111 results about "Radiotherapy dose" patented technology

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.

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.

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.

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.

The invention discloses a radiotherapy dose calculation optimization method and device based on a generative adversarial network, and relates to the technical field of medical plans, and the method comprises the steps: obtaining phase space files and condition vectors of different accelerators, and making a data set; training and verifying the constructed generative adversarial network model by using the data set; determining the value of each parameter in the condition vector according to the actual operation parameter of the specific accelerator, inputting the value to the verified generative adversarial network model, and generating particle distribution data corresponding to the condition vector; and taking particle distribution data generated by the model as initial input of a Monte Carlo (MC) simulation method to obtain a radiotherapy dose distribution result simulated by the MC. Radiotherapy dose calculation is realized through a method of combining the generative adversarial network and MC simulation, and the duration of MC simulation and quality inspection processes can be greatly shortened while the dose calculation precision is ensured.

An external beam radiation therapy dose prediction system through a latent diffusion model is adapted to predict an external beam radiation therapy plan according to a clinical data of a patient and includes a storage unit adapted to store a training data and the clinical data and a processor signally connected to the storage unit. The training data includes a plurality of medical images, a plurality of dose distribution data, and a plurality of training prompts. The processor is adapted to input the training data to a latent diffusion training model to execute a latent diffusion model training and generate an external beam radiation therapy plan model and input the clinical data and at least one prompt to the external beam radiation therapy plan model to generate an external beam radiation therapy plan.

An automated adaptive radiotherapy system based on machine learning for designing and adapting personalized dosing plans and a corresponding system, the system comprising: • a data acquisition module to collect multimodal patient data such as anatomical images, genomics, physiological signals and electronic health records; • a preprocessing department that performs the normalization, alignment, segmentation and transformation of the acquired data into data structures suitable for predictive modeling; • a dose prediction engine containing at least one machine learning model trained to predict personalized three-dimensional radiotherapy dose distributions and dose-volume histograms from the preprocessed data; • an adaptation module to receive updated clinical data and automatically adapt the dosing schedule to intra-fraction and inter-fraction changes in patient anatomy and response to treatment; • a clinician dashboard that displays predicted dosing schedules, allows clinician interaction, and can make the model interpretable through explainable AI; • a data security and compliance layer that protects data through encryption, access control, and regulatory compliance.

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.

A neural network is trained using a training corpus having a plurality of information features, each of the information features including both a reference radiation treatment dose and at least one corresponding post-treatment patient datum. By one approach, the at least one corresponding post-treatment patient datum comprises patient imagery such as, but not limited to, one or more computed tomography images. That trained neural network facilitates radiation treatment planning by generating resultant treatment efficacy probability information and resultant treatment complications probability information.

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 relates to the technical field of radiotherapy dose analysis, in particular to a radioactive cardiotoxicity prediction system based on multi-modal dynamic fusion and a quantitative evaluation method. According to the system, medical images, radiotherapy plans and clinical parameters are acquired through the multi-modal data acquisition module; the image preprocessing and three-dimensional reconstruction module processes the image data and reconstructs a heart model containing a substructure; the dynamic dose module is combined with the four-dimensional respiratory gating image and the dose matrix to generate time sequence dose distribution; the feature fusion and prediction module extracts multi-modal features, performs calculation by using a graph convolutional network, a long-short term memory network and an attention mechanism, and outputs a cardiotoxicity risk quantitative index; and finally generating a heart substructure level risk thermodynamic diagram and a structured report. According to the method, dynamic dose modeling, heart substructure level refined analysis, multi-source information fusion and individualized prediction are realized, and the accuracy, interpretability and clinical auxiliary decision-making value of cardiac toxicity risk assessment are remarkably improved.

