153 results about "Radiotherapy treatment planning" patented technology

Various embodiments of the present invention provide processes for applying deterministic radiation transport solution methods for calculating doses and predicting scatter in radiotherapy and imaging applications. One method embodiment of the present invention is a process for using deterministic methods to calculate dose distributions resulting from radiotherapy treatments, diagnostic imaging, or industrial sterilization, and for calculating image scatter for the purposes of image reconstruction. In one embodiment of the present invention, a method provides a means for transport of external radiation sources through field-shaping devices. In another embodiment of the present invention, a method includes a process for calculating the dose response at selected points and volumes prior to radiotherapy treatment planning.

Method and systems are disclosed for radiating a moving target inside a heart. The method includes acquiring sequential volumetric representations of an area of the heart and defining a target tissue region and / or a radiation sensitive structure region in 3D for a first of the representations. The target tissue region and / or radiation sensitive structure region are identified for another of the representations by an analysis of the area of the heart from the first representation and the other representation. Radiation beams to the target tissue region are fired in response to the identified target tissue region and / or radiation sensitive structure region from the other representation.

Disclosed are a method and an apparatus for manufacturing a radiation intensity bolus. The method comprises the steps of: (a) calculating, by a radiotherapy treatment planning unit, a received 3D radiation dose distribution, planning a bolus to be manufactured, and outputting radiation intensity modulation information; (b) receiving, by a bolus design unit, the radiation intensity modulation information, generating a conversion file for manufacturing bolus, and outputting information about a 3D structure of the bolus to be manufactured; (c) receiving, by a bolus manufacturing unit, the conversion file for manufacturing bolus, verifying a type, location, and size of the bolus to be manufactured, sending the verified data to a 3D printer, and manufacturing the bolus; and (d) obtaining, by an accuracy verification unit, information about a 3D structure of the manufactured bolus and evaluating manufacturing accuracy by comparing the information about the 3D structure of the manufactured bolus with the information about the planned bolus.

The present invention belongs to the technical field of medical images and computer, and relates to a target region automatic drawing method in radiotherapy treatment planning based on deep learning.The method comprises the following steps of: (1) performing preprocessing of patient's image data; (2) inputting the image data through preprocessing to a trained convolutional neural network model for prediction of the target region; and (3) performing edge extraction of the predicted target region to obtain an automatically drawing target region. The method provided by the invention can achieverapid target drawing and has high precision, for the same organ, doctors manually draw the organ for 5-10 min, the target region automatic drawing algorithm only needs 15 seconds, and therefore, compared to manual drawing, the time is reduced about 95%, the working efficiency of doctors can be greatly improved, and valuable time is provided for patients for timely treatment.

Systems and methods for radiation treatment planning in proton therapy using pencil beam scanning (“PBS”) are described. More particularly, the systems and methods described in the present disclosure related to quantifying the output from a proton therapy system implementing PBS. The systems and methods described in the present disclosure can therefore be implemented when commissioning a new proton therapy system, or when performing quality assurance (“QA”) on a proton therapy system. A spot delivery pattern that includes a spiral out pattern is used for beam delivery. A number of control points along the spot delivery pattern define a beam pause time during which delivery of the proton beam is paused. Radiation measurements are obtained at the control points and at the end of the spot delivery pattern, and these radiation measurements are used to compute field size factors for field sized associated with the segments of the spot delivery pattern.

Method and system that allows patient dose response functions to be calculated, prior to treatment planning, for external photon beam radiotherapy. In one embodiment, for each location where a separate dose value is desired, referred to as a dose region, adjoint calculations are performed to calculate the adjoint solution fields associated with that dose region. Once potential beam directions are specified, a ray tracing process is performed to transport the adjoint solution fields out of the patient and to locations where treatment plan parameters may be specified. The output of this process is the dose response at each dose region resulting from a prescribed photon fluence, at a given location, direction, and energy.

A computer-implemented method for optimizing a radiation treatment plan for a radiotherapy machine providing independently controlled radiation along a plurality of rays j directed toward a patient and configured to account for the effects of patient motion. The method includes generating a probability distribution function quantitatively expressing patient motion, identifying a prescribed total dose Dip at the voxels i in a treatment area, assigning a fluence value wj for each ray j based on an iterative function, calculating an actual total dose Did produced each voxel i within the assigned fluence values and calculating an expectation value of the dose per energy fluence, dij based on the actual total dose Did and the probability distribution function.

A low-dose-rate (LDR) brachytherapy device having a spatiotemporal radiation profile includes an elongated body having a radioactive material in a spatial pattern to provide a spatial radiation profile with a radiation intensity that varies along a length of the elongated body. The radioactive material includes at least first and second radioisotopes having at least first and second respective decay profiles that together provide a temporal radiation profile that is different from the first and second decay profiles. The spatial radiation profile and the temporal radiation profile form a net spatiotemporal radiation profile configured to provide a radiotherapy plan for a patient.

