Abstract: 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.

Abstract: A system plans a radiation therapy treatment of a target volume based on inputs in the form of: a set of candidate beams (B), where each beam defines an arrangement of a therapeutic beam relative to the target volume; a treatment plan (x) for the radiation therapy treatment that uses a subset of the candidate beams (B); an objective function (F) describing a quality of the treatment plan (x); and a feasible region (X) describing requirements on the treatment plan (x) that must be fulfilled. The objective function (F) and/or the feasible region (X) also reflect a first complexity criterion (?(x)?{circumflex over (?)}) limiting a first complexity measure (?(x)) to be less than or equal to a maximum first complexity ({circumflex over (?)}). An optimization step is executed repeatedly; whereby, in each iteration, an updated treatment plan (x?) is calculated by optimizing the treatment plan (x) with respect to the objective function (F) and the feasible region (X).

Abstract: An output radiation treatment plan for at least one target in a treatment volume is determined. Each target is associated with a prescribed radiation dose. An updated treatment plan causes the prescribed radiation dose to be delivered to the target when implemented by a radiation therapy machine. The updated treatment plan requires an updated delivery time to complete and is calculated by: [i] receiving a numerical value designating an upper bound on modulation efficiency of the updated treatment plan, [ii] receiving a current treatment plan, [iii] calculating a current delivery time for the current treatment plan, [iv] calculating an un-modulated delivery time for the current treatment plan, and [v] calculating the updated treatment plan by executing an optimization process while satisfying the upper bound on the modulation efficiency. Steps [ii] to [v] are traversed a predetermined number of times. Thereafter, the output radiation treatment plan is generated.

Abstract: A scenario-based radiotherapy treatment plan optimization method is used to define an extended region of interest for treatment planning purposes, based on the union of the positions of a region of interest in a number of different scenarios, each scenario representing a realization of a possible location of the region of interest. Preferably a number of scenarios are defined and some of these scenarios are used to define the extended region.

Abstract: A radiation treatment plan is determined by: [1] receiving a current fluence map defining a radiation dose; [2] receiving a current control-point sequence describing machine settings for a collimator associated with a radiation source; [3] determining an updated fluence map and an updated control-point sequence based on the current fluence map; [4] determining a further updated control-point sequence based on the updated control-point sequence and the updated fluence map; [5] determining a further updated fluence map based on the updated fluence map, the updated control-point sequence and the further updated control-point sequence; [6] checking if a stopping criterion is fulfilled; if so: stopping the process, and producing an output radiation treatment plan based on the further updated control-point sequence; and otherwise: setting the further updated fluence map, or zero, to the current fluence map; setting the further updated control-point sequence to the current control-point sequence; and returning to st

Abstract: A method for generating a robust radiotherapy treatment plan, using scenario-based robust optimization, is provided. Weights which are dependent on the overlap of different scenario-specific mappings of a region of interest is used in the optimization.

Abstract: Radiation therapy treatment planning methods facilitate predicting achievable dose distributions by training a prediction model for clinical DVH curves based on simplified DVH curves obtained using standardized methods. Machine learning applied on pairs of clinical and simplified DVH curves enables predicting actual clinical DVH curves based on simplified DVH curves.

Abstract: A method for generating a robust radiotherapy treatment plan, using scenario-based robust optimization, is provided. Weights which are dependent on the overlap of different scenario-specific mappings of a region of interest is used in the optimization.

Abstract: A radiation treatment plan is determined by: [1] receiving a current fluence map defining a radiation dose; [2] receiving a current control-point sequence describing machine settings for a collimator associated with a radiation source; [3] determining an updated fluence map and an updated control-point sequence based on the current fluence map; [4] determining a further updated control-point sequence based on the updated control-point sequence and the updated fluence map; [5] determining a further updated fluence map based on the updated fluence map, the updated control-point sequence and the further updated control-point sequence; [6] checking if a stopping criterion is fulfilled; if so: stopping the process, and producing an output radiation treatment plan based on the further updated control-point sequence; and otherwise: setting the further updated fluence map, or zero, to the current fluence map; setting the further updated control-point sequence to the current control-point sequence; and returning to st

Abstract: An output radiation treatment plan for at least one target in a treatment volume is determined. Each target is associated with a prescribed radiation dose. An updated treatment plan causes the prescribed radiation dose to be delivered to the target when implemented by a radiation therapy machine. The updated treatment plan requires an updated delivery time to complete and is calculated by: [i] receiving a numerical value designating an upper bound on modulation efficiency of the updated treatment plan, [ii] receiving a current treatment plan, [iii] calculating a current delivery time for the current treatment plan, [iv] calculating an un-modulated delivery time for the current treatment plan, and [v] calculating the updated treatment plan by executing an optimization process while satisfying the upper bound on the modulation efficiency. Steps [ii] to [v] are traversed a predetermined number of times. Thereafter, the output radiation treatment plan is generated.