received by the simulated doctor and the simulated patient under different C-arm imaging device configuration information and the image quality data of the acquired images are obtained in the following manner: the simulated doctor is controlled to move in all possible positions in the simulated surgical environment, the simulated patient is fixed on the catheter bed and different regions of interest are set; for each combination of the position of the simulated doctor and the position of the region of interest of the simulated patient, the radiation dose data received by the simulated doctor and the simulated patient under different C-arm imaging device configuration information and the image quality data of the acquired images are obtained.

[0012] Optionally, the method for generating a C-arm imaging device configuration scheme also includes: analyzing the impact of different C-arm imaging device configuration information on radiation dose and image quality based on radiation dose data and image quality data; and adjusting the setting strategy of the detector position and the gantry position, the exposure time setting strategy, and the position layout strategy of the simulated doctor and the simulated patient based on the analysis results.

[0013] Optionally, training is performed in the following manner to obtain a configuration information recommendation model: an objective function is set according to the radiation dose data and image quality data of a simulated doctor and a simulated patient; an initial state is determined; the initial state includes the position of a simulated doctor, the position of a simulated patient's region of interest, the detector position, the rack position, and the exposure time; a reinforcement learning Markov decision process is used to perform model training on the initial state, and the model input parameters of the doctor's position, the patient's region of interest position, the detector position, the rack position, and the exposure time are adjusted so that the obtained configuration information recommendation model can output a solution with the highest objective function score.

[0014] Optionally, a Markov decision process of reinforcement learning is used to train the model of the initial state, including one or more iterative operations: the iterative operation includes: performing a target action in the current state according to the current strategy to obtain a target state; the target action includes adjusting the detector position, adjusting the rack position, and adjusting the exposure time. One or more; calculating the objective function score of the target state; when the objective function score is the highest score, updating the current strategy according to the objective function score of the target state, and setting the target state as the current state; or, when the objective function score is not the highest score, maintaining the current strategy and the current state; wherein the convergence condition of the iterative operation is to reach a preset number of iterations, or the objective function reaches the highest score.

[0015] Optionally, the method for generating a C-arm imaging device configuration plan also includes: using an evaluation network in combination with a configuration information recommendation model and an objective function to evaluate the recommended configuration information of the C-arm imaging device for each surgical operation in the operation; and according to the evaluation results, using a timing method to adjust the recommended configuration information of the C-arm imaging device for each surgical operation in the operation.

[0016] Optionally, the method for generating a C-arm imaging device configuration plan also includes: after obtaining recommended configuration information of the C-arm imaging device, in response to confirmation information from a doctor, performing surgery according to the detector position, gantry position and exposure time in the recommended configuration information.

[0017] In some embodiments, an apparatus for generating a C-arm imaging device configuration plan includes a processor and a memory storing program instructions, wherein the processor is configured to execute the method for generating a C-arm imaging device configuration plan as described above when running the program instructions.

[0018] In some embodiments, the C-arm imaging device includes: a C-arm imaging device body; and the device for generating a configuration scheme of the C-arm imaging device as described above, which is installed on the C-arm imaging device body.

[0019] The C-arm imaging device and the method and device for generating a configuration scheme thereof provided by the embodiments of the present disclosure can achieve the following technical effects:

[0020] In the disclosed embodiment, the doctor's current position can be obtained, and reasoning is performed on the doctor's current position and the position of the target patient's area of ​​interest based on the configuration information recommendation model to generate recommended configuration information. Under the recommended configuration information, the image quality of the target patient's area of ​​interest obtained meets the quality requirements, and the radiation dose received by the doctor and the target patient is low, thereby minimizing the radiation dose of the patient and doctor while ensuring the image quality, thereby ensuring the accuracy and safety of the surgical operation.

