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.

A virtual beam's-eye view of a planning target volume is generated based on volumetric image data acquired immediately prior to radiation therapy by a radiation therapy system. The virtual beam's-eye view can then be displayed to confirm that, with the patient disposed in the current position, the planned beam-delivered treatment extends beyond the surface of the skin. In some embodiments, the virtual beam's-eye view can be displayed in conjunction with a beam's-eye view that is generated based on volumetric image data acquired during treatment planning, to create a blended beam's-eye view. In some embodiments, a field outline of a treatment beam can be superimposed on the blended beam's-eye view, thereby illustrating whether the planned beam-delivered treatment extends beyond the surface of the skin of the patient. The blended beam's-eye view can facilitate a manual confirmation process that verifies the planned beam-delivered treatment extends beyond the surface of the skin.

Systems and methods are disclosed for performing operations comprising: receiving first and second images depicting an anatomy of a subject; obtaining a segmentation associated with the first image; applying a trained neural network to estimate the adapted segmentation corresponding to the anatomy depicted in the second image, the trained network consisting of three sub-networks: a registration sub-network, generating an initial segmentation estimate representing a deformation of the segmentation associated with the first image to fit the anatomy depicted in the second image, a segmentation sub-network, generating a second initial segmentation estimate for the second image, and a third refinement sub-network, combining the two initial segmentations and generating a refined segmentation for the second image.

Particle radiation therapy and planning utilizing magnetic resonance imaging (MRI) data. Radiation therapy prescription information and patient MRI data can be received and a radiation therapy treatment plan can be determined for use with a particle beam. The treatment plan can utilize the radiation therapy prescription information and the patient MRI data to account for interaction properties of soft tissues in the patient through which the particle beam passes. Patient MRI data may be received from a magnetic resonance imaging system integrated with the particle radiation therapy system. MRI data acquired during treatment may also be utilized to modify or optimize the particle radiation therapy treatment.

Methods for dose or treatment planning for a radiotherapy system including a radiotherapy unit are provided. A spatial dose delivered can be changed by adjusting beam shape settings, and the delivered radiation is determined using an optimization problem that steers the delivered radiation according to objectives reflecting criteria for regions of interest including at least one of: targets to be treated during treatment of the patient, organs at risk and/or healthy tissue. The method includes determining an inner set of voxels and providing a first frame description for the inner set of voxels, where the first frame description reflects criteria for the inner set of voxels. Determining an outer set of voxels encompassing the target volume and the inner set of voxels and a frame description for the outer set of voxels is provided where each reflecting criteria for the outer set of voxels. The frame descriptions are then used in the optimization problem that steers the delivered radiation.

A multi-mode cone beam computed tomography radiotherapy simulator and treatment machine is disclosed. The radiotherapy simulator and treatment machine both include a rotatable gantry on which is positioned a cone-beam radiation source and a flat panel imager. The flat panel imager captures x-ray image data to generate cone-beam CT volumetric images used to generate a therapy patient position setup and a treatment plan.

Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.

An imaging marker comprises a flexible, axially-elongated line-shaped marker, an underlying adhesive, and a flexible, axially-elongated foam spacer extending between an underside of the line-shaped marker and the adhesive. The line-shaped marker is partially radiopaque, partially radiolucent, or is radiopaque, and the foam spacer and adhesive are radiolucent at the levels of radiation used during an imaging procedure. The marker and foam spacer flex and substantially conform to a curvilinear contour of the skin, follow the curvilinear contour of a scar or other anatomical feature on the skin, and prevent forces exerted thereon in flexibly conforming to and following the curvilinear counters from detaching the adhesive from the skin during the imaging procedure.

An implantable device has a body that is substantially rigid and has a predetermined shape. The body is further bioabsorbable and has a density less than or equal to about 1.03 g/cc. When the device is implanted in a resected cavity in soft tissue, it causes the cavity to conform to the predetermined shape. The implantable device is further imageable due to its density being less than that of soft tissue such that the boundaries of the tissue corresponding to the predetermined shape can be determined.

In one embodiment, a method includes receiving treatment information relating to a treatment plan for proton- or ion-beam therapy intended to irradiate a target tissue; receiving machine-limitation information relating to one or more limitations of one or more machines involved in the proton- or ion-beam therapy; determining a time-optimized beam current for a proton or ion beam based on the treatment information and the machine-limitation information, wherein the time-optimized beam current minimizes the time required to deliver a required quantity of monitor units to one of a plurality of spots, wherein each of the plurality of spots is a particular area of the target tissue; and delivering the time-optimized beam current to the particular area.