507 results about "Dose distribution" patented technology

The invention relates to the technical field of deep learning, and discloses a real-time dose monitoring and adjusting method and system for tumor radiotherapy, and the method comprises the steps: carrying out the real-time dose data collection of a treatment region, obtaining the surface dose distribution data and pH value change data, carrying out the respiratory movement feature recognition of a patient, and carrying out the recognition of the surface dose distribution data and the pH value change data; generating a respiratory movement feature set; performing four-dimensional space-time division, and constructing a dynamic dose distribution network; performing feature extraction and integration to obtain a fusion distribution feature set; inputting the fusion distribution feature set and the respiratory movement feature set into a preset dose distribution prediction model for real-time dose distribution prediction to obtain real-time dose distribution prediction data; according to the method, the original control parameters of the radiotherapy equipment are dynamically adjusted to obtain the target control parameters, multi-source heterogeneous data are integrated, an accurate dose distribution prediction model is constructed, and intelligent adjustment of the parameters of the radiotherapy equipment is realized.

The invention provides a radiation therapy dose distribution prediction method and system based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: collecting a data set containing a CT scanning image of a patient, radiation therapy plan parameters and actual dose distribution, taking an electron density map and a three-dimensional flux map as the input of a training deep learning model, and carrying out the prediction of the radiation therapy dose distribution. Taking the endangered organ mask image, the radiotherapy target area mask image and the three-dimensional dose distribution image corresponding to the CT scanning image of the patient as the output of the model, and training to obtain an optimal dose distribution prediction model; and predicting the three-dimensional dose distribution of the new patient by using the trained dose distribution prediction model. According to the invention, by using the data of the patient and the radiotherapy plan information in the past, the dose distribution can be predicted more quickly and accurately, the delineation dependence of the doctor on the region of interest is reduced, and the treatment efficiency and the result stability are improved.

The invention belongs to but is not limited to the technical field of three-dimensional space dose visualization, and discloses a three-dimensional nuclear emergency radiation field calculation and visualization method and system, based on a point nuclear method, a cumulative factor, mass attenuation factor and flux rate-dose rate conversion factor database is established, a mean free path solving algorithm is optimized, and a three-dimensional nuclear emergency radiation field is obtained. The geometric types of radioactive sources are expanded, and the dose calculation efficiency is improved through parallel calculation; plane dose rate drawing at a certain height is realized based on a two-dimensional texture mapping method; three-dimensional space dose rate contour surface drawing is realized based on a moving cube algorithm; and three-dimensional space dose rate volume rendering is realized based on a three-dimensional texture volume rendering technology. Radiation field dose calculation, three-dimensional radiation field dose visualization and GIS are integrated, and the system is used as a subsystem of a nuclear emergency aid decision simulation platform. The dose distribution diagram in the area can be judged more visually, and the method has a good help effect on nuclear emergency command.

The invention discloses a self-adaptive aid decision-making method, system and device for patient radiotherapy, and a medium, and relates to the field of smart medical treatment. The method comprises the following steps: standardizing and preprocessing multi-source original data; performing automatic segmentation and change detection on tumor and normal tissues based on the processed data; delineating a tumor target region and a normal tissue in radiotherapy in a detection result; generating radiation therapy dose distribution according to the sketching result; evaluating the radiation treatment dose distribution by adopting an optimization evaluation method to generate a plurality of alternative treatment schemes; determining an optimal treatment regimen based on the plurality of alternative treatment regimens; and performing radiotherapy based on the optimal treatment scheme, monitoring anatomical changes of the patient in real time in the radiotherapy process, and dynamically adjusting the optimal treatment scheme. According to the invention, a plurality of operations of patient radiotherapy can be effectively integrated, and precision and individuation of patient radiotherapy can be realized, so that complex changes in the treatment process can be better coped with, and the overall treatment effect can be improved.

The invention discloses a BNCT dose calculation method, which comprises the following steps: S1, constructing a voxel model, initializing one or more particles in a simulation system, and respectively acquiring the initial position, energy and movement direction of each particle; s2, adaptively selecting a Monte Carlo particle tracking algorithm and a flux counting mode based on the region where the particles are located and the energy of the particles; s3, repeating the step S2 until the particles leave the simulation system or are killed when meeting a truncation condition; and S4, cumulatively calculating dose distribution. Through the BNCT dose calculation method, the Monte Carlo particle transport simulation time is greatly shortened, the BNCT dose calculation efficiency is optimized, and the method is suitable for rapid and high-precision radiation dose simulation under a BNCT complex tissue structure.