[0021] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0023] Figure 1 is a schematic diagram of a method for generating a configuration scheme of a C-arm imaging device provided by an embodiment of the present disclosure;

[0024] Figure 2 is a schematic diagram of a method for obtaining a configuration information recommendation model provided by an embodiment of the present disclosure;

[0025] Figure 3 is a schematic diagram of another method for obtaining a configuration information recommendation model provided by an embodiment of the present disclosure;

[0026] Figure 4 is a schematic diagram of another method for generating a configuration scheme of a C-arm imaging device provided by an embodiment of the present disclosure;

[0027] Figure 5 is an application schematic diagram of a method for generating a configuration scheme of a C-arm imaging device provided by an embodiment of the present disclosure;

[0028] Figure 6 It is a schematic diagram of an apparatus for generating a configuration scheme of a C-arm imaging device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0030] The terms "first", "second", etc. in the technical solutions described in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the disclosed embodiments described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0031] Unless otherwise stated, the term "plurality" means two or more.

[0032] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0033] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0034] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0035] Combination Figure 1 As shown, the embodiment of the present disclosure provides a method for generating a configuration scheme of a C-arm imaging device. The C-arm imaging device is an X-ray system used for medical imaging. It is named because of its C-arm structure. It is widely used in operating rooms, interventional radiology, orthopedics and other fields. It provides real-time images and assists doctors in precise operations. DSA is a type of C-arm imaging device. The execution subject of the method can be a processor, and the method includes:

[0036] S101, the processor obtains the current location of the doctor.

[0037] S102, the processor inputs the current position of the doctor and the position of the region of interest of the target patient into the configuration information recommendation model to obtain the recommended configuration information of the C-arm imaging device.

[0038] Among them, under the recommended configuration information, the image quality of the target patient's region of interest obtained meets the quality requirements, and the radiation dose received by the doctor and the target patient is low.

[0039] In the disclosed embodiment, the current position of the doctor can be obtained, and the current position of the doctor and the position of the region of interest of the target patient can be inferred based on the configuration information recommendation model to generate recommended configuration information. Under the recommended configuration information, the image quality of the region of interest of the target patient obtained meets the quality requirements, and the radiation dose received by the doctor and the target patient is low, thereby minimizing the radiation dose of the patient and the doctor while ensuring the image quality, and ensuring the accuracy and safety of the surgical operation. It should be noted that the region of interest of the target patient can be the lesion area, or it can be the area where the guide wire passes during the operation, that is, the region of interest of the target patient is the area of ​​concern during the operation.

[0040] Optionally, the C-arm imaging device configuration information includes parameters selected from the following parameters: detector position, gantry position and exposure time.

[0041] In this embodiment, the detector position refers to the specific position of the X-ray detector in the C-arm imaging device in space. The detector position directly affects the image acquisition effect and radiation dose distribution. The rack position refers to the angle and position of the C-arm rack of the C-arm imaging device in space. The movement and position of the C-arm rack directly affect the direction and angle of the X-ray beam, thereby affecting the image acquisition effect. The exposure time refers to the duration of the X-ray emission, which is one of the key parameters for controlling image quality and radiation dose.

[0042] Optionally, the configuration information recommendation model is trained by obtaining radiation dose data received by simulated doctors and simulated patients and image quality data of acquired images under different DSA configuration information in a simulated surgical environment.

[0043] In other embodiments, the training data of the configuration information recommendation model may be data in an actual surgical environment. Furthermore, the data in an actual surgical environment may be used as a test set to verify the effectiveness of the configuration information recommendation model.

[0044] Optionally, the radiation dose data received by the simulated doctor and the simulated patient under different C-arm imaging device configuration information and the image quality data of the acquired images are obtained in the following manner: the simulated doctor is controlled to move in all possible positions in the simulated surgical environment, the simulated patient is fixed on the catheter bed and different regions of interest are set; for each combination of the position of the simulated doctor and the position of the region of interest of the simulated patient, the radiation dose data received by the simulated doctor and the simulated patient under different C-arm imaging device configuration information and the image quality data of the acquired images are obtained.

