Abstract: A control circuit optimizes a radiation treatment plan for a patient treatment volume using an automatically iterating optimization process that optimizes as a function, at least in part, of predetermined cost functions, wherein at least one of the cost functions favors apertures for the multi-leaf collimation system having local curvature that deviates only minimally from a reference curvature. By one approach the control circuit determines the reference curvature as a function, at least in part, of at least one of setting the reference curvature to a static minimal local curvature, a shape of a projective mapping of the treatment volume onto an isocenter plane, and/or a fluence map associated with an amount of radiation to be administered to the treatment volume from a particular direction. By one approach the control circuit dynamically determines when to employ one or more such cost functions.

Abstract: A multi-layer multi-leaf collimation system includes at least a two layers of collimation leaves. The first multi-leaf collimator layer is configured to primarily perform a first function to affect a radiation beam traveling from a radiation source to a target and a second multi-leaf collimator layer is configured to primarily perform a second function, different from the first function, to affect the radiation beam for the administration of a treatment plan.

Abstract: A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

Abstract: A multi-layer multi-leaf collimation system includes at least a two layers of collimation leaves. The first multi-leaf collimator layer is configured to primarily perform a first function to affect a radiation beam traveling from a radiation source to a target and a second multi-leaf collimator layer is configured to primarily perform a second function, different from the first function, to affect the radiation beam for the administration of a treatment plan.

Abstract: A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

Abstract: A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm.

Abstract: Deep learning approaches automatically segment at least some breast tissue images while non-deep learning approaches automatically segment organs-at-risk. Both three-dimensional CT imaging information and two-dimensional orthogonal topogram imaging information can be used to determine virtual-skin volume. The foregoing imaging information can also serve to automatically determine a body outline for at least a portion of the patient. That body outline, along with the virtual-skin volume and registration information can serve as inputs to automatically calculate radiation treatment platform trajectories, collision detection information, and virtual dry run information of treatment delivery per the optimized radiation treatment plan.

Abstract: A control circuit accesses topograms of a patient that include patient content that is beyond the portion of the patient that appears in the three-dimensional computed tomography (CT) images for that patient. The control circuit uses those topograms to derive a virtual volumetric structure representing at least some of the patient content that is beyond the aforementioned portion of the patient that appears in the 3D CT images. That virtual volumetric structure can then be used to predict potential collisions when assessing a radiation treatment plan for the patient that utilizes the aforementioned radiation treatment platform. By one approach the topograms include at least two substantially orthographic views of the aforementioned patient content.

Abstract: An apparatus for use in a treatment planning process or in a treatment process, includes: an input for obtaining a parameter representing a number of beam on-off transitions; and a treatment planner configured to optimize a treatment plan based on parameter representing the number of beam on-off transitions. An apparatus includes: an input configured to obtain a width of a gating window for a treatment plan; and a gating window adjustor configured to adjust the width of the gating window during a treatment session. An apparatus includes: a dose calculator configured to calculate doses for different treatment variations; an evaluator configured to evaluate treatment acceptance criteria against the calculated doses; and a delivery limit module configured to determine one or more limits for one or more delivery parameters based on an evaluation of the treatment acceptance criteria by the evaluator.

Abstract: A control circuit accesses historical information regarding previously optimized radiation treatment plans for different patients and processes that information to determine the relative importance of different clinical goals. The circuit then facilitates development of a particular plan for a particular patient as a function of the relative importance of the clinical goals. By one approach the control circuit can be configured as a radiation treatment plan recommendation resource that accesses a database of radiation treatment plan formulation content items including at least one of a radiation treatment plan template, an auto-planning algorithm, and an auto-segmentation algorithm.

Abstract: A control circuit forms a radiation therapy treatment plan by automatically generating a base dose that references dosing information from multiple sources and then using that base dose to optimize a radiation therapy treatment plan. That radiation therapy treatment plan is then used to administer radiation therapy to a patient. That automatically generated base dose can represent any or all of earlier radiation therapy treatments for the patient, a same fraction as a dose presently being optimized per the radiation therapy treatment plan, and future planned fractions for the patient.

Abstract: An apparatus for creating a radiation treatment plan for execution by a radiation treatment machine, includes: a waypoint module configured to obtain imaging waypoint data representing imaging waypoints, the imaging waypoints at least partially defining one or more positions for obtaining images of a patient during a treatment session; a treatment trajectory module configured to obtain treatment data at least partially defining a beam-on direction; a treatment plan generator configured to create the radiation treatment plan based at least in part on the imaging waypoint data and the treatment data.

Abstract: System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.

Abstract: A control circuit accesses topograms of a patient that include patient content that is beyond the portion of the patient that appears in the three-dimensional computed tomography (CT) images for that patient. The control circuit uses those topograms to derive a virtual volumetric structure representing at least some of the patient content that is beyond the aforementioned portion of the patient that appears in the 3D CT images. That virtual volumetric structure can then be used to predict potential collisions when assessing a radiation treatment plan for the patient that utilizes the aforementioned radiation treatment platform. By one approach the topograms include at least two substantially orthographic views of the aforementioned patient content.

Abstract: Deep learning approaches automatically segment at least some breast tissue images while non-deep learning approaches automatically segment organs-at-risk. Both three-dimensional CT imaging information and two-dimensional orthogonal topogram imaging information can be used to determine virtual-skin volume. The foregoing imaging information can also serve to automatically determine a body outline for at least a portion of the patient. That body outline, along with the virtual-skin volume and registration information can serve as inputs to automatically calculate radiation treatment platform trajectories, collision detection information, and virtual dry run information of treatment delivery per the optimized radiation treatment plan.

Abstract: System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.

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: A radiation-treatment plan that comprises a plurality of dose-delivery fractions can be optimized by using fraction dose objectives and at least one other, different dose objective. This use of fraction dose objectives can comprise accumulating doses delivered in previous dose-delivery fractions. The other, different dose objective can comprise a remaining total dose objective, a predictive dose objective, or some other dose objective of choice. An existing radiation-treatment plan having a corresponding resultant quality and that is defined, at least in part, by at least one delivery parameter can be re-optimized by specifying at least one constraint as regards that delivery parameter as a function, at least in part, of that resultant quality and then applying that constraint when re-optimizing the existing radiation-treatment plan.

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 control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.