Abstract: A cost function is constructed so as to guide an optimization process to achieve similar coverage for all targets simultaneously in a concurrent radiation treatment of multiple targets, so that a single scaling factor may be used in a plan normalization to achieve the desired coverage for all the targets. The cost function includes a component that favors a solution that attains similar target coverages for all targets, as well as a component that favors a solution that approaches the desired target coverage value for each individual target. The cost function includes a max term relating to deficiencies of actual target coverages with respect to a desired target coverage, or alternatively a soft-max term relating to deviations of actual target coverages with respect to an average target coverage, as well as to deficiencies of actual target coverages with respect to a desired target coverage.

Abstract: In radiation therapy, treatment objectives applicable to different identified structures in a patient's body (e.g., a target structure and an organ at risk (OAR)) may conflict due to overlap between the structures. Automated systems and methods can detect and resolve such conflicts. For example, a set of modified regions that do not overlap with each other can be defined, and a modified treatment objective for each modified region can be determined based on the original treatment objectives. The modified regions and modified treatment objectives can be used in treatment planning processes.

Abstract: An apparatus for use in a medical process that involves a particle accelerator, includes: a processing unit configured to obtain medical information, obtain a viewing direction of a user of the apparatus, and process the medical information based on the viewing direction of the user of the apparatus to create a graphical representation of the medical information for presentation to the user of the apparatus; and a screen for displaying the graphical representation.

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: A treatment planning apparatus include: a quality metric generator configured to electronically generate a first quality metric based on input received via a user interface; an optimizer configured to determine treatment parameters for a treatment plan that is executable by a processing unit of a treatment machine to operate the treatment machine, wherein the optimizer is configured to determine the treatment parameters for the treatment plan based on the first quality metric; and a controller configured to influence how the optimizer performs optimization, wherein the controller is configured to provide a first continuous function for the first quality metric.

Abstract: A treatment planning apparatus includes: a modeler configured to obtain a model definition, wherein the model definition comprises a first quality metric of a first clinical goal; and a treatment planner having: a model trainer configured to obtain a set of existing treatment plans following desired clinical practice, and to perform model training to obtain a trained model based on the existing treatment plans and the first quality metric of the first clinical goal; an objective generator configured to generate a cost function based on the trained model; and an optimizer configured to determine a treatment plan based on the cost function.

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: A cost function is constructed so as to guide an optimization process to achieve similar coverage for all targets simultaneously in a concurrent radiation treatment of multiple targets, so that a single scaling factor may be used in a plan normalization to achieve the desired coverage for all the targets. The cost function includes a component that favors a solution that attains similar target coverages for all targets, as well as a component that favors a solution that approaches the desired target coverage value for each individual target. The cost function includes a max term relating to deficiencies of actual target coverages with respect to a desired target coverage, or alternatively a soft-max term relating to deviations of actual target coverages with respect to an average target coverage, as well as to deficiencies of actual target coverages with respect to a desired target coverage.

Abstract: A control circuit has access to information that correlates a plurality of patient-experience outcomes with corresponding delivered-radiation metrics and uses that plurality of patient-experience outcomes in conjunction with developing a radiation-therapy treatment plan for a particular patient. Those patient-experience outcomes can comprise, for example, information regarding how various patients fared post-radiation treatment as regards such things as post-radiation treatment life expectancy, tumor eradication success, and one or more other quality-of-life metrics. These teachings will accommodate using a particular one of the patient-experience outcomes as an optimization goal when optimizing candidate radiation-therapy treatment plans to develop the radiation-therapy treatment plan. In such a case these teachings will further accommodate, if desired, permitting a user to modify one or more optimization objectives while displaying intermediate optimization results.

Abstract: A cost function is constructed so as to guide an optimization process to achieve similar coverage for all targets simultaneously in a concurrent radiation treatment of multiple targets, so that a single scaling factor may be used in a plan normalization to achieve the desired coverage for all the targets. The cost function includes a component that favors a solution that attains similar target coverages for all targets, as well as a component that favors a solution that approaches the desired target coverage value for each individual target. The cost function includes a max term relating to deficiencies of actual target coverages with respect to a desired target coverage, or alternatively a soft-max term relating to deviations of actual target coverages with respect to an average target coverage, as well as to deficiencies of actual target coverages with respect to a desired target coverage.

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 radiation-delivery treatment plan includes a first value for a limit as pertains to motion compensation-based adjustment of a radiation-delivery parameter during a first portion of a radiation treatment session as well as a second value for that same limit during a second, different portion of the treatment session. By one approach, the radiation-delivery treatment plan selects between this first and second value as a function of a preselected parameter. Examples of such parameters include, but are not limited to, radiation-beam orientation parameters (such as a gantry-based parameter) and/or any of a variety of external surrogates.

Abstract: In radiation therapy, treatment objectives applicable to different identified structures in a patient's body (e.g., a target structure and an organ at risk (OAR)) may conflict due to overlap between the structures. Automated systems and methods can detect and resolve such conflicts. For example, a set of modified regions that do not overlap with each other can be defined, and a modified treatment objective for each modified region can be determined based on the original treatment objectives. The modified regions and modified treatment objectives can be used in treatment planning processes.

Abstract: An apparatus for use in a medical process that involves a particle accelerator, includes: a processing unit configured to obtain medical information, obtain a viewing direction of a user of the apparatus, and process the medical information based on the viewing direction of the user of the apparatus to create a graphical representation of the medical information for presentation to the user of the apparatus; and a screen for displaying the graphical representation.

Abstract: A cost function is constructed so as to guide an optimization process to achieve similar coverage for all targets simultaneously in a concurrent radiation treatment of multiple targets, so that a single scaling factor may be used in a plan normalization to achieve the desired coverage for all the targets. The cost function includes a component that favors a solution that attains similar target coverages for all targets, as well as a component that favors a solution that approaches the desired target coverage value for each individual target. The cost function includes a max term relating to deficiencies of actual target coverages with respect to a desired target coverage, or alternatively a soft-max term relating to deviations of actual target coverages with respect to an average target coverage, as well as to deficiencies of actual target coverages with respect to a desired target coverage.

Abstract: An apparatus for use in a medical process that involves a particle accelerator, includes: a processing unit configured to obtain treatment plan information, obtain a viewing direction of a user of the apparatus, and process the treatment plan information based on the viewing direction of the user of the apparatus to create a graphical representation of the treatment plan information for presentation to the user of the apparatus; and a screen for displaying the graphical representation.

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: 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: A control circuit provides a user with an opportunity to designate one or more patient structures for a particular patient that are to be protected from radiation. When optimizing a radiation-treatment plan for that patient, then, the control circuit uses the designated patient structure(s) as an area to be masked from radiation during administration of the radiation-treatment plan. By one approach the aforementioned opportunity to designate patient structures as being designated patient structures comprises providing the user with an opportunity to so designate patient structures via a display. The aforementioned masking can be accomplished as desired including by appropriate use of a multi-leaf collimator.

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.