[0045] Combination Figure 2 As shown, the embodiment of the present disclosure provides a method for obtaining a configuration information recommendation model. The execution subject of the method may be a processor, and the method includes:

[0046] S201, the processor controls the simulated doctor to move in all possible positions of the simulated surgical environment, the simulated patient is fixed on the catheter bed and different positions of the region of interest are set.

[0047] S202, the processor obtains the radiation dose data received by the simulated doctor and the simulated patient under different C-arm imaging device configuration information, as well as the image quality data of the acquired image, for each combination of the simulated doctor's position and the simulated patient's region of interest position.

[0048] S203: The processor performs intensive training based on the radiation dose data and the image quality data to obtain a trained configuration information recommendation model.

[0049] In this embodiment, different robots or movable objects can be used to simulate doctors and patients in a simulated surgical environment, thereby fully covering all possible positions and operating conditions of doctors and patients in the operating room, ensuring the comprehensiveness and accuracy of data collection, and shielding the radiation doses received by doctors and patients during the data collection process. For each combination of the simulated doctor's position and the simulated patient's region of interest position, different C-arm imaging device configuration information is set, which can simulate various C-arm imaging device configurations during surgery, thereby obtaining radiation dose data sets and image quality data covering all possible situations in the operating room, providing a solid data foundation for the subsequent enhanced training of the configuration information recommendation model.

[0050] Optionally, a first robot is used to simulate a doctor, and the first robot is equipped with a high-precision radiation dosimeter; a second robot is used to simulate a patient, and the region of interest of the second robot is equipped with a high-precision radiation dosimeter.

[0051] In this embodiment, during the data collection process, the first robot simulating the doctor needs to move in all possible positions in the operating room, and each position needs to stay for a period of time to record the radiation dose. Among them, the possible positions include different corners of the operating room, positions close to the equipment, and common operating positions during the operation. The second robot simulating the patient lies on the catheter bed, and the position of the region of interest, that is, the position of the lesion, wears a radiation dosimeter. At each data collection, the patient's position, including different parts such as the head, trunk, limbs, and posture should remain unchanged to simulate the situation in a real operation. For each doctor position, the position of the detector and the rack is adjusted to cover all possible operating angles and distances. This includes different distances between the detector and the patient, the rotation angle of the rack, and the tilt angle. Under each combination of the detector and the rack position, different exposure times are recorded. The exposure time should cover a range from extremely short to extremely long to observe its impact on the radiation dose and image quality. In addition, a high-precision positioning system can also be used to record the three-dimensional position information of the lesion and the two robots, including the absolute position of the robot in the operating room, the position of the lesion relative to the patient, and the position of the detector and the rack relative to the patient. In the subsequent reasoning process, the doctor and the patient replace the position of the first robot and the second robot respectively to carry out specific operational practices.

[0052] Optionally, the method for generating a C-arm imaging device configuration plan also includes: cleaning and verifying radiation dose data and image quality data to ensure the accuracy and completeness of the data; storing the cleaned and verified data set in a secure and reliable database for subsequent analysis and mining.

[0053] Optionally, the method for generating a C-arm imaging device configuration scheme also includes: analyzing the impact of different C-arm imaging device configuration information on radiation dose and image quality based on radiation dose data and image quality data; and adjusting the setting strategy of the detector position and the gantry position, the exposure time setting strategy, and the position layout strategy of the simulated doctor and the simulated patient based on the analysis results.

[0054] In this embodiment, by analyzing the position of the simulated doctor, the position combination of the detector and the rack, the influence of different equipment layouts on the radiation dose and image quality can be clarified. For example, some position combinations can significantly reduce the radiation dose while ensuring image quality, while other combinations may require higher radiation doses to obtain clear images. By analyzing the influence of different exposure times on radiation dose and image quality, the optimal exposure time range can be found. In general, a shorter exposure time can reduce the radiation dose but affect the image quality, while a longer exposure time can increase the radiation dose but improve the image quality. Through data analysis, the balance point between radiation dose and image quality can be found. Through systematic analysis, the quantitative influence of each parameter on radiation dose and image quality can be clarified, providing a scientific basis for subsequent optimization and adjustment. According to the analysis results, the setting strategy of the position combination of the detector and the rack, the exposure time setting strategy, and the position layout strategy of the simulated doctor and the simulated patient are dynamically adjusted to ensure the optimal conditions for surgical operations. Using the optimized parameter setting strategy, the system can generate a personalized surgical plan for each target patient.

