Abstract: A prescribing user can designate and prioritize clinical goals that can, in turn, serve as the basis for optimization objectives to guide the development of a radiation treatment plan. The prescribing user can then alter that prioritization of one or more clinical goals and view information regarding changes to fluence-based dose distributions that occur in response to those changes to relative prioritization amongst the clinical goals to thereby understand dosing tradeoffs that correspond to prioritization amongst the clinical goals.

Abstract: Image information regarding a particular patient is provided, which image information includes, at least in part, a tumor to be irradiated. These teachings can also include providing non-image clinical information that corresponds to the particular patient. A control circuit accesses the foregoing image information and non-image clinical information and automatically generates a clinical target volume that is larger than the tumor as a function of both the image information and the non-image clinical information. The control circuit can then generate a corresponding radiation treatment plan based upon that clinical target volume, which plan can be utilized to irradiate the clinical target volume.

Abstract: At least one example embodiment provides a method including obtaining a first image, the first image including a light field on a patient, the light field being generated by a treatment system; obtaining a treatment plan outline, the treatment plan outline including an area of the patient for a treatment; determining positioning information based on the first image and the treatment plan outline, the positioning information including an indicator of a position of the treatment system with respect to the patient; and controlling the treatment system based on the positioning information.

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: At least one example embodiment provides a method including obtaining a surface of a patient, obtaining first treatment information for the patient, the first treatment information associated with a treatment for the patient, the first treatment information corresponding to at least one of a treatment intent for the patient, a treatment plan for the patient or a structure of the patient, obtaining at least one model based on the first treatment information for the patient and determining a region of interest of the patient based on the surface of the patient and the at least one model.

Abstract: At least one example embodiment provides a method including obtaining surface information of a patient using a camera; determining a motion of the patient based on the surface information; and providing an indicator of a region of interest (ROI) of the patient based on the motion.

Abstract: Embodiments described herein provide for radiotherapy treatment plan analysis and guidance using voice commands and human language processing. A processor monitors a computer model generating a radiation therapy treatment plan comprising a projected dose-volume distribution for a structure of a patient or an attribute of the radiation therapy treatment plan. The processor receives a query indicating the structure of the patient. The processor generates a query term based on the received query. The processor retrieves data associated with the query term by querying data generated based on monitoring the computer model. The processor transmits the retrieved data to computer device.

Abstract: A computer-implemented method of generating dose information for a region of patient anatomy includes determining a first set of dose values for a first three-dimensional (3D) image of the region, wherein each value in the first set of dose values is associated with a different voxel of the first 3D image, and wherein the first 3D image is associated with a specific application of dose to the region; determining a second set of dose values for a representative 3D image of the region, wherein each value in the second set of dose values is associated with a different voxel of the representative 3D image; determining a set of geometric error models for the representative 3D image of the region, wherein each geometric error model in the set of geometric error models indicates a geometric error between a voxel of the representative 3D image and one or more voxels of a treatment fraction 3D image of the region; and based on the second set of dose values and the set of geometric error models, determining a set of do

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: Provided herein are methods and systems to train and execute a motion model that uses artificial intelligence methodologies (e.g., deep-learning) to learn and predict location of a patient's internal structures. A method comprises receiving respiratory data of a patient from an electronic sensor in addition to a medical image, such as kV image; executing an artificial intelligence model using the respiratory data and predicting deformation data for at least one internal structure of the patient, wherein the artificial intelligence model is trained in accordance with a training dataset comprising a set of participants, their corresponding respiratory data, and their corresponding deformation data; and outputting the predicted deformation data.

Abstract: Disclosed herein are systems and methods for identifying radiation therapy treatment data for patients. A processor accesses a neural network trained based on a first set of data generated from characteristic values of a first set of patients that received treatment at one or more first radiotherapy machines. The processor executes the neural network using a second set of data comprising characteristic values of a second set of patients receiving treatment at one or more second radiotherapy machines. The processor executes a calibration model using an output of the neural network based on the second set of data to output a calibration value. The processor executes the neural network using a set of characteristics of a first patient to output a first confidence score associated with a first treatment attribute. The processor then adjusts the first confidence score according to the calibration value to predict the first treatment attribute.

