In a radiation treatment plan that includes a plurality of treatment fields of multiple treatment modalities, such as IMRT modality and dynamic treatment path modality (e.g., VMAT and conformal arc therapy), an optimized spatial point sequence may be determined that optimizes the total treatment time, which includes both the beam-on time (i.e., during the delivery of radiation dose) and the beam-off time (i.e., during transitions between consecutive treatment fields). The result is a time-ordered field trajectory that intermixes and interleaves different treatment fields. In one embodiment, a dynamic treatment path may be cut into a plurality of sections, and one or more IMRT fields may be inserted between the plurality of sections.

A system, medium, and method including obtaining a plurality of positions for multiple components defined by a plan; obtaining a set of constraints that express limitations for the multiple components at the plurality of positions, the constraints being applicable to a plan where the multiple components synchronously change their positions with time to traverse a prescribed sequence of the plurality of positions, at least one of the multiple components being further constrained to change its position over time by staying within a predefined tolerance to a predefined smooth function of position over time between different positions; determining a trajectory of position and a minimum duration in which the multiple components completely synchronously traverse the prescribed sequence of positions while satisfying the constraints for the multiple components; and generating a record of the determined trajectory of position and the minimum duration for the plurality of components.

A device for optimizing a radiation therapy plan (30) for delivering therapeutic radiation to a patient using a therapeutic radiation source (16) while modulated by a multi-leaf collimator (MLC) (14) includes at least one electronic processor (25) connected to a radiation therapy device (12). A non-transitory computer readable medium (26) stores instructions readable and executable by the at least one electronic processor to perform a radiation therapy plan optimization method (102) including: optimizing MLC settings of the MLC respective to an objective function wherein the MLC settings define MLC leaf tip positions for a plurality of rows of MLC leaf pairs at a plurality of control points (CPs). The optimizing is performed in two or more iterations with a resolution of the MLC settings increasing in successive iterations.

A multi-mode cone beam computed tomography radiotherapy simulator and treatment machine is disclosed. The radiotherapy simulator and treatment machine both include a rotatable gantry on which is positioned a cone-beam radiation source and a flat panel imager. The flat panel imager captures x-ray image data to generate cone-beam CT volumetric images used to generate a therapy patient position setup and a treatment plan.

A radiation treatment planning system and method for generating plans to treat plural target volumes, each associated with a prescribed dose, does not require delivery of radiation to every target volume from every beam direction. Allowing target volumes to be omitted for some control points facilitates generation of treatment plans that deliver less radiation dose to non-target tissues by allowing beam shaping to more closely fit the remaining target volumes. Simulated annealing using an objective function may be applied to determine parameters such as the number of control points for which a target volume is not targeted.

Systems and methods are provided for using prior radiotherapy treatment machine parameter trajectory files to determine or predict the machine parameter trajectory at treatment delivery for a new radiotherapy plan, and to quantify the corresponding dosimetric effect of the difference between these machine parameters and the original radiotherapy plan. A pre-treatment quality assurance may thereby be generated that requires no extra beam-on time and provides preemptive insight into the plan quality. The system may include a multi-leaf collimator configured to deliver a treatment plan to a subject and configured to interact with the computer-based algorithm and/or any associated equipment used to perform the quality assurance tasks.

A system, medium, and method including obtaining a plurality of positions for multiple components defined by a plan; obtaining a set of constraints that express limitations for the multiple components at the plurality of positions, the constraints being applicable to a plan where the multiple components synchronously change their positions with time to traverse a prescribed sequence of the plurality of positions, at least one of the multiple components being further constrained to change its position over time by staying within a predefined tolerance to a predefined smooth function of position over time between different positions; determining a trajectory of position and a minimum duration in which the multiple components completely synchronously traverse the prescribed sequence of positions while satisfying the constraints for the multiple components; and generating a record of the determined trajectory of position and the minimum duration for the plurality of components.

In a continuous arc radiation therapy planning method for planning a radiation therapy session parameterized by a set parameters for control points (CPs) along at least one radiation source arc, a geometric optimization (40) is performed that does not include calculating radiation absorption profiles to generate optimized values for a sub-set of the parameters. After the geometric optimization, a main optimization (42) is performed that includes calculating radiation absorption profiles. The main optimization is performed with the sub-set of parameters initialized to the optimized values from the geometric optimization. The sub-set of parameters optimized by the geometric optimization may include collimator angle parameters for a multileaf collimator (MLC) (58). The geometric optimization may optimize a cost function comprising a sum over the CPs of a per-CP cost function dependent on a target-only region (62) defined as a planning target volume excluding any portion overlapping an organ at risk.

Systems, devices, and methods for imaging-based calibration of radiation treatment couch position compensations.

Example methods and systems for quality-aware continuous learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining an artificial intelligence (AI) engine that is trained to perform a radiotherapy treatment planning task. The method may also comprise: based on input data associated with a patient, performing the radiotherapy treatment planning task using the AI engine to generate output data associated with the patient; and obtaining modified output data that includes one or more modifications made by a treatment planner to the output data. The method may further comprise: performing quality evaluation based on (a) first quality indicator data associated with the modified output data, and/or (b) second quality indicator data associated with the treatment planner. In response to a decision to accept, a modified AI engine may be generated by re-training the AI engine based on the modified output data.