[0055] Optionally, training is performed in the following manner to obtain a configuration information recommendation model: an objective function is set according to the radiation dose data and image quality data of a simulated doctor and a simulated patient; an initial state is determined; the initial state includes the position of a simulated doctor, the position of a simulated patient's region of interest, the detector position, the rack position, and the exposure time; a reinforcement learning Markov decision process is used to perform model training on the initial state, and the model input parameters of the doctor's position, the patient's region of interest position, the detector position, the rack position, and the exposure time are adjusted so that the obtained configuration information recommendation model can output a solution with the highest objective function score.

[0056] Generally speaking, under the same image quality conditions, the lower the radiation dose data of the simulated doctor and simulated patient, the higher the score obtained based on the objective function.

[0057] Combination Figure 3 As shown, the embodiment of the present disclosure provides another method for obtaining a configuration information recommendation model, including:

[0058] S301, the processor controls the simulated doctor to move in all possible positions of the simulated surgical environment, the simulated patient is fixed on the catheter bed and different positions of the region of interest are set.

[0059] S302, the processor obtains the radiation dose data received by the simulated doctor and the simulated patient under different C-arm imaging device configuration information, as well as the image quality data of the acquired image, for each combination of the simulated doctor's position and the simulated patient's region of interest position.

[0060] S303: The processor sets an objective function according to the radiation dose data and image quality data of the simulated doctor and the simulated patient.

[0061] S304, the processor determines an initial state; the initial state includes the position of the simulated doctor, the position of the region of interest of the simulated patient, the position of the detector, the position of the rack, and the exposure time.

[0062] S305, the processor uses the Markov decision process of reinforcement learning to train the model of the initial state, and adjusts the model input parameters of the doctor's position, the patient's area of ​​interest position, the detector position, the rack position and the exposure time, so that the obtained configuration information recommendation model can output the solution with the highest objective function score.

[0063] S306: The processor obtains a trained configuration information recommendation model.

[0064] Optionally, the objective function is set according to the following formula:

[0065] F(x, y, z) = a*x+b*y+c*z

[0066] Among them, F(x, y, z) represents the score of the objective function, x represents the total radiation dose received by the patient, and a is its coefficient; y represents the total radiation dose received by the doctor, and b is its coefficient; z represents the image quality evaluation, and c is its coefficient; among them, a, b and c represent the weights of the corresponding variables x, y and z, a and b are negative numbers between -1 and 0, and c is a positive number between 0 and 1; the sum of a, b, and c can be equal to zero.

[0067] Through the design of the objective function, the model can clearly optimize the goal: while ensuring image quality, minimize the radiation dose of doctors and patients. This quantitative objective function provides a clear optimization direction for reinforcement learning.

[0068] Optionally, the initial state is the starting point of reinforcement learning training, including the following key parameters: the position of the simulated doctor, the position of the simulated patient's region of interest, the detector position, the gantry position, and the exposure time. Determining the initial state from the dataset ensures that the training process is based on data from actual surgical scenarios, which improves the practicality and reliability of the training results. The dataset covers a variety of possible operating positions, equipment layouts, and exposure time combinations in the operating room, providing rich samples for model training. At the same time, the diverse selection of initial states enables the model to learn a wider range of optimization paths. Finally, the Markov decision process of reinforcement learning is used to train the model on the initial state to adjust the model parameters so that the model can output the solution with the highest score of the objective function.

