78 results about "Delivered radiation dose" patented technology

The invention relates to the technical field of radiotherapy dose prediction, in particular to a glioma radiation dose prediction system based on an attention-enhanced convolutional neural network, which comprises a data collection module, a data preprocessing module, a convolutional neural network module, an attention mechanism module and an output layer. According to the invention, by introducing the attention mechanism, the important feature region can be effectively highlighted, so that the prediction accuracy of the radiation dose of the glioma patient is improved, and the individuation and precision of the treatment scheme are ensured; a dose distribution result can be quickly generated through automatic dose prediction, time and energy investment of doctors in radiotherapy plan design is reduced, working efficiency is improved, accuracy and efficiency of glioma radiotherapy are remarkably improved, clinical decisions are optimized, medical cost is reduced, and support is provided for intelligentization and automation of radiotherapy. Through the research of the project, the application of the deep learning technology in the field of medical image analysis is promoted, and the overall level of radiotherapy is improved.

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.

A method for a system for characterizing a radiation dose distribution in a radiotherapy treatment plan is disclosed. The radiotherapy treatment plan is used to treat an anatomical structure with a radiotherapy device. The system includes a processor and a user interface. The processor is configured to: receive data obtained from a radiation simulation at the anatomical structure under a nominal condition and one or more artificially applied uncertainty conditions; generating a plurality of dose distributions including dose-volume histograms (DVHs) using the received data, each DVH representing a tissue volume of the anatomical structure receiving a respective radiation dose, the DVHs including a nominal DVH corresponding to a nominal condition, and one or more uncertain DVHs corresponding to one or more artificially applied uncertain conditions; and determining a dose distribution characteristic based on statistical characteristics of the generated DVH. The user interface is configured to display the determined dose distribution characteristic to a user.

The present invention relates to the field of neural network technology, and specifically to a radiation dose adjustment system based on deep learning. The system includes: an image analysis module that scans and distinguishes the boundaries between tumors and normal tissues based on medical image data input, refines the size and shape of the tumor in the image through edge detection, extracts key features in the image through contrast enhancement, and generates tumor feature data. In the present invention, by distinguishing the boundaries between tumors and normal tissues and refining the features of the tumor in the image, the distribution of radiation doses is made more accurate and personalized. This detailed image analysis optimizes the treatment plan, ensures that the dose is concentrated in the lesion area, and reduces the impact on healthy tissues. In addition, the analysis of integrated clinical parameters enhances the understanding of the patient's radiation sensitivity and provides a treatment plan that better meets individual needs. This dose adjustment method based on in-depth analysis effectively adjusts the dose deviation in treatment by real-time monitoring of the execution effect of radiotherapy.

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 utility model relates to a radiation dose adjusting device. The device comprises a radioactive source, a source switch and a shielding plate device, the radioactive source is arranged above the source switch, the shielding plate device is arranged below the source switch, the shielding plate device comprises a base, a light shielding plate, a light shielding plate driving device for driving the light shielding plate to rotate and a beam outlet, and the light shielding plate and the light shielding plate driving device are arranged in the base. The beam outlet is arranged at the bottom of the base, and the shading plate is provided with a notch corresponding to the beam outlet. According to the utility model, aiming at the irradiation dose differentiation requirement in the radiation treatment process, after the collimator with the proper focal length is selected, a certain dose of irradiation treatment is carried out on the focus area of a patient through the dose adjusting device.

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.

The present invention relates to a method (200) of a control unit (108) of a radiation monitoring device (100) for determining a delivered radiation dose from a plurality of radiation pulses sensed by a sensor unit (101, wherein the method (200) comprises: sampling (S10) a sensor signal from the sensor unit (101); storing (S20) the sampled sensor signal in a data buffer with length S; calculating (S30) a second derivative of the sampled sensor signal in the data buffer; determining (S40) if a valid pulse is stored in the data buffer based on the calculated second derivative of the sampled sensor signal in the data buffer; calculating (S50) the integrated value of the pulse, IP, in the buffer, upon determining that a valid pulse is stored in the data buffer, wherein the data buffer comprises at least two partitions, one partition for storing the valid pulse and the other partition for storing the sensor signal without the valid pulse, wherein the integrated value of the pulse, IP, in the buffer is calculated as the difference between these at least two partitions; and sum (S60) the integrated value of the pulse IP to a delivered radiation dose

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.

Described herein are methods of beam station delivery for radiotherapy in which a patient platform is moved to a series of discrete patient platform positions or beam stations determined during a treatment plan and stopped at each of these positions as a radiation source rotates about the patient, radiation is delivered to a target area that intersects the radiation beam path, and then moved to the next position after a prescribed dose of radiation (e.g., according to the calculated fluence map) for that position has been delivered to the patient.

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.

The present invention discloses a method and device for predicting radiation dose distribution based on CT images. A convolutional neural network is used to process a CT image to be predicted to obtain a first feature map and a second feature map of different resolutions corresponding to the CT image to be predicted, and a first initial differential field and a second initial differential field. The feature map and the initial differential field are further predicted through a decoder stream to obtain a first differential field and a second differential field. The shape of the prior image is continuously sampled through a continuous differential field, so that a prior with correct topological features is deformed through a continuous differential field to maintain the topology. Since the first differential field and the second differential field are differential homomorphic fields, which are continuous deformation fields that lead to a one-to-one mapping, their derivatives are reversible and can obtain a positive Jacobian determinant, so that a clear mapping between the prior shape and the predicted image coordinates is given in dose registration, so that 100% of the topology is maintained, and the accuracy of radiation dose distribution prediction is improved.

Generating a plan for radiation treatment of multiple target volumes such as, for example, multiple brain tumors, involves optimizing a grouping of the target volumes into subsets and generating treatment plans for each subset. Resulting treatment plans may minimize radiation dose to tissues outside of the target volumes. A radiation treatment planning system may be configured to operate in this manner and to upload control signals which cause a radiation therapy device such as a linear accelerator to execute the radiation treatment plans.