Method and systems are disclosed for radiating a moving target inside a heart. The method includes acquiring sequential volumetric representations of an area of the heart and defining a target tissue region and / or a radiation sensitive structure region in 3D for a first of the representations. The target tissue region and / or radiation sensitive structure region are identified for another of the representations by an analysis of the area of the heart from the first representation and the other representation. Radiation beams to the target tissue region are fired in response to the identified target tissue region and / or radiation sensitive structure region from the other representation.

The invention discloses a radiotherapy planning system, a determining device and a storage medium. The system includes one or more processors, and a storage device for storing one or more programs; the one or more programs are executed by one or more processors, and the one or more processors perform the following steps: acquiring image sequences distributed with time, wherein the image sequencesincluding sample points corresponding to the same location of an interest region; establishing an optimization model based on the sampling points and the weight of each image in the image sequences; and solving the optimization model to determine the radiation therapy plan. The above technical scheme solves the problem that in the prior art, a tumor target area generates periodic movements due tobreathing, heartbeat, and the like, so that the actual exposure dose of the target area and surrounding endangered organs is different from the target dose, the system provides a more precise radiotherapy plan for the movement target area, facilitates more precise treatment, effectively protects the surrounding endangered organs of the target area, and is suitable for use with most existing radiotherapy equipment that implements radiotherapy plans.

The invention relates to the field of radiotherapy and provides an image sampling method used in radiotherapy plan optimization. The sampling method comprises the steps that an image to be obtained is obtained, wherein the image to be obtained includes at least one interested area, the upper limit of the total number of sampling points and the number of sampling points included in subareas in the interested area are obtained, the interested area is divided into multiple subareas according to the upper limit of the total number of the sampling points and the number of the sampling points included in subareas in the interested area, a clustering analysis algorithm is utilized to conduct downsampling on the subareas to obtain the sampling points in the interested area. The sampling method can retain shape characteristics of organs or target section while decreasing the number of the sampling points and is conductive to improvement of radiotherapy plan effectiveness and accuracy of dosage distribution calculation.

A method for generating a robust radiotherapy treatment plan for a treatment volume of a subject. An adjusted voxel-specific dose objective is determined by “smearing” an initial voxel-specific dose objective a specified distance in at least one direction. A treatment plan based on such adjusted dose objective will be more robust with respect to setup uncertainties and organ movements. Smearing of the dose objective corresponds to adjusting the dose objective dose value of a voxel in accordance with dose objective dose values of voxels within the specified distance.

The invention discloses an accurate radiotherapy planning system, comprising a CT data input module, a machine data module, a radiotherapy planning database, a target positioning module, a dosage computation module, a computer, an on-line dosage inversion module, a dosage collection module, a beam setting and preset ideal dosage distribution module, a setting beam module, a calculating beam dosage response matrix and determining beam intensity distribution module, a first determination module, a first evaluation result module, a calculating beam generated dosage and optimization module, a second evaluation result module, a second determination module and an output module. The accurate radiotherapy planning system has the characteristics of reasonable design and simple structure, and can accurately and rapidly calculate a target dosage, and substantially increase radiotherapy efficiency; in addition, an evaluation system makes dosages more accurate, is suitable for clinical usage, improves radiotherapy effects and reduces patient treatment pains.

The present disclosure relates to systems, methods, and computer-readable storage devices for radiotherapy treatment planning. For example, a method may generate a treatment plan for a patient. The method may receive training data reflecting radiotherapy treatment data. The training data may include a feature vector and a target vector. The method may further determine a training model based on the feature vector and the target vector. The method may further receive testing data associated with the patient. The testing data may include a descriptive feature vector. The method may further determine a therapy model based on the descriptive feature vector and the training model. The therapy model may be used to generate the treatment plan.

The invention discloses a dialog-type radiotherapy planning system based on voice recognition and a dialog-type radiotherapy making method based on voice recognition. The dialog-type radiotherapy planning system based on speech recognition comprises a voice data acquisition module, an acoustic feature extraction module, a training acoustic model module, a training language model module, a decodermodule, and a radiotherapy command library comparison module, a command execution module, and a self-learning module. The language commands of a radiotherapy physician can quickly and efficiently control the operation execution of the radiotherapy planning system, and the radiotherapy planning system intelligently recognizes and understands the voice commands of the radiotherapy physician and converts voice signals into computer instructions that can be understood by the system. A dialog-type new interaction mode replaces the traditional mechanized human-machine interaction mode of the physician so as to improve the efficiency of the radiotherapy physician. Artificial intelligence and existing cases improve accuracy and improve radiotherapy effects.

A deep convolutional neural network can be trained to provide a patient radiation treatment plan. Training can include collecting patient data based on at least one image of patient anatomy from patients, determining a treatment plan including a set of control points from the collected patient data, and using the determined treatment plans and the corresponding collected patient data to train a deep convolutional neural network for regression to determine a treatment plan including a set of control points from collected patient data. The trained model can be used to provide a radiation treatment plan, such as in real-time.