The invention belongs to the technical field of medical treatment, and discloses a radiotherapy protection system for reducing normal tissue damage, and the system comprises a real-time imaging and monitoring module which employs high-speed CT or MRI equipment to obtain 4D images of a target region and surrounding tissues of a patient in real time in a treatment process; the dynamic protection dose distribution module is used for dynamically calculating dose distribution of each radiation according to the real-time imaging data, optimizing an irradiation path and reducing the stress of normal tissues; the intelligent feedback control module is used for detecting the accumulated dose of a target region and normal tissues in real time and dynamically adjusting an irradiation strategy; inputting the real-time image and the dose monitoring data into a treatment planning system, and updating an irradiation plan; the user interaction module provides a radiotherapy real-time display interface; patient physiological data is monitored in real time. According to the invention, through dynamic real-time monitoring, dose distribution optimization and intelligent feedback control, the limitation of the traditional radiotherapy technology on real-time performance and protection effect is broken through. The system significantly reduces damage to normal tissues, improves treatment precision and patient experience, and has wide clinical application value.

The invention provides a non-equidistant lattice distribution optimization method based on local dose constraint. The method comprises the following steps: generating an initial lattice based on a patient image and a delineated target region; calculating and storing a dose kernel in advance based on a Monte Carlo algorithm; calling a dose core to carry out rapid dose optimization and calculation; establishing an independent peripheral subspace dose evaluation region and a local dose constraint condition for each lattice target region; establishing a multi-objective function for lattice target region position optimization; dynamically adjusting lattice positions and intervals by using a rapid simulated annealing method or a gradient descent method until target region position distribution and dose distribution meeting a dose multi-objective function and constraint conditions are generated; according to the invention, through dynamic adjustment of the lattice spacing and position and combination of the patient specific anatomical structure, accurate control of the valley dose around each lattice is realized, so that the problems of non-uniform target dose distribution and insufficient valley dose control in traditional equal spacing lattice radiotherapy are solved.

The present disclosure provides systems and methods for automated scan preparation and real-time monitoring / adjustment in medical imaging. The automated scan preparation may include positioning a target subject to be scanned by a medical imaging device, determining a rotation scheme of the medical imaging device, targeting a scan region of the target subject with an imaging isocenter of the medical imaging device, determining target position(s) of component(s) of the medical imaging device, performing a virtual scan, generating a reference subject model representing an internal structure of the target subject, or the like. The real-time monitoring / adjustment operations may include achieving automatic brightness stabilization, monitoring a posture of the target subject, adjusting the position of component(s) of the medical imaging device, estimating a dose distribution, monitoring a treatment of the target subject, performing a motion correction, or the like.

The invention discloses a gravitational field model-based lattice space segmentation radiotherapy lattice point optimization method, and relates to the technical field of radiotherapy. According to the method, by combining a PET-CT functional image, a key region, especially a hypoxic region (functional target region), of a tumor is recognized more accurately, and the positions and the distances of lattice points are dynamically adjusted according to biological characteristics of the tumor; arrangement of lattice points is dynamically adjusted through a gravitational field model, it can be guaranteed that a high-dose area accurately covers a key area of a tumor, unnecessary side effects can be reduced to the maximum extent, the risk of tumor recurrence is reduced, the method can better adapt to functional heterogeneity of the tumor, personalized treatment is achieved, and the method has good application prospects. And the influence on normal tissues is reduced while the treatment accuracy is improved, a more reliable treatment scheme is provided for treatment of complex tumors, and accurate dose distribution aiming at different tumor biological characteristics is realized.

