Tumor target pose real-time tracking and predicting method and system, equipment and medium

Abstract

The invention provides a tumor target pose real-time tracking and predicting method and system, equipment and a medium. An abdominal respiration monitoring device of the system is used for detecting abdominal respiration deformation; the multi-channel array flexible acquisition sensor has deformability and is used for detecting back myoelectricity and pressure change signals; the body surface information acquisition device is used for acquiring chest and abdomen point cloud data of a human body; and the main controller is used for processing data acquired by the abdominal respiration monitoring device, the multi-channel array flexible acquisition sensor and the body surface information acquisition device to obtain a predicted position of an active tumor target region. In order to solve the problem of respiratory movement in the radiotherapy process, back pressure myoelectricity information, abdomen respiratory deformation information and binocular vision information respiratory movement evaluation are adopted, and a mapping relation from in-vitro movement to in-vivo tumor movement is constructed through in-vitro multi-modal data and 4D-CT information in combination with a big data model. And in-vivo tumor pose prediction and evaluation are realized.

Description

Technical Field

[0001] The present invention relates to the field of detection technology, and in particular to a method, system, device and medium for real-time tracking and prediction of tumor target area posture. Background Art

[0002] The impact of chest and abdominal respiratory motion on organ shape and position changes will directly affect surgical positioning and the target accuracy of tumor radiotherapy. Existing tumor target monitoring methods usually rely on real-time CT irradiation. Although it can provide real-time monitoring data, it will cause patients to be exposed to higher radiation doses, and the invasiveness of the gold label implantation monitoring method increases the risk of complications such as pneumothorax in patients. In order to deal with the interference of respiratory motion, surface-based monitoring and prediction technology is particularly important. The current respiratory motion prediction models are divided into two categories: model algorithms and model-free algorithms. Model algorithms predict through time series models, but there are problems such as limited prediction time and long calculation time, and it is difficult to effectively compensate for system delays. Model-free algorithms can automatically adjust parameters according to different respiratory states of individuals, adapt to non-strictly periodic respiratory motion, and update faster, but their prediction accuracy still needs to be improved. In addition, traditional surface electromyography (sEMG) sensors and pressure sensors have problems such as poor adhesion, easy damage, and poor biocompatibility in applications, which limits their effective application in tumor tracking. Summary of the invention

[0003] In order to achieve the above-mentioned purpose and other advantages of the present invention, the first purpose of the present invention is to provide a real-time tracking and prediction system for the position of a tumor target area, comprising an abdominal respiratory monitoring device based on a fiber Bragg grating sensor, a multi-channel array flexible acquisition sensor, a body surface information acquisition device based on binocular structured light, and a main controller, wherein the fiber Bragg grating sensor and the multi-channel array flexible acquisition sensor are both wearable sensors; wherein,

[0004] The abdominal respiratory monitoring device is used to detect abdominal respiratory deformation;

[0005] The multi-channel array flexible acquisition sensor is deformable and is used to detect back electromyography and pressure change signals;

[0006] The body surface information acquisition device is used to collect point cloud data of the chest and abdomen of the human body;

[0007] The main controller is used to process the data collected by the abdominal respiratory monitoring device, the multi-channel array flexible acquisition sensor and the body surface information acquisition device to obtain the predicted position of the active tumor target area.

[0008] Furthermore, the abdominal respiratory monitoring device includes a light source, a beam expander, a fiber Bragg grating sensor module, a beam combiner, a spectrum analyzer, and a computer data processing system;

[0009] The light emitted by the light source is expanded by the beam expander, and the expanded parallel light passes through the fiber Bragg grating sensor module. After wavelength selection, the fiber Bragg grating sensor module transmits light of different wavelengths, and then the converged light beam is transmitted to the spectrum analyzer through the beam combiner to analyze the intensity distribution of light beams of different wavelengths. The computer data processing system performs deformation analysis to obtain results.

[0010] Furthermore, the fiber Bragg grating sensor module includes a fiber Bragg grating sensor and silica gel, and the fiber Bragg grating sensor is embedded in the silica gel.

[0011] Furthermore, the fiber Bragg grating sensor is arranged in a twisted manner, and is bent into two layers, both of which are horizontal S-shaped waves, and the S-shapes of the upper and lower layers are opposite, and the upper and lower layers overlap to form a circle.

