50 results about "Radiotherapy dose" patented technology

The invention discloses a brain tumor disease treatment effect evaluation method, and belongs to the technical field of radiotherapy plan evaluation, and the method specifically comprises the steps: obtaining the brain multi-modal image data of a patient, and extracting a three-dimensional tumor region and a corresponding perfusion parameter; constructing a biological target region hierarchical structure containing a tumor overall region and an anoxic subregion; a relative hypoxia degree parameter is obtained by calculating the cerebral blood flow ratio of the hypoxia subregion to other regions of the tumor; historical treatment case data are deconstructed, and a dose-curative effect associated parameter set is established; training and outputting a dose adjustment strategy model for the hypoxia subregion; and generating a final radiotherapy dose planning scheme according to a dose adjustment value output by the model in combination with a clinical guide basic dose. Through a data-driven dose decision-making mechanism, the limitation of traditional uniform dose irradiation is overcome, so that an objective and reliable evaluation basis is provided for clinical selection and optimization of personalized treatment schemes.

The invention discloses an AI auxiliary radiotherapy dose intelligent optimization device, which comprises a multi-modal image fusion module, a dose distribution prediction module, a dynamic adaptive optimization module and a prognosis model integration module, and is characterized in that the multi-modal image fusion module is used for aligning anatomical features of different images, eliminating respiratory motion artifacts and predicting the dose distribution of the different images; the dose distribution prediction module is used for rapidly predicting radiotherapy dose distribution based on anatomical features and historical data, the dynamic adaptive optimization module is used for monitoring anatomical changes in real time and dynamically adjusting a dose plan, and the prognosis model integration module is used for quantifying correlation between dose distribution and radioactive injury risks. High-precision alignment and respiratory motion artifact elimination of an anatomical structure are achieved through the multi-modal image fusion module, three-dimensional dose calculation is rapidly and accurately conducted through the dose distribution prediction module, organ displacement and deformation are effectively coped with through the dynamic self-adaptive optimization module through a real-time image monitoring and reinforcement learning algorithm, and the accuracy of the three-dimensional dose calculation is improved. And the prognosis model integration module constructs an individualized risk prediction model.

The invention discloses a radiation therapy dose inspection system and method based on a space body distributed multi-layer ionization chamber. The radiation therapy dose inspection system comprises a cylindrical die body with a space body distributed ionization chamber matrix, a suspended supporting base capable of being adjusted to be horizontal, other functional plug-ins and matched software. According to the system and the method, a novel high-pressure transmission mode is adopted, a cylinder distributed air leakage type ionization chamber with higher spatial resolution is used for small radiation field measurement in radiotherapy, dose body distribution of radiotherapy radiation at various angles and depths can be collected and measured at a time without rotating a die body and angle correction, and the measurement accuracy is improved. And finally, the spatial distribution of the radiation dose in the cylindrical die body is obtained through software calculation, and high-precision measurement is realized.

The embodiment of the application provides a kind of based on Kd-Net's tumor radiotherapy response prediction method and model, the method includes the following steps: extracting tumor feature fusion forms point cloud, according to SUV variation gradient, point cloud is classified and labeled, and is divided into training set, verification set and test set;Based on the network architecture of Kd-Net, construct deep learning model;Based on training set and verification set, the deep learning model is trained, and model parameter is adjusted;Based on the optimized model parameters and test set, the performance of the optimized deep learning model is evaluated, and the trained deep learning model is obtained;The tumor data set to be predicted is input into the trained deep learning model, and the tumor radiotherapy response is predicted.The prediction method and model provided in the application can predict whether the tumor responds after radiotherapy, thereby intelligently assisting the doctor to adjust the radiotherapy dose, implementing adaptive precision radiotherapy decision for the patient, reducing the harm of radiotherapy to the patient, and improving the work efficiency and diagnosis and treatment quality of the doctor.

