5550results about "X-ray/gamma-ray/particle-irradiation therapy" patented technology

The invention discloses a multi-angle particle therapy system without a rotating rack and a using method thereof, and relates to the technical field of particle therapy equipment, and the treatment system comprises an accelerator, an irradiation system, a control system, a patient supporting system and an imaging system. The patient supporting system comprises a containing and fixing device, a two-dimensional translation system, a rotating system and a multi-degree-of-freedom mechanical positioning system, the containing and fixing device is connected with the two-dimensional translation system, the two-dimensional translation system is connected with the rotating system, and the two-dimensional translation system controls the relative position of a patient and a rotating center; the rotating system rotates a patient to an appointed angle of a treatment plan or cooperates with the imaging system to obtain a three-dimensional image of the patient, and the irradiation system irradiates a tumor target area; the use method comprises the following steps: generating a treatment plan by reconstructing a three-dimensional CT image, and completing irradiation at different angles according to the treatment plan. According to the treatment system, multi-angle irradiation on a patient can be realized under the condition that a rotating rack is not used, and the manufacturing period and cost of the particle treatment system can be reduced.

The invention discloses a tumor radiotherapy dose calibration method, and relates to the technical field of tumor radiotherapy, and the method comprises the steps: collecting multi-modal data in real time through 4D-CT, MRI and the like, extracting dynamic features through space-time registration, calculating a risk organ dose ratio and a tumor target dose deviation rate, and constructing a comprehensive evaluation function. The method has the advantages that the multi-modal dynamic data of the tumor target area are synchronously collected in real time, the space-time joint registration algorithm and the time sequence weight factor are combined, the limitation that existing static calibration depends on fixed parameters is broken through, and the method is suitable for the multi-modal dynamic data of the tumor target area. According to the method, 95% dose coverage of a tumor target region is guaranteed, and meanwhile, organ-endangering peak value receiving amount is controlled within a certain range of a clinical tolerance threshold value, so that the risk of excessive irradiation of normal tissues is reduced, and the radiotherapy dose calibration precision is prompted based on the deviation rate of a traditional method.

A medical image processing device of an embodiment includes first and second image acquirers, a generator, and a calculator. The first image acquirer acquires a first fluoroscopic image of a patient. The second image acquirer acquires a second fluoroscopic image according to radiation of the patient at a time point different from that of acquisition of the first image using a detector. The generator generates a reconstructed image by reproducing the second image from the first image virtually arranged in a three-dimensional space on the basis of a position of the detector in the three-dimensional space. The calculator obtains a suitable position on the first image in the three-dimensional space on the basis of a degree of similarity between the second image and the reconstructed image. The generator generates the reconstructed image for use in the calculator and has a range larger than a range corresponding to the second image.

Described herein are methods for monitoring the radiation delivery during a radiotherapy delivery session and providing a graphical representation of radiation delivery to an operator (e.g., a clinician, a medical physicist, a radiation therapy technologist). The graphics are updated in real-time, as radiation data is collected by the radiotherapy system, and in some variations, can be updated every 15 minutes or less. A variety of graphical representations (“graphics”) can be used to indicate the status of radiation delivery relative to the planned radiation delivery. Methods optionally include calculating a range of acceptable metric values, generating graphics that represent the range of acceptable metrics values, and generating a graphic that depicts the real-time values of those metrics overlaid with the range of acceptable metrics values.

The invention relates to the technical field of radiotherapy, in particular to a space-time compensation method and system for radiotherapy of pancreas respiratory movement variability, and the method comprises the steps: setting an infrared reflective mark point on the body surface of a patient, and obtaining a body surface three-dimensional CT image and an infrared camera real-time image under respiratory movement; establishing a pancreas three-dimensional model, a CTV target region model and a respiratory movement model; predicting body surface mark point displacement by adopting a long-short-term memory network algorithm and generating a respiration curve; determining a pancreas three-dimensional coordinate motion state; predicting lung motion by using optical flow motion estimation, and reversely solving a pancreas motion trail; determining the opening and closing states of the blades of the multi-blade collimator; according to the method, the mapping relation between lung movement and pancreatic movement is established through reverse mapping, an improved optical flow movement estimation algorithm and an LSTM network are adopted to predict respiratory movement, and the CTV leakage volume can be remarkably reduced to 0.4 cm from 2.1 cm of a traditional gating technology.