[0012] Furthermore, the multi-channel array flexible acquisition sensor includes an electromyographic sensor and a pressure-variable sensor, wherein the electromyographic sensor is used to collect electromyographic signals of the back, and the pressure-variable sensor is used to detect stress changes of the back.

[0013] Furthermore, the electromyographic sensor is prepared by a combination of graphene, silk fibroin and Ca2+.

[0014] Furthermore, the pressure-varying sensor is made of graphene material.

[0015] Furthermore, the body surface information acquisition device adopts structured light technology, and projects specially encoded structured light onto the surface of the measured object through a projector to generate texture.

[0016] Furthermore, the main controller fuses the data collected by the abdominal respiratory monitoring device, the multi-channel array flexible acquisition sensor and the body surface information acquisition device through a multimodal data fusion model to form a fusion feature, and uses the fusion feature as an input of a tumor target position prediction model for prediction, matches and transforms the predicted information with the constructed mapping model containing CT image information, and finally obtains the predicted position of the active tumor target.

[0017] The second object of the present invention is to provide a method for real-time tracking and prediction of tumor target area posture, based on the above system, comprising the following steps:

[0018] A multimodal data fusion model is used to fuse back electromyography and pressure change signals, abdominal respiratory deformation, and human chest and abdomen point cloud data to form fusion features;

[0019] Using the fusion feature as input data of a tumor target location prediction model for prediction;

[0020] The predicted information is matched and transformed with the constructed mapping model containing CT image information, and finally the predicted position of the active tumor target area is obtained.

[0021] Furthermore, the multimodal data fusion model includes a data input layer, a feature extraction layer, and a feature fusion layer; wherein,

[0022] The data input layer is used to input back electromyography and pressure change signals, abdominal respiratory deformation, and human chest and abdomen point cloud data into the model;

[0023] The feature extraction layer is used to extract useful features of the data by passing the input data through the corresponding feature extraction module in combination with the self-attention mechanism module;

[0024] The feature fusion layer is used to fuse the features of different input data using a cross-attention mechanism to obtain more comprehensive and consistent environmental perception information.

[0025] Furthermore, the tumor target area location prediction model adopts a prediction model based on time series.

[0026] Furthermore, the method further includes the step of constructing the mapping model:

[0027] Collect CT image data and in vitro test information before surgery and perform data processing and feature extraction;

[0028] Construct multimodal fusion models and prediction models including CT image information;

[0029] A mapping relationship between CT images and external detection information is constructed to obtain a mapping model containing CT image information.

[0030] A third object of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0031] A fourth object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0032] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0033] To address the problem of respiratory movement during radiotherapy, the present invention uses back pressure electromyography information, abdominal respiratory deformation information, and binocular vision information to evaluate respiratory movement. Through in vitro multimodal data and 4D-CT information, combined with a big data model, a mapping relationship from in vitro movement to in vivo tumor movement is constructed, thereby achieving prediction and evaluation of in vivo tumor posture.

[0034] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0036] Figure 1 This is a schematic diagram of the real-time tracking and prediction system for the tumor target area position;

[0037] Figure 2 It is a schematic diagram of an abdominal respiratory monitoring device;

[0038] Figure 3 This is a schematic diagram of a fiber Bragg grating sensor module;

[0039] Figure 4 Schematic diagram of a multi-channel array flexible data acquisition sensor;

[0040] Figure 5 A schematic diagram of the attachment position of the wearable sensor;

[0041] Figure 6 It is a flowchart for real-time tracking and prediction of tumor target position;

[0042] Figure 7 This is a flow chart of the real-time tracking and prediction method of the tumor target area position;

[0043] Figure 8 It is a schematic diagram of computer equipment;

[0044] Fig. 9 A schematic diagram of a computer-readable storage medium. DETAILED DESCRIPTION

[0045] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. It should be noted that, under the premise of no conflict, the embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0046] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0047] The figure numbers in this application are only used to distinguish the various steps in the scheme, and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0049] Example 1

[0050] A real-time tracking and prediction system for tumor target position 1, such as Figure 1 As shown, it includes an abdominal respiratory monitoring device 100 based on a fiber Bragg grating sensor, a multi-channel array flexible acquisition sensor 110, a body surface information acquisition device 120 based on binocular structured light, and a main controller 130. The fiber Bragg grating sensor and the multi-channel array flexible acquisition sensor are both wearable sensors; wherein,

[0051] The abdominal respiratory monitoring device is used to detect abdominal respiratory deformation;

[0052] The multi-channel array flexible acquisition sensor is deformable and is used to detect back electromyography and pressure change signals;

[0053] The body surface information acquisition device is used to collect point cloud data of the chest and abdomen of the human body;

[0054] The main controller is used to process the data collected by the abdominal respiratory monitoring device, the multi-channel array flexible acquisition sensor and the body surface information acquisition device to obtain the predicted position of the active tumor target area.

