Systems for planning delivery of radiation dose to a target region within a subject comprise a processor configured to: iteratively optimize a simulated dose distribution relative to a set of one or more optimization goals comprising a desired dose distribution in the subject over a first plurality of control points located on a trajectory, the trajectory comprising relative movement between a treatment radiation source and the subject; reach one or more initial termination conditions, and after reaching the one or more initial termination conditions: specify a second plurality of control points along the trajectory, the second plurality of control points comprising a larger number control points than the first plurality of control points; and iteratively optimize a simulated dose distribution relative to the set of one or more optimization goals over the second plurality of control points to thereby determine a radiation delivery plan.

Systems for planning delivery of radiation dose to a target region within a subject comprise a processor configured to: initialize a first plurality of control points located on a trajectory, the trajectory comprising relative movement between a radiation source and the subject, wherein initializing the first plurality of control points comprises assigning, to each the first plurality of control points, one or more axis positions which specify a position of the radiation source relative to the subject; specify a second plurality of control points along the trajectory, the second plurality of control points comprising a larger number of control points than the first plurality of control points; and iteratively optimize a simulated dose distribution over the second plurality of control points to thereby determine a radiation delivery plan by assigning each of the second plurality of control points optimized values for one or more radiation delivery parameters.

Systems and methods are disclosed for exploration and adaptation of radiotherapy treatment plans. Example operations for radiotherapy treatment planning include: obtaining a plurality of solutions (e.g., Pareto-optimal solutions) of a radiotherapy problem, exploring the plurality of solutions to identify an additional solution in a submanifold space (e.g., exploration of a Pareto surface), and generating treatment plan parameters based on the additional solution for use in a radiation therapy treatment. In an example, exploring the plurality of solutions includes: establishing a submanifold space from a manifold space representing the plurality of solutions in fewer dimensions than the weights; producing additional sets of weights in the submanifold space based on derivatives of first-order optimality conditions of the radiotherapy problem, the derivatives determined with respect to the weights; and navigating in the submanifold space to arrive at the additional solution, corresponding to one of the additional sets of weights.

A control circuit automatically models a multi-field treatment arrangement as a single composite trajectory that includes a plurality of treatment fields to provide a single composite/multi-field treatment arrangement, and then optimizes the multi-field radiation treatment plan as a function of the single composite/multi-field treatment arrangement.

Systems and methods for determining a target multi-source model of a radiation source corresponding to an energy spectrum is provided. The systems may obtain an initial multi-source model of the radiation source, which includes an initial phase space file that includes information of a plurality of simulated particles of a plurality of energy levels. The systems may estimate, based on the initial phase space file, a plurality of component PDD curves corresponding to the plurality of energy levels. The systems may obtain a measured PDD curve corresponding to radiation of the energy spectrum. For each energy level, the systems may determine, based on the plurality of component PDD curves and the measured PDD curve, a weight for the each energy level. The systems may further determine the target multi-source model of the radiation source based at least in part on the initial multi-source model and the weights.

A radiation irradiation system and a control method therefor. On the basis of weight proportions of elements in a human body and reaction intensities the elements with a neutron and a photon, the element that has influence on simulation calculation results of the neutron and the photon in an application scenario of the radiation irradiation system is screened out, and during a simulation process, only the screened-out element is simulated, so that the calculation speed can be greatly improved, and the calculation time can be reduced.

Systems and methods for generating a beam model for radiotherapy treatment planning are discussed. An exemplary system includes a memory to store a trained deep learning model, and a processor circuit to generate a beam model. The deep learning model can be trained to establish a relationship between machine scanning data and values of beam model parameters, and validated for accuracy. The processor circuit can receive machine scanning data indicative of a configuration or an operation status of the radiation therapy device, apply the machine scanning data to the trained deep learning model to determine values for the beam model parameters, and generate a beam model based on the determined values of the plurality of beam model parameters. The beam model may be provided to a user, or a treatment planning system.

A method for generating a radiation treatment plan is provided. The method may include determining a set of one or more optimization goals for radiation delivery by a therapeutic radiation delivery apparatus. The method may also include determining a plan for radiation delivery from a radiation source of the therapeutic radiation delivery apparatus. The radiation source may be capable of continuously rotating around a subject. The plan may include a plurality of radiation segments. Each radiation segment may be characterized by at least one parameter selected from a start angle, a stop angle, a two-dimensional segment shape, or a segment MU value such that the plurality of radiation segments satisfy the set of one or more optimization goals by superimposing at least two radiation segments from at least two different rotations into a target volume of the subject.

The present application relates to a control method and apparatus for a dose calculation system, and a dose calculation system. The method includes: building a dose calculation geometric model; determining a CPU and GPU joint dose calculation mode from a computing speed of a CPU and a computing speed of a GPU; and performing, by the CPU and the GPU, particle simulation according to the CPU and GPU joint dose calculation mode and the dose calculation geometric model to obtain a dose calculation result. By using this method, computing resources of the dose calculation system can be fully used, thereby maximizing the improvement of a dose calculation speed.

Systems and methods are disclosed for optimizing a treatment plan using all degrees of freedom including those related to beam geometry parameters, the optimization including a step for limiting the search space for the beam geometry parameters using a trained machine learning model, and systems and methods are disclosed for obtaining beam geometry parameters for treatment planning that do not require knowledge of the beam delivery device isocenter.