A method for extending initial image data of a subject for dose estimation includes obtaining first image data of the subject for dose calculation, wherein the first image data has a first field of view. The method further includes obtaining second image data for extending the field of view of the first image data. The second image data has a second field of view that is larger than the first field of view. The method further includes extending the first field of view based on the second image data, producing extended image data.

A photon source emits a flattening filter-free photon beam. A control circuit operably couples to a multi-layer multi-leaf collimator that is disposed between the photon source and a treatment area of a patient. The control circuit automatically arranges operation of some, but not all, of the layers of the multi-layer multi-leaf collimator to serve as a virtual flattening filter with respect to the flattening filter-free photon beam emitted by the photon source. By one approach, another of the layers of the multi-layer multi-leaf collimator serves to form a treatment aperture corresponding to a shape of the treatment area of the patient. By one approach the control circuit comprises an integral part of a treatment platform (as versus a dedicated treatment planning platform) and can carry out most or even essentially all of the planning steps that lead to administration of the treatment to a patient.

Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.

These teachings provide for accessing optimization information comprising at least one isocenter that corresponds to a body outline for a particular patient, field geometry information for a particular radiation treatment platform, and dosimetric data. The optimization information can further comprise a model of a body outline for the patient. A control circuit optimizes a radiation treatment plan as a function of the optimization information to provide an optimized radiation treatment plan where radiation dose levels delivered to the particular patient from a particular field depends on the relative volume magnitude of field path intersections to thereby reduce radiation dose delivery to healthy patient tissue in regions having relatively more overlapping fields.

Disclosed herein are methods and systems to optimize a radiation therapy treatment plan using dose distribution values predicted via a trained artificial intelligence model. A server trains the AI model using a training dataset comprising data associated with a plurality of previously implemented radiation therapy treatments on a plurality of previous patients and dose distributions associated with one or more organs of each previous patient. The server then executes the trained AI model to predict dose distribution for a patient. The server then displays a heat map illustrating the predicted values, transmits the predicted values to a plan optimizer to generate an optimized treatment plan for the patient, and/or transmits an alert when a treatment plan generated by a plan optimizer deviates from rules and thresholds indicated within the patient's plan objectives.

The present invention relates to a method for evaluating radiotherapy planning. The present invention provides a method for evaluating radiotherapy planning, the method comprising the steps of: (a) acquiring a dose distribution of generated radiotherapy planning for the target volume according to the prescribed dose (S100); (b) calculating dose gradient index (DGI) of 3-dimensional isodose levels of the acquired dose distribution, by using distances between the isodose levels (S200); and (c) generating a differential dose gradient curve (dDGC) by plotting the calculated dose gradient index (DGI) as a function of the dose over the range of dose distributions (S300)

A method and apparatus for radiation therapy using functional measurements of branching structures. The method includes determining a location of each voxel of a plurality of voxels in a reference frame of a radiation device. The method further includes obtaining measurements that indicate a tissue type at each voxel. The method further includes determining a subset of the voxels based on an anatomical parameter of a respective branching structure of a set of branching structures indicated by the measurements. The method further includes determining a subset of the voxels that enclose an organ-at-risk (OAR) volume. The method further includes determining a value of a utility measure at each voxel. The method further includes determining a series of beam shapes and intensities which minimize a value of an objective function based on a computed dose delivered to each voxel and the utility measure for that voxel summed over all voxels.

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.

Generating a three-dimensional radiation dose matrix for a patient for controlling the delivery of radiation dose to patients. The three-dimensional radiation dose matrix for the patient based on an intensity of radiation fields delivered by a radiation therapy delivery system that intersect with volume elements of a patient and determined by a predictive model. The intensity of the radiation fields at volume elements of the patient determined from spatial position data of the volume elements in a patient and radiation therapy delivery system data.

Radiation treatment planning includes accessing values of parameters such as a number of beams to be directed into sub-volumes in a target, beam directions, and beam energies. Information that specifies limits for the radiation treatment plan are accessed. The limits include a limit on irradiation time for each sub-volume outside the target. Other limits can include a limit on irradiation time for each sub-volume in the target, a limit on dose rate for each sub-volume in the target, and a limit on dose rate for each sub-volume outside the target. The values of the parameters are adjusted until the irradiation time for each sub-volume outside the target satisfies the maximum limit on irradiation time.