[0055] In some embodiments, Figure 2 As shown, the abdominal respiratory monitoring device includes a light source, a beam expander, a fiber Bragg grating sensor module, a beam combiner, a spectrum analyzer, and a computer data processing system;

[0056] The light emitted by the light source is expanded by the beam expander, and the expanded parallel light passes through the transmission fiber Bragg grating sensor module. After wavelength selection, the fiber Bragg grating sensor module transmits light of different wavelengths, and then the converged light beam is transmitted to the spectrum analyzer through the beam combiner to analyze the intensity distribution of light beams of different wavelengths. The computer data processing system performs deformation analysis to obtain results.

[0057] Fiber Bragg Grating (FBG) sensors are a type of fiber optic sensor widely used for respiratory monitoring. They work by creating periodic refractive index modulation in the optical fiber. This allows light of a specific wavelength to continue to be transmitted, and the transmission wavelength is directly related to the strain or temperature change in the optical fiber. When the optical fiber is subjected to mechanical stress or temperature changes, the transmission wavelength changes, thereby enabling the measurement of physical quantities. FBG sensors have the advantages of high sensitivity, small size, and strong resistance to electromagnetic interference. In respiratory monitoring, FBG sensors can be installed in chest straps or other wearable devices to measure respiratory rate by detecting the expansion of the chest or abdomen caused by breathing.

[0058] The chest wall movement caused by breathing is an important indicator for monitoring respiratory rate. Considering that the FBG sensor is poorly attached to the human abdomen, the FBG sensor is embedded in the silicone material through a 3D printed mold. Figure 3 As shown, the fiber Bragg grating sensor module includes a fiber Bragg grating sensor 101 and a silicone gel 102, wherein the fiber Bragg grating sensor is embedded in the silicone gel. Furthermore, the fiber Bragg grating sensor is arranged in a twisted pattern, and the fiber Bragg grating sensor is bent into an upper and lower layer, both of which are horizontal S-shaped waves, and the S-shapes of the upper and lower layers are opposite, and the upper and lower layers overlap to form a circle, which can increase the evanescent field around the bent structure without destroying the fiber structure, and amplify the influence of human breathing induced motion on the sensor, see Figure 3 The FBG sensor can accurately capture the chest and abdominal movements caused by each breath by measuring the changes in the FBG wavelength.

[0059] Considering that traditional electromyographic sensors and pressure sensors have poor adhesion, and that a few electrodes alone cannot collect complete electrophysiological activities of the back, the rigid pressure sensor needs to be in contact with the back to detect valid data, resulting in a high sensor waste rate. For the collection of electromyographic signals of the back, combined with the position arrangement of the back muscle groups, the present invention designs a multi-channel array flexible collection sensor suitable for back electromyographic and pressure variable signal detection, see Figure 4 .

[0060] Specifically, the multi-channel array flexible acquisition sensor includes an electromyographic sensor and a pressure-variable sensor. The electromyographic sensor is used to collect electromyographic signals of the back, and the pressure-variable sensor is used to detect stress changes of the back.

[0061] Taking into account the deviations in height and body shape of users, the muscle groups of different users are not exactly the same. The multi-channel array flexible acquisition sensor in the present invention is deformable and can be deformed within a certain range, and has better adhesion and biocompatibility. In order to ensure normal operation under the CT environment and avoid affecting the CT image acquisition process, the multi-channel array flexible acquisition sensor provided by the present invention adopts non-magnetic materials and is prepared as follows: the electromyographic sensor is prepared by using a combination of graphene / silk fibroin / Ca2+ (Gr / SF / Ca2+), and the silk fibroin material has the property of swelling in water. After damage, only a small amount of water needs to be added to complete self-repair in a short time, thereby improving the utilization rate and service life. The pressure sensor is also made of graphene material. A high signal-to-noise ratio, long-term and stable detection of human physiological signals is achieved.