The invention discloses an ultra-high dose rate radiotherapy plan optimization system based on compensator modulation, and relates to the technical field of radiotherapy. A data acquisition module is used for acquiring medical image data of a patient and receiving a clinical target; the clinical target comprises a target region prescription dose, an organ-endangering limit and a minimum dose rate threshold value required for triggering and maintaining a FLASH effect; and the FLASH biological effect evaluation module is used for calculating FLASH biological effective dose distribution corresponding to the physical dose distribution through an embedded biological effect model based on the input physical dose distribution and dose rate distribution. According to the method, the physical dose is converted into the accurate FLASH biological effective dose through the improved linear quadratic model embedded with the FLASH correction factor, the target tumor killing effect is guaranteed, normal tissue protection is enhanced with the help of the tissue differentiation correction factor, the industrial pain point that the physical dose reaches the standard but the biological effect does not reach the expectation is effectively solved, and the application prospect is wide. And the treatment plan better meets the clinical curative effect and safety core requirements.

The invention discloses an intensity modulated radiotherapy plan quality assurance method and system based on virtual prediction, and the method comprises the steps: extracting complexity parameters from a radiotherapy plan RTplan of a patient, extracting three-dimensional dose distribution characteristics from RTdense, calculating the preprocessing data of a flux diagram through combining with TPS, and designing and training an end-to-end convolutional neural network model. The model comprehensively analyzes plan complexity, dosiomics features, and flux image features to predict virtual measurement flux and Gamma pass rate. Through multi-dimensional data fusion, the prediction accuracy and the QA efficiency are remarkably improved, the quality of the radiotherapy plan can be automatically evaluated in advance, the radiotherapy process is assisted to be accelerated, and the treatment precision is improved.

Disclosed herein are systems and methods for adapting and / or updating radiotherapy treatment plans based on biological and / or physiological data and / or anatomical data extracted or calculated from imaging data acquired in real-time (e.g., during a treatment session). Functional imaging data acquired at the time of radiation treatment is used to modify a treatment plan and / or dose delivery instructions to provide a prescribed dose distribution to patient target regions. Also disclosed herein are methods for evaluating treatment plans based on imaging data acquired in real-time.

A hospital proton zone radiation protection performance intelligent monitoring and evaluation method and device relate to the radiation monitoring field, and the method comprises the following steps: carrying out three-dimensional scanning on an actual building structure and an actual device layout of a target proton treatment center to obtain three-dimensional scanning data, and correcting a preset BIM model by using the three-dimensional scanning data to obtain a three-dimensional building model; after the detection equipment is started, obtaining a measured dose rate level of the target proton treatment center at a preset target measurement point; and performing curve fitting on the measured dose rate level to obtain a measured dose rate curve, fusing the simulated dose rate level and the measured dose rate curve to obtain an actual dose rate level of each building structure position, and mapping the actual dose rate level into a three-dimensional building model to obtain a three-dimensional dose distribution diagram. By implementing the method, the radiation protection level of the proton treatment area of the hospital can be displayed more visually.

The invention provides a pencil beam dose calculation method. The pencil beam dose calculation method comprises the following steps: acquiring human body image data; determining a plurality of particle types for treatment; calculating initial pencil beam dose distribution corresponding to each particle type; based on the particle types, target action layers, distributed in the human body image data, of all the doses are determined, and the positions of the target action layers corresponding to different particle types are different; based on the target action layer corresponding to each particle type, adjusting each initial dose distribution, and obtaining particle pencil beam dose distribution corresponding to each particle type; superposing the particle pencil beam dose distribution corresponding to each particle type to obtain target pencil beam dose distribution; wherein the dose proportion of the particle pencil beam dose distribution corresponding to the same particle type in the target action layer is greater than the dose proportion of the particle pencil beam dose of the particle type in other parts in the human body image data.

The invention provides an industrial ray radiation dose visual simulation method and system based on FLUKA. The method comprises the following steps: creating and optimizing an X-ray machine model, setting radiation parameters, establishing an optimized radiation propagation model, carrying out simulation calculation, establishing the radiation intensity of a freely selected detection point or focus point according to a calculation result, and generating two-dimensional and three-dimensional dose and energy distribution diagrams. And establishing a dynamic radiation intensity calculation model and carrying out mistaken radiation accident simulation and evaluation. The system can accurately simulate and predict the dose of industrial ray radiation in real time, outputs a visual dose distribution diagram, provides a convenient and visual operation interface for workers, and is beneficial to improving the efficiency and safety of ray protection work. The method has wide application value in the aspects of industrial ray protection, mistaken irradiation accident early warning, ray device design optimization and the like.