The invention provides a radiation pneumonitis risk prediction method and system, and the method comprises the steps: obtaining a CT image and a radiotherapy dose image, and carrying out the rigid and non-rigid registration; preprocessing the registered radiotherapy dose image by adopting a piecewise linear normalization method; performing first feature extraction on the CT image by using a CT feature extraction network, and performing second feature extraction on the preprocessed radiotherapy dose image by using an improved 3D convolutional network; performing interactive modeling on the first feature and the second feature according to a multi-head cross attention mechanism and a position self-adaptive dynamic weighted fusion strategy to obtain an interactive feature; the interaction features are respectively transmitted to a main task branch used for predicting an RP risk score and an auxiliary task branch used for generating a sensitive heat map, and the RP risk score is output through the main task branch, so that more accurate and personalized RP risk assessment is realized.

The present application relates to the technical field of radiotherapy protection material testing, in particular to an evaluation method and system for isolation protection hydrogel, the system comprises an X-ray device, an aluminum ladder, a test disc and a sample manufacturing device, the sample manufacturing device comprises a workbench, a lifting mechanism, a horizontal moving mechanism, a uniform smearing assembly, an injection assembly, a rotating mechanism and a control module. The evaluation method comprises the following steps: automatic sample preparation, sample shaping, placing aluminum ladder control sample, simulating clinical radiotherapy irradiation, X-ray detection and evaluation. The automatic equipment is used to realize the standardization of hydrogel sample preparation, the 4Gy single radiotherapy dose and 10-minute irradiation time are set to accurately simulate the clinical radiotherapy conditions, the aluminum ladder control and X-ray gray value detection are combined to quantitatively evaluate the radiation shielding effect and performance attenuation of the hydrogel. The present application realizes the automation of the sample preparation process, the test is consistent with the clinical practice, the evaluation result is accurate and objective, and reliable data support is provided for the research and development and clinical application of the isolation protection hydrogel.

1. The name of the design product: radiotherapy simulation phantom. 2. The use of the design product: radiotherapy dose verification mold, for measuring, verifying, teaching of radio dose. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view.

The application discloses a 3D printing customized tissue compensation and a manufacturing method thereof, and relates to the technical field of radiotherapy. The application comprises the following steps: a model is prepared by using 3D printing technology according to a radiotherapy plan formulated by a doctor, and a tissue compensation is prepared by pouring, wherein the tissue compensation comprises an inner surface consistent with the body surface topography. The application provides a manufacturing method of a personalized and non-uniform thickness tissue compensation. The thickness of the radiotherapy compensation pad is determined according to the radiotherapy plan formulated by the doctor, and the manufacturing process does not require basic medical knowledge. The obtained tissue compensation pad is consistent with the thickness, shape and size required by the radiotherapy plan formulated by the doctor. The tissue compensation pad can better adhere to the skin of a patient, meet the requirements of the radiotherapy plan, and guarantee the accuracy and stability of the radiotherapy dose, so that the purpose of precise treatment is achieved.

The utility model discloses a non-uniform die body used for radiotherapy dose verification, firstly, the non-uniform die body mainly comprises a die assembly and a supporting assembly, secondly, the supporting assembly comprises a supporting seat and a supporting beam, the supporting seat is mainly used for fixing the die assembly so as to prevent the die body assembly from transversely moving when the die body assembly rotates, and the supporting beam is used for supporting the die body assembly. In order to guarantee the supporting effect of the mold body, a supporting beam is further arranged between the two supporting bases, the final model assembly comprises a fixing part, a film used for dose measurement and verification is horizontally inserted in the center of the axis of the fixing part, and the film is placed at the horizontal position of the center axis of the fixing part. In this way, it can be ensured that the dose distribution received by the film is an accurate representation of the dose distribution in the treatment plan, in the radiotherapy dose distribution, the central area is usually the area with the highest and most uniform dose, and therefore measurement can provide the most accurate dose verification data.

The application belongs to the technical field of radiotherapy dose simulation measurement, and provides a heavy ion fixed head radiotherapy dose simulation measurement method and system. The method first performs limited point and / or slice dose measurement in the solid water phantom according to the layered and key area encryption sampling principle to obtain sparse dose data and sampling positions; then inputs the sampling positions into the coding-decoding structure and the mask perception neural network with attention mechanism in the decoding stage, and outputs the complete three-dimensional dose distribution; finally, the simulation results containing the dose distribution and the uncertainty distribution are output. The application improves the dose reconstruction accuracy and the clinical quality control efficiency, and is suitable for heavy ion radiotherapy dose verification.

