Siemens Healthineers International Ag

Varian Medical Systems International Ag

Varian Medical Systems, Inc.

Tobias Gass

Marco Lessard

Thomas Coradi

Tomasz Morgas

Benjamin M Haas

Jonas Honegger

Christopher Boylan

Thomas Schwere

Joerg Desteffani

Fernando Franco

Joao Neta

Pawel Zak

Yarema Mazuryk

Michael Waschbüesch

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.

Systems, methods and devices for automated target volume generation

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 dose probability values for each voxel of the representative 3D image, wherein each set of dose probability values includes at least one dose value and a probability value that corresponds to the dose value.

Determining uncertainty in radiation therapy delivered dose accumulation

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.

Streamlined, guided on-couch adaptive workflow

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.

Deformable image registration based on an image mask generated based on a two-dimensional (2D) computed tomography (CT) image

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.

Patient object mapping techniques

Deformable image registration based on masked computed tomography (CT) image

Growing community of inventors

Vogelsang, Switzerland

Tobias Gass