The invention discloses an equivalent biological dose calculation and plan optimization method and system, and the method comprises the steps: determining dose objective functions and constraint conditions of target regions and radiation-endangered organs, and setting parameters of loss functions of each target region and radiation-endangered organ; calculating the physical dose and irradiation time of each voxel by using a dose calculation method based on the parameters of the current radiotherapy machine; calculating the equivalent biological dose of each voxel according to the physical dose and the irradiation time of each voxel; and calculating a loss function according to the target function and the constraint condition based on the statistical distribution state of the equivalent biological dose, so as to optimize the parameters of the radiotherapy machine by using a preset optimization algorithm. According to the invention, the technical problem of how to utilize the FLASH effect to protect normal tissues and enhance tumor killing in a treatment plan can be solved.

A method for processing medical data by a client application and a chatbot, includes: receiving, by the client application, a user command comprising natural language, the user command further comprising personal information; pre-processing, by the client application, the user command to mask the personal information; invoking the chatbot to determine an action and / or an argument from the pre-processed user command; and processing the medical data, by the client application, using a feature of the client application to perform the chatbot-determined action and / or argument on the medical data.

The disclosure provides a method of treating a human subject afflicted with lung cancer (e.g., non-small cell lung cancer (NSCLC)) with a programmed death-1 (PD-1) pathway inhibitor (e.g., an anti-PD-1 antibody) and a concurrent chemoradiotherapy (CCRT, e.g., a platinum doublet chemotherapy (PDCT) and a radiation therapy) followed by a combination of a PD-1 pathway inhibitor (e.g., an anti-PD-1 antibody) and a lymphocyte activation gene-3 (LAG-3) antagonist (e.g., an anti-LAG-3 antibody). In some aspects, the method comprises a recovery period that begins upon completion of the treatment with the PD-1 pathway inhibitor and the CCRT and ends at the start of the treatment with the combination of the PD-1 pathway inhibitor and the LAG-3 antagonist.

The invention belongs to the field of medical image processing and radiotherapy quality control, particularly relates to a gamma distribution diagram calculation method based on a diffusion model, and aims to solve the problem that the traditional radiotherapy quality guarantee efficiency is low. The method includes deriving a field flux profile image from a radiotherapy planning system. A reverse process noise estimation network is improved based on a conditional diffusion model, a field flux distribution diagram image is taken as conditional input, field flux distribution diagram information is extracted layer by layer at an encoder part, information fusion with noise path coding is carried out at different coding scales, diffusion noise is predicted by adopting multi-scale fusion Unet, and the prediction precision is improved. And constructing to obtain a gamma distribution diagram generation model. A dual-path residual block is adopted as a basic module of an encoder and a decoder of the multi-scale fusion Unet, a moving window jump connection module is adopted to integrate global and local features of different stages, and the moving window jump connection module introduces spatial offset through a moving window mechanism.

The invention relates to a capsule for a pneumatic rabbit system to produce medical isotopes, which comprises a capsule shell, a plurality of hemispherical limiting blocks, a heat dissipation hole channel, an isotope target fixing structure, an annular driving fan blade and a target cabin, the positioning device is used for being matched with a limiting groove in the end of a conveying pipeline in the pneumatic rabbit system for positioning. The heat dissipation hole channel penetrates through the capsule shell in the radial direction and is in a circular truncated cone shape, and the diameter of the front end of the heat dissipation hole channel is smaller than that of the rear end of the heat dissipation hole channel. The isotope target fixing structure is arranged inside or on the surface of the capsule shell; the annular driving fan blade is composed of a plurality of fan-shaped fan blades, and the fan-shaped fan blades are annularly distributed in the heat dissipation hole channel; the isotope target fixing structure and the annular driving fan blade are arranged in a staggered manner; the target cabin is fixedly connected to the isotope target fixing structure and is used for sealing the isotope target. The capsule can achieve posture balance and self-positioning in the pneumatic transmission process, and has efficient heat dissipation performance.