The application discloses a head and neck cancer radiotherapy dose prediction method based on cross perception fusion, comprising the following steps: (1) establishing a training data set containing a patient CT image, a planning target volume, an organ at risk and a real dose distribution; (2) constructing a generative adversarial network based on cross perception fusion, introducing a cross perception fusion module in the generator, realizing the deep interaction and fusion of local and global features, and introducing a multi-scale cross window self-attention coding network to constrain the difference between the generated and real doses at multiple scales; (3) constructing a hybrid loss function based on the discriminator and the multi-scale cross window self-attention coding network; (4) training the generative adversarial network by using the training data set; and (5) testing the data to be tested by using the trained network, and the output of the generator is the dose prediction result. The application can realize the direct generation of the head and neck cancer radiotherapy dose distribution, assist doctors in formulating accurate radiotherapy plans, and improve the radiotherapy effect.

The application relates to the technical field of medical image processing, and discloses a nasopharyngeal carcinoma radiotherapy dose prediction system and method of a cascade network, which solves the technical problem that nasopharyngeal carcinoma radiotherapy dose prediction lacks global-local structure collaborative modeling and adaptive cross-stage feature interaction in the prior art. A first-stage three-dimensional U-Net network captures anatomical global features, a second-stage improved MedNeXt network performs dose fine prediction through residual learning, explicit division avoids the multi-scale feature extraction limitation of a single network, and the overall dose score and the DVH score are respectively improved; through instance normalization, the first-stage feature activation distribution is stabilized, after being spliced with original input, the convolution adaptive layer is seamlessly integrated into the second stage, the dimension mismatch and the gradient saturation problem are solved, and flexible multi-stage feature reuse is supported.

This application relates to the field of radiotherapy technology, specifically to a method for predicting three-dimensional radiotherapy dose distribution for proton and heavy ion radiotherapy equipment. The method includes: generating a reference dose curve containing detector response and a standard three-dimensional dose distribution map under ideal conditions; calculating the gradient weights of each depth point to divide different sampling intervals and perform differentiated sampling to obtain observation depth points; obtaining the measured dose values ​​collected by the detector at each observation depth point; matching and mapping the sparse observation depth points onto the dense reference dose curve to determine the matching depth point corresponding to each observation depth point on the curve; calculating the intensity scaling factor characterizing the beam output intensity; calculating the range deviation characterizing the beam penetration depth; and performing spatial translation transformation and global dose correction on the standard three-dimensional dose distribution map to generate a predicted three-dimensional dose distribution map that eliminates detector interference. This application improves the accuracy and efficiency of dose verification for radiotherapy equipment.

The application discloses a preparation method of a nitrate-based three-dimensional gel color change dosimeter, and comprises the following steps: dissolving a certain amount of nitrate in water to obtain a mixed solution I; adding a certain amount of a hydroxyl shielding agent to obtain a mixed solution II; adding a certain amount of a gel monomer, uniformly mixing, and heating to a certain temperature to obtain a mixed solution III; forming the gel monomer in the mixed solution III into a gel by a physical cross-linking method to obtain the gel dosimeter IV; adding a certain amount of a color developing agent to the surface of the gel dosimeter after the gel dosimeter is irradiated by high-energy rays for a certain dose, placing for 5 min to 20 min, removing the color developing agent on the surface of the gel, and thus the color change of gradually deepening color with the increase of the dose appears. The gel dosimeter can be used for detecting the treatment level of high-energy rays in clinical radiotherapy, the minimum detection limit is 0.1 Gy, the sensitivity is higher, the response speed is faster, and the gel dosimeter is more suitable for clinical radiotherapy dose detection.

In the present invention, the mRNA of the genes VTN, CXCL5, NEDD4, LCN2, SYTL4, TNFR2, and SERPING1, or the proteins encoded by these genes, exhibit differential expression levels in radioresistant lung cancer cells compared to control lung cancer cells and have a close correlation with a decrease in the survival rate of lung cancer patients. Accordingly, this preparation for measuring the expression levels of the mRNA of the genes selected from the group consisting of VTN, CXCL5, NEDD4, LCN2, SYTL4, TNFR2, and SERPING1 of the present invention or the expression levels of the proteins encoded by the genes can effectively diagnose radiation resistance in lung cancer or prognose radiation therapy for lung cancer, whereby it is possible to determine whether to perform radiation therapy and the optimal dose of radiation therapy, thus improving the efficiency and therapeutic outcomes of radiation therapy for lung cancer.

