Simulation system based on tumor marginal particle positioning
Through the integration of tumor edge particle positioning simulation system with modules such as image generation, particle generation and injection, radiation transmission, etc., the problem of generation does not meet user needs is solved, the tumor model and particle characteristics are customized, the accuracy and flexibility of the simulation system are improved, and medical image processing and particle therapy optimization are supported.
Patent Information
- Application Number
- CN202410130581.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-31
- Publication Date
- 2025-07-22
AI Technical Summary
The existing tumor edge particle positioning simulation system is difficult to generate tumor models and particle characteristics that meet user needs, and cannot meet the needs of specific research or training.
Through the combination of image generation unit, particle generation and injection unit, radiation transmission unit, detector unit, data processing unit, particle positioning unit and visualization unit, the user parameterized input tumor information and particle characteristics are realized, and the localization process of particles at the tumor edge is simulated, including the integration of multiple modules such as medical image type selection, tumor model definition, particle type selection, radiation transmission simulation, etc.
The generated image and particle positioning results meet user needs, support the testing and verification of medical image processing algorithms, provide training data for machine learning, optimize particle therapy solutions, and improve the accuracy and flexibility of the simulation system.
Smart Images
Figure CN120356681A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of tumor margin particle technology, and particularly relates to a simulation system for tumor margin particle positioning. Background Art
[0002] A tumor margin particle positioning simulation system is a system used to simulate and study the positioning of tumor margin particles. In this field, multiple technologies and concepts are involved, and the following are the related background technologies: Medical imaging technology: Tumor margin particle positioning usually relies on medical imaging technologies such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and ultrasound imaging. These technologies can provide detailed views of the tissue structure inside the patient, providing necessary information for particle positioning. Image processing and analysis: Image processing and analysis technologies are used to process medical images, extract features therein, and assist in algorithms for particle positioning. Edge detection, segmentation, and feature extraction technologies play important roles in the simulation system for tumor margin positioning. Particle positioning algorithms: including but not limited to probability models, machine learning algorithms, and computational geometry algorithms. These algorithms identify the positions of tumor margin particles by processing medical image data, providing quantitative and qualitative information. Simulation technology: Simulation technology is used to simulate the process of particle positioning at the tumor margin, which involves mathematical modeling of physical processes, radiation transport, and simulating the behavior of devices and sensors. Real-time positioning system: In the simulation system, the concept of a real-time positioning system is involved, which is related to the need for real-time monitoring and navigation in actual clinical practice. This includes the processing and visualization of real-time data. Computer graphics: used to visualize the simulation results, providing an intuitive graphical interface. Computer graphics technology can be used to draw the movement trajectories of particles in tumor tissue, showing the results of the simulation process. Biomedical engineering: Knowledge in the field of biomedical engineering is crucial for understanding and simulating processes within the living body. This includes knowledge in biomedical signal processing and medical sensor technology. Data security and privacy protection: Since it involves patients' medical data, the system needs to consider data security and privacy protection, meeting relevant regulations and ethical requirements. The comprehensive application of these background technologies constitutes a tumor margin particle positioning simulation system, which has important application potential in simulating and studying tumor treatment and surgical navigation. Summary of the Invention
[0003] The purpose of the present invention is to provide a simulation system for tumor margin particle positioning. Through an image generation unit including a medical image type selection module, a tumor model definition module, a tumor margin generation module, and a tumor internal feature simulation module, it helps to ensure that the generated images meet the research or training needs of users. Users can input relevant information about the tumor in a parameterized manner to generate a tumor model that meets the user's requirements, solving the existing problems.
[0004] To solve the above technical problems, the present invention is achieved through the following technical solutions:
[0005] The present invention is a tumor edge particle localization simulation system, including an image generation unit. The output end of the image generation unit is provided with a particle generation and injection unit. The output end of the particle generation and injection unit is provided with a radiation transmission unit. The output end of the radiation transmission unit is provided with a detector unit, and the output end of the detector unit is provided with a data processing unit. The output end of the data processing unit is provided with a particle localization unit. The output end of the particle localization unit is provided with a visualization unit. The output end of the visualization unit is provided with a data analysis unit.