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: Radiation treatment planning includes accessing values of parameters such as a number of beams to be directed into sub-volumes in a target, beam directions, and beam energies. Information that specifies limits for the radiation treatment plan are accessed. The limits include a limit on irradiation time for each sub-volume outside the target. Other limits can include a limit on irradiation time for each sub-volume in the target, a limit on dose rate for each sub-volume in the target, and a limit on dose rate for each sub-volume outside the target. The values of the parameters are adjusted until the irradiation time for each sub-volume outside the target satisfies the maximum limit on irradiation time.

Abstract: After accessing optimization information for a particular patient and for a particular radiation treatment platform, a control circuit generates an optimized radiation treatment plan by processing the optimization information using direct-aperture-optimization that includes fluence-based sub-optimization. By one approach, the control circuit includes the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization. By one approach, the control circuit is configured to include only a few iterations of the fluence-based sub-optimization when including the fluence-based sub-optimization in at least some, but not necessarily all, iterations of the direct-aperture-optimization.

Abstract: Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques. With each iteration, a training agent applies a random radiation therapy treatment attribute corresponding to the radiation therapy treatment attribute associated with previously performed radiation therapy treatments when an epsilon value indicative of a likelihood of exploration and exploitation training of the artificial intelligence model satisfies a threshold. When the epsilon value does not satisfy the threshold, the agent generates, using an existing policy, a first predicted radiation therapy treatment attribute, and generates, using a predefined model, a second predicted radiation therapy treatment attribute. The agent applies one of the first predicted radiation therapy treatment attribute or the second predicted radiation therapy treatment attribute that is associated with a higher reward.

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: A system for treating a patient during radiation therapy is disclosed. The system includes a shell, a plurality of material inserts disposed in the shell, where each material insert of the plurality of material inserts respectively shapes a distribution of a dose delivered to the patient by a respective beam of a plurality of beams emitted from a nozzle of a radiation treatment system, and a scaffold component disposed in the shell that holds the plurality material inserts in place relative to the patient such that each material insert lies on a path of at least one of the beams.

Abstract: Disclosed herein are systems and methods for identifying radiation therapy treatment data for patients. A processor accesses a neural network trained based on a first set of data generated from characteristic values of a first set of patients that received treatment at one or more first radiotherapy machines. The processor executes the neural network using a second set of data comprising characteristic values of a second set of patients receiving treatment at one or more second radiotherapy machines. The processor executes a calibration model using an output of the neural network based on the second set of data to output a calibration value. The processor executes the neural network using a set of characteristics of a first patient to output a first confidence score associated with a first treatment attribute. The processor then adjusts the first confidence score according to the calibration value to predict the first treatment attribute.

Abstract: Information associated with a radiation treatment plan includes, for example, values of dose per voxel in a target volume, values of dose rate per voxel in the target volume, and values of parameters used when generating the values of dose per voxel and the values of dose rate per voxel. Renderings that include, for example, a rendering of an image of or including the target volume, and a rendering of selected values of the radiation treatment plan, are displayed. When a selection of a region of one of the renderings is received, a displayed characteristic of another one of the renderings is changed based on the selection.

Abstract: Systems and methods for radiation treatment planning can include a computing system determining an estimate of radiation dose distribution within an anatomical region of a patient, and determining a cost matrix representing an objective function, using the estimate of radiation dose distribution. The computing system can project the cost matrix on each of a plurality of fluence planes. Each of the plurality of fluence planes can be associated with a corresponding gantry-couch orientation of a plurality of gantry-couch orientations of a medical linear accelerator. The computing system can determine, using projections of the cost matrix on each of the plurality of fluence planes, a sequence of gantry-couch orientations among the plurality of gantry-couch orientations representing a treatment path.