55 results about "Delivered radiation dose" patented technology

An automated clinical decision support method for treatment re-planning that enhances a CBCT scan using information from an initial CT scan of the patient to create a synthetic CT scan; adapts contours of organ(s) and tumour(s) from the initial CT scan to align with the synthetic CT scan; and calculates a delivered radiation dose from the synthetic CT scan; compares these against an original treatment plan to detect deviations exceeding preset thresholds, and triggers alerts with assigned priority levels for a re-plan decision.

A computer-implemented system for improving radiation dose reconstruction in adaptive real-time radiotherapy, comprising a data acquisition module for capturing treatment data in real time during radiotherapy, a preprocessing module for preprocessing the captured data, a deep-learning dose reconstruction engine for reconstructing the delivered radiation dose distribution using a trained deep-learning model, a dose evaluation and comparison module for comparing the reconstructed dose distribution with a planned dose distribution, and an adaptive decision support module to assist in adjusting radiotherapy treatment parameters based on the comparison results.

The application relates to the technical field of medical data processing, and discloses a macroscopic cross-section database construction method, a dose determination method, a device and equipment, which comprises the following steps: setting multiple boron concentration values for the tissue types corresponding to a human body model; selecting corresponding neutron source parameters for the tissue types and the multiple boron concentration values, wherein the neutron source parameters comprise energy range values; dividing the energy range values into multiple discrete neutron energy groups; performing particle transport simulation based on the selected neutron source parameters, jumping between different neutron energy groups in the simulation process, and obtaining neutron reaction rates and neutron fluxes of the tissue types under the multiple boron concentration values and the multiple neutron energy groups; and obtaining neutron macroscopic cross-section values of the tissue types under the multiple boron concentration values and the multiple neutron energy groups based on the neutron reaction rates and the neutron fluxes, and constructing a multi-group macroscopic cross-section database. The application can improve the calculation efficiency of the radiation dose and improve the adaptability to individual metabolic differences during dose calculation.

Provided are methods and compositions for enhancing radiotherapy in various cancers by administering dopamine receptor (DRD2)‐binding phenylpiperazine derivatives such as brexpiprazole, cariprazine, pipamperone, and perospirone. In vitro studies show that combining these agents with ionizing radiation (e.g., 5 Gy) significantly reduces cancer cell survival, suppresses metastatic and stemness markers (e.g., CD44, MMP‑2, Snail, Nanog), and promotes apoptosis. Fractionated or single‐fraction radiation regimens can be paired with DRD2 antagonists to lower treatment‐resistant phenotypes, decrease the likelihood of recurrence, and potentially reduce necessary radiation dosages. The approach applies to breast, prostate, lung, pancreatic, and brain cancers, among others. Depending on tumor characteristics, additional chemotherapeutic or immunotherapeutic agents may further improve outcomes.

The invention discloses a heterogeneity correction model applied to radiation dose calculation, and relates to the technical field of radiotherapy. According to the content, the model comprises an input layer, a block embedding layer, an encoder, a bottleneck layer, a decoder and an output head. The bottleneck layer comprises two feature modeling modules. The input layer is used for receiving four-dimensional tensor data. And the block embedding layer is used for performing division processing on the four-dimensional tensor data through the three-dimensional convolution layer to determine a plurality of small blocks to be processed. The encoder comprises a sampling module used for sampling a plurality of small blocks to be processed and determining high-level features. The feature modeling module comprises a continuous window multi-head attention sub-block and a shift window multi-head attention sub-block, and is used for extracting local and global features and determining high-quality features based on input features. The output head is used for determining a heterogeneity correction coefficient based on the target features. Therefore, heterogeneity correction is carried out through the heterogeneity correction model, the calculation accuracy is improved, and the radiation dose calculation precision is improved.