[0069] The Markov decision process is a classic framework in reinforcement learning, which is used to model environments with sequential decisions. Its core includes: state, action, reward and transition probability. In the disclosed embodiment, the state refers to the current conditions in the surgical operation, including the position of the simulated doctor and the simulated patient, the equipment layout and the exposure time. The action refers to the operation that the model can take, such as adjusting the position of the detector and the rack, or changing the exposure time. The reward refers to the score calculated according to the objective function, which is used to evaluate the pros and cons of the current operation. The transition probability refers to the probability of transition from one state to another, reflecting the dynamic changes in the surgical operation.

[0070] Optionally, a Markov decision process of reinforcement learning is used to train a model of the initial state, including one or more iterative operations: the iterative operation includes: executing a target action in the current state according to the current strategy to obtain a target state; the target action includes adjusting the detector position, adjusting the rack position, and adjusting the exposure time. One or more of the following; calculating the objective function score of the target state; when the objective function score is the highest score, updating the current strategy according to the objective function score of the target state, and setting the target state as the current state; or, when the objective function score is not the highest score, maintaining the current strategy and the current state; wherein the convergence condition of the iterative operation is to reach a preset number of iterations, or the objective function reaches the highest score.

[0071] In this embodiment, the Markov decision process is used for model training, and it is necessary to first select an initial state from the data set as the current state. Then the model performs the target action in the current state according to the current strategy, obtains the target state, and calculates the score of the target state according to the objective function. When the objective function score is the highest score, the current strategy is updated according to the objective function score of the target state to adjust the model parameters, and the target state is set to the current state. Or when the objective function score is not the highest score, the current strategy and the current state are maintained. Repeat the above steps until the convergence condition is reached, the optimal strategy is obtained, and the trained configuration information recommendation model is obtained. The configuration information recommendation model can adopt CNN, DNN and Transformer model frameworks, preferably, Transformer model framework.

[0072] Optionally, the doctor's current position and the target patient's area of ​​interest position are input into a configuration information recommendation model to obtain recommended configuration information of the C-arm imaging device, including: obtaining recommended configuration information of the C-arm imaging device for each step of the surgical operation based on the configuration information recommendation model according to the doctor's current position and the target patient's area of ​​interest position; wherein the recommended configuration information includes detector position, gantry position and exposure time.

[0073] Combination Figure 4 As shown, the embodiment of the present disclosure provides another method for generating a configuration scheme of a C-arm imaging device, comprising:

[0074] S401, the processor obtains the current location of the doctor.

[0075] S402, the processor obtains the recommended configuration information of the C-arm imaging device for each surgical operation in the operation based on the configuration information recommendation model according to the current position of the doctor and the position of the area of ​​interest of the target patient; wherein the recommended configuration information includes the detector position, the rack position and the exposure time.

[0076] In this embodiment, according to the actual situation of the target patient, such as the location of the lesion, body thickness, surgical requirements, etc., the configuration information recommendation model dynamically generates the recommended configuration information of the C-arm imaging device for each surgical operation during the operation according to the input patient information and the current position of the doctor. The recommended configuration information output by the configuration information recommendation model includes: detector position, the optimal position of the detector to ensure image quality while reducing radiation dose; rack position, the optimal angle and position of the rack to adapt to the location of the lesion and surgical requirements; exposure time, the optimal exposure time for each step of the operation to balance image quality and radiation dose. In addition, the configuration information recommendation model can also indicate the doctor's position and recommend the doctor's optimal position during the operation to minimize radiation exposure. The configuration information recommendation model has the ability to reason about new surgical scenarios by learning the optimal operation strategies under different surgical scenarios in the data set. Through the reasoning of the configuration information recommendation model, a personalized surgical plan can be generated for each target patient, which not only takes into account the individual differences of the patients, but also combines the surgical requirements to ensure the safety and accuracy of the surgical operation.