[0062] The muscle group collection position in this embodiment is as follows Figure 5 As shown in (a), the final results are the back electromyographic signal diagram and back stress change diagram. Figure 5 Middle (b) is an abdominal respiratory monitoring device based on FBG sensors, which is attached to the abdomen in a distributed manner and uses multiple optical fibers in an integrated manner to better detect respiratory movements.

[0063] The traditional binocular vision ranging system is composed of only binocular cameras. By capturing the left and right views of the spatial object, it can quickly calculate the depth information of the object in the image, but it is easily affected by the texture information of the measured object. When the texture information is weak, it will increase the difficulty of stereo matching and easily lead to matching errors. The present invention adopts structured light technology, and projects specially encoded structured light onto the surface of the measured object through a projector to generate texture. With the help of a customized texture method, the difficulty of stereo matching is reduced, and the speed and accuracy of measurement are improved. The calibration method uses a motion platform to drive the camera to move along a known trajectory, and at the same time controls the camera to collect multiple sets of images, and solves the camera parameters by establishing a corresponding relationship between image information and known displacement.

[0064] In some embodiments, Figure 6 As shown, the main controller fuses the data collected by the abdominal respiratory monitoring device, the multi-channel array flexible acquisition sensor and the body surface information acquisition device through a multimodal data fusion model to form a fusion feature, and uses the fusion feature as the input of the tumor target position prediction model for prediction, and matches and transforms the predicted information with the constructed mapping model containing CT image information, and finally obtains the predicted position of the active tumor target.

[0065] Among them, the multimodal data fusion model fuses the back array pressure change and electromyography monitoring map, the abdominal respiratory deformation based on the FBG sensor, and the human chest and abdomen point cloud based on the body surface information acquisition device based on binocular structured light to form fusion features, including:

[0066] Data input layer: inputs data from multiple sensors into the model.

[0067] Feature extraction layer: In the feature extraction layer, the data of each sensor passes through the corresponding feature extraction module and is combined with the self-attention mechanism module to extract useful features of the sensor data, including the edges and corners of the image, the point cloud features of the human body, etc.

[0068] Feature fusion layer: In the feature fusion layer, the cross-attention mechanism is used to fuse features from different sensors to obtain more comprehensive and consistent environmental perception information. In order to better align features, the cross-attention mechanism is used to dynamically capture the correlation between multiple modalities.

[0069] The prediction of the internal tumor target location needs to take into account the time delay, and the tumor location needs to be predicted and the target location needs to be issued in advance. The prediction model needs to predict the location of the tumor at different times within a whole period of time in the future (such as 0 to 1000ms), so a time series model is needed for location prediction.

[0070] At present, the classic method for modeling sequence information is recurrent neural network (RNN) and its various variants, but RNN training is difficult to parallelize, and long sequence modeling faces problems such as gradient vanishing and gradient explosion. The present invention uses Informer to predict the tumor target location, and monitors the surface point cloud information of the human chest and abdomen, the back compression deformation and electromyographic array images in real time. The abdominal respiratory deformation is passed through a multimodal fusion model, and the fusion features are used as input data for prediction.

[0071] Then, a multimodal fusion model and prediction model including CT image information are constructed to build a mapping relationship from CT images to external detection information.

[0072] Finally, the information obtained during the prediction is matched and transformed with the constructed mapping model containing CT image information, and the predicted position of the active tumor target area is finally obtained. Among them, the mapping model containing CT image information is constructed before the radiotherapy operation, and the collected information includes in vitro detection information and CT images. In the implementation stage of the operation, the in vitro detection information is collected and the position of the active target area is predicted through the tumor target area position prediction model. For details, see Figure 6 .

[0073] Example 2

[0074] A method for real-time tracking and prediction of tumor target position is provided, based on the above system. For detailed description of the system, please refer to the corresponding description in the above system embodiment, which will not be repeated here. Figure 6 , Figure 7 As shown, the method comprises the following steps:

[0075] S1. Use a multimodal data fusion model to fuse back electromyography and pressure change signals, abdominal respiratory deformation, and human chest and abdomen point cloud data to form fusion features;

[0076] The multimodal data fusion model includes a data input layer, a feature extraction layer, and a feature fusion layer;

[0077] The data input layer is used to input back electromyography and pressure change signals, abdominal respiratory deformation, and human chest and abdomen point cloud data into the model;

[0078] The feature extraction layer is used to extract useful features of the input data through the corresponding feature extraction module in combination with the self-attention mechanism module, including the edges and corners of the image, the point cloud features of the human body, etc.