[0006] Further, the image generation unit includes a medical image type selection module, a tumor model definition module, a tumor edge generation module, and a tumor internal feature simulation module.
[0007] Further, the particle generation and injection unit includes a particle type selection module, a particle parameter setting module, a particle injection strategy module, an energy spectrum distribution simulation module, and a multi-particle simulation module.
[0008] Further, the radiation transmission unit includes a scattering and absorption model selection module, a tissue parameter setting module, a radiation transport equation solving module, a radiation field distribution calculation module, and a scattering angle distribution simulation module.
[0009] Further, the detector unit includes a detector type selection module, a detector parameter setting module, a detector response model selection module, and a scintillation light generation simulation module.
[0010] Further, the data processing unit includes a data input module, a noise filtering module, an energy calibration module, a spatial localization algorithm module, and an energy localization algorithm module.
[0011] Further, the particle localization unit includes a spatial coordinate conversion module, a multi-channel fusion module, a model matching module, a fine adjustment algorithm module, and a real-time localization processing module.
[0012] Further, the visualization unit includes a three-dimensional reconstruction module, an energy spectrum diagram drawing module, a spatial distribution diagram drawing module, and a trajectory visualization module.
[0013] Further, the data analysis unit includes a data preprocessing module, an energy spectrum analysis module, a spatial distribution analysis module, and an energy resolution evaluation module.
[0014] The present invention has the following beneficial effects:
[0015] 1. The image generation unit of the present invention includes a medical image type selection module, a tumor model definition module, a tumor edge generation module, and a tumor internal feature simulation module, which helps to ensure that the generated images meet the research or training needs of users. Users can input relevant information about tumors in a parametric manner to generate tumor models that meet their needs.
[0016] 2. The particle generation and injection unit of the present invention includes a particle type selection module, a particle parameter setting module, a particle injection strategy module, an energy spectrum distribution simulation module, and a multi-particle simulation module, which provides selectivity for further simulations. Users can customize the characteristic parameters of particles according to their needs to meet the requirements of specific research or training, provide energy spectrum distribution information for subsequent radiation transport simulations or image generation, and the multi-particle simulation module can simulate the behaviors and interactions of multiple particles.
[0017] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0019] Figure 1 is a flowchart of the present invention.
[0020] In the accompanying drawings, the components represented by each reference numeral are listed as follows:
[0021] 1. Image generation unit; 2. Particle generation and injection unit; 3. Radiation transport unit; 4. Detector unit; 5. Data processing unit; 6. Particle positioning unit; 7. Visualization unit; 8. Data analysis unit. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.
[0023] Please refer to Figure 1, the present invention is a tumor margin particle localization simulation system, including an image generation unit 1. The output end of the image generation unit 1 is provided with a particle generation and injection unit 2. The output end of the particle generation and injection unit 2 is provided with a radiation transmission unit 3. The output end of the radiation transmission unit 3 is provided with a detector unit 4, and the output end of the detector unit 4 is provided with a data processing unit 5. The output end of the data processing unit 5 is provided with a particle localization unit 6. The output end of the particle localization unit 6 is provided with a visualization unit 7. The output end of the visualization unit 7 is provided with a data analysis unit 8.
[0024] The image generation unit 1 includes a medical image type selection module, a tumor model definition module, a tumor margin generation module, and a tumor internal feature simulation module.
[0025] Medical image type selection module: Through the medical image type selection module, the user can specify the type of medical image to be generated, such as X-ray, CT scan, MRI. This helps to ensure that the generated image meets the user's research or training needs. Tumor model definition module: The tumor model definition module allows the user to define the characteristics of the tumor, such as shape, size, and location. The user can input the relevant information of the tumor in a parametric way to generate a tumor model that meets the user's needs. Tumor margin generation module: The tumor margin generation module is responsible for generating the margin information of the tumor. The tumor margin is an important feature in medical images and is of great significance for diagnosis and research. This module uses various algorithms and technologies, such as edge detection algorithms, to simulate the boundary between the tumor and the surrounding tissues. Tumor internal feature simulation module: The tumor internal feature simulation module is used to generate the internal tissue structure and features of the tumor, which includes the tissue density, blood flow condition, and internal heterogeneity of the tumor. These internal features are very important for simulating the physical properties and morphology of the tumor in medical images. The purpose of the entire image generation unit 1 is to generate a tumor medical image that meets the user's specified conditions. This is of great value in the research of medical image processing algorithms, the training and simulation of medical image diagnosis, and the development of medical image processing software. The images generated by this unit can be used to test the robustness of algorithms, verify the performance of medical image analysis tools, and provide training data for the training of machine learning models.
