Example methods and systems for generating dose estimation models for radiotherapy treatment planning are provided. One example method may comprise obtaining model configuration data that specifies multiple anatomical structures based on which dose estimation is performed by a dose estimation model. The method may also comprise obtaining training data that includes a first treatment plan associated with a first past patient and multiple second treatment plans associated with respective second past patients. The method may further comprise: in response to determination that automatic segmentation is required for the first treatment plan, performing automatic segmentation on image data associated with the first past patient to generate an improved first treatment plan, and generating the dose estimation model based on the improved first treatment plan and the multiple second treatment plans.

Techniques for generating a radiotherapy treatment plan are provided. The techniques include receiving a radiotherapy optimization problem, the radiotherapy problem comprising a plurality of parameters; processing the radiotherapy optimization problem to instantiate a first set of candidate parameters; converting the first set of candidate parameters into an adapted representation; defining an adapted radiotherapy optimization problem as a function of the adapted representation such that a given solution to the adapted optimization problem estimates a solution to the radiotherapy optimization problem; processing the adapted radiotherapy optimization problem to estimate a function of the solution to the adapted radiotherapy optimization problem; and processing the estimated function of the solution to the adapted optimization problem to generate a deliverable radiotherapy treatment plan.

Systems and methods for an oncology radiation therapy treatment review are disclosed. The systems include a radiation therapy delivery system, a processor, and a non-transitory memory containing computer-readable instructions. The instructions are executed by the processor to receive information relating to a subject undergoing radiation therapy treatment in accordance with a treatment plan, identify at least one clinical requirement associated with the treatment plan, and analyze the received information and the at least one clinical requirement for identifying an action, the action comprising at least one of the following: configuring a user interface, performing a corrective action, storing results of the analyzing to a database, outputting an alert, or disabling treatment, or a document related action.

The treatment planning engine empowers radiation treatment decision makers, such as a physician, to efficiently identify effective radiation treatment outcomes for a given patient during the contouring stage. Specifically, using the treatment planning engine, the physician may iteratively and in real-time evaluate different treatment outcomes for a patient before selecting an optimal outcome that will guide the delivery of radiation treatment to the patient. By providing real-time information as to potential toxicity and treatment efficacy during the contouring stage, the physician is empowered to make informed decisions at the preliminary contouring stage.

A system for estimating a dose from a proton therapy plan includes a memory that stores machine instructions and a processor coupled to the memory that executes the machine instructions to subdivide a representation of a volume of interest in a patient anatomy traversed by a planned proton field into a plurality of voxels. User input received by a GUI can be used to define the representation. The processor further executes the machine instructions to determine the distance from the source of the planned proton beam to one of the voxels. The processor also executes the machine instructions to compute the discrete contribution at the voxel to an estimated dose received by the volume of interest from the planned proton beam based on the distance between the source and the volume of interest.

The present disclosure provides methods for user adaptive radiation therapy planning and a system for using the same. In some aspect, method for generating a radiation therapy plan is provided. The method includes receiving imaging information acquired from a patient and producing a preliminary radiation therapy plan, using a treatment planning system, based on the imaging information. The method also includes generating an indication for modifying the preliminary radiation therapy plan in accordance with a predetermined clinician profile, wherein the predetermined clinician profile is based on a trained learning machine. The method further includes producing, using the indication, an updated radiation therapy plan that is adapted to the predetermined clinician profile.

A scenario-based treatment plan optimization method for radiotherapy treatment is proposed, in which a first and a second possible scenario are defined. Different optimization functions are defined for the scenarios and the treatment plan is optimized applying the first optimization function under the first scenario and the second optimization function under the second scenario, thereby obtaining a first optimized radiotherapy treatment plan.

Described here are systems and methods for knowledge-based brachytherapy planning. These systems and methods are capable of automatically generating treatment plans for prostate brachytherapy in clinically relevant times. Primitive features computed from anatomical contours of the target organ are used to retrieve a template plan from a database. The template plan is then adjusted in a stochastic search algorithm.

Systems and methods for shuttle mode radiation delivery are described herein. One method for radiation delivery comprises moving the patient platform through the patient treatment region multiple times during a treatment session. This may be referred to as patient platform or couch shuttling (i.e., couch shuttle mode). Another method for radiation delivery comprises moving the therapeutic radiation source jaw across a range of positions during a treatment session. The jaw may move across the same range of positions multiple times during a treatment session. This may be referred to as jaw shuttling (i.e., jaw shuttle mode). Some methods combine couch shuttle mode and jaw shuttle mode. Methods of dynamic or pipelined normalization are also described.

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.