The present invention relates to the technical field of medical images, and more specifically, to an image quality improvement method based on low-resolution mammograms, comprising the following steps: S1. Obtain two-dimensional breast tomosynthesis images and a set of low-resolution mammogram images; S2. Correspondingly pair all the two-dimensional breast tomosynthesis images with the images in the set of low-resolution mammogram images, and then divide them into a training set and a test set; S3. Construct a deep learning reconstruction model based on the deep learning method; S4. Input the training set to train the deep learning reconstruction model; S5. Use the trained deep learning reconstruction model to test on the test set, and then select the model corresponding to the synthetic mammogram image with the best performance in the test set as the output model. The present invention can obtain synthetic mammograms of higher quality on the basis of reducing the radiation dose that patients need to receive during imaging screening.

A method for dose guidance for a radiotherapy system during a radiotherapy session includes the steps of: performing substantially real-time dose reconstruction to obtain a delivered radiation dose generated by a radiotherapy beam of the radiotherapy system in at least one volume; repeatedly estimating a remaining radiation dose based on an observed and / or simulated motion of the at least one volume; repeatedly estimating a final delivered radiation dose as a sum of the delivered radiation dose and the estimated remaining radiation dose; and providing dose guidance for a remaining part of the radiotherapy session based on the estimated final delivered radiation dose. A method of continuous estimation of final delivered radiation dose during a radiotherapy session and a decision support system for a radiotherapy system are also provided.

Disclosed herein are methods for radiotherapy treatment plan optimization for irradiating one or more target regions using both an internal therapeutic radiation source (ITRS) and an external therapeutic radiation source (ETRS). One variation of a method comprises iterating through ITRS radiation dose values and ETRS radiation dose values to attain a cumulative dose that meets prescribed dose requirements. In some variations, an ITRS is an injectable compound that has a targeting backbone and a radionuclide, and images acquired using an imaging compound that has the same targeting backbone as the injectable compound can be used to calculate the radiation dose deliverable using the injectable ITRS, and also to calculate firing filters for delivering radiation using a biologically-guided radiation therapy (BGRT) system. Image data acquired from a previous treatment session may be used to adapt the dose provided by an ITRS and / or ETRS for a future treatment session.

The present application relates to the technical field of radiation dose calculation, and particularly relates to a method and system for automatically modeling radiation dose, wherein on one hand, a plurality of intelligent optimization algorithms are used to perform step-by-step and region-by-region optimization, to obtain a radiation source model corresponding to each intelligent optimization algorithm, and then to obtain an optimal radiation source model, so that automatic modeling can be realized, the efficiency is high, and the radiation source model can be optimized to reduce errors; on the other hand, step-by-step and region-by-region optimization is performed in a search interval of a target empirical energy spectrum, so that convergence is easy, and the problem that a solution found due to an excessively large search range is easy to fall into local optimization can be avoided.

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.

Systems and methods for radiation therapy for cancerous tumors using biocompatible quantum dots to provide radiation dose enhancement and resistance to metastasis are described. In an aspect, a method for radiation therapy treatment of cancer cells, includes, but is not limited to, preparing a radiosensitizer fluid, the radiosensitizer fluid including a plurality of biocompatible quantum dots; introducing the radiosensitizer fluid to cancer cells; and irradiating the cancer cells with the radiosensitizer fluid to promote generation of reactive oxygen species and inhibit radiation-induced enhancement of cell-migration.

A radiation treatment system includes a radiation therapy machine configured to output radiation pulses to provide a radiation dose to a patient and a beam generation and monitoring (“BGM”) board including a dose servo algorithm. The BGM board is configured to execute the dose servo algorithm in accordance with a treatment plan to control the radiation dose on a pulse-by-pulse basis according to the treatment plan.

1. Name of the Design Product: Scintillator Performance Tester. 2. Use of the Design Product: Used to test the emission spectrum, absorption spectrum, detection limit of X-ray radiation dose rate, radiation damage, scintillation decay time, afterglow, temperature effect, and scintillator imaging performance of scintillators, etc. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Perspective view. 5. The bottom surface of the Design Product is a part that is not easily seen or not seen during use, and the bottom view is omitted.