The invention relates to a real-time target region tracking and dynamic regulation and control method and system. The method comprises the steps that a phase prediction model is obtained based on 4DCT reconstruction data and three-dimensional point cloud body surface contour data; obtaining a two-stage model according to a result output by the phase prediction model and 4DCT reconstruction data; superposing the phase prediction model and the two-stage model, and fusing to obtain a target region prediction model; acquiring real-time body surface contour data and breathing signals; inputting the body surface contour data and the breathing signal into a target region prediction model, wherein the target region prediction model predicts the current position of an in-vivo target region; and calculating the target pose of the accelerator treatment bed according to the current position of the in-vivo target region, and converting the accelerator treatment bed to the target pose. Through the target region prediction model, the real-time in-vivo target region position can be obtained by inputting data in real time, the dynamic change of the target region can be responded in real time, and dynamic regulation and control are realized. And the target region prediction model is modeled based on 4DCT reconstruction data, so that the prediction result is high in accuracy.

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 radiotherapy dose simulation measurement, and provides a heavy ion fixed machine head radiotherapy dose simulation measurement method and system. The method comprises the following steps: firstly, in a solid water model, according to a layering and key area encryption sampling principle, carrying out finite point and / or cutting layer dose measurement to obtain sparse dose data and a sampling position; inputting the three-dimensional dose distribution and a sampling position mask into a coding-decoding structure and a mask perception neural network with an attention mechanism in a decoding stage, and outputting complete three-dimensional dose distribution; and finally, outputting a simulation result containing dose distribution and uncertainty distribution. The method improves dose reconstruction precision and clinical quality control efficiency, and is suitable for dose verification before heavy ion radiotherapy.

A radiotherapy system is configured to receive an open-field X-ray beam from a superficial radiotherapy machine and direct the beam through a grid module configured to spatially fractionate the open-field beam into a group of spaced-apart microbeams. A patient applicator defines an enclosed space having a patient support surface for supporting a target body part. A beam receiver of the patient applicator receives the group of microbeams directed at the target body part on the support surface. The enclosed space is defined by a wall structure comprising shielding sufficient to absorb the radiation emitted by the superficial radiotherapy machine. The patient applicator 80 can be portable, and the superficial radiotherapy machine can be wheeled.

A machine learning-based method of generating a radiotherapy treatment plan for a patient, comprises dose prediction and dose mimicking, wherein the dose prediction step involves using a machine learning system that has been trained to consider at least one optimality criterion related to physical or technical restrictions that will affect the delivery of the treatment plan. Thus, at least one of the factors that are normally taken into account in the dose mimicking step is introduced in the dose prediction step. The invention also relates to a method of training such a machine learning system for use in radiotherapy treatment planning, a computer program product and a computer system.

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 relates to the field of navigation signal processing, and discloses a radiation therapy bolus dose feedback and tracking system based on image registration, the system comprises an image acquisition module, an image registration module, a bolus identification module, a dose recalculation module, a dose deviation analysis module and a feedback and adjustment module, space registration is carried out through an FBCT image acquired every day and an original plan CT, and the radiation therapy bolus dose feedback and tracking system based on image registration is obtained. The fitting state between the bolus and the skin is automatically identified, the position of an air gap is detected, and dose recalculation is carried out based on an actual structure. Through comparison with an original planned dose, the system can output a deviation index of a key dose parameter and provide visual feedback, if the deviation exceeds a set threshold value, a prompt is automatically sent out, and a radiotherapy technician and a physician are supported to carry out bolus adjustment or call an adaptive planning engine to reconstruct a treatment plan. Quantitative analysis of bolus fitting quality and closed-loop control of dose delivery consistency are realized, radiotherapy precision and safety are effectively improved, and the method is suitable for superficial radiotherapy scenes containing bolus design.