[0077] Optionally, the method for generating a C-arm imaging device configuration plan also includes: using an evaluation network in combination with a configuration information recommendation model and an objective function to evaluate the recommended configuration information of the C-arm imaging device for each surgical operation in the operation; and according to the evaluation results, using a timing method to adjust the recommended configuration information of the C-arm imaging device for each surgical operation in the operation.

[0078] Optionally, the method for generating a C-arm imaging device configuration plan also includes: after obtaining the recommended configuration information of the C-arm imaging device, in response to the doctor's confirmation information, performing C-arm imaging device surgery according to the detector position, gantry position and exposure time in the recommended configuration information.

[0079] In the embodiment of the present disclosure, the radiation dose of the doctor and the patient is focused on the doctor's position, the patient's position, the gantry position, and the detector position. In practical applications, the doctor's position can be obtained by a camera, the patient's position is predetermined by the catheter bed, and the gantry position includes the angles of each axis of the gantry. Figure 5As shown in the figure, an evaluation network combining the configuration information recommendation model and the objective function is designed to evaluate the operation information of each step of the operation performed in the operating room environment. According to the evaluation results, the recommended configuration information of the C-arm imaging device for each step of the operation can be adjusted by the time sequence method to further optimize the radiation dose of doctors and patients, as well as the image quality. During the training process, the intelligent group algorithm is used. If the current position is the best score, it is kept and recorded; if it is suboptimal, the position is not updated. At this time, the evaluation network will comprehensively consider each previous step, but the influence weight of the previous step will gradually decrease. The deep network is responsible for indicating the next step of operation information and executing it in the action network to change the operating room environment, including the position of doctors and patients, the direction of the rack movement, the exposure time, the detector position, and the position of other equipment. The external reinforcement signal comes from the real-time surgical operation information, such as the position and exposure time of the above-mentioned doctors, patients, racks, detectors, etc.; while the internal reinforcement signal is based on all previous surgical operation information. Ultimately, the doctor only needs to check the output results of the configuration information recommendation model, confirm that the position is reasonable, and then use the smart button to automatically adjust the detector position and frame position of the C-arm imaging device, set the exposure time, and perform the operation.

[0080] Optionally, during surgery, the configuration information recommendation model can dynamically adjust the surgical plan based on the real-time monitored surgical environment. For example, if the patient's position changes, the model can update the position of the detector and the gantry in real time.

[0081] In this embodiment, the configuration information recommendation model can also dynamically adjust the exposure time according to the real-time image quality and radiation dose data to ensure that the operation is always in the optimal state. Through dynamic adjustment and real-time optimization, it can adapt to various changes in the operation process, ensure the flexibility and safety of the operation, and improve the success rate and efficiency of the operation.

[0082] Combination Figure 6 As shown, an embodiment of the present disclosure provides a device 600 for generating a configuration scheme of a C-arm imaging device, including a processor 601 and a memory 602. Optionally, the device may also include a communication interface 603 and a bus 604. The processor 601, the communication interface 603, and the memory 602 may communicate with each other through the bus 604. The communication interface 603 may be used for information transmission. The processor 601 may call the logic instructions in the memory 602 to execute the method for generating a configuration scheme of a C-arm imaging device of the above embodiment.

[0083] In addition, the logic instructions in the memory 602 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.

[0084] The memory 602 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 601 executes the function application and data processing by running the program instructions / modules stored in the memory 602, that is, the method for generating a configuration scheme of a C-arm imaging device in the above embodiment is implemented.

[0085] The memory 602 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 602 may include a high-speed random access memory and may also include a non-volatile memory.

[0086] The embodiments of the present disclosure provide a C-arm imaging device, comprising: a C-arm imaging device body, and the above-mentioned device for generating a configuration scheme for the C-arm imaging device. The device for generating a configuration scheme for the C-arm imaging device is installed on the C-arm imaging device body. The installation relationship described here is not limited to placement inside the C-arm imaging device, but also includes installation connections with other components of the C-arm imaging device, including but not limited to physical connections, electrical connections, or signal transmission connections. It can be understood by those skilled in the art that the device for generating a configuration scheme for the C-arm imaging device can be adapted to a feasible C-arm imaging device body, thereby realizing other feasible embodiments.

