Abstract: 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.

Abstract: Streamlined and partially automated methods of setting normal tissue objectives in radiation treatment planning are provided. These methods may be applied to multiple-target cases as well as single-target cases. The methods can impose one or more target-specific dose falloff constraints around each target, taking into account geometric characteristics of each target such as target volume and shape. In some embodiments, methods can also take into account a planner's preferences for target dose homogeneity. In some embodiments, methods can generate additional dose falloff constraints in locations between two targets where dose bridging is likely to occur.

Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: The present invention proposes a method for automatically training a dose prediction model based on existing clinical knowledge that is accumulated from multiple sources without collaborators establishing communication links between each other. According to embodiments of the claimed subject matter, clinics can collaborate in creating a dose prediction model by submitting their treatment plans into a remote computer system (such as a cloud-based system) which aggregates information from various collaborators and produces a model that captures clinical information from all submitted treatment plans, and can be used to predict dose distributions (and other dose parameters) in subsequent treatment plans.

Abstract: Streamlined and partially automated methods of setting normal tissue objectives in radiation treatment planning are provided. These methods may be applied to multiple-target cases as well as single-target cases. The methods can impose one or more target-specific dose falloff constraints around each target, taking into account geometric characteristics of each target such as target volume and shape. In some embodiments, methods can also take into account a planner's preferences for target dose homogeneity. In some embodiments, methods can generate additional dose falloff constraints in locations between two targets where dose bridging is likely to occur.

Abstract: A control circuit stores a plurality of radiation treatment plan states as pertain to optimization of a given radiation treatment plan. The control circuit detects a user's selection of a particular one of the plurality of radiation treatment plan states and responsively displays dose distribution information as corresponds to that selected state. The control circuit can automatically store at least some of those states and/or can provide the user with an opportunity to selectively save a particular state. The control circuit can provide the user with an opportunity to modify optimization objectives such that at least two of the states correspond to different optimization objectives for the radiation treatment plan. The control circuit can also display a radiation treatment plan state selector to facilitate the user selecting the particular state. An individual selector for each state can provide a visual indication of merit as pertains to each such state.

Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: A method for determining MLC leaf sequences for radiation treatment includes obtaining BEV projections of a first target volume and a second target volume along one or more treatment paths of a radiation treatment plan, analyzing the BEV projections to determine one or more contiguous ranges of spatial points where there exists an interstitial region between the first target volume and the second target volume in the direction of MLC leaf motion, and determining a first set of MLC leaf sequences such that an aperture formed by the MLC in a first portion of the one or more contiguous ranges of spatial points exposes radiation to the first target volume but not the second target volume, and an aperture formed by the MLC in a second portion of the one or more contiguous ranges of spatial points exposes radiation to the second target volume but not the first target volume.

Abstract: A control circuit identifies (prior to optimizing a radiation-treatment plan) at least one angle (such as one or more gantry angles as correspond to an arc-therapy treatment platform) to provide one or more identified angles. The control circuit then optimizes a radiation-treatment plan with respect to a particular patient target volume by using the identified angle(s) as preferred angles as regards irradiating the patient target volume. Being “preferred,” the radiation-treatment plan is thereby influenced (without being required) towards changing a rate of moving the gantry when traversing the preferred angle(s). For example, the plan can be biased towards slowing down movement of the gantry at such angles to thereby deliver a relatively greater amount of radiation to the patient target volume at the preferred angle.

Abstract: Methods and systems are provided for developing radiation therapy treatment plans. A treatment template with radiation fields can be chosen for a patient based on a tumor location. Static radiation field positions can be adjusted for the patient, while arc radiation fields may remain the same. Static radiation field positions can be adjusted using dose gradient, historical patient data, and other techniques.

Abstract: Streamlined and partially automated methods of setting normal tissue objectives in radiation treatment planning are provided. These methods may be applied to multiple-target cases as well as single-target cases. The methods can impose one or more target-specific dose falloff constraints around each target, taking into account geometric characteristics of each target such as target volume and shape. In some embodiments, methods can also take into account a planner's preferences for target dose homogeneity. In some embodiments, methods can generate additional dose falloff constraints in locations between two targets where dose bridging is likely to occur.

Abstract: 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.

Abstract: An optimized radiation treatment plan may be developed in which the total monitor unit (MU) count is taken into account. A planner may specify a maximum treatment time. An optimization algorithm may convert the specified maximum treatment time to a maximum total MU count, which is then used as a constraint in the optimization process. A cost function for the optimization algorithm may include a term that penalizes any violation of the upper constraint for the MU count.

Abstract: An existing radiation treatment plan is accessed for a given patient as well as first information (such as automatically generated updated information) regarding at least one physical characteristic as corresponds to the radiation treatment of this patient. One then initiates, prior to receiving second information (such as user input) regarding the first information, an automatic adaptation process to adapt the treatment plan to accommodate the first information. Upon later receiving second information regarding the first information, one then modifies the automatic adaptation process itself to incorporate the second information regarding the first information.

Abstract: A patient support system includes a patient support having a surface for supporting a patient and a longitudinal axis, and a positioner coupled to the patient support for positioning the patient support, wherein the positioner is configured to move the patient support along a path that has an arc, circular, or zig-zag shape, and wherein the path lies within a plane that forms an angle with the surface of the patient support.

Abstract: Optimization of a radiation-delivery treatment plan can be facilitated by permitting the making of automatic changes to one or more treatment objectives. This can comprise processing radiation-delivery treatment plan parameters with respect to one or more predetermined treatment objectives and then automatically changing that predetermined treatment objective to provide one or more changed treatment objectives (including altered, deleted, and/or added treatment objectives) that can then be used to at least attempt to optimize a radiation-delivery treatment plan. That predetermined treatment objective can comprise, for example, a treatment objective as regards a given treatment volume. The automatic changing of the predetermined treatment objective can occur in response to a variety of stimuli such as detecting a change with respect to a treatment condition such as a change in the presentation of a patient volume of interest.

Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: A method of generating a treatment plan for treating a patient with radiotherapy, the method includes obtaining a plurality of sample plans, which are generated by use of a knowledge base comprising historical treatment plans and patient data. The method also includes performing a multi-criteria optimization based on the plurality of sample plans to construct a Pareto frontier, where the plurality of sample plans are evaluated with at least two objectives measuring qualities of the plurality of sample plans such that treatment plans on the constructed Pareto frontier are Pareto optimal with respect to the objectives. The method further includes identifying a treatment plan by use of the constructed Pareto frontier.

Abstract: A control circuit that operably couples to a memory having a radiation-treatment plan stored therein that is based upon a particular patient volume receives subsequent information regarding that particular patient volume and automatically uses that subsequent information to calculate supplemental patient volume information to be used by the radiation-treatment plan. By one approach this supplemental patient volume information comprises replacement information to be used in lieu of the presumed patient volume information. The foregoing may include applying at least one rule to effect the aforementioned calculation, applying one or more deformable registration algorithms, and/or one or more Boolean operators.

Abstract: The present invention proposes a method for automatically training a dose prediction model based on existing clinical knowledge that is accumulated from multiple sources without collaborators establishing communication links between each other. According to embodiments of the claimed subject matter, clinics can collaborate in creating a dose prediction model by submitting their treatment plans into a remote computer system (such as a cloud-based system) which aggregates information from various collaborators and produces a model that captures clinical information from all submitted treatment plans, and can be used to predict dose distributions (and other dose parameters) in subsequent treatment plans.