The present application provides a plan adjustment method for radiotherapy, a radiotherapy system, and a related apparatus. The method comprises the following steps: S1, acquiring simulated CT information and treatment plan information of a patient, the simulated CT information comprising a simulated CT image and region division information corresponding thereto; S2, measuring, in the current session of radiotherapy corresponding to the treatment plan information, Cerenkov radiation emitted by the patient due to particle radiation exposure; S3, acquiring irradiation exposure dose information of an irradiation target region and an organ at risk in the treated region; S4, determining, according to the dose information of the target region and the organ at risk and the treatment plan, whether the treatment plan for subsequent irradiation fractions needs adjustment; and S5, if so, adjusting the treatment plan for subsequent fractions according to dose distribution of an irradiation exposure region and a therapeutic prescription corresponding to the patient. In the present application, the Cerenkov radiation emitted by the patient is detected to judge the quality of the treatment plan, which improves the accuracy of plan adjustments in radiotherapy and the safety of radiotherapy.

The invention discloses an intelligent medical management method and system based on malignant tumor radiotherapy. The method comprises the following steps: acquiring holographic image data corresponding to clinical image data of a patient; inputting the holographic image data into a trained deep learning model to identify a malignant tumor target region and attributes thereof; calculating the dose of radioactive particles based on the malignant tumor target region and the attributes thereof; generating a 3D printing instruction according to the malignant tumor target region; obtaining dose distribution monitoring data of radioactive particles in the treatment area; analyzing the uniformity of radioactive particles according to the dose distribution monitoring data; and according to the malignant tumor target region, the attributes and the uniformity of the radioactive particles, generating a treatment scheme and performing treatment risk assessment. Effective connection of all links can be achieved, the treatment time of a patient is shortened, and the treatment effect is improved.

The embodiment of the invention particularly relates to a dosage monitoring device based on a scintillation fiber array and a beam control device. In the dose monitoring device, when a beam passes through a two-dimensional scintillation optical fiber array detector, a plurality of first scintillation optical fibers arranged in parallel in a first scintillation optical fiber layer and a plurality of second scintillation optical fibers arranged in parallel in a second scintillation optical fiber layer are excited to emit scintillation fluorescence, and the scintillation fluorescence is transmitted along the optical fibers and received by a photoelectric detection unit; and the photocurrent signal is further converted into a voltage pulse signal by the current-voltage conversion unit, and the output dose information and the radiation field dose distribution information of the electron accelerator can be further obtained according to the voltage pulse signal.

The invention relates to the technical field of computers, and discloses an automatic segmentation and intelligent plan generation method and device suitable for self-adaptive radiotherapy of prostate cancer. Comprising the following steps: acquiring medical image data of a treated object, clinical dose data of a target area and an organ at risk in a focus area of the treated object, and clinical treatment data of a preset priority level; performing automatic segmentation based on the medical image data to obtain target segmentation data; performing dose distribution prediction according to the target segmentation data to obtain initial dose distribution data, and performing iterative optimization on the initial dose distribution data by using clinical treatment data to obtain target dose distribution data of the treated object; and performing iterative optimization of a radiotherapy plan based on the target dose distribution data, and generating a target radiotherapy plan of the treated object. Therefore, on the basis of realizing accurate, efficient and automatic segmentation of the prostate cancer target region and the endangered organ, multi-round iterative optimization of dose distribution and the radiotherapy plan is carried out, and finally a high-quality target radiotherapy plan is obtained.

The invention provides a dose distribution determination method and device based on a graphics processor, and electronic equipment, which can be applied to the technical field of medical radiation dose calculation. The method comprises the following steps: based on radiography data of a target object, simulating sampling data of action of each tissue of a organism and particles, and constructing a digital phantom; based on the configuration parameters, the accelerator head model is used for processing a primary particle beam determined based on the simulation particle source to generate a target particle beam, the shape of the target particle beam is matched with the shape of the tumor tissue of the target object, and the configuration parameters comprise the upper limit parameter of the photon reaction action times; processing the target particle beam and the digital phantom by using a physical action model to obtain a particle deposition energy distribution result generated when the target particle beam is conveyed to the digital phantom; and obtaining a dose distribution result according to the particle deposition energy distribution result and the energy dose conversion factor.

