A method for constructing a photon source model function of a medical linear accelerator, for calculating the dose of rays in a radiation therapy scheme is disclosed. A source model of a therapeutic photon beam of the accelerator includes a primary ray photon source model and a scattered ray photon source model. Physical parameters in the two parts of source model functions include the position coordinates of an emission point of a particle, a projection value of a unit momentum vector in a three-dimensional orthogonal direction, and the energy of the particle. By utilizing the model functions, photon fluence information, energy spectrum information, and unit momentum direction information of photons on any phase space plane can be accurately calculated. The method and thought for constructing the source model are applicable to construction of source models of photon beams with various nominal energies of the accelerator used in a radiation therapy.

These teachings facilitate the administration of therapeutic radiation to a heterogeneous patient volume using a radiation beam source. More particularly, these teachings provide for determining a cross-sectional size of a radiation beam as corresponds to that radiation beam source and also for determining density information corresponding to the aforementioned heterogeneous body. These teachings then provide for generating a three-dimensional radiation dose calculation for the heterogeneous body using a control circuit configured as a convolution/superposition based dose calculator using a three-dimensional energy-spreading kernel. By one approach, these teachings provide for the calculator scaling total energy released per mass as a function of the cross-sectional size and energy of the radiation beam and the aforementioned density information.

A reinforcement learning agent facilitates optimization of a radiation-delivery treatment plan. The reinforcement learning agent is configured to generate a radiation-delivery treatment plan that can exceed the quality of a plan or plans employed to train the reinforcement learning agent. The reinforcement learning agent is trained to evaluate a radiation-delivery treatment plan that is output by an optimization software application, modify one or more dose-volume objective parameters of the evaluated radiation-delivery treatment plan, and then input the modified radiation-delivery treatment plan to the optimization software application for further optimization. The reinforcement learning agent adaptively adjusts the one or more dose-volume objective parameters based on an action policy learned during a reinforcement learning training process.

A method for treating a tumor, comprising identifying a tumor as a colon cancer tumor and implanting in the tumor identified as a colon cancer tumor, as least one diffusing alpha-emitter radiation therapy (DaRT) source with a suitable radon release rate and for a given duration, such that the source provides during the given duration a cumulated activity of released radon between 3.5 Mega becquerel (MBq) hour and 8.4 MBq hour, per centimeter length.

Parameterized model reconstruction is used for internal dose tomography. The parameterized model, solved for within the reconstruction, models the dose level and may account for diffusion, isotope half-life, and/or biological half-life. Using the detected emissions from different scans (e.g., from different scan sessions in a given cycle) as input for the one reconstruction, the parameterized model reconstruction determines the biodistribution of dose at any time.

The present disclosure provides a system and method for generating radiation treatment plan. The method may include obtaining a plurality of beam angles of an arc for radiation treatment and preliminary segment parameters of control points associated with the plurality of beam angles. The method may also include grouping the plurality of beam angles into at least two sets so that each pair of two consecutive beam angles of the plurality of beam angles belong to different sets of the at least two sets, and determining the target segment parameters of the control points in each of the at least two sets by optimizing, based on a leaf motion constraint, the preliminary segment parameters of the control points associating with the plurality of beam angles. The method may further include generating a treatment plan based on the target segment parameters.

Systems, kits and methods for preparing an injection system and/or treating target lesions with a selective internal radiation therapy which includes a double-barrel syringe loaded with a two-component tissue glue and radioisotope loaded microspheres. The microspheres are loaded into the syringe based on the size of the target location and are administered with a needle or dual-lumen catheter. Dosing regimens for treating breast cancer lesions or surgical beds up to 130 mm in diameter and hepatocellular carcinoma lesions up to 50 mm are included.

Systems and methods can include a method for training a deep convolutional neural network to provide a patient radiation treatment plan, the method comprising collecting patient data from a group of patients, the patient data including at least one image of patient anatomy and a prior treatment plan, wherein the treatment plan includes predetermined machine parameters, and training a deep convolution neural network for regression by using the prior treatment plans and the corresponding collected patient data to determine a new treatment plan. Systems and methods can also include a method of using a deep convolutional neural network to provide a radiation treatment plan, the method comprising retrieving a trained deep convolution neural network previously trained on patient data from a group of patients, collecting new patient data, wherein the new patient data includes at least one image of patient anatomy, and determining a new treatment plan for the new patient using the trained deep convolutional neural network for regression, wherein the new treatment plan has a new set of machine parameters.

In radiation treatment planning, a plurality of optimization loops are performed. In each optimization loop computes a dose distribution (60) in a patient represented by a planning image (42) with regions of interest (ROIs) defined in the planning image. Weights (64) for objective functions (50) are determined from objective function value (OFV) goals (52) for the objective functions. An optimized dose distribution is produced by adjusting the plan parameters to optimize the computed dose distribution respective to composite objective function (62). At least one optimization loop may include updating (70) at least one OFV goal to be used in at least the next performed optimization loop. At least one optimization loop may include updating an objective function quantifying compliance with a target dose for a target ROI based on a comparison of a metric of coverage of the target ROI and a desired coverage of the target ROI.

A method for delivering radiation treatment may include defining a preliminary trajectory including a plurality of control points. Each control point may be associated with position parameters of a gantry and a couch. The method may also include generating a treatment plan based on the preliminary trajectory by optimizing an intensity and position parameters of a collimator and MLC leaves for each control point. The method may also include decomposing the treatment plan into a delivery trajectory including the plurality of control points. Each of the plurality of control points may be further associated with the optimized intensity, the optimized position parameters of the collimator and the MLC leaves, an output rate, and a motion parameter of each of the gantry, the couch, the collimator, and the MLC leaves. The method may further include instructing a radiation delivery device to deliver the treatment plan according to the delivery trajectory.