[0079] The feature fusion layer is used to fuse the features of different input data using a cross-attention mechanism to obtain more comprehensive and consistent environmental perception information. In order to better align features, the cross-attention mechanism is used to dynamically capture the correlation between multiple modalities.

[0080] S2, using the fusion feature as input data of a tumor target location prediction model for prediction;

[0081] The prediction of the internal tumor target position needs to take into account the time delay, and the tumor position needs to be predicted and the target position needs to be issued in advance. The prediction model needs to predict the position of the tumor at different times within a whole period of time in the future (such as 0 to 1000ms), so a time series model is needed for position prediction, that is, the tumor target position prediction model adopts a prediction model based on time series.

[0082] At present, the classic method for modeling sequence information is recurrent neural network (RNN) and its various variants, but RNN training is difficult to parallelize, and long sequence modeling faces problems such as gradient vanishing and gradient explosion. The present invention uses Informer to predict the tumor target location, and monitors the surface point cloud information of the human chest and abdomen, the back compression deformation and electromyographic array images in real time. The abdominal respiratory deformation is passed through a multimodal fusion model, and the fusion features are used as input data for prediction.

[0083] S3. Match and transform the predicted information with the constructed mapping model containing CT image information, and finally obtain the predicted position of the active tumor target area.

[0084] In some embodiments, the mapping model is constructed by:

[0085] Before surgery, CT imaging data and in vitro test information are collected and data processing and feature extraction are performed. For details, see Figure 6 ;

[0086] Construct multimodal fusion models and prediction models including CT image information;

[0087] A mapping relationship between CT images and external detection information is constructed to obtain a mapping model containing CT image information.

[0088] The mapping model containing CT image information is constructed before the radiotherapy operation. During the operation, the in vitro detection information is collected and the position of the active target area is predicted through the tumor target area position prediction model. For details, see Figure 6 .

[0089] Example 3

[0090] A computer device 200, such as Figure 8 As shown, it includes a memory 210, a processor 220, and a computer program 230 stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for real-time tracking and prediction of tumor target posture are implemented. For a detailed description of the method, reference can be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0091] Example 4

[0092] A computer readable storage medium such as Fig. 9 As shown, a computer program is stored thereon, and when the computer program is executed by the processor, the steps of a method for real-time tracking and prediction of tumor target position and posture are implemented. For a detailed description of the method, reference can be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0093] Example 5

[0094] A computer program product, the computer program product comprising a computer program, the computer program when executed by a processor implements the steps of a method for real-time tracking and prediction of tumor target posture. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0095] The number of devices and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention will be obvious to those skilled in the art.

[0096] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

[0097] The apparatus, computer device, non-volatile computer storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, computer device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, computer device and non-volatile computer storage medium will not be repeated here.

[0098] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software units for implementing the method and structures within the hardware component.

[0099] The systems, devices or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions in various units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or more software and / or hardware.

[0100] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0101] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0104] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0105] The specification may be described in the general context of computer-executable instructions executed by a computer, such as program units. Generally, program units include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program units may be located in local and remote computer storage media, including storage devices.

[0106] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0107] The above description is only an embodiment of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of one or more embodiments of this specification.

Claims

1. A real-time tracking and prediction system for tumor target area position, characterized by: It includes an abdominal respiratory monitoring device based on a fiber Bragg grating sensor, a multi-channel array flexible acquisition sensor, a body surface information acquisition device based on binocular structured light, and a main controller. The fiber Bragg grating sensor and the multi-channel array flexible acquisition sensor are both wearable sensors; wherein, The abdominal respiratory monitoring device is used to detect abdominal respiratory deformation; The multi-channel array flexible acquisition sensor is deformable and is used to detect back electromyography and pressure change signals; The body surface information acquisition device is used to collect point cloud data of the chest and abdomen of the human body; The main controller is used to process the data collected by the abdominal respiratory monitoring device, the multi-channel array flexible acquisition sensor and the body surface information acquisition device to obtain the predicted position of the active tumor target area.