The invention provides a dynamic trajectory intensity modulated radiotherapy plan optimization method and system, and belongs to the technical field of radiotherapy equipment. A patient data management module is used for acquiring basic information, patient image data and patient radiotherapy plan data of a patient; the region-of-interest sketching module is used for determining a radiotherapy region-of-interest; the conventional plan design module is used for calculating a radiation physical dose according to the conventional plan parameters and performing reverse optimization of a conventional dose rate; and the dynamic track intensity-modulated radiotherapy plan design module is used for calculating the intensity-modulated radiation physical dose according to the dynamic track intensity-modulated radiotherapy plan parameters, and performing dynamic track intensity-modulated radiotherapy dose reverse optimization. According to the method, the optimization speed and quality of the dynamic trajectory intensity modulated radiation therapy plan are improved by using an advanced motion planning algorithm in automatic driving, and the quality of the radiation therapy plan can be improved without hardware modification; the blank of the dynamic trajectory intensity modulated radiotherapy technology is filled, and the cancer treatment level is improved.

The invention discloses a radiotherapy sensitivity and toxic and side effect detection system based on multiple omics, and relates to the technical field of radiotherapy, and the system comprises the steps: obtaining genomics data, proteomics data and metabonomics data of a patient at different stages, and generating a patient multi-omics time series data set; based on the patient multi-omics time sequence data set, extracting patient multi-omics time sequence features, and constructing a patient radiotherapy sensitivity dynamic prediction model; combining clinical manifestation and treatment history in clinical data of the patient, generating an adversarial network by utilizing a cGAN condition, generating toxicity simulation data under different radiotherapy doses, and establishing a patient toxic and side effect risk prediction model; and according to the patient radiotherapy sensitivity dynamic prediction model and the toxic and side effect risk prediction model, obtaining an optimal radiotherapy dose interval of the patient, and generating a patient personalized radiotherapy digital twinning scheme. The method has the beneficial effects that the treatment safety and effect of patients are improved, and the method has higher clinical application value and personalized treatment potential.

The invention belongs to the technical field of radiooncology, artificial intelligence and medical image fusion, and particularly relates to a model construction method and system for predicting the radiation pneumonitis risk of a patient receiving immune combined radiotherapy. According to the method, for a non-small cell lung cancer patient receiving immune checkpoint inhibitor (ICIs) combined radiotherapy, clinical information and radiotherapy dose parameters of the patient are collected, a positioning CT image and dynamic immune factor data are simulated, multi-source features including radiomics features, an immune change curve and dose volume indexes are extracted, and a multi-modal fusion prediction model is constructed. The system integrates automatic data input, feature extraction, model training and risk output modules, can output individual radiation pneumonitis risk scores, provides risk grading and dose intervention suggestions, and has high prediction accuracy and clinical interpretability. The method can be widely applied to individualized risk management and precise treatment of non-small cell lung cancer immune combined radiotherapy patients.

The invention provides a radioactive source personal dose real-time monitoring device and a radioactive source personal dose real-time monitoring method based on a double-range self-adaptive mechanism. The radioactive source personal dose real-time monitoring device and method are suitable for intraoperative individual dose collection and dynamic evaluation in the tumor radiotherapy process. The device comprises a flexible attaching substrate, a first measuring range detection sub-module, a second measuring range detection sub-module, a self-adaptive scheduling unit, a temperature compensation module, a breathing synchronous correction module and a wireless communication module. The device can respond to different dose intensities in real time and adaptively switch or fuse channels; the collected dose projection is restored to a planned coordinate system through a breathing phase recognition and target region drift model, and dynamic target region dose tracking is achieved; temperature correction and wireless transmission functions are combined, and intraoperative data visualization and postoperative evaluation are supported. According to the invention, the precision and safety of radiotherapy dose monitoring are improved, and the method is especially suitable for radiotherapy management of areas with significant respiration-induced drift or adjacent key organs.

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 relates to the technical field of medical artificial intelligence, and provides a radiotherapy decision support system based on genetic characteristics of tumors, and the system comprises a data collection module which collects clinical data and tumor tissue samples of patients, and obtains gene mutation data and gene expression data; the data preprocessing module performs quality evaluation, duplicate removal and cleaning on the data and integrates the data into a data set; an XGBoost algorithm is adopted to screen out key features related to radiotherapy sensitivity from the cleaned data set; inputting the training set into an LSTM model combined with an attention mechanism for model training to obtain the prediction model, and performing training and parameter adjustment on the model; and the input and output module takes the collected data as input and outputs recommended radiotherapy dose. The system integrates genetic features and clinical features of patients, predicts the radiotherapy dose through a machine learning model, provides a personalized radiotherapy scheme for each patient, and improves the treatment effect.

