Abstract: Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

Abstract: A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Abstract: A search space used for multicriteria optimization in radiation treatment planning can be expanded using extrapolation. For instance, given an initial set of base plans, one or more virtual plans can be generated by assigning a weight to each base plan such that at least one of the weights is less than zero and/or such that the sum of the weights is not normalized to 1. The dose distribution for the virtual plan is computed as the weighted sum of the dose distributions of the base plans. Virtual plans criteria can be used together with the initial set of base plans to define an expanded search space within which interpolation can be performed to identify an optimal treatment plan.

Abstract: In accordance with at least some embodiments of the present disclosure, a process to improve computed tomography (CT) to cone beam computed tomography (CBCT) registration is disclosed. The process may include receiving a CT image generated by CT-scanning of an object, and receiving a CBCT image generated by CBCT-scanning of the object. The process may include generating an image mask based on Digital Imaging and Communications in Medicine (DICOM) information extracted from the CBCT image. For a specific pixel in the CBCT image, the image mask contains a corresponding data-field indicating whether the specific pixel contains image data generated based on the CBCT-scanning of the object. The process may further include generating a registered image by utilizing the image mask to perform a DIR between the CT image and the CBCT image.

Abstract: Techniques for the mapping, selection and import of patient medical objects can include receiving metadata of a plurality of patient medical objects from one or more data sources. Metadata icons can be generated for the received metadata of the plurality of patient medical objects. The metadata icons can be filtered based on one or more received criteria. A patient map including the filtered metadata icons can be generated. In addition, the selection of one or more metadata icons in the patient map can be received and used to determine one or more selected patient medical objects. The selected patient medical objects can thereafter be imported from the one or more data sources.

Abstract: In a method of interactive manipulation of the dose distribution of a radiation treatment plan, after an initial candidate treatment plan has been obtained, a set of clinical goals are transferred into a set of constraints. Each constraint may be expressed in terms of a threshold value for a respective quality index of the dose distribution. The dose distribution can then be modified interactively by modifying the threshold values for the set of constraints. Re-optimization may be performed based on the modified threshold values. A user may assign relative priorities among the set of constraints. When a certain constraint is modified, a re-optimized treatment plan may not violate those constraints that have priorities that are higher than that of the modified constraint, but may violate those constraints that have priorities that are lower than that of the modified constraint.

Abstract: In an x-ray imaging method, the acquisition of a signal image is split off into acquisition of two or more subimages or frames. The first subimage may be acquired with an exposure of a low dose followed by a readout cycle. The dose of the exposure for acquiring the first subimage can be chosen such that it is below the default dose for a particular anatomy. The first subimage may be used to calculate or estimate the parameters of exposure for acquiring a second or subsequent images subimage. The estimation may be such that the total dose received by the imager, in acquiring the first and second subimages, achieves an expected target value to provide an image of good quality. The first and second subimages can be combined to form the final image. A detector array supporting automatic exposure control (AEC) includes AEC pixels providing AEC signals. The AEC pixels are independently or individually addressable and/or readable.

Abstract: Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

Abstract: Methods of beam angle optimization for intensity modulated radiotherapy (IMRT) treatment include determining beam's eye view (BEV) regions and a BEV region connectivity manifold by evaluating dose response of each region of interest for each vertex in a delivery coordinate space (DCS). The information contained in the BEV regions and the BEV region connectivity manifold is used to guide an optimizer to find optimal field geometries in the IMRT treatment. To improve the visibility of insufficiently exposed voxels of planning target volumes (PTVs), a post-processing step may be performed to enlarge certain BEV regions, which are considered for exposing during treatment trajectory optimization.

Abstract: Methods of treatment trajectory optimization for radiotherapy treatment of multiple targets include determining beam's eye view (BEV) regions and a BEV region connectivity manifold for each target group of a plurality of target groups separately. The information contained in the BEV regions and the BEV region connectivity manifolds for all target groups is used to guide an optimizer to find optimal treatment trajectories. To improve the visibility of insufficiently exposed voxels of planning target volumes (PTVs), a post-processing step may be performed to enlarge certain BEV regions, which are considered for exposing during treatment trajectory optimization.

Abstract: A method of calculating radiation dose includes dosimetric projection of a collimator geometry. The method includes defining a three-dimensional (3D) geometry of a collimating device which defines an aperture configured to allow a radiation beam passing through, projecting the collimating device along the radiation beam into a two-dimensional (2D) geometry in a plane, calculating dosimetric opacity values of the collimating device at locations adjacent to the aperture based on the 3D geometry of the collimating device, and calculating transport of the radiation beam through the collimating device based on the 2D geometry projected in the plane and using the dosimetric opacity values of the collimating device at the locations adjacent to the aperture.