Systems and methods for generating a beam model for radiotherapy treatment planning are discussed. An exemplary system includes a memory to store a trained deep learning model, and a processor circuit to generate a beam model. The deep learning model can be trained to establish a relationship between machine scanning data and values of beam model parameters, and validated for accuracy. The processor circuit can receive machine scanning data indicative of a configuration or an operation status of the radiation therapy device, apply the machine scanning data to the trained deep learning model to determine values for the beam model parameters, and generate a beam model based on the determined values of the plurality of beam model parameters. The beam model may be provided to a user, or a treatment planning system.

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 invention relates to a multi-robot image guide radiotherapy system and an imaging method thereof, and relates to the technical field of radiotherapy. The multi-robot image guide radiotherapy system comprises a treatment device, a main robot, an auxiliary robot and an imaging device. One or both of the radiation blocker and the imaging detector are movable such that, upon emission of the imaging beam, the imaging detector can face the X-ray source and the radiation blocker faces away from the X-ray source, then the radiation blocker does not interfere with detection by the imaging detector; and when the treatment beam is emitted, the radiation blocker can face the treatment device, and the imaging detector deviates from the treatment device, so that the treatment beam cannot directly irradiate the imaging detector, and the purpose of protecting the imaging detector to ensure the quality of an image detected by the imaging detector is achieved.

An apparatus comprising a radiation source, coincident positron emission detectors configured to detect coincident positron annihilation emissions originating within a coordinate system, and a controller coupled to the radiation source and the coincident positron emission detectors, the controller configured to identify coincident positron annihilation emission paths intersecting one or more volumes in the coordinate system and align the radiation source along an identified coincident positron annihilation emission path.

The invention discloses a real-time respiratory movement prediction and compensation method for thoracic and abdominal radiotherapy. A respiratory signal acquisition module, a respiratory movement prediction module, a body surface mark point position monitoring module, a body surface mark point displacement prediction module, a body surface mark point coordinate conversion module, a tumor target center point prediction module and a tumor target visualization module are included. The invention discloses a respiratory movement prediction system based on a causal convolutional network, and the system achieves the precise prediction of the position of a tumor target region in the future by collecting a respiratory waveform signal and a body surface mark point movement track of a patient in real time, extracting a periodic movement mode, and predicting the three-dimensional displacement of the tumor target region in 0.5-2 seconds in the future.

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.

Carriers for embodying radioactive seeds, as well as a device for loading and customizing brachytherapy carriers based on the principles of optimizing a more precise and predictable dosimetry, and adaptable to the geometric challenges of a tumor bed in a real-time setting. The present disclosure relates to a specialized loading device designed to enable a medical team to create a radionuclide carrier for each patient and tumor reliably, reproducibly and efficiently.

The invention relates to an Agent AI-based full-process intelligent radiotherapy system and a working method thereof, the system comprises a treatment Agent, a feedback Agent and a QA Agent, the feedback Agent is connected with the treatment Agent, the input of the treatment Agent is multi-modal data, and an initial radiotherapy scheme is dynamically output through deep learning and an evolutionary algorithm; the feedback Agent is connected with the multi-sensor system and used for monitoring user state data of the patient in real time, correspondingly adjusting treatment parameters according to an initial treatment scheme and outputting an optimized treatment scheme; and the QA Agent is used for monitoring key parameters in the treatment process, performing early treatment verification and later quality evaluation, and outputting a quality assurance report. Compared with the prior art, multi-Agent mutual cooperation is designed, multi-modal data are integrated, full-process automation from treatment scheme determination, data acquisition, target region sketching, plan generation, treatment execution to QA is achieved, dependence on manual intervention can be reduced, and treatment individuation and efficiency are improved.

