A photon source emits a flattening filter-free photon beam. A control circuit operably couples to a multi-layer multi-leaf collimator that is disposed between the photon source and a treatment area of a patient. The control circuit automatically arranges operation of some, but not all, of the layers of the multi-layer multi-leaf collimator to serve as a virtual flattening filter with respect to the flattening filter-free photon beam emitted by the photon source. By one approach, another of the layers of the multi-layer multi-leaf collimator serves to form a treatment aperture corresponding to a shape of the treatment area of the patient. By one approach the control circuit comprises an integral part of a treatment platform (as versus a dedicated treatment planning platform) and can carry out most or even essentially all of the planning steps that lead to administration of the treatment to a patient.

The disclosure relates to a system and method for adapt a treatment plan. The method may include: obtaining an initial treatment plan of a region of interest, wherein the initial treatment plan includes a first initial treatment fraction and a second initial treatment fraction; causing a radiation treatment device to deliver the first initial treatment fraction; obtaining a treatment record related to the first initial treatment fraction; and generating an updated second treatment fraction based on the second initial treatment fraction and the treatment record.

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.

Systems and methods are disclosed for monitoring anatomic position of a human subject and modifying a radiotherapy treatment based on anatomic position changes, as determined with a regression model trained to estimate movement of a region of interest. Example operations for movement monitoring and therapy control include: obtaining 3D image data for a subject, which provides a reference volume and at least one defined region of interest; obtaining real-time 2D image data corresponding to the subject, captured during the radiotherapy treatment session; extracting features from the 2D image data; producing a relative motion estimation of a region of interest with a machine learning regression model, the model trained to estimate a spatial transformation from the 2D image data based on training from the reference volume; and controlling a radiotherapy beam of a radiotherapy machine used in the radiotherapy session, based on the relative motion estimation.

A radiation delivery system that includes a gantry to extend along one or more axes. The gantry is to provide a continuous rotation. The radiation delivery system includes a linear accelerator (LINAC) coupled to the gantry. The LINAC is to generate a treatment beam. The radiation delivery system includes a rotary joint coupled to the gantry. The rotary joint provides a physical connection from the LINAC to an external system that is positioned off the gantry. The physical connection is to transport radio frequency (RF) power.

The invention comprises a method and apparatus for reducing a kinetic energy of positively charged particles, comprising the steps of: (1) transporting the positively charged particles from an accelerator into an exit nozzle system along a beam line; (2) providing a first chamber of the exit nozzle system, the first chamber comprising: an incident side comprising an incident aperture, an exit side comprising an exit aperture, and a beam path of the positively charged particles from the incident aperture to the exit aperture; (3) filling the beam path in the chamber with a liquid; and (4) using the liquid to reduce the kinetic energy of the positively charged particles. The kinetic energy dissipater is optionally used in combination with a proton therapy cancer treatment system and/or a proton tomography imaging system.

A method and apparatus for radiation therapy using functional measurements of branching structures. The method includes determining a location of each voxel of a plurality of voxels in a reference frame of a radiation device. The method further includes obtaining measurements that indicate a tissue type at each voxel. The method further includes determining a subset of the voxels based on an anatomical parameter of a respective branching structure of a set of branching structures indicated by the measurements. The method further includes determining a subset of the voxels that enclose an organ-at-risk (OAR) volume. The method further includes determining a value of a utility measure at each voxel. The method further includes determining a series of beam shapes and intensities which minimize a value of an objective function based on a computed dose delivered to each voxel and the utility measure for that voxel summed over all voxels.

A method of determining treatment geometries for a radiotherapy treatment includes providing a patient model having one or more regions of interest (ROIs); defining a delivery coordinate space (DCS); for each beam's eye view (BEV) plane of each vertex in the DCS, and for each ROI, evaluating a dose of the ROI using transport solutions; evaluating a BEV scores of each pixel of the BEV plane using the doses of the one or more ROIs; determining one or more BEV regions in the BEV plane based on the BEV scores; determining a BEV region connectivity manifold based on the BEV regions; determining a set of treatment trajectories based on the BEV region connectivity manifold; and determining one or more IMRT fields. Each treatment trajectory defines a path through a set of vertices in the DCS. Each IMRT field defines a direction of incidence corresponding to a vertex in the DCS.

A computer-implemented method for a radiation therapy system includes: acquiring a first X-ray image of a region while the region is in a first location, the gantry is in a first imaging position, and a center axis of an imaging beam passes through an isocenter of the radiation therapy system along a first imaging path; acquiring a second X-ray image of the region while the region of patient anatomy is in the first location, the gantry is in a second imaging position, and the center axis of the imaging beam passes through the isocenter along a second imaging path, wherein an angle between the first imaging path and the second imaging path is a non-orthogonal angle; and based on the first X-ray image, the second X-ray image, and a three-dimensional treatment planning image of the region, determining an offset between a planning location for the region and the first location.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for segmenting a medical image. In one aspect, a method comprises: receiving a medical image that is captured using a medical imaging modality and that depicts a region of tissue in a body; and processing the medical image using a segmentation neural network to generate a segmentation output. The segmentation neural network can include a sequence of multiple encoder blocks and a decoder subnetwork. Training the segmentation neural network can include determining a set of error values for a segmentation channel; identifying the highest error values from the set of error values for the segmentation channel; and determining a segmentation loss based on the highest error values identified for the segmentation channel.