Abstract: Systems and methods for radioablation treatment planning are disclosed. In some examples, a computing device provides for display a user interface that allows a medical professional to define a target region of a patient for treatment. The user interface may allow the medical professional to select a treatment area using interactive target maps generated for the patient. The computing device also receives image data from an imaging system for the patient, such as image data identifying a 3D volume of the patient's scanned structure. The computing device may generate for display a 3D image of the scanned structure based on the received image data, and may superimpose on the 3D image a target region map that the medical professional can manipulate to define the target region of treatment for the patient. Once defined, the computing device may transmit the defined target region to a treatment system for treating the patient.

Abstract: Systems and methods for cardiac radioablation treatment planning are disclosed. In some examples, a computing device receives an image volume and a dose matrix data. The computing device generates a first mesh of organ substructures for the organ based on substructure contours for the organ. Further, the computing device generates a second mesh of an isodose volume based on the dose matrix data. The computing device displays the first mesh of the organ substructures and the second mesh of the isodose volume within a same scene. In some examples, the computing device samples a plurality of dosage values, determines representative dosage values for each of a plurality of points along surfaces of a dose matrix, and generates an image for display based on the representative dosage values. In some examples, a segmentation model is generated for display based on the representative dosage values.

Abstract: Deep learning approaches automatically segment at least some breast tissue images while non-deep learning approaches automatically segment organs-at-risk. Both three-dimensional CT imaging information and two-dimensional orthogonal topogram imaging information can be used to determine virtual-skin volume. The foregoing imaging information can also serve to automatically determine a body outline for at least a portion of the patient. That body outline, along with the virtual-skin volume and registration information can serve as inputs to automatically calculate radiation treatment platform trajectories, collision detection information, and virtual dry run information of treatment delivery per the optimized radiation treatment plan.

Abstract: Systems and methods for cardiac radioablation treatment planning are disclosed. In some examples, a computing device receives a first signal identifying a first event within a first workspace from a second computing device. The computing device determines a first action to apply to a first image displayed within a second workspace based on the first signal. The computing device generates a second image based on applying the first action to the first image within the second workspace, and displays the second image within the second workspace. In some examples, the first workspace is a radiation oncologist workspace and the second workspace is an electrophysiologist workspace. In some examples, the first workspace is an electrophysiologist oncologist workspace and the second workspace is a radiation oncologist workspace.

Abstract: Deep learning approaches automatically segment at least some breast tissue images while non-deep learning approaches automatically segment organs-at-risk. Both three-dimensional CT imaging information and two-dimensional orthogonal topogram imaging information can be used to determine virtual-skin volume. The foregoing imaging information can also serve to automatically determine a body outline for at least a portion of the patient. That body outline, along with the virtual-skin volume and registration information can serve as inputs to automatically calculate radiation treatment platform trajectories, collision detection information, and virtual dry run information of treatment delivery per the optimized radiation treatment plan.