Abstract: A method for improving patient safety during medical treatment involves comparing medical images to verify patient identity. The method retrieves a first medical image of a patient and captures a second image using a medical imaging sensor. An artificial intelligence model transforms these images into feature vectors in a latent space, where several features are identified and compared. The model predicts a distance between corresponding features in the two images, associated with the likelihood that both images belong to the same patient. If this distance exceeds a set threshold, indicating a possible mismatch, a warning signal is sent to a radiotherapy computing device. This method helps prevent incorrect patient identification during medical procedures.

Abstract: A control circuit can access multi-dimensional information for a particular patient and then automatically determine a supplemental boundary for at least one portion of the particular patient (such as a treatment target comprising a part of the patient's heart and/or one or more organs-at-risk) as a function, at least in part, of the multi-dimensional information. The latter may comprise determining a margin that is added to a boundary of the at least one portion of the particular patient. The control circuit can then, for example, determine a planning treatment volume as a function, at least in part, of that supplemental boundary. These teachings will then permit, for example, optimizing a cardiac radioablation treatment plan for the particular patient as a function of various dimensions of movement as derived, at least in part, from the aforementioned multi-dimensional information.

Abstract: A control circuit can access multi-dimensional information for a particular patient and then automatically determine a supplemental boundary for at least one portion of the particular patient (such as a treatment target and/or one or more organs-at-risk) as a function, at least in part, of the multi-dimensional information. The latter may comprise determining a margin that is added to a boundary of the at least one portion of the particular patient. The control circuit can then, for example, determine a planning treatment volume as a function, at least in part, of that supplemental boundary. These teachings will then permit, for example, optimizing a radiation-based therapeutic treatment plan for the particular patient as a function of various dimensions of movement as derived, at least in part, from the aforementioned multi-dimensional information.

Abstract: Embodiments described herein provide for determining a probability distribution of a three-dimensional point in a template feature map matching a three-dimensional point in space. A dual-domain target structure tracking end-to-end system receives projection data in one dimension or two dimensions and a three-dimensional simulation image. The end-to-end system extracts a template feature map from the simulation image using segmentation. The end-to-end system extracts features from the projection data, transforms the features of the projection data into three-dimensional space, and sequences the three-dimensional space to generate a three-dimensional feature map. The end-to-end system compares the template feature map to the generated three-dimensional feature map, determining an instantaneous probability distribution of the template feature map occurring in the three-dimensional feature map.

Abstract: A universal phantom includes a first phantom and a second phantom. The first phantom comprises a plurality of radiation markers. The second phantom comprises a plurality of optical markers. The second phantom is fixedly attachable to the first phantom in a predetermined position. A calibration method employs a universal phantom to consolidate the tasks of determining the isocenter of a radiation machine, calibrating optical devices, and registering the optical devices in a radiation coordinate system with origin at the isocenter.

Abstract: A control circuit accesses projection data for a given patient (such as cone-beam computed tomography images). The control circuit can then background process the projection data to generate a plurality of different images. The control circuit then stores these images to provide stored images. Upon receiving a request that corresponds to at least one item of task-supportive content that corresponds to a particular radiation therapy workflow step, the control circuit may then access the aforementioned stored images to select at least one particular image that corresponds to the particular radiation therapy workflow step. That at least one particular image can then be transmitted, for example, to the functionality that requested (or that otherwise requires and is awaiting) this content.

Abstract: Embodiments described herein provide for determining a probability distribution of a three-dimensional point in a template feature map matching a three-dimensional point in space. A dual-domain target structure tracking end-to-end system receives projection data in one dimension or two dimensions and a three-dimensional simulation image. The end-to-end system extracts a template feature map from the simulation image using segmentation. The end-to-end system extracts features from the projection data, transforms the features of the projection data into three-dimensional space, and sequences the three-dimensional space to generate a three-dimensional feature map. The end-to-end system compares the template feature map to the generated three-dimensional feature map, determining an instantaneous probability distribution of the template feature map occurring in the three-dimensional feature map.

Abstract: Embodiments described herein provide for determining a probability distribution of a three-dimensional point in a template feature map matching a three-dimensional point in space. A dual-domain target structure tracking end-to-end system receives projection data in one dimension or two dimensions and a three-dimensional simulation image. The end-to-end system extracts a template feature map from the simulation image using segmentation. The end-to-end system extracts features from the projection data, transforms the features of the projection data into three-dimensional space, and sequences the three-dimensional space to generate a three-dimensional feature map. The end-to-end system compares the template feature map to the generated three-dimensional feature map, determining an instantaneous probability distribution of the template feature map occurring in the three-dimensional feature map.

Abstract: A universal phantom includes a first phantom and a second phantom. The first phantom comprises a plurality of radiation markers. The second phantom comprises a plurality of optical markers. The second phantom is fixedly attachable to the first phantom in a predetermined position. A calibration method employs a universal phantom to consolidate the tasks of determining the isocenter of a radiation machine, calibrating optical devices, and registering the optical devices in a radiation coordinate system with origin at the isocenter.

Abstract: Embodiments described herein provide for determining a probability distribution of a three-dimensional point in a template feature map matching a three-dimensional point in space. A dual-domain target structure tracking end-to-end system receives projection data in one dimension or two dimensions and a three-dimensional simulation image. The end-to-end system extracts a template feature map from the simulation image using segmentation. The end-to-end system extracts features from the projection data, transforms the features of the projection data into three-dimensional space, and sequences the three-dimensional space to generate a three-dimensional feature map. The end-to-end system compares the template feature map to the generated three-dimensional feature map, determining an instantaneous probability distribution of the template feature map occurring in the three-dimensional feature map.

Abstract: Dosimetrical end-to-end quality assurance devices, systems, and methods for radiation devices using X-ray imaging, optical surface imaging, and electromagnetic navigational systems to position the quality assurance device either absolute or relative in space.

Abstract: Dosimetrical end-to-end quality assurance devices, systems, and methods for radiation devices using X-ray imaging, optical surface imaging, and electromagnetic navigational systems to position the quality assurance device either absolute or relative in space.

Abstract: Dosimetrical end-to-end quality assurance devices, systems, and methods for radiation devices using X-ray imaging, optical surface imaging, and electromagnetic navigational systems to position the quality assurance device either absolute or relative in space.

Abstract: Systems, devices, and methods for pre-treatment verification of radiation dose delivery in arc-based radiation therapy devices using a time-dependent gamma evaluation method.

Abstract: Dosimetrical end-to-end quality assurance devices, systems, and methods for radiation devices using X-ray imaging, optical surface imaging, and electromagnetic navigational systems to position the quality assurance device either absolute or relative in space.