A system for delivering radiation dose includes: a gantry for moving around a target to be irradiated; and a radiation source mounted to the gantry and guided inward toward the target. The system further includes: a collimator mounted to the gantry and in front of the radiation source, the collimator being used to shape the radiation beam guided to the target, wherein the collimator is used to modulate the sub-beam intensity of the radiation beam on a plurality of sub-beams for subdividing the flux field into a two-dimensional (2D) grid, and to deliver a plurality of independent two-dimensional (2D) sub-beam intensity patterns from multiple gantry angles while the gantry is continuously moved.

The invention discloses a brain glioma sub-target region personalized radiotherapy planning method and system. The method comprises the following steps: inputting a to-be-detected T1C image and a corresponding AAL image into a segmentation model to obtain a brain region segmentation mask file; inputting a to-be-tested T1C image into the mapping model to obtain a pseudo MRS image; inputting the T1C image to be detected and the registered brain glioma labeling file into the habitat model to obtain a habitat map; based on the brain region segmentation mask file, identifying whether the habitat subregion is located in the functional key brain region or not to determine whether a safe dose threshold value is set as a specific tolerance dose corresponding to the functional key brain region or not; and determining the radiation dose of the brain glioma subregion based on the to-be-detected T1C image, the ASL, the pseudo MRS image and a safe dose threshold. Therefore, the stability, repeatability and efficiency of target area sketching can be effectively improved, and biological conformity of radiotherapy dosage can be realized, so that objective and accurate quantitative reference basis is provided for formulating a radiotherapy scheme.

Provided is a dose radiotherapy optimization system suitable for subtotal liver diffuse tumor thrombus liver cancer. The system consists of the following modules: a data acquisition module; an image analysis module; an image processing module; a solution optimization module. The data acquisition module is used for acquiring original radiotherapy plan data, the image analysis module is used for obtaining original radiation dose distribution data from an original radiotherapy plan image, the image processing module is used for performing image processing on an original medical image to obtain a target medical image, and the solution optimization module is used for optimizing the original radiation dose distribution data by combining a plan optimization model with the target medical image, to obtain a radiation dose distribution optimization result. The system enables a radiation dose line of a radiotherapy plan to be more densely distributed and reasonable, and to satisfy actual application requirements, so as to solve the current international problem that radiotherapy cannot be performed on subtotal liver diffuse tumor thrombus liver cancer by means of existing radiotherapy plans.

A reference radiotherapy treatment plan having a plurality of control points, such as a sequence of gantry angles, aperture leaf positions, and intensity weights, can be adapted during the radiotherapy treatment session. Treatment imaging data of a patient may be obtained during the radiotherapy treatment session and one or more parameters may be determined using the treatment imaging data. Then, a current radiotherapy treatment plan may be generated based on the parameter(s). The reference radiotherapy treatment plan for the radiotherapy treatment session may be modified during the radiotherapy treatment session by updating one of the plurality of control points of the reference radiotherapy treatment plan with a control point of a current radiotherapy treatment plan, which may compensate for patient deformations during the delivery of radiotherapy treatment, or intrafraction patient deformations and thereby improve the delivery accuracy and efficacy of radiation doses to a patient undergoing radiotherapy treatment.

The utility model relates to a portable X-ray machine which comprises a foldable base, a telescopic rod, an arm assembly, a main machine base, a ball head connecting piece and a main machine which are connected in sequence. The foldable base is placed on the ground and comprises a base and folding legs evenly distributed on the periphery of the base, and a first screw hole is formed in the center of the base. The folding legs can be folded unidirectionally; the telescopic rod comprises a lower rod and an upper rod; a locking knob is arranged in the middle of the telescopic rod; the arm assembly comprises an upper arm and a front arm which can be connected in a locking mode, and self-locking units are installed in the upper arm and the front arm. The main machine base comprises a fixing base and a screwing cover, the fixing base and the screwing cover are connected through threads, and the fixing base and the screwing cover form a nearly-spherical cavity. Compared with the prior art, the portable radiation device is portable in design, low in radiation dose, wide in application range, capable of being used in complex environments such as slits and the like, capable of improving operation efficiency and reducing labor intensity, easy to carry and convenient to fold.

