Abstract: A control circuit accesses a radiation treatment plan for a given patient. The control circuit then generates dose volume histogram information as a function of the radiation treatment plan and automatically assesses the dose volume histogram information to identify any anomalous results. Generating that information can comprise, at least in part and for example, generating at least one dose volume histogram curve. The latter may comprise generating at least one dose volume histogram curve for each of a plurality of different patient structures (such as one or more treatment volumes and/or one or more organs-at-risk).

Abstract: Provided herein are methods and systems to train and execute a computer model that uses artificial intelligence methodologies (e.g., deep learning) to learn and predict Multi-leaf Collimator (MLC) openings and control weights for a radiation therapy treatment plan.

Abstract: Embodiments described herein provide for training a machine learning model for automatic organ segmentation. A processor executes a machine learning model using an image to output at least one predicted organ label for a plurality of pixels of the image. Upon transmitting the at least one predicted organ label to a correction computing device, the processor receives one or more image fragments identifying corrections to the at least one predicted organ label. Upon transmitting the one or more image fragments and the image to a plurality of reviewer computing devices, the processor receives a plurality of inputs indicating whether the one or more image fragments are correct. When a number of inputs indicating an image fragment of the image fragments is correct exceeds a threshold, the processor aggregates the image fragment into a training data set. The processor trains the machine learning model with the training data set.

Abstract: These teachings include accessing energy dosing information along with at least one quality-of-care model that correlates at least one categorical energy-based treatment patient quality-of-care outcome with at least one resultant energy-based treatment description. The model can be created via probabilistic mapping that maps patient impact information to dose impartation information to infer non-biological impact to a patient. A patient treatment plan can be optimized for a particular patient as a function of the foregoing information to provide corresponding resultant benefit trade-of evaluation information. This benefit trade-off evaluation information can be displayed to a user to permit the user to explore the benefit trade-off evaluation information to thereby identify a resultant energy-based treatment plan having a selected balance between dosing a treatment target with energy and a quality-of-care impact on the particular patient.

Abstract: In accordance with at least some embodiments of the present disclosure, a process to estimate scattered radiation contained in x-ray projections for computed tomography (CT) reconstruction is provided. The process may construct an object model based on a plurality of projection images generated by CT scanning of an object using an x-ray radiation source and a detector panel. The process may construct a virtual radiation source based on the x-ray radiation source, and a virtual detector panel based on the detector panel. The process may perform a simulated CT scanning of the object model by simulating macroscopic behavior of particles being emitted from the virtual radiation source, passing through the object model, and being detected by the virtual detector panel. And the process may generate a simulated scatter image based on a first subset of particles scattered during the simulated CT scanning of the object model.

Abstract: In accordance with at least some embodiments of the present disclosure, a process to estimate scattered radiation contained in x-ray projections for computed tomography (CT) reconstruction is provided. The process may construct an object model based on a plurality of projection images generated by CT scanning of an object using an x-ray radiation source and a detector panel. The process may construct a virtual radiation source based on the x-ray radiation source, and a virtual detector panel based on the detector panel. The process may perform a simulated CT scanning of the object model by simulating macroscopic behavior of particles being emitted from the virtual radiation source, passing through the object model, and being detected by the virtual detector panel. And the process may generate a simulated scatter image based on a first subset of particles scattered during the simulated CT scanning of the object model.