A memory has stored therein a fluence map that corresponds to a particular patient and a deep learning model. The deep learning model is trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. The deep learning model comprises a neural network model that was trained, at least in part, via a reinforcement learning method. A control circuit accesses the memory and is configured to iteratively optimize a radiation treatment plan to administer the therapeutic radiation to the patient by, at least in part, generating a leaf sequence as a function of the deep learning model and the fluence map by employing a plurality of agents to each separately use the deep learning model to each generate a leaf sequence for only a single leaf pair of the multi-leaf collimator.

Methods and apparatus are provided for planning and delivering radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. In some embodiments the radiation source is moved continuously along the trajectory while in some embodiments the radiation source is moved intermittently. Some embodiments involve the optimization of the radiation delivery plan to meet various optimization goals while meeting a number of constraints. For each of a number of control points along a trajectory, a radiation delivery plan may comprise: a set of motion axes parameters, a set of beam shape parameters and a beam intensity.

A method of determining treatment geometries for a radiotherapy treatment includes providing a patient model having one or more regions of interest (ROIs); defining a delivery coordinate space (DCS); for each beam's eye view (BEV) plane of each vertex in the DCS, and for each ROI, evaluating a dose of the ROI using transport solutions; evaluating a BEV scores of each pixel of the BEV plane using the doses of the one or more ROIs; determining one or more BEV regions in the BEV plane based on the BEV scores; determining a BEV region connectivity manifold based on the BEV regions; determining a set of treatment trajectories based on the BEV region connectivity manifold; and determining one or more IMRT fields. Each treatment trajectory defines a path through a set of vertices in the DCS. Each IMRT field defines a direction of incidence corresponding to a vertex in the DCS.

The present disclosure relates a multi-leaf collimator. The multi-leaf collimator may include a plurality of leaves. At least two leaves of the plurality of leaves may be movable parallel to each another. For each leaf of at least some of the plurality of leaves, at least one portion of the leaf may have thicknesses varying along a longitudinal direction of the each leaf. The each leaf may have a first end and a second end along the longitudinal direction of the each leaf.

A multileaf collimator includes a plurality of movable leaves for shaping a radiotherapy beam, wherein each leaf is independently movable in a same linear travel direction. Each leaf includes a linear array of magnets disposed on a measurement surface of the leaf and an array of magnetoresistive sensors that is disposed proximate the measurement surfaces of the leaves.

Methods and apparatus are provided for planning and delivering radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. In some embodiments the radiation source is moved continuously along the trajectory while in some embodiments the radiation source is moved intermittently. Some embodiments involve the optimization of the radiation delivery plan to meet various optimization goals while meeting a number of constraints. For each of a number of control points along a trajectory, a radiation delivery plan may comprise: a set of motion axes parameters, a set of beam shape parameters and a beam intensity.

The invention relates to a method and apparatus for control of a charged particle cancer therapy system. A treatment delivery control system is used to directly control multiple subsystems of the cancer therapy system without direct communication between selected subsystems, which enhances safety, simplifies quality assurance and quality control, and facilitates programming. For example, the treatment delivery control system directly controls one or more of: an imaging system, a positioning system, an injection system, a radio-frequency quadrupole system, a ring accelerator or synchrotron, an extraction system, a beam line, an irradiation nozzle, a gantry, a display system, a targeting system, and a verification system. Generally, the control system integrates subsystems and/or integrates output of one or more of the above described cancer therapy system elements with inputs of one or more of the above described cancer therapy system elements.

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.

Systems and methods for delivering a radiation beam using a linear accelerator (LINAC). Optimal beam alignment parameters may be determined and stored for each of N gantry angles. The beam alignment parameters may adjust a current supplied to one or more bending magnets of the LINAC and, thus, change an angle and direction of the radiation beam. An optimum beam alignment parameter for a gantry angle may be determined by adjusting the beam alignment parameter until a center of a radiation field of the radiation beam in a radiation transmission image is at a center of shadow of a radiation opaque marker, which may be placed at a radiation isocenter. The beam alignment parameters stored for the N gantry angles may be used to adjust the beam steering current as the gantry is rotated through any arbitrary gantry angle.

A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm. By one approach the control circuit can be configured to, when presenting automatically-generated radiation treatment plans to a user, also co-present an opportunity for the user to signal to a remote entity that none of the plans are acceptable and that the user will instead employ a user-generated plan for the particular patient.