Abstract: Systems and methods for using electronic portal imaging devices (EPIDs) as absolute radiation beam measuring devices and as radiation beam alignment devices without implementation of elaborate and complex calibration procedures.

Abstract: Embodiments of the present disclosure are directed to methods and computer systems for converting datasets into three-dimensional (“3D”) mesh surface visualization, displaying the mesh surface on a computer display, comparing two three-dimensional mesh surface structures by blending two primary different primary colors to create a secondary color, and computing the distance between two three-dimensional mesh surface structures converted from two closely-matched datasets. For qualitative analysis, the system includes a three-dimensional structure comparison control engine that is configured to convert dataset with three-dimensional structure into three-dimensional surfaces with mesh surface visualization. The control engine is also configured to assign color and translucency value to the three-dimensional surface for the user to do qualitative comparison analysis. For quantitative analysis, the control engine is configured to compute the distance field between two closely-matched datasets.

Abstract: A computing system comprising a central processing unit (CPU), and memory coupled to the CPU and having stored therein instructions that, when executed by the computing system, cause the computing system to execute operations to generate a radiation treatment plan. The operations include accessing a minimum prescribed dose to be delivered into and across the target, determining a number of beams and directions of the beams, and determining a beam energy for each of the beams, wherein the number of beams, the directions of the beams, and the beam energy for each of the beams are determined such that the entire target receives the minimum prescribed dose. The operations further include prescribing a dose rate and optimizing dose rate constraints for FLASH therapy, and displaying a dose rate map of the FLASH therapy.

Abstract: Systems and method for automatically generating structures, such as target volumes, in a treatment image using structure-guided deformation to propagate the structures from a planning image onto the subsequently acquired treatment image.

Abstract: Multi-criteria optimization of radiation therapy tools for receiving a plurality of criteria including one or more sets of competing objectives. The competing objective can include delivery time criteria, fractionation criteria or similar time-based criteria. One or more radiation therapy plans can be determined based on a multi-criteria optimization of the plurality of criteria for the radiation therapy treatment. The one or more optimized radiation therapy plans can be output for consideration by one or more members of a radiation oncology team.

Abstract: A radiation-treatment plan that comprises a plurality of dose-delivery fractions can be optimized by using fraction dose objectives and at least one other, different dose objective. This use of fraction dose objectives can comprise accumulating doses delivered in previous dose-delivery fractions. The other, different dose objective can comprise a remaining total dose objective, a predictive dose objective, or some other dose objective of choice. An existing radiation-treatment plan having a corresponding resultant quality and that is defined, at least in part, by at least one delivery parameter can be re-optimized by specifying at least one constraint as regards that delivery parameter as a function, at least in part, of that resultant quality and then applying that constraint when re-optimizing the existing radiation-treatment plan.

Abstract: In various embodiments, a radiation therapy method can include loading a planning image of a target in a human. In addition, the position of the target can be monitored. A computation can be made of an occurrence of substantial alignment between the position of the target and the target of the planning image. Furthermore, after the computing, a beam of radiation is triggered to deliver a dosage to the target in a short period of time (e.g., less than a second).

Abstract: Example methods and systems for radiotherapy treatment planning based on continuous deep learning are provided. One example method may comprise: obtaining a deep learning engine that is trained to perform a radiotherapy treatment planning task based on first training data associated with a first planning rule. The method may also comprise: based on input data associated with a particular patient, performing the radiotherapy treatment planning task using the deep learning engine to generate output data associated with the particular patient; and obtaining modified output data that includes one or more modifications to the output data generated by the deep learning engine. The method may further comprise: based on the modified output data, generating second training data associated with a second planning rule; and generating a modified deep learning engine by re-training the deep learning engine using a combination of the first training data and the second training data.

Abstract: Streamlined and partially automated methods of setting normal tissue objectives in radiation treatment planning are provided. These methods may be applied to multiple-target cases as well as single-target cases. The methods can impose one or more target-specific dose falloff constraints around each target, taking into account geometric characteristics of each target such as target volume and shape. In some embodiments, methods can also take into account a planner's preferences for target dose homogeneity. In some embodiments, methods can generate additional dose falloff constraints in locations between two targets where dose bridging is likely to occur.