The invention provides a flux graph generation method and device and a treatment plan generation method and device, relates to the technical field of computers, and solves the technical problems that a convolutional neural network lacks understanding of detailed information of a CT image, and a flux graph predicted based on the convolutional neural network is too smooth and is greatly different from a real flux graph in related technologies. The method comprises: determining a dose distribution or an initial flux map of a target volume at each of a plurality of field angles, the target volume comprising a target region and an organ at risk; inputting the dose distribution or the initial flux diagram of the target volume at each radiation field angle into a first flux diagram generation model based on a denoising diffusion probability model to obtain a flux diagram corresponding to each radiation field angle; wherein the flux diagram corresponding to each radiation field angle is used for representing the intensity of each small beam in the target volume corresponding to each radiation field angle.

The invention provides a composite collimator and a control method thereof, a radiotherapy system and a dose delivery method thereof, and relates to the technical field of radiotherapy, and the composite collimator comprises a grating and a diaphragm which are sequentially arranged in the direction of a beam center axis, the plurality of blades move to form a grating channel through which a radiation beam can pass; the diaphragm is provided with a collimation channel, and the diaphragm can move relative to a beam center axis; when the diaphragm moves to enable the collimation channel to be located at the central axis of the beam, the radiation beam forms a focusing radiation field in a target area through the grating channel and the collimation channel; and when the diaphragm moves to enable the collimation channel to deviate from the beam center axis, the radiation beam forms a conformal radiation field in the target area through the grating channel. Through switching between the conformal radiation field and the focusing radiation field, a flexible irradiation scheme is provided, so that the dose distribution effect can be improved, and the radiotherapy effect is improved.

The invention discloses a deep learning model for radiotherapy dose distribution prediction and a radiotherapy plan generation method, and the deep learning model is constructed based on a UNet model, is obtained by inputting a training data set into the UNet model for training, and comprises a coding part which is composed of a mixing module and a down-sampling layer and is used for extracting data features, and the down-sampling layer uses maxpoo; the decoding part is composed of a decoding layer mixing module and an up-sampling layer and is used for gradually recovering the size of the original resolution, and the up-sampling layer adopts transposition convolution; the jump connection part is used for connecting the encoding part and the decoding part, and extracting features which are beneficial for the decoding part to recover to the original resolution from the encoding part; wherein the mixing module is composed of a plurality of branch paths, and outputs of the plurality of branch paths are added to serve as final output. The deep learning model is constructed by using a multi-branch path combination strategy, three-dimensional dose distribution can be quickly and accurately predicted, and plan generation in radiotherapy can be quickly completed.

The present disclosure provides systems and methods for automated scan preparation and real-time monitoring / adjustment in medical imaging. The automated scan preparation may include positioning a target subject to be scanned by a medical imaging device, determining a rotation scheme of the medical imaging device, targeting a scan region of the target subject with an imaging isocenter of the medical imaging device, determining target position(s) of component(s) of the medical imaging device, performing a virtual scan, generating a reference subject model representing an internal structure of the target subject, or the like. The real-time monitoring / adjustment operations may include achieving automatic brightness stabilization, monitoring a posture of the target subject, adjusting the position of component(s) of the medical imaging device, estimating a dose distribution, monitoring a treatment of the target subject, performing a motion correction, or the like.

The invention relates to a self-adaptive radiotherapy plan determination method, a self-adaptive radiotherapy system and a storage medium. The method comprises the following steps: acquiring a current region-of-interest segmentation result and current image features corresponding to a current image of an object; according to the segmentation result of the current region of interest and the current image features, candidate radiotherapy plans are selected from a plan library of the object, and the plan library comprises historical images of the object and corresponding historical radiotherapy plans; calculating a calculated dose distribution corresponding to the current image based on parameters in the candidate radiotherapy plan; determining the applicability of the candidate radiotherapy plan for the current image according to the calculated dose distribution, the segmentation result of the current region of interest and the features of the current image; and the target radiotherapy plan for the current image is determined based on the applicability, so that the generation efficiency and effectiveness of the adaptive radiotherapy plan are improved.

