Abstract: An apparatus for developing an intensity-modulated radiation therapy treatment plan includes a memory that stores machine instructions and a processor that executes the machine instructions to receive a clinical goal associated with the treatment plan as a user input. The processor further executes the machine instructions to determine a plan objective based on the clinical goal, generate a cost function comprising a term based on the plan objective, and assign an initial value to a parameter associated with the term. The processor also executes the machine instructions to identify a microstate that results in a reduced value associated with the cost function, evaluate a fulfillment level associated with the clinical goal, and adjust the value of the parameter to improve the fulfillment level.

Abstract: A computer implemented method of determining a resultant treatment plan for a proton radiation therapy system based on given dose volume constraints, wherein the resultant treatment plan is optimized for treatment time comprises accessing the dose volume constraints and range information, wherein the range information indicates acceptable deviations from the dose volume constraints. Based on the proton radiation therapy system, the method further comprises accessing machine configuration information comprising a plurality of machine parameters that define a maximum resolution achievable in irradiating a patient. Further, the method comprises iteratively adjusting the plurality of machine parameters to values which decrease the maximum resolution and simulating a plurality of candidate treatment plans to generate a plurality of treatment plan results, wherein each treatment plan result comprises: a respective treatment time and a respective plan quality.

Abstract: A patient supporting device includes: a base; a positioner; a platform having a first end and a second end; and a controller; wherein the positioner is operable by the controller to place the platform at one of a first plurality of possible positions or at one of a second plurality of possible positions, wherein in any of the first plurality of possible positions, the second end of the platform is closer to one of a left side and a right side of a treatment machine; wherein in any of the second plurality of possible positions, the second end of the platform is closer to another one of the left side and the right side; and wherein a size of a first spatial region defined by the first plurality of possible positions is different from a size of a second spatial region defined by the second plurality of possible positions.

Abstract: Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Abstract: Example methods and systems for radiotherapy treatment planning based on treatment delivery efficiency are described. One example method may comprise a computer system configuring dosimetric planning objective(s) and non-dosimetric planning objective(s) associated with efficiency of treatment delivery. A set of multiple treatment plan variants may be generated based on the dosimetric planning objective(s) and non-dosimetric planning objective(s). A first treatment plan associated with a first tradeoff and a second treatment plan associated with a second tradeoff may then be identified from the set of multiple treatment plan variants. The second treatment plan may be associated with improved efficiency of treatment delivery compared to the first treatment plan.

Abstract: Cone-beam computer tomography systems, devices, and methods for image acquisition of large target volumes using partial scans.

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. A quantitative time-dependent model-based charged particle pencil beam scanning optimization is then implemented for FLASH therapy.

Abstract: Methods and systems are provided for developing radiation therapy treatment plans. A treatment template with radiation fields can be chosen for a patient based on a tumor location. Static radiation field positions can be adjusted for the patient, while arc radiation fields may remain the same. Static radiation field positions can be adjusted using dose gradient, historical patient data, and other techniques.

Abstract: Embodiments of the present invention provide an integrated solution to radiotherapy treatment planning that enables accurate recording and accumulation of physical parameters as input (e.g., dose, dose rate, irradiation time per voxel, etc.). User-defined functions are evaluated to correlate the input parameters with 4D biological outcomes. The resulting biological parameters can be visualized as a biological outcome map to evaluate decisions, support decisions, and optimize decisions regarding the parameters of the radiotherapy treatment plan, for example, for supporting clinical trials and related clinical research.

Abstract: A radiation dose received by a patient from a radiation therapy system can be verified by acquiring a cine stream of image frames from an electronic portal imaging device (EPID) that is arranged to detect radiation exiting the patient during irradiation. The cine stream of EPID image frames can be processed in real-time to form exit images providing absolute dose measurements at the EPID (dose-to-water values), which is representative of the characteristics of the radiation received by the patient. Compliance with predetermined characteristics for the field can be determined during treatment by periodically comparing the absolute dose measurements with the predetermined characteristics, which can include a predicted total dose in the field after full treatment and/or a complete irradiation area outline (CIAO). The system operator can be alerted or the irradiation automatically stopped when non-compliance is detected.

Abstract: Systems, devices, and methods are presented for automatic tuning, calibration, and verification of radiation therapy systems comprising control elements configured to control parameters of the radiation therapy systems based on images obtained using electronic portal imaging devices (EPIDs) included in the radiation therapy system.

Abstract: System and method for automatically generate therapy plan parameters by use of an integrate model with extended applicable regions. The integrated model integrates multiple predictive models from which a suitable predictive model can be selected automatically to perform prediction for a new patient case. The integrated model may operate to evaluate prediction results generated by each predictive model and the associated prediction reliabilities and selectively output a satisfactory prediction. Alternatively, the integrated model may select a suitable predictive model by a decision hierarchy in which each level corresponds to divisions of a patient data feature set and divisions on a subordinate level are nested with divisions on a superordinate level.

Abstract: Cone-beam computer tomography systems, devices, and methods for image acquisition of large target volumes using partial scans.

Abstract: Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.

Abstract: Example methods and systems for tomographic image reconstruction are provided. One example method may comprise: obtaining two-dimensional (2D) projection data and processing the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating 2D feature data by processing the 2D projection data using the multiple first processing layers, reconstructing first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and generating second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers.

Abstract: Example methods and systems for training deep learning engines for radiotherapy treatment planning are provided. One example method may comprise: obtaining a set of training data that includes unlabeled training data and labeled training data; and configuring a deep learning engine to include (a) a primary network and (b) a deep supervision network that branches off from the primary network. The method may further comprise: training the deep learning engine to perform the radiotherapy treatment planning task by processing training data instance to generate (a) primary output data and (b) deep supervision output data; and updating weight data associated with at least some of the multiple processing layers based on the primary output data and/or the deep supervision output data. The deep supervision network may be pruned prior to applying the primary network to perform the radiotherapy treatment planning task for a patient.

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: 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: 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: An apparatus for determining material property, includes: an interface configured to obtain a first HU value associated with a first image of an object, and to obtain a second HU value associated with a second image of the object, wherein the first image is created using a first energy having a first energy level, and wherein the second image is created using a second energy having a second energy level that is different from the first energy level; and a processing unit configured to determine a weighted property value for the object based at least in part on the first HU value and the second HU value.