The invention discloses an X-ray thermoacoustic image reconstruction method based on self-supervised multi-view fusion, and relates to the technical field of medical imaging and radiotherapy dose monitoring. The method comprises the following steps: firstly, acquiring initial sound pressure distribution after radiotherapy simulation by utilizing a tissue HU value and related physical attributes, and simulating X-ray induced ultrasonic signals under different sampling conditions under a K-Wave platform so as to construct a diversified training data set, further, providing a multi-view fusion and artifact decoupling network model MFDRNet based on self-supervision, according to the model, sparse acoustic signals and image feature information can be extracted and fused at the same time, reconstructed global and local detail expressions are enhanced through a multi-scale attention mechanism, and separation of a real structure and artifact components is achieved by means of an artifact decoupling structure; in the training process, a self-supervision loss mechanism is adopted, and stable training can still be completed under the condition of lack of high-quality full-sampling labels.

The invention discloses a tumor radiotherapy dose prediction method based on a diffusion model. The implementation scheme is as follows: 1) data collection; 2) data preprocessing; 3) constructing a dose prediction model; 4) training a prediction model; and 5) dose prediction. According to the dose prediction model constructed by the invention, a three-branch encoder is adopted to efficiently extract key features of input information, and accurate fusion of multi-source input features is realized by means of a feature fusion module MFF; the designed full-scale jump connection based on deformable convolution can make full use of the feature distribution of input conditions on different scales, thereby significantly improving the accuracy and robustness of dose prediction. The method can quickly generate a dose distribution result rich in high-frequency information, and has important scientific value and application prospect in the field of precise tumor radiotherapy.

The invention relates to the technical field of medical image processing and radiotherapy, in particular to an OVH-based breast cancer automatic radiotherapy plan design method and system. Constructing a historical database, and integrating the image data of the patient, the delineation structure of the target region and the organ at risk and the dose information; calculating an OVH index, and quantifying a spatial geometrical relationship between the breast cancer tumor target region and the key endangered organ; and extracting a target area external expansion distance Lx corresponding to the specific volume x of the organ in the OVH curve and a dose value Dx corresponding to the volume point in the dose-volume histogram. And constructing a function model between Lx and Dx through linear regression analysis. And for a new patient, the system calculates the Lx value of the organ of interest of the new patient, inputs the value into the linear model, and predicts the corresponding radiotherapy dose Dx. According to the method provided by the invention, intelligent aided design of the breast cancer radiotherapy plan can be realized, and a scientific and acceptable dose prediction basis is provided.

The invention discloses a three-dimensional in-vivo radiotherapy dose monitoring method and system, and the method comprises the steps: collecting an image sequence {In} containing time dimensions at a theta t time interval through CBCT scanning equipment in a radiotherapy process; calculating target area displacement of adjacent frames of images in the image sequence {In} by using a bidirectional optical flow estimation method; state estimation and prediction are carried out on the displacement data based on an adaptive Kalman filtering algorithm, a dynamic displacement rule of the target area in the respiratory cycle is generated, and a respiratory movement prediction result is output; acquiring radiotherapy plan parameters from a radiotherapy plan system, and calculating planned dose distribution without considering respiratory movement; dynamically adjusting a ray transmission path and energy deposition according to a respiratory movement prediction result, and calculating real-time dose distribution of each respiratory time phase; mapping the dose distribution of each time phase to a reference time phase coordinate system to generate accumulated dose distribution; and comparing and verifying the accumulated dose distribution and the planned dose distribution.