The invention relates to a closed circulation 226Ra solution target system for producing 225Ac. The closed circulation 226Ra solution target system comprises a solution type 226Ra target, an isotope target bin, a valve, a 226Ra solution circulation structure, a radium actinide separation structure, a radium lead separation structure, a photon detection system I, a photon detection system II, a heat exchange system, a pressure reduction radon evaporator and a radon treatment system, and the solution type 226Ra target comprises a solution circulation pipeline and a radium-containing solution. The radium-containing solution is contained in the closed solution circulating pipeline; the solution circulating pipeline comprises a main loop pipeline and two branch pipelines; the photon detection system I, the 226Ra solution circulation structure, the pressure reduction radon evaporator, the heat exchange system, the valve and the photon detection system II are sequentially arranged on the main loop pipeline; and the radium actinide separation structure and the radium lead separation structure are respectively arranged on the two branch pipelines. The system can improve the preparation efficiency and yield of 225Ac.

Systems and methods for detecting and diagnosing faults in a radiotherapy system, such as a fault related to a dynamic leaf guide (DLG), are discussed. An exemplary predictive maintenance system includes a processor configured to receive machine data indicative of configuration and operation of a DLG in a target radiotherapy machine, apply a trained deep learning model to the received machine data, and detect and diagnose a DLG fault. The predictive maintenance system can train the deep learning model using data sequences constructed from the received machine data of the one more normal DLGs and the one or more faulty DLGs. Diagnosis of the DLG fault in the target radiotherapy machine includes classifying the DLG faults into different fault types or different fault severities.

The invention discloses an ion source automatic beam adjusting method, a radiotherapy system and related equipment, and relates to the technical field of radiotherapy, and the method comprises the steps: monitoring and adjusting a plurality of control parameters in real time, and obtaining an optimal control parameter combination; under the optimal control parameter combination, the variable quantity of each control parameter before and after adjustment and the performance change of the proton beam are obtained; performing a pulse width charge test according to the variation of each parameter before and after adjustment and the performance change of the proton beam; obtaining an optimal adjustment parameter and an adjustment amount through a pulse width charge test result; and through the optimal adjustment parameter and adjustment amount, automatic beam adjustment is realized, stable and efficient acceleration of the proton beam in the synchrocyclotron system is realized, and the stability and consistency of the proton beam are ensured.

The invention relates to the technical field of nasopharyngeal carcinoma radiotherapy, in particular to a nasopharyngeal carcinoma radiotherapy dose automatic prediction method based on deep learning, and the method comprises the following steps: S1, analyzing obtained original data; s2, writing a program to pre-process all the original data in batches; s3, calculating a corresponding dose volume histogram according to the anatomical contour and the patient dose distribution; and S4, inputting the preprocessed data into a designed deep learning model for dose distribution training and prediction, and comparing with an existing dose prediction method to verify the innovativeness and feasibility of the scheme. Dose prediction is automatically carried out through the deep learning model, manual rule design is not needed, high-quality dose distribution can be rapidly generated, batch preprocessing is carried out on all original data, dose distribution training and prediction are carried out by inputting the data into the designed deep learning model, the calculation performance is improved, and the method is suitable for large-scale popularization and application. And the training and reasoning efficiency is improved.

Embodiments described herein provide for radiotherapy treatment plan generation using a machine learning language processing model. A processor can present a user interface providing an interaction interface between a user and a machine learning language processing model. The processor can receive a first input comprising a first patient attribute of a patient. The processor can execute the machine learning language processing model using the first patient attribute as an input to generate a response requesting a second patient attribute. The processor can present the response requesting the second patient attribute of the patient. The processor can receive a second input comprising the second patient attribute of the patient. The processor can transmit the first patient attribute of the patient and the second patient attribute of the patient to a radiotherapy plan optimizer. The radiotherapy plan optimizer can be configured to generate a radiotherapy treatment plan for the patient.