[0087] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned method for generating a configuration plan for a C-arm imaging device.

[0088] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0089] The above description and accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the technical solutions recorded in this application. As used in the technical solutions recorded in this application, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0090] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0091] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0092] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for generating a C-arm imaging device configuration scheme, characterized in that: include: Get the doctor's current location; Input the current position of the doctor and the position of the region of interest of the target patient into the configuration information recommendation model to obtain the recommended configuration information of the C-arm imaging device; Among them, under the recommended configuration information, the image quality of the target patient's region of interest obtained meets the quality requirements, and the radiation dose received by the doctor and the target patient is low.

2. The method according to claim 1, characterized in that The C-arm imaging device configuration information includes parameters selected from the following parameters: detector position, gantry position, and exposure time.

3. The method according to claim 1, characterized in that The configuration information recommendation model is trained by obtaining the radiation dose data received by the simulated doctor and simulated patient and the image quality data of the acquired images under different C-arm imaging device configuration information in a simulated surgical environment.

4. The method according to claim 3, characterized in that The radiation dose data received by the simulated doctor and simulated patient and the image quality data of the acquired images under different C-arm imaging device configuration information are obtained as follows: Control the simulated doctor to move in all possible positions of the simulated surgical environment, fix the simulated patient on the catheter bed and set different positions of the region of interest; For each combination of the position of the simulated doctor and the position of the region of interest of the simulated patient, the radiation dose data received by the simulated doctor and the simulated patient under different C-arm imaging device configuration information and the image quality data of the acquired image are obtained.

5. The method according to claim 4, characterized in that Also includes: Based on the radiation dose data and image quality data, analyze the impact of different C-arm imaging device configuration information on radiation dose and image quality; According to the analysis results, the setting strategies of the detector position and the rack position, the exposure time setting strategy, and the position layout strategy of the simulated doctor and simulated patient are adjusted.

6. The method according to claim 3, characterized in that Train as follows to obtain the configuration information recommendation model: Setting the objective function according to the radiation dose data of the simulated doctor and the simulated patient and the image quality data; Determine an initial state; the initial state includes a position of a simulated doctor, a position of a region of interest of a simulated patient, a detector position, a gantry position, and an exposure time; The Markov decision process of reinforcement learning is used to train the model of the initial state, and the model input parameters such as the doctor's position, the patient's region of interest position, the detector position, the rack position and the exposure time are adjusted so that the obtained configuration information recommendation model can output the solution with the highest objective function score.

7. The method according to claim 6, characterized in that The Markov decision process of reinforcement learning is used to train the model for the initial state, including one or more iterative operations: the iterative operations include: Execute a target action in the current state according to the current strategy to obtain a target state; the target action includes one or more of adjusting the detector position, adjusting the rack position, and adjusting the exposure time; Calculate the objective function score of the target state; When the objective function score is the highest, the current strategy is updated according to the objective function score of the target state, and the target state is set as the current state; or, when the objective function score is not the highest, the current strategy and the current state are maintained; The convergence condition of the iterative operation is that the preset number of iterations is reached, or the objective function reaches the highest score.

8. The method according to claim 6, characterized in that Also includes: The evaluation network is used to combine the configuration information recommendation model and the objective function to evaluate the recommended configuration information of the C-arm imaging device for each surgical operation during the operation. Based on the evaluation results, a timing method is used to adjust the recommended configuration information of the C-arm imaging device for each surgical step during the operation.

9. A device for generating a configuration scheme of a C-arm imaging device, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for generating a configuration plan of a C-arm imaging device according to any one of claims 1 to 8 when running the program instructions.

10. A C-arm imaging device, characterized in that: include: C-arm imaging device body; And the device for generating a configuration plan of a C-arm imaging device as described in claim 9 is installed on the C-arm imaging device body.

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