The embodiment of the invention provides a head and neck lymph node and drainage area automatic sketching method based on deep learning. The method utilizes the symmetry of a human body structure to divide a head area into a left part and a right part for training and prediction, thereby indirectly increasing the data volume of model training, and reducing the scale of a deep learning model at thesame time. According to the method, a step-by-step processing optimized sketching strategy is used, the lymphatic drainage area easy to segment is achieved through the deep learning model, then the lymphatic drainage area is optimized and lymph nodes are segmented through the multi-task deep learning model, the relevance between the lymphatic drainage area and the lymph nodes is fully utilized, and the segmentation accuracy of the lymphatic drainage area and the lymph nodes is improved. According to the invention, an AI (artificial intelligence)-assisted contour sketching method is implemented in a radiotherapy plan work flow, so that the sketching consistency of the work efficiency of medical workers can be effectively improved, and the head and neck tumor radiotherapy precision is improved.

The invention discloses a method for calculating dose of a radiotherapy ray plane detector and a matched system. The method comprises the content of calibrating parameters of a depth deviation model of a measurement plane; receiving a three-dimensional external irradiation treatment plan for a patient and a depth position of the measurement plane from a radiotherapy treatment planning system (TPS); calculating a depth deviation of the measurement plane of each beam according to a therapy apparatus head angle of the beam and the depth deviation model of the measurement plane; calculating a dosedistribution on the measurement plane after the depth position shifts according to the treatment plan and the depth deviation of each beam; superposing the dose distribution of each beam on the measurement plane after shifting, and generating the dose on the calibrated measurement plane of the detector.

The invention discloses a method for calculating the invasion probability of a tumor clinical target area based on a hidden Markov model. The frequency of joint appearance of grid points in the three-dimensional tumor area is used to obtain the corresponding correlation rules; the primary tumor data of the new patient is read in, and the distance transformation is used to obtain the spatial distance from each voxel point to the primary tumor area; The probability of the primary tumor area is set as 1, and the invasion probability of each voxel point is related to the probability of adjacent voxels and the probability of the last observed state, and iteratively calculates outward from the primary tumor area; finally, all voxels are calculated The probability of a point being invaded by a tumor. The invention can assist the formulation of clinical radiotherapy plans, realize the automatic determination of the range of clinical radiotherapy target areas with different emission doses, reach the delineation standard for clinical application, and improve the working efficiency of doctors.

The invention discloses a computed tomography (CT) image reconstruction method based on magnetic resonance imaging (MRI). The CT image reconstruction method comprises the following steps: 1) reconstructing MRI by using a deep learning network, training the deep learning network, acquiring undersampling k spatial data of a to-be-detected object, and inputting the undersampling k spatial data of theto-be-detected object in the trained deep learning network to acquire online MRI of the to-be-detected object; and 2) using a bidirectional generative adversarial network to reconstruct a CT image through MRI. The CT image reconstruction method has the following advantages: (1) the MRI imaging speed is high; (2) the method has wide application ranges and can be applied to imaging of other parts of a human body as well as imaging of the lung; (3) the CT image is obtained through MRI reconstruction, and ionizing radiation of CT examination is avoided; and (4) the CT image obtained through reconstruction can also be used for radiotherapy plan formulation and PET attenuation correction.

The invention discloses radiotherapy plan generation equipment, a radiotherapy plan generation device and a storage medium. The equipment comprises one or more processors; and a memory for storing oneor more programs. When one or more programs are executed by one or more processors, the one or more processors realize the following operations: inputting image information and prescription dose information into a preset neural network model to obtain a current operation instruction; controlling a radiotherapy plan module to generate a current radiotherapy plan according to the current operationinstruction; if the current radiotherapy plan does not meet a preset cycle end condition, feeding back the current score corresponding to the current radiotherapy plan to a preset neural network modelto update the output current operation instruction, and returning to execute the operation of controlling the radiotherapy plan generation module; and if the current radiotherapy plan satisfies the preset cycle end condition, determining the current radiotherapy plan as a target radiotherapy plan so as to improve plan generation efficiency and plan generation accuracy.

The invention discloses a method for quickly realizing a Gamma analysis method in the posology verification of the radiotherapy treatment planning. Compared with the common method, the method disclosed by the invention has the differences that a GPU (graphic processing unit) is combined to read data, carry out logic processing and output; the GPU is adopted for processing volume element refining existing in the Gamma analysis method process in the posology verification of the radiotherapy treatment planning and processing the process of calculating the Gamma factor based on the refined volume element; and the whole process has the characteristics of large data volume, low data correlation, high degree of parallelism, high computing density and the like, and data have the same executive program. More calculating units simultaneously execute in parallel by the GPU, the operation time is shortened, the efficiency of the Gamma analysis method is higher, and the posology verification speed is improved.

The application discloses a system and a method for adaptive radiation therapy. The method may include: obtaining an initial treatment plan of a region of interest, wherein the initial treatment planincludes a first initial treatment fraction and a second initial treatment fraction; causing a radiation treatment device to deliver the first initial treatment fraction; obtaining a treatment fraction based on the second initial treatment fraction and the treatment record.