[0026] The particle generation and injection unit 2 includes a particle type selection module, a particle parameter setting module, a particle injection strategy module, an energy spectrum distribution simulation module, and a multi-particle simulation module.
[0027] Particle type selection module: The particle type selection module allows users to select the types of particles to be simulated, such as photons, protons, and neutrons. The role of this module is to provide selectivity for further simulations. Particle parameter setting module: The particle parameter setting module allows users to set the basic parameters of particles, such as energy, incident angle, and emission position. Users can customize the characteristic parameters of particles according to their needs to meet the requirements of specific research or training. Particle injection strategy module: The particle injection strategy module is responsible for defining the injection strategy and path of particles. According to the user's needs, the injection speed, injection position, and injection direction of particles can be set. This module can simulate the particle injection situation in a real environment, such as the process of particle injection into a tumor in medical treatment. Energy spectrum distribution simulation module: The energy spectrum distribution simulation module is used to simulate the energy distribution of particles in tissues. According to the energy of particles, incident angle, and tissue property parameters, this module can simulate the energy deposition of particles in tissues and provide energy spectrum distribution information for subsequent radiation transport simulations or image generation. Multi-particle simulation module: The multi-particle simulation module can simulate the behaviors and interactions of multiple particles. By simulating the movement trajectories and mutual collisions of multiple particles, the physical processes of particles in tissues can be described more accurately. The role of the particle generation and injection unit is to generate particles that meet the user-specified conditions and inject them into the simulation environment. This is of great value in simulating radiation transport, studying the behavior of particles in tissues, and developing particle therapy programs. The particles generated by this unit can be used to test the accuracy and robustness of particle transport simulation algorithms, optimize particle injection strategies, and evaluate the effectiveness and safety of particle therapy.
[0028] The radiation transport unit 3 includes a scattering and absorption model selection module, a tissue parameter setting module, a radiation transport equation solving module, a radiation field distribution calculation module, and a scattering angle distribution simulation module.
[0029] Scattering and Absorption Model Selection Module: This module allows users to select appropriate scattering and absorption models to describe the interaction of radiation in tissues. Selecting the appropriate model is crucial for accurately simulating radiation transport because different types of tissues have different interactions with radiation. Tissue Parameter Setting Module: The tissue parameter setting module allows users to set the physical and optical properties of tissues, such as refractive index, scattering coefficient, and absorption coefficient. These parameters are very important for solving the radiation transport equation because they affect the propagation and absorption behavior of radiation in tissues. Radiation Transport Equation Solving Module: The radiation transport equation solving module is responsible for solving the radiation transport equation, which describes the propagation and absorption of radiation in tissues. Based on the selected scattering and absorption models and tissue parameters, this module can simulate the transport behavior of radiation and calculate the distribution of the radiation field. Radiation Field Distribution Calculation Module: The radiation field distribution calculation module uses the results of the solved radiation transport equation to calculate the radiation field distribution in tissues. This includes the spatial distribution of radiation intensity and energy spectrum distribution information, which can help researchers understand the distribution of radiation in tissues. Scattering Angle Distribution Simulation Module: The scattering angle distribution simulation module is used to simulate the distribution of scattering angles. This is very important for understanding the way and angular distribution of the interaction between radiation and tissues, especially in cases involving multiple scattering. The role of the radiation transport unit is to simulate the transport behavior of radiation in biological tissues, including the processes of scattering and absorption. This is crucial for researching radiation therapy, medical imaging, and optical diagnosis. The results simulated by this unit can be used to evaluate the effect of radiation transport, optimize treatment plans, and provide references for medical image reconstruction.