The invention provides a particle dose determination method and device, electronic equipment and a storage medium, and relates to the field of physics and computers, in particular to application of physics and computer technologies in the field of radiation medical treatment. The particle dose determination method comprises the following steps: respectively guiding a particle beam to move through M groups of scanning parameters to obtain a plurality of transverse radiation field dose distribution data; wherein each group of scanning parameters corresponds to a plurality of transverse radiation field dose distribution data, and M is a positive integer; according to the plurality of transverse radiation field dose distribution data, calculating to obtain a plurality of first radiation field dose distribution data corresponding to each group of scanning parameters; wherein the dimension of the first radiation field dose distribution data is higher than that of the transverse radiation field dose distribution data; based on scanning parameters and the first radiation field dose distribution data, obtaining a target model used for determining radiation field center dose distribution; and inputting the target scanning parameter into the target model, and determining the delivery dose according to the target radiation field center dose distribution output by the target model.

Systems and methods for evaluating and presenting robustness of a radiotherapy treatment plan for use in radiotherapy are discussed. An exemplary system includes a processor to generate, in a radiation simulation in accordance with the treatment plan under evaluation, dose distributions at an anatomical structure under a nominal condition and one or more artificially imposed uncertainty conditions, determine a dose distribution characteristic for the anatomical structure using the received dose distributions, and generate a robustness indicator of the treatment plan. The dose distributions may be determined at a target structure and one or more structures at risk, and presented graphically in a three-dimensional dose-volume-structure space. An output circuit can output the dose distribution characteristic or the robustness indicator to a user or a treatment planning system.

The invention relates to the technical field of intelligent disinfection, and discloses an intelligent ultraviolet disinfection control system and method based on environmental perception, and the system comprises an information extraction module, a radiation model construction module, a dose distribution module, a disinfection module, a data feedback module and a disinfection termination module. Wherein an initial model is constructed based on three-dimensional point cloud data and reflectivity characteristics, reflectivity compensation is performed on the initial model, and an ultraviolet radiation field attenuation model is obtained; coupling the ultraviolet radiation field attenuation model with an ultraviolet sterilization threshold database to obtain an ultraviolet dose spatial distribution map; the to-be-disinfected object is disinfected according to the ultraviolet dose space distribution diagram; dynamically correcting the ultraviolet radiation field attenuation model based on irradiance feedback data of the to-be-disinfected object during disinfection to obtain an ultraviolet dose regulation and control instruction of the to-be-disinfected object; when the ultraviolet dose regulation and control instruction reaches a biosafety threshold value, the disinfection process is terminated; the intelligent disinfection efficiency can be improved.

The embodiment of the invention particularly relates to a beam parameter monitoring device and a beam control device of an electron accelerator. In the monitoring device, a beam output by an electron accelerator sequentially passes through a first annular channel and a second annular channel, and a processor is connected with a first cylindrical electrode, a second cylindrical electrode, a third cylindrical electrode, a fourth cylindrical electrode, a fifth cylindrical electrode, a sixth cylindrical electrode, a seventh cylindrical electrode and an eighth cylindrical electrode. Induced currents respectively corresponding to a first cylindrical electrode, a second cylindrical electrode, a third cylindrical electrode, a fourth cylindrical electrode, a fifth cylindrical electrode, a sixth cylindrical electrode, a seventh cylindrical electrode and an eighth cylindrical electrode when a beam output by an electron accelerator continuously passes through a first annular channel and a second annular channel in sequence are monitored; and obtaining output dose information and radiation field dose distribution information corresponding to the output beam of the electron accelerator. And output dose information and radiation field dose distribution information corresponding to the output beam of the electron accelerator can be accurately monitored.

The invention relates to the technical field of clinical medicine, and discloses an AI prediction system for relapse after radiotherapy of local advanced rectal cancer, and the system comprises a multi-modal biological image database building module which is used for collecting clinical data, image data, radiotherapy target region and dose distribution data, radiobiological data and genomics information of a patient, integrating the data to establish a database; a feature extraction and screening module; a prediction model construction module; a model verification and optimization module; and a clinical application evaluation module. According to the invention, information of patients in multiple aspects such as clinic, image, radiotherapy target area and dose distribution, radiobiology and genomics is widely collected, and the multi-modal biological image database is integrated and established, so that various factors influencing rectum cancer relapse after radiotherapy can be comprehensively covered; a rich, accurate and comprehensive data basis is provided for subsequent accurate analysis and prediction, and the accuracy and reliability of prediction of relapse conditions after radiotherapy are improved.