These teachings facilitate the administration of therapeutic radiation to a heterogeneous patient volume using a radiation beam source. More particularly, these teachings provide for determining a cross-sectional size of a radiation beam as corresponds to that radiation beam source and also for determining density information corresponding to the aforementioned heterogeneous body. These teachings then provide for generating a three-dimensional radiation dose calculation for the heterogeneous body using a control circuit configured as a convolution/superposition based dose calculator using a three-dimensional energy-spreading kernel. By one approach, these teachings provide for the calculator scaling total energy released per mass as a function of the cross-sectional size and energy of the radiation beam and the aforementioned density information.

The present disclosure relates to the field of radiotherapy, in particular, methods of and apparatus for treating cancer using ultra-high dose rate radiotherapy (FLASH). The apparatus may comprise a device configured to administer to the subject no more than five fractions of proton ultra-high dose rate radiotherapy (FLASH), said fractions having a range of radiation from 1.5 Gy to 60 Gy, collectively. The device may be configured such that the treatment is sufficient to prevent further growth of the tumor for at least 10% longer than standard of care radiotherapy, induce at least 10% more tumor regression than standard of care radiotherapy and/or delay tumor regrowth by at least about 2 months longer than standard of care radiotherapy.

A base dose calculation tool for determining a base dose employed in treatment planning. Base therapy information is considered on a voxelized and temporal basis such that subsequent treatments may be planned. The base dose output is operable with existing treatment planning systems.

A radiation treatment session is initiated to deliver a therapeutic radiation beam from a therapeutic radiation source to a target. One or more X-ray radiation sources are caused to deliver an imaging radiation beam from the one or more X-ray radiation sources through the target to one or more X-ray detectors to acquire imaging data associated with the target during therapeutic radiation beam delivery. One or more volumetric images are constructed using the acquired imaging data.

Methods and systems are provided which relate to the planning and delivery of radiation treatments by modalities which involve moving a radiation source along a trajectory relative to a subject while delivering radiation to the subject. An artificial intelligence (AI) agent trained using reinforcement learning (and/or some other suitable form of machine learning) is used to control the radiation delivery parameters in effort to achieve desired delivery of radiation therapy. In some embodiments, the AI agent selects suitable control steps (e.g. radiation delivery parameters for particular time steps), while accounting for patient motions, difference(s) in patient anatomical geometry and/or the like.

Apparatus for improving image guidance in radiation therapy applications are described. In one aspect, a patient support with integrated radiopaque markers is described. In another aspect, a bite block with integrated sensors, radiopaque markers, or both, is described. The integrated sensors may include a radiation detector, a pH sensor, or both. Radiation detectors that may be used include a dosimeter. As an example, the dosimeter may include a film dosimeter, an ion chamber dosimeter, a diode dosimeter, or other suitable dosimeters and combinations thereof.

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.

An example method of radiation therapy in a radiation therapy system that includes a gantry with a treatment-delivering X-ray source and an imaging X-ray source mounted thereon is described. The method includes rotating the gantry in a first direction at a first rotational velocity about an open bore and concurrently rotating an annular support structure at a second rotational velocity about the open bore, wherein the second rotational velocity is less than the first rotational velocity. While continuing to rotate the gantry in the first direction about the open bore from a first position to a treatment position, the method also includes generating multiple images of a target volume disposed in the bore using the imaging X-ray source. Upon rotating the gantry to the treatment position, the method includes initiating delivery of a treatment beam to the target volume with the treatment-delivering X-ray source.

For planning radiation treatment using spot scanning, also known as pencil beam scanning, the size and/or shape of the treatment target is considered when determining the placement and density of spots in the treatment target. For example, when generating a radiation treatment plan, the size and/or shape of the treatment target can be considered when determining the placement and density of spots in the treatment target. During treatment planning, the treatment target can be separated into regions corresponding to different target volumes in the treatment target, and the placement and density of spots (the amount of spacing between spots) in each region can be determined independently for each region according to the size and/or shape of the region.

A scanning candidate route extracting unit which extracts plural candidates of scanning routes in which each of the scanning routes connects all spot positions in one layer is provided, in an evaluation function using necessary scanning time Tk and weight coefficient wk for a kth partial route among partial routes which are routes between the spot positions which are adjacent on one of the plural candidates of scanning routes, and number n of spot in the layer, wk with respect to a partial route which passes through affected tissue is set to be 1, wk with respect to a partial route which passes through normal tissue is set to be bigger than 1, and wk with respect to a partial route which passes through an important internal organ is set to be bigger than wk with respect to a partial route which passes through normal tissue.