Abstract: Imaging systems and methods are provided, which involve acquiring static volume data using a first imaging technique; segmenting the static volume data to generate a static segmentation; annotating the static segmentation with at least one annotation; acquiring initial dynamic volume data using a second imaging technique different to the first imaging technique; segmenting the initial dynamic volume data to generate a plurality of dynamic segmentations; comparing the static segmentation to each one of the plurality of dynamic segmentations and determining, using the comparisons, a single dynamic segmentation that most closely corresponds to the static segmentation; storing the corresponding single dynamic segmentation in the memory as a reference segmentation; acquiring subsequent dynamic volume data; segmenting the subsequent dynamic volume data to generate at least one subsequent dynamic segmentation; determining a difference between the reference segmentation and the subsequent dynamic segmentation; updati

Abstract: Imaging systems and methods are provided, which involve acquiring static volume data using a first imaging technique; segmenting the static volume data to generate a static segmentation; annotating the static segmentation with at least one annotation; acquiring initial dynamic volume data using a second imaging technique different to the first imaging technique; segmenting the initial dynamic volume data to generate a plurality of dynamic segmentations; comparing the static segmentation to each one of the plurality of dynamic segmentations and determining, using the comparisons, a single dynamic segmentation that most closely corresponds to the static segmentation; storing the corresponding single dynamic segmentation in the memory as a reference segmentation; acquiring subsequent dynamic volume data; segmenting the subsequent dynamic volume data to generate at least one subsequent dynamic segmentation; determining a difference between the reference segmentation and the subsequent dynamic segmentation; updati

Abstract: The present invention relates to determining optimal C-arm angulation for heart valve positioning. In order to further improve the workflow in relation with the correct positioning of a valve prosthesis, it is described to provide (12) a 2D image of an aortic root of a heart of a patient, wherein the 2D image comprises three cusps of the heart in a visible and distinct manner. Further, 2D positions of the three cusps are indicated (14) in the 2D image. An analytical 3D model of the aortic cusp locations is provided (16), and the 3D positions of the three cusps are computed (18) based on the 2D positions and the analytical 3D model. Still further, an optimal C-arm angulation is computed (20) based on the computed 3D positions of the cusps, wherein, in the optimal C-arm angulation, the three cusps are aligned in a 2D image. The optimal C-arm angulation is then provided for further image acquisition steps.

Abstract: A system and method for vascular roadmapping include an X-ray imaging device for generating a fluoroscopy image, a data base in which a plurality of contrast-enhanced images is stored, a user interface element and a processor. Each of the contrast-enhanced images is stored together with the imaging parameters based on which the image is generated. The user interface element may be configured for selecting any intended imaging parameters, such as by manually adjusting the imaging device in any position and orientation. The processor may be configured for identifying a stored contrast-enhanced image out of the plurality of contrast-enhanced images, which is generated with specific imaging parameters, where the deviation of the specific imaging parameters from the intended imaging parameters is as small as possible, thus the processor may be configured for identifying the nearest available roadmap.

Abstract: The present invention relates to a system (1) for adaptive segmentation. The system (1) comprises a configurator (10), which is configured to determine an adapted angular range (AR) with respect to an operation mode of the system (1) and which is configured to determine a segmentation parameter (SP) based on the adapted angular range (AR). Further, the system comprises an imaging sensor (20), which is configured to acquire images (I1, . . . , IN) within the adapted angular range (AR). Still further, the system comprises a segmentator (30), which is configured to generate a segmentation model based on the acquired images (I1, . . . , IN) using the determined segmentation parameter (SP).

Abstract: The present invention relates to determining optimal C-arm angulation for heart valve positioning. In order to further improve the workflow in relation with the correct positioning of a valve prosthesis, it is described to provide (12) a 2D image of an aortic root of a heart of a patient, wherein the 2D image comprises three cusps of the heart in a visible and distinct manner. Further, 2D positions of the three cusps are indicated (14) in the 2D image. An analytical 3D model of the aortic cusp locations is provided (16), and the 3D positions of the three cusps are computed (18) based on the 2D positions and the analytical 3D model. Still further, an optimal C-arm angulation is computed (20) based on the computed 3D positions of the cusps, wherein, in the optimal C-arm angulation, the three cusps are aligned in a 2D image. The optimal C-arm angulation is then provided for further image acquisition steps.

Abstract: The present invention relates to a system (1) for adaptive segmentation. The system (1) comprises a configurator (10), which is configured to determine an adapted angular range (AR) with respect to an operation mode of the system (1) and which is configured to determine a segmentation parameter (SP) based on the adapted angular range (AR). Further, the system comprises an imaging sensor (20), which is configured to acquire images (I1, . . . , IN) within the adapted angular range (AR). Still further, the system comprises a segmentator (30), which is configured to generate a segmentation model based on the acquired images (I1, . . . , IN) using the determined segmentation parameter (SP).

Abstract: A system and method for vascular roadmapping is proposed, the system comprising an X-ray imaging device for generating a fluoroscopy image, a data base in which a plurality of contrast-enhanced images is stored, a user interface element and a processing device. Each of the contrast-enhanced images is stored together with the imaging parameters based on which the image is generated. The user interface element may be configured for selecting any intended imaging parameters, for example by manually adjusting the imaging device in any position and orientation. The processing device may be configured for identifying a stored contrast-enhanced image out of the plurality of contrast-enhanced images, which is generated with specific imaging parameters, wherein the deviation of the specific imaging parameters from the intended imaging parameters is as small as possible, thus the processing device may be configured for identifying the nearest available roadmap.