[0030] The detector unit 4 includes a detector type selection module, a detector parameter setting module, a detector response model selection module, and a scintillation light generation simulation module.
[0031] Detector Type Selection Module: This module allows users to select the type of detector to be used, such as a photomultiplier tube (PMT), a silicon detector, or an indium gallium germanium detector. Different types of detectors have different sensitivities and response modes to radiation or particles. Detector Parameter Setting Module: The detector parameter setting module allows users to set the basic parameters of the detector, including but not limited to sensitivity, resolution, noise level, and operating temperature. These parameters directly affect the performance and response of the detector. Detector Response Model Selection Module: This module allows users to select a model for the detector response to simulate the response process of the detector to incident radiation or particles. This includes simulations of radiation energy deposition, generated charge, or optical signals. Scintillation Light Generation Simulation Module: The scintillation light generation simulation module is used to simulate certain types of detectors, especially those involving scintillation materials. In this case, when radiation or particles interact with the detector, scintillation light is generated. This module simulates the process of this light generation to more comprehensively understand the response characteristics of the detector.
[0032] The data processing unit 5 includes a data input module, a noise filtering module, an energy calibration module, a spatial positioning algorithm module, and an energy positioning algorithm module.
[0033] Data input module: The data input module is responsible for receiving the raw data from radiation detectors or other data sources, which may include radiation data measured by sensors, image data acquired by imaging devices. This module passes the raw data to subsequent processing steps. Noise filtering module: The noise filtering module is used to remove the noise or interference in the raw data to improve the quality and accuracy of the data. This may include using digital filtering techniques, signal processing algorithm methods to eliminate the interference irrelevant to the real signal. Energy calibration module: The energy calibration module is responsible for calibrating the measured radiation energy spectrum to match the actual energy measurement values. This is very important for accurate radiation measurement and analysis, especially in the fields of medical imaging and radiation therapy. Spatial positioning algorithm module: The spatial positioning algorithm module is used to perform spatial positioning on the data, that is, to determine the spatial positions of radiation sources or objects in images. This may include image processing techniques, positioning algorithms for extracting the position information of the target in space. Energy positioning algorithm module: The energy positioning algorithm module is used to determine the energy distribution or characteristics of radiation sources. In some applications, not only the position of the radiation source needs to be known, but also its energy spectrum distribution needs to be understood, which is particularly important in the fields of radiation therapy and nuclear medicine.
[0034] The particle positioning unit 6 includes a spatial coordinate conversion module, a multi-channel fusion module, a model matching module, a fine-tuning algorithm module, and a real-time positioning processing module.
[0035] Spatial coordinate conversion module: The spatial coordinate conversion module is used to convert the positions of particles in different coordinate systems. This is crucial for integrating data from different sensors or devices to ensure that they are processed and analyzed in the same coordinate system. Multi-channel fusion module: The multi-channel fusion module is responsible for fusing the data from multiple sensors or channels. By integrating the information from different channels, the accuracy and robustness of the particle position can be improved, especially in the face of complex environments or noise. Model matching module: The model matching module uses the previously established model to match the actual observed data. This can help determine the actual position of the particle and improve the positioning accuracy through model comparison. Fine-tuning algorithm module: The fine-tuning algorithm module is used to perform more refined position adjustment, which includes considering error correction, dynamic environmental change factors to ensure the particle position is more accurately and stably located. Real-time positioning processing module: The real-time positioning processing module can process the particle positioning information under real-time requirements. This is very important for applications that require immediate response, such as real-time monitoring and navigation fields.
[0036] The visualization unit 7 includes an interactive visualization module, a multimodal fusion module, a spatial distribution map drawing module, and a visualization parameter adjustment module.