The present application relates to the medical physics technical field, disclose a kind of radiotherapy dose prediction method, device, equipment and medium.The method comprises: obtaining the medical image of the radiotherapy region of patient, including the multiple two-dimensional slice images of radiotherapy region, planned target volume and organ at risk in radiotherapy region are region of interest;Each two-dimensional slice image and its adjacent two-dimensional slice image are input into conditional diffusion generation network, with the anatomical information of its region of interest as control condition, guide the process of gradually denoising of the noise map generated by diffusion model to the random dose distribution map of radiotherapy region, generate initial dose distribution map;Two-dimensional slice image is input into dose calibration network, and the dose of its region of interest is predicted, to calibrate the dose of region of interest in initial dose distribution map, generate target dose distribution map;The three-dimensional dose distribution of patient radiotherapy is determined in combination with the target dose distribution map of all two-dimensional slice images.

This invention relates to the field of medical imaging assessment technology, specifically a prognostic assessment system for liver cancer portal vein tumor thrombosis radiotherapy that integrates multiple MRI parameters. The system includes modules for MRI multi-parameter acquisition, data preprocessing, feature extraction, fusion assessment, dynamic updating, result output, and data storage. The system acquires MRI structural, functional, and temporal parameters. After preprocessing such as adaptive noise reduction, it extracts core features using an attention mechanism combined with CNN. An attention fusion algorithm integrates MRI features, radiotherapy dose, and clinical parameters. An improved algorithm is used to construct a prognostic model, which is dynamically optimized through transfer learning. The system outputs visualized prognostic results, treatment recommendations, and abnormal warnings, and the data is stored with encryption. This system addresses the problems of low assessment accuracy, lack of dynamic adjustment, and poor practicality in existing technologies, improving assessment accuracy and timeliness, providing reliable support for individualized radiotherapy decisions, and balancing data security with clinical research needs.

The invention discloses a self-adaptive radiotherapy plan re-optimization method and system, an electronic device and a medium wherein the self-adaptive radiotherapy plan re-optimization system comprises a deformation registration module for performing deformation registration to obtain a deformation field; the multi-time plan data module is used for importing multi-time plan data which comprises an ROI (Region of Interest) sketching area; the multi-time plan creating module is used for creating multi-time plan parameters; the deformation identification module is used for carrying out deformation analysis on the deformation field mu and identifying a deformation area; the grid optimization module is used for adjusting the priority of the ROI sketching region and optimizing the dose grid resolution of the deformation region to obtain the optimized dose grid resolution and distribution thereof; the plan re-optimization module is used for performing re-optimization to obtain an optimized radiotherapy plan; according to the method and the device, the technical problem that the dose optimization speed is difficult to improve under the condition of relatively low dose error in the setting of the radiotherapy dose grid resolution in the prior art is solved.

The invention relates to the technical field of medical physics, and discloses a radiotherapy dose prediction method and device, equipment and a medium. The method comprises the steps that a medical image of a radiotherapy area of a patient is acquired, the medical image comprises a plurality of two-dimensional slice images of the radiotherapy area, and a planned target area and an organ at risk in the radiotherapy area are areas of interest; inputting each two-dimensional slice image and the two-dimensional slice image adjacent to the two-dimensional slice image into a conditional diffusion generation network, and by taking anatomical information of a region of interest of the conditional diffusion generation network as a control condition, guiding a process of gradually denoising a noise graph generated by adding noise to a random dose distribution graph of a radiotherapy region by a diffusion model to generate an initial dose distribution graph; inputting the two-dimensional slice image into a dose calibration network, predicting the dose of the region of interest of the two-dimensional slice image, calibrating the dose of the region of interest in the initial dose distribution diagram, and generating a target dose distribution diagram; and determining the three-dimensional dose distribution of the radiotherapy of the patient by combining the target dose distribution diagrams of all the two-dimensional slice images.