The utility model relates to the field of quality control of medical electron linear accelerators. Multi-dimensional radiotherapy dose measuring equipment comprises a multifunctional water tank, mounting grooves arranged in a matrix mode are formed in a bottom plate of the multifunctional water tank, the mounting grooves are used for placing thermoluminescence dosimeters, and film slots used for inserting films are formed in the bottom of the inner wall of the multifunctional water tank; three mounting holes with different heights are formed in a side plate of the multifunctional water tank, the mounting holes are used for being in butt joint with an ionization chamber plug or a water tank plug, and the water tank plug is used for blocking the mounting holes; the ionization chamber plug is used for installing the ionization chamber, and a through hole for inserting the ionization chamber is formed in the ionization chamber plug; the thermoluminescence dosimeter box further comprises a bearing bottom plate, bearing grooves distributed in a matrix mode are formed in the bearing bottom plate, and the bearing grooves are used for containing thermoluminescence dosimeters. By optimizing the structure of the water tank, compared with a traditional one-dimensional water tank, the dose measurement dimensionality is greatly improved.

The present invention relates to the field of clinical radiotherapy technology, and in particular to a test system and method for the effect of an iodized oil area on photon radiotherapy dose. The system comprises: a plurality of iodized oil samples, each of which is made by mixing minced pig liver and iodized oil in different proportions; a cylindrical sample tube, each of which is composed of a plurality of detachably connected sample chambers, each of which is provided with the iodized oil samples in the sample chambers, each corresponding to the iodized oil sample. The linear accelerator irradiates an abdominal simulation phantom into which the sample tube is inserted. The CT scanner is connected to a radiotherapy planning system, which simulates the irradiation of the sample tube with a photon beam based on a CT image and obtains a simulated dose of electron density. The present application can characterize the effect of the iodized oil area on the photon radiotherapy dose by comparing the simulated dose of electron density obtained by the radiotherapy planning system with the actual dose of electron density obtained by an ionization chamber detector.

The invention discloses a forward LatticeRT dose distribution optimization method and device based on an orthogonal double-layer grating. The method comprises the following steps: setting a prescription dose, arranging a radiation field with an equal interval angle, setting a blade position of an upper-layer grating, and forming a vertical stripe-shaped irradiation area; dose calculation is carried out in the forward direction, and a prescription dose line is stored as an organ structure; setting an irradiation range, and adjusting an irradiation angle within the irradiation range; according to the position of the structure on the BEV, the blade position of the upper grating is adjusted, so that the irradiation range can cover the structure; setting the blade position of the lower grating to form a lattice radiotherapy dose mode; generating a lattice wave crest irradiation area and a wave trough irradiation area according to the dose distribution; setting a comprehensive objective function; and an iterative optimization algorithm is adopted to adjust the blade position and the radiation field weight, so that the dose distribution is gradually close to the target function. The unique space structure of the orthogonal double-layer grating is utilized, the wave crests and the wave troughs are rapidly formed, and the Lattice plan manufacturing process is simplified.

The utility model relates to a full-sphere detector array-motif device, which belongs to the technical field of radiotherapy dose verification equipment and comprises a first sphere, a second sphere and a detector array. The first ball body is a hollow ball body; the second ball body is a solid ball body; the detector array is respectively connected with the first sphere and the second sphere; the detector array comprises a plurality of groups of detector units; the plurality of groups of detector units are sequentially arranged in rows on the outer surface of the second sphere from top to bottom; the full-spherical-surface detector array-motif device has the capability of full-angle dose measurement, is suitable for multi-angle, multi-depth and non-coplanar test items, is especially suitable for small-radiation-field and high-precision test items, obtains multiple x-ray signal data through the full-spherical-surface detector array, performs fitting calculation of the dose through the multiple x-ray signal data, and is high in accuracy and high in accuracy. Influence errors caused by the fitting difference value dose are reduced, and additional errors are prevented from being introduced in the testing process.

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 invention discloses a head and neck cancer radiotherapy dose prediction method based on cross perception fusion. The method comprises the following steps: (1) establishing a training data set containing a patient CT image, a planned target region, an organ at risk and real dose distribution; (2) constructing a generative adversarial network based on cross-aware fusion, introducing a cross-aware fusion module into a generator to realize 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 generation and real dose on 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 data to be tested by using the trained network, wherein the output of the generator is a dose prediction result. According to the invention, direct generation of head and neck cancer radiotherapy dose distribution can be realized, doctors can be assisted in making an accurate radiotherapy plan, and the radiotherapy curative effect is improved.

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.