2. A real-time tracking and prediction system for tumor target area position as claimed in claim 1, characterized in that: The abdominal respiratory monitoring device includes a light source, a beam expander, a fiber Bragg grating sensor module, a beam combiner, a spectrum analyzer, and a computer data processing system; The light emitted by the light source is expanded by the beam expander, and the expanded parallel light passes through the fiber Bragg grating sensor module. After wavelength selection, the fiber Bragg grating sensor module transmits light of different wavelengths, and then the converged light beam is transmitted to the spectrum analyzer through the beam combiner to analyze the intensity distribution of light beams of different wavelengths. The computer data processing system performs deformation analysis to obtain results.

3. A real-time tracking and prediction system for tumor target area position as claimed in claim 2, characterized in that: The fiber Bragg grating sensor module comprises a fiber Bragg grating sensor and silica gel, and the fiber Bragg grating sensor is embedded in the silica gel.

4. A real-time tracking and prediction system for tumor target area position as claimed in claim 3, characterized in that: The fiber Bragg grating sensor is arranged in a twisted pattern, and is bent into two layers, both of which are horizontal S-shaped waves, and the S-shapes of the upper and lower layers are opposite, and the upper and lower layers overlap to form a circle.

5. The real-time tracking and prediction system for tumor target area position as claimed in claim 1, characterized in that: The multi-channel array flexible acquisition sensor comprises an electromyographic sensor and a pressure-variable sensor. The electromyographic sensor is used to collect electromyographic signals of the back, and the pressure-variable sensor is used to detect stress changes of the back.

6. A real-time tracking and prediction system for tumor target area position as claimed in claim 5, characterized in that: The electromyographic sensor is prepared by combining graphene, silk fibroin and Ca2+.

7. A real-time tracking and prediction system for tumor target area position and posture as claimed in claim 5, characterized in that: The pressure-varying sensor is made of graphene material.

8. The real-time tracking and prediction system for tumor target area position and posture according to claim 1, characterized in that: The body surface information acquisition device adopts structured light technology, and projects specially encoded structured light onto the surface of the measured object through a projector to generate texture.

9. A real-time tracking and prediction system for tumor target area position and posture as claimed in claim 1, characterized in that: The main controller fuses the data collected by the abdominal respiratory monitoring device, the multi-channel array flexible acquisition sensor and the body surface information acquisition device through a multimodal data fusion model to form a fusion feature, and uses the fusion feature as the input of the tumor target position prediction model for prediction, matches and transforms the predicted information with the constructed mapping model containing CT image information, and finally obtains the predicted position of the active tumor target.

10. A method for real-time tracking and prediction of tumor target position, based on the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: A multimodal data fusion model is used to fuse back electromyography and pressure change signals, abdominal respiratory deformation, and human chest and abdomen point cloud data to form fusion features; Using the fusion feature as input data of a tumor target location prediction model for prediction; The predicted information is matched and transformed with the constructed mapping model containing CT image information, and finally the predicted position of the active tumor target area is obtained.

11. A method for real-time tracking and prediction of tumor target position and posture according to claim 10, characterized in that: The multimodal data fusion model includes a data input layer, a feature extraction layer, and a feature fusion layer; wherein, The data input layer is used to input back electromyography and pressure change signals, abdominal respiratory deformation, and human chest and abdomen point cloud data into the model; The feature extraction layer is used to extract useful features of the data by passing the input data through the corresponding feature extraction module in combination with the self-attention mechanism module; The feature fusion layer is used to fuse the features of different input data using a cross-attention mechanism to obtain more comprehensive and consistent environmental perception information.

12. A method for real-time tracking and prediction of tumor target position and posture according to claim 10, characterized in that: The tumor target area position prediction model adopts a prediction model based on time series.

13. The method for real-time tracking and prediction of tumor target position and posture according to claim 10, characterized in that: The construction steps of the mapping model are also included: Collect CT image data and in vitro test information before surgery and perform data processing and feature extraction; Construct multimodal fusion models and prediction models including CT image information; A mapping relationship between CT images and external detection information is constructed to obtain a mapping model containing CT image information.

14. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 10 to 13 are implemented.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 10 to 13 are implemented.

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