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: A computer-implemented method of generating dose information for a region of patient anatomy includes determining a first set of dose values for a first three-dimensional (3D) image of the region, wherein each value in the first set of dose values is associated with a different voxel of the first 3D image, and wherein the first 3D image is associated with a specific application of dose to the region; determining a second set of dose values for a representative 3D image of the region, wherein each value in the second set of dose values is associated with a different voxel of the representative 3D image; determining a set of geometric error models for the representative 3D image of the region, wherein each geometric error model in the set of geometric error models indicates a geometric error between a voxel of the representative 3D image and one or more voxels of a treatment fraction 3D image of the region; and based on the second set of dose values and the set of geometric error models, determining a set of do

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: 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: 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: Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

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: 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: Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

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: 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: Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

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: 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: 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: 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: 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: 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: 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: 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.