Abstract: A method of trajectory optimization for radiotherapy treatment includes providing a patient model having one or more regions of interest (ROIs), defining a delivery coordinate space (DCS), for each ROI, solving an adjoint transport to obtain an adjoint solution field from the ROI, for each vertex in the DCS, evaluating an adjoint photon fluence by performing ray tracing of the adjoint solution field, evaluating a dose of the ROI using the adjoint photon fluence, for each vertex in the DCS, evaluating a respective beam's eye view (BEV) score of each pixel of a 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, and determining one or more optimal treatment trajectories based on the BEV region connectivity manifold.

Abstract: These teachings serve to facilitate radiating a treatment target in a patient during a radiation treatment session with a radiation treatment platform having a moving source of radiation and using an optimized radiation treatment plan. These teachings in particular provide for configuring the radiation treatment platform in a half-fan trajectory arrangement. These teachings then provide for beginning the radiation treatment session with the source of radiation in a first location and an isocenter for the treatment target in a first position. Then, during the radiation treatment session, these teachings provide for moving the source of radiation from that first location in synchronization with moving the isocenter from the aforementioned first position.

Abstract: A computer-implemented method of performing a treatment fraction of radiation therapy comprises: determining a current position of a target volume of patient anatomy; based on the current position of the target volume, computing an accumulated dose for non-target tissue proximate the target volume; determining that the accumulated dose is less than a current value for a dose budget of the non-target tissue; and in response to the accumulated dose being less than the current value for the dose budget, applying a treatment beam to the target volume while the target volume is in the current position.

Abstract: 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.

Abstract: A radiation therapy system includes an X-ray imaging system that is configured with a combined and simplified filter and collimator positioning mechanism. In addition, an X-ray imager of the RT system is only positioned at a few discrete locations within a plane that is a fixed distance from the imaging X-ray source when generating X-ray images. As a result, for each of these discrete imaging positions, the simplified filter and collimator positioning mechanism positions a specific collimator-filter combination in a specific location between the X-ray source and the imager.

Abstract: A radiation therapy system is configured with fast readout of X-ray images with significantly reduced image lag. A reset phase is included in the process of acquiring an X-ray image to reduce image lag in a subsequently acquired X-ray image. During the reset phase, residual charge is concurrently transferred from multiple arrays of pixel detector elements in an X-ray detector panel. As a result, image lag present in a subsequent X-ray image is minimized or otherwise reduced.

Abstract: Systems, devices, and methods for imaging-based calibration of radiation treatment couch position compensations.

Abstract: A technology enables interactive control of simultaneously displayed multiple images with high dynamic ranges, which software automation processes are programmed to reduce the complexity in managing and viewing the post window/level adjustment of the multiple images. An image control engine provides several synchronous functional capabilities, which comprises an input module, a blending factor synchronization module, a window/level synchronization module, a display module, and an image storage. For window/level adjustment of the images in blended views, the blending factor synchronization module automatically links the activation of a window/level control of one image with a transparency blending factor that affects both images.

Abstract: A cone-beam computed tomography (CBCT) method uses a continuous beam and an area detector to carry out fast acquisition of projection data. The acquired projection data are then reconstructed to generate tomographic images. In acquisition of the projection data, a radiation source continuously irradiates a subject with a cone beam of radiation from a plurality of angles and an area detector continuously reads out data. A CBCT system including a source operable to produce a cone beam of radiation and an area detector movable in synchrony with the source to rapidly acquire projection data for CBCT construction is also disclosed.

Abstract: 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.

Abstract: Cost functions and cost function gradients for use in radiation treatment planning can be computed based on an approximation of an “isodose” surface. Where a clinical goal is expressed by reference to a threshold isodose surface, a corresponding cost function component can be defined directly by reference to that isodose surface, and a corresponding contribution to the cost function gradient can be approximated by identifying voxels that are intersected by the threshold isodose surface and approximating the gradient of the dose distribution within each such voxel.

Abstract: Systems and methods for the automatic generation and optimization of radiation therapy treatment plans, and systems and methods for the automatic generation and optimization of an adapted plan in an adaptive radiation therapy workflow.

Abstract: Systems, devices, and methods for quality assurance for verification of radiation dose delivery in arc-based radiation therapy devices using a 3D gamma evaluation method.

Abstract: Example methods and systems for deep transfer learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining (310) a base deep learning engine that is pre-trained to perform a base radiotherapy treatment planning task; and based on the base deep learning engine, generating a target deep learning engine to perform a target radiotherapy treatment planning task. The target deep learning engine may be generated by configuring (330) a variable base layer among multiple base layers of the base deep learning engine, and generating (340) one of multiple target layers of the target deep learning engine by modifying the variable base layer. Alternatively or additionally, the target deep learning engine may be generated by configuring (350) an invariable base layer among the multiple base layers, and generating (360) one of multiple target layers of the target deep learning engine based on feature data generated using the invariable base layer.

Abstract: One or more medical images of a patient are processed by a first neural network model to determine a region-of-interest (ROI) or a cut-off plane. Information from the first neural network model is used to crop the medical images, which serves as input to a second neural network model. The second neural network model processes the cropped medical images to determine contours of anatomical structures in the medical images of the patient. Each of the first and second neural network models are deep neural network models. By use of cropped images in the training and inference phases of the second neural network model, contours are produced with sharp edges or flat surfaces.

Abstract: Systems and methods for determining beam asymmetry in a radiation treatment system using electronic portal imaging devices (EPIDs) without implementation of elaborate and complex EPID calibration procedures. The beam asymmetry is determined based on radiation scattered from different points in the radiation beam and measured with the same region of interest ROI of the EPID.

Abstract: A reinforcement learning agent facilitates optimization of a radiation-delivery treatment plan. The reinforcement learning agent is configured to generate a radiation-delivery treatment plan that can exceed the quality of a plan or plans employed to train the reinforcement learning agent. The reinforcement learning agent is trained to evaluate a radiation-delivery treatment plan that is output by an optimization software application, modify one or more dose-volume objective parameters of the evaluated radiation-delivery treatment plan, and then input the modified radiation-delivery treatment plan to the optimization software application for further optimization. The reinforcement learning agent adaptively adjusts the one or more dose-volume objective parameters based on an action policy learned during a reinforcement learning training process.

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: A control circuit operably couples to a memory having a plurality of different radiation treatment applications stored therein, a data store having patient data stored therein, and at least a first user interface comprising a first display and a first user input interface. The control circuit can be configured to present simultaneously, via the first display and for a given patient, at least two workspaces that each correspond to a different one of the radiation treatment applications wherein the workspaces are using patient data from the data store for the given patient. The control circuit can also be configured to present, via the user input interface, a bookmark capture opportunity, such that a user of the apparatus can selectively create a bookmark that captures a present state for both of the at least two workspaces and hence for the radiation treatment applications that correspond to the at least two workspaces.

Abstract: Systems, devices, and methods for dosimetric verification of radiation therapy treatments by selective evaluation of measurement points. Systems, methods, and computer program-products for providing dosimetric verification of radiation therapy treatments by evaluating measurement points using different evaluation criteria.