[0037] Interactive visualization module: The interactive visualization module allows users to interact with the system. Through a graphical user interface (GUI) or other interaction means, users can view and operate the visualization results of particle positioning in real time. This helps users understand the system's performance, adjust parameters, and perform real-time control and feedback. Multimodal fusion module: The multimodal fusion module is responsible for integrating data from different modalities or sensors and presenting it in a comprehensive way. This can provide more comprehensive information to help users better understand the position distribution and characteristics of particles, especially in the case of multimodal dataset synthesis. Spatial distribution map drawing module: The spatial distribution map drawing module is used to draw the distribution map of particles in space, which includes two-dimensional or three-dimensional graphical representations to intuitively display the position, density, and distribution law of particles. Visualization parameter adjustment module: The visualization parameter adjustment module allows users to adjust visualization parameters according to needs, such as color mapping, transparency, and chart type. Such flexibility can help users better customize the visualization effect to meet different needs and preferences.
[0038] The data analysis unit 8 includes a result visualization module, a positioning accuracy analysis module, a spatial distribution analysis module, and an energy resolution evaluation module.
[0039] Result visualization module: The result visualization module is used to graphically present the output results of the particle positioning system. This helps users more intuitively understand the analysis results, identify potential problems or trends, and conduct further research and decision-making. Positioning accuracy analysis module: The positioning accuracy analysis module evaluates the positioning accuracy of the system for particle positions. By comparing the actual position and the system output position, accuracy metrics, error distribution, and root mean square error can be calculated to evaluate the system's performance. Spatial distribution analysis module: The spatial distribution analysis module is used to study the distribution law of particles in space, which can include particle density distribution and aggregation trend information to help users understand the system's performance in different regions and the existing spatial characteristics. Energy resolution evaluation module: The energy resolution evaluation module is used to evaluate the system's ability to resolve particle energies. In some applications, such as particle physics experiments, energy resolution is a key performance indicator. By analyzing the difference between the actual energy and the energy measured by the system, the accuracy of the system's energy measurement can be evaluated.
[0040] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example" mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.
[0041] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and effects.
Claims
1. A tumor-edge particle localization simulation system, comprising an image generation unit (1), characterized in that: A particle generation and injection unit (2) is provided at the output end of the image generation unit (1). A radiation transmission unit (3) is provided at the output end of the particle generation and injection unit (2). A detector unit (4) is provided at the output end of the radiation transmission unit (3), and a data processing unit (5) is provided at the output end of the detector unit (4). A particle positioning unit (6) is provided at the output end of the data processing unit (5). A visualization unit (7) is provided at the output end of the particle positioning unit (6). A data analysis unit (8) is provided at the output end of the visualization unit (7).
2. The tumor-edge particle localization simulation system according to claim 1, wherein The image generation unit (1) includes a medical image type selection module, a tumor model definition module, a tumor edge generation module, and a tumor internal feature simulation module.
3. The tumor-edge particle positioning simulation system according to claim 1, wherein, The particle generation and injection unit (2) includes a particle type selection module, a particle parameter setting module, a particle injection strategy module, an energy spectrum distribution simulation module, and a multi-particle simulation module.
4. The tumor-edge particle localization simulation system according to claim 1, wherein The radiation transmission unit (3) includes a scattering and absorption model selection module, a tissue parameter setting module, a radiation transport equation solving module, a radiation field distribution calculation module, and a scattering angle distribution simulation module.
5. The tumor edge particle positioning simulation system according to claim 1, wherein The detector unit (4) includes a detector type selection module, a detector parameter setting module, a detector response model selection module, and a scintillation light generation simulation module.
6. The tumor-edge particle positioning simulation system according to claim 1, wherein The data processing unit (5) includes a data input module, a noise filtering module, an energy calibration module, a spatial positioning algorithm module, and an energy positioning algorithm module.
7. A tumor-edge particle localization simulation system according to claim 1, characterized in that, The particle positioning unit (6) includes a spatial coordinate conversion module, a multi-channel fusion module, a model matching module, a fine adjustment algorithm module, and a real-time positioning processing module.
8. The tumor-edge particle positioning simulation system according to claim 1, characterized in that, The visualization unit (7) includes a three-dimensional reconstruction module, an energy spectrum diagram drawing module, a spatial distribution diagram drawing module, and a trajectory visualization module.
9. The tumor-edge particle localization simulation system according to claim 1, wherein The data analysis unit (8) includes a data preprocessing module, an energy spectrum analysis module, a spatial distribution analysis module, and an energy resolution evaluation module.