The invention relates to the technical field of medical image processing, discloses a cascaded network nasopharyngeal carcinoma radiotherapy dose prediction system and method, and solves the technical problem that nasopharyngeal carcinoma radiotherapy dose prediction in the prior art lacks collaborative modeling and self-adaptive cross-stage feature interaction for a global-local structure. According to the method, in the first stage, the three-dimensional U-Net network captures anatomical global features, in the second stage, the improved MedNeXt network carries out dosage refined prediction through residual learning, explicit division avoids the limitation of multi-scale feature extraction of a single network, and the overall dosage score and the DVH score are improved respectively; first-stage feature activation distribution is stabilized through instance normalization, and is seamlessly integrated to a second stage through a convolution adaptation layer after being spliced with original input, so that the problems of dimension mismatching and gradient saturation are solved, and flexible multi-stage feature multiplexing is supported.

Provided are a FLASH radiotherapy dose control method and apparatus, and an electronic device. The method comprises: calculating the number of delivered pulses of at least two beam pulses having different pulse widths, the total beam delivery time, and the beam repetition frequency of the beam pulses, issuing the beam repetition frequency to a beam generation system, and issuing a set beam termination dose threshold, a pulse conversion dose threshold obtained by means of calculation, and a beam termination time threshold to a beam sampling controller; determining whether a real beam delivery dose is greater than or equal to the pulse conversion dose threshold; determining, on the basis of the determination result, whether to output a level control signal for adjusting the pulse width to the beam generation system; then determining whether the real beam delivery dose is greater than or equal to a beam termination dose threshold, and at the same time, determining whether a real beam delivery time is greater than or equal to the beam termination time threshold; and determing, on the basis of the determination result, whether to output a level control signal for beam termination to the beam generation system. The accuracy of FLASH beam dose control is improved.

The invention discloses a urethra protection device for radiotherapy of prostatic cancer, and relates to the technical field of radiotherapy, and the urethra protection device comprises a CT which obtains a CT image of a prostate area of a user; the 3D T2WI nuclear magnetic sequence is used for obtaining a three-dimensional nuclear magnetic image sequence of the prostate area of the user; the radiotherapy dose and frequency determination module is used for determining the total radiotherapy dose and frequency of the user; the radiotherapy planning module is used for performing three-dimensional fusion processing on the CT image and the three-dimensional nuclear magnetic image sequence to obtain a three-dimensional fusion image sequence containing a prostate cancer target region and a whole urethra contour, and dividing the whole urethra contour in the three-dimensional fusion image sequence into a plurality of urethra protection regions according to the prostate cancer target region, according to the total radiotherapy dose, the number of times and the plurality of urethra protection areas, determining each radiotherapy dose of each urethra protection area; and the linear accelerator is used for carrying out radiotherapy treatment according to the radiotherapy dosage of each urethra protection area.

The application discloses a brain tumor disease treatment effect evaluation method, and belongs to the technical field of radiotherapy plan evaluation, and specifically comprises the following steps: acquiring patient brain multi-modal image data and extracting three-dimensional tumor regions and corresponding perfusion parameters; constructing a biological target region hierarchical structure containing tumor overall regions and hypoxic subregions; obtaining a relative hypoxia degree parameter by calculating the cerebral blood flow ratio of the hypoxic subregion to other regions of the tumor; deconstructing historical treatment case data to establish a dose-therapeutic effect correlation parameter set; training a dose adjustment strategy model for the hypoxic subregion; and generating a final radiotherapy dose plan scheme according to the dose adjustment value output by the model combined with a clinical guideline basic dose. The application overcomes the limitations of traditional uniform dose irradiation through a data-driven dose decision mechanism, thereby providing an objective and reliable evaluation basis for clinical selection and optimization of personalized treatment schemes.

The application discloses a compensation film preparation method based on optical body surface data reconstruction of high-precision three-dimensional models, relates to the technical field of postoperative radiotherapy of breast cancer, and comprises the following steps: sequentially performing body surface data collection, high-precision model reconstruction, compensation film optimization design, 3D printing preparation, CT positioning scanning and radiotherapy plan optimization, collecting three-dimensional data of a related region of a patient through an optical body surface monitoring system, and generating a high-precision body surface model through feature screening and reconstruction. The method does not need additional ionizing radiation, effectively solves the problems of poor compensation film fitting degree and insufficient quality of existing optical scanning data, improves the compensation film and body surface fitting precision and radiotherapy dose accuracy through whole-process personalized optimization and accurate preparation, is suitable for postoperative radiotherapy of breast cancer, and has the characteristics of strong clinical applicability and high safety and efficiency.