The disclosure is directed to compounds, compositions, and methods for radiation dose enhancement and the delineation of tumors, and in particular, to improve dose of megavoltage radiation therapy using barium nanoparticles such as BaTiO3 nanoparticles for the delineation of tumors.

The present invention relates to a system for determining a radiotherapy plan for a radiotherapy system (100), comprising a multi-leaf collimator. The radiotherapy plan determination system (110) comprises: a treatment system characteristic providing unit (111), wherein the characteristics include possible leaf positions and possible radiation fluence values; a planning target providing unit (112), wherein the planning target indicates a desired therapeutic radiation dose distribution; an optimization function providing unit (113), wherein the optimization function indicates a deviation of the radiation dose distribution from the planning target and an uncertainty of the radiation dose distribution at the edge of the possible aperture; and a treatment plan optimization unit (114), which is adapted to determine a sequence of possible apertures and possible radiation fluence values, for which the optimization function is optimized. Thus, an optimal treatment plan can be provided for each individual patient.

The present invention provides a rapid in vitro detection method for predicting the radiosensitivity of tumor patients for non-diagnostic purposes. The method includes: preprocessing cancer tissue samples and adjacent tissue samples; transferring tissue blocks into a well plate and grouping them; irradiating each group of tissue blocks at a second preset time according to the radiation time and a preset dose rate; detecting the absorbance values of each group and calculating the inhibition rate of the cancer tissue and the activity value of the adjacent tissue. Furthermore, the radiosensitivity corresponding to each radiation dose is predicted. The rapid in vitro detection method for predicting the radiosensitivity of tumor patients for non-diagnostic purposes provided by the present invention can rapidly and accurately predict the radiosensitivity of tissues, which is beneficial to providing a strong basis for routine clinical implementation.

The invention relates to the technical field of medical data processing, and discloses a radiotherapy image information fusion method based on a neural network model, and the method comprises the steps: constructing a dual-domain neural network model comprising an image domain and a feature domain, and carrying out the combined training of multi-modal radiotherapy image data through a self-supervised learning method, and deep information fusion from a global structure to local features is realized. A multi-resolution pyramid fusion mechanism is introduced into an image domain to enhance spatial consistency among multi-modal images, and deep feature expression is optimized by combining radiation dose distribution physical constraints and beam path feature constraints in a feature domain. According to the invention, accurate fusion of multi-modal radiotherapy image information is realized on the whole, the accuracy and safety of radiotherapy plan making are improved, the risk of image information distortion of a key area is reduced, and the higher requirement of tumor radiotherapy on the image fusion effect is met.

[Problem] To provide a radiotherapy planning assistance system that can efficiently achieve a dose distribution for a radiotherapy device that is close to the established ideal dose distribution, and that can stabilize the quality of a treatment plan by ensuring that the achieved dose distribution does not vary greatly even when different types of radiation therapy devices are used. [Solution] A radiotherapy planning assistance system comprising: a dose distribution storage unit 21b that stores an ideal radiation dose distribution established on the basis of information about the location and cross-sectional contour of an affected area to be irradiated with radiation; a region-generating processing unit 20c that divides the cross-section into two or more regions for each prescribed dose range on the basis of dose distribution information; and a dose parameter setting unit 30a that, for each region, associates prescribed parameter values ​​for radiation doses suitable for achieving the dose distribution with the radiotherapy device to be used.

An embodiment of the present invention discloses a method, device, equipment and medium for generating a simulated postoperative CBCT image. The method includes: before implanting the implant, obtaining a preoperative tomographic image collected by a cone beam tomography system, and after implanting the implant, obtaining the implant position and pose data of the implant collected by a surgical navigation system; determining the position data between the implant model and the preoperative tomographic image based on a transformation matrix and the implant position and pose data; wherein the transformation matrix is used to represent the transformation relationship between the coordinate system of the cone beam tomography system and the coordinate system of the surgical navigation system; performing a pixel filling operation on the preoperative tomographic image based on the position data and the three-dimensional model data corresponding to the implant model to obtain a simulated postoperative CBCT image. The embodiment of the present invention omits the step of collecting the postoperative CBCT image by using a cone beam tomography system after the operation, and reduces the radiation dose and economic pressure received by the patient.