To provide improved methods of radiation therapy, including reducing the dose of radiation therapy to tissue proximate to the site of radiation therapy.SOLUTION: A method of spacing a first tissue site in a subject from a second tissue site in the subject, the method comprising: (a) placing a viscoelastic medium in spaced relation between the first tissue site and the second tissue site, wherein the viscoelastic medium comprises non-animal stabilized hyaluronic acid ("NASHA") and a gadolinium complex.SELECTED DRAWING: Figure 9

The invention discloses a radiotherapy dose dynamic prediction and plan optimization method and system based on artificial intelligence. The method comprises the following steps: S1, acquiring initial radiotherapy plan data of a patient; s2, simulating to generate N virtual treatment CT images representing different anatomical states possibly occurring in the treatment process, wherein N is an integer greater than 1; s3, quickly predicting a three-dimensional dose deviation distribution diagram; s4, generating a group of risk indexes; and S5, outputting the optimized radiotherapy plan parameters. According to the method, in the treatment plan design stage, the three-dimensional dose deviation possibly generated by the current plan due to anatomical change in the future fractional treatment can be quickly and accurately predicted, the influence of the three-dimensional dose deviation on key clinical indexes is quantitatively evaluated, the optimization process is actively guided to generate a treatment plan with high robustness to uncertainty, and the treatment plan design efficiency is improved. And the treatment accuracy and safety are improved from the source.

The ion beam radiotherapy dose measurement system comprises an ion beam emitter, an acoustic sensor, a front-end receiving circuit and a back-end processing circuit. The ion beam emitter emits an ion beam towards a living body, so that the ion beam generates a dose peak point and generates a sound signal after reaching the living body. The acoustic sensor captures the sound signal and converts the sound signal into an electrical signal. The front-end receiving circuit is electrically connected with the acoustic sensor and is used for receiving the electrical signal output by the acoustic sensor to determine the time when the acoustic sensor receives the sound signal. The back-end processing circuit is electrically connected with the front-end receiving circuit and is used for calculating the position of the dose peak point and the dose distribution according to the time when the acoustic sensor receives the sound signal, in combination with the time of ion beam emission and the position of the acoustic sensor in the living body. The ion beam radiotherapy dose measurement system of the present patent application has the advantages of small volume, low cost, high precision and real-time measurement of dose distribution.

This invention discloses a method and system for personalized radiotherapy planning of subtarget areas in gliomas. The method includes: inputting the target T1C image and its corresponding AAL image into a segmentation model to obtain a brain region segmentation mask file; inputting the target T1C image into a mapping model to obtain a pseudo-MRS image; inputting the target T1C image and the registered glioma annotation file into a habitat model to obtain a habitat atlas; based on the brain region segmentation mask file, identifying whether the habitat subregion is located in a functionally critical brain region to determine whether its safe dose threshold is set to the specific tolerable dose corresponding to the functionally critical brain region; and determining the radiation dose of the glioma subregion based on the target T1C image, ASL, pseudo-MRS image, and safe dose threshold. This effectively improves the stability, repeatability, and efficiency of target delineation and achieves biological conformity of radiotherapy dose, thus providing an objective and accurate quantitative reference for developing radiotherapy plans.

This application discloses a method, apparatus, device, medium, and product for early warning of proton or heavy ion radiotherapy dose deviation, relating to the field of radiotherapy dose deviation early warning. The method includes: acquiring beam parameters for proton or heavy ion radiotherapy on a patient, as well as planned CT images of the patient's treatment site, planned WET distribution maps, and guiding images for the current treatment session; performing image registration between the planned CT images and the guiding images to obtain a registered image; constructing a beam path on the registered image based on the beam parameters to obtain a superimposed image; obtaining an actual WET distribution map based on the tissue type of each voxel along the beam path in the superimposed image; comparing the actual WET distribution map with the planned WET distribution map to obtain a WET deviation value; and issuing an early warning based on the WET deviation value, thereby achieving early warning of proton or heavy ion radiotherapy dose deviation. This application features physical interpretability, real-time performance, universality, and low cost.