An automated clinical decision support method for treatment re-planning that enhances a CBCT scan using information from an initial CT scan of the patient to create a synthetic CT scan; adapts contours of organ(s) and tumour(s) from the initial CT scan to align with the synthetic CT scan; and calculates a delivered radiation dose from the synthetic CT scan; compares these against an original treatment plan to detect deviations exceeding preset thresholds, and triggers alerts with assigned priority levels for a re-plan decision.

This invention relates to the field of interventional tumor therapy technology and discloses a real-time optimization method for interventional radiotherapy planning. The method includes: dimensionality reduction processing of preoperative three-dimensional angiography images to generate an initial three-dimensional spatial radiation dose distribution matrix; acquisition of in-situ physical and electrophysiological physical quantities corresponding to the injection phase, outputting multimodal synchronous temporal features; analysis of the multimodal synchronous temporal features based on feature decoupling and cross-tissue dielectric cross-validation logic to exclude vasospasm and arteriovenous shunt states, and calculation of dynamic flow resistance increments; reconstruction of blood flow distribution ratios based on dynamic flow resistance increments, accumulating to generate a real-time cumulative radiation dose distribution matrix; monitoring the real-time cumulative radiation dose distribution matrix and catheter reflux pressure gradient, sending a low-level command when a threshold is reached to stop the operation of the radiation-proof high-pressure injection pump. This invention solves the problem of deviation between actual radiation dose distribution and static plan, preventing excessive radiation in non-target areas and microsphere reflux.

Dose-volume criteria may be equivalently expressed in terms of quantiles. This re-formulation of a dose-volume criteria allows incorporation of dose-volume criteria into a full optimization problem. Radiotherapy treatment techniques are described that may apply optimization-based formulation of quantiles to express dose-volume criterion as an inequality involving an optimization problem, which may improve DVH modeling, improve computational speed and accuracy of radiation treatment planning, and improve the delivery accuracy and efficacy of radiation doses to a patient undergoing radiotherapy treatment.

The invention discloses a radiation dose adjustment system based on deep learning, and relates to the technical field of neural networks, the system comprises an image analysis module, a clinical parameter analysis module, a dose decision module and a dose implementation monitoring module which work cooperatively in sequence, and each module forms a complete radiation dose adjustment closed loop through data interaction. According to the radiation dose adjusting system based on deep learning, tumor boundaries are accurately recognized through multi-step image processing, individualized dose distribution is achieved in combination with clinical parameter analysis, it is ensured that the dose is concentrated in a lesion area, radiation damage to healthy tissue is reduced, and the occurrence rate of side effects is reduced.

The invention relates to the technical field of radiation medicine, in particular to an intelligent radiation dose estimation system which comprises a data acquisition module, a data preprocessing module, a chromosome distortion analysis module, a radiation dose estimation module and a result output module. The data preprocessing module is used for preprocessing the collected multivariate heterogeneous data, the chromosome distortion analysis module is used for analyzing the preprocessed data, the radiation dose estimation module is used for intelligently estimating the radiation dose, and the result output module is used for visually presenting the radiation dose estimation result to a user. By adopting the technical scheme, the radiation exposure condition and chromosome distortion characteristics of a patient can be more comprehensively known, so that the accuracy of radiation dose estimation is improved, modular design and an automatic processing flow are adopted, data acquisition, preprocessing, analysis and result output can be quickly completed, and the analysis efficiency is improved.

The application provides an image processing method, a readable storage medium and medical equipment, and the image processing method comprises the following steps: acquiring a first image; inputting the first image into a preset generative adversarial network, so that the preset generative adversarial network processes the first image to obtain a second image, and the preset generative adversarial network is an adversarial network which is trained based on image blocks of different sizes in sequence; and outputting the second image; wherein the first image is a computed tomography image which is photographed under a first radiation dose, the second image is a computed tomography image which is photographed under a second radiation dose, and the first radiation dose is less than the second radiation dose.