Abstract: A therapy planner is configured to construct a therapy plan based on a planning image segmented into segments delineating features of a subject. A predictive plan adaptation module is configured to adjust the segments to represent a foreseeable change in the subject and to invoke the therapy planner to construct a therapy plan corresponding to the foreseeable change. A data storage stores a plurality of therapy plans generated for a subject by the therapy planner and the predictive plan adaptation module based on at least one planning image of the subject. A therapy plan selector is configured to select one of the plurality of therapy plans for use in a therapy session based on a preparatory image acquired preparatory to the therapy session.

Abstract: A system and method is provided for visualizing a volumetric image of an anatomical structure. Using a first view of the volumetric image showing a non-orthogonal cross-section of a surface of the anatomical structure, a local orientation of the surface within the volumetric image is determined, namely by analyzing the image data of the volumetric image. Having determined the local orientation of the surface, a second view is generated of the volumetric image, the second view being geometrically defined by a viewing plane intersecting the surface of the anatomical structure in the volumetric image orthogonally. Accordingly, the surface is shown in a sharper manner in the second view than would typically be the case in the first view. Advantageously, the user can manually define or correct a delineation of the outline of the anatomical structure in a more precise manner.

Abstract: A method displays spectral image data reconstructed from spectral projection data with a first reconstruction algorithm and segmented image data reconstructed from the same spectral projection data with a different reconstruction algorithm, which is different from the first reconstruction algorithm. The method includes reconstructing spectral projection data with the first reconstruction algorithm, which generates the spectral image data and displaying the spectral image data. The method further includes reconstructing the spectral projection data with the different reconstruction algorithm, which generates segmentation image data, segmenting the segmentation image data, which produces the segmented image data, and displaying the segmented image data.

Abstract: A system and a method are provided for enabling a user to interactively navigate through a set of slice images, the set of slice images jointly representing an image volume showing an anatomical structure of a patient. A user may be enabled to switch from a static viewing mode to a navigation mode based on navigation commands received from a user input device operable by the user. A display processor may be configured for, in the static viewing mode, generating an output image comprising one slice image of the set of slice images. The display processor may be configured for, in the navigation mode, replacing the said one slice image in the output image by a volume rendering of a slab of the image volume, the slab comprising more than one slice image. The system and method thus selectively switch to volume rendering, namely during navigation, whereas in a static (i.e., non-navigation) viewing mode, a slice image is shown.

Abstract: A method includes determining, with a computer implemented classifier, a health state of tissue interest of a subject in at least one pair of images of the subject based on a predetermined set of longitudinal features of the tissue of interest of the subject in the at least one pair of images of the subject. The at least one pair of images of the subject includes a first image of the tissue of interest acquired at a first moment in time and a second image of the tissue of interest acquired at a second moment in time. The first and second moments in time are different moments in time. The method further includes visually displaying indicia indicative of the determined health state.

Abstract: An imaging system (10) analyzes airways of a patient. The system (10) includes a hardware phantom (50) including tubes (54) representative of airways. The tubes (54) include different lumen sizes and/or wall thicknesses. The system further includes an imaging scanner (12) for scanning a region of interest (ROI), including the airways, and the hardware phantom (50) to create raw image data. At least one processor (32) is programmed to at least one of: (1) correct measurements of walls of the airways based on measurements of lumen size and/or wall thickness of the tubes (54) and known lumen size and/or wall thickness of the tubes (54); and (2) generate an image of the ROI in which color and/or opacity of the airways are based on a comparison of images or maps of the tubes (54) and images or maps of the airways. A corresponding method is also provided.

Abstract: The present invention relates to a device for encoding an anatomical shape (13) of a living being, comprising a receiving unit (12) for receiving an anatomical shape (13); a shape representation generating unit (14) for generating a shape representation of the anatomical shape (13) by using one or more shape representation models and determining the value of one or more shape representation coefficients of the one or more shape representation models; and a conversion unit (16) for converting the determined value of the one or more shape representation coefficients into a human-readable code comprising one or more human-readable characters.

Abstract: The present invention provides for means for linking breast lesion locations across imaging studies. In particular, a generic three-dimensional representation of the female breast is used. Automatic translation of the lesion location into standard clinical terminology and aligning the breast model with individual patient images is comprised. Moreover, a mechanism for linking image locations showing a lesion to a location in the breast model is presented. If desired, a region of interest can be calculated by a region of interest definition module that predicts a region of interest of a known lesion in terms of the breast model representation in a new imaging study.

Abstract: A method includes obtaining contrast-enhanced image data having a plurality of voxels, each voxel having an intensity value. The method further includes determining a vesselness value for each voxel. The method further includes determining a hypo-density value for each voxel. The method further includes weighting each of the intensity values by a corresponding vesselness value. The method further includes weighting each of the hypo-density values by the corresponding vesselness value. The method further includes combining the weighted intensity values and the weighted hypo-density values, thereby generating composite image data. The method further includes visually displaying the composite image data.

Abstract: A perfusion analysis system includes a perfusion modeller and a user interface. The perfusion modeller generates a patient specific perfusion model based on medical imaging perfusion data for the patient, a general perfusion model, and a quantification of one or more identified pathologies of the patient that affect perfusion in the patient. The user interface accepts an input indicative of a modification to the quantification of the one or more identified pathologies. In response, the perfusion modeller updates the patient specific perfusion model based on the medical imaging perfusion data for the patient, the general perfusion model, and the quantification of the one or more identified pathologies of the patient, including the modification thereto.

Abstract: A method includes determining a registration transform between first three dimensional pre-scan image data and second three dimensional pre-scan image data based on a predetermined registration algorithm. The method further includes registering first volumetric scan image data and second volumetric scan image data based on the registration transform. The method further includes generating registered image data. A system (100) includes a pre-scan registerer (122) that determines a registration transform between first three dimensional pre-scan image data and second three dimensional pre-scan image data based on a predetermined registration algorithm. The system further includes a volume registerer (126) that registers first volumetric scan image data and second volumetric scan image data based on the registration transform, generating registered image data.

Abstract: A method includes determining, with a computer implemented classifier, a health state of tissue interest of a subject in at least one pair of images of the subject based on a predetermined set of longitudinal features of the tissue of interest of the subject in the at least one pair of images of the subject. The at least one pair of images of the subject includes a first image of the tissue of interest acquired at a first moment in time and a second image of the tissue of interest acquired at a second moment in time. The first and second moments in time are different moments in time. The method further includes visually displaying indicia indicative of the determined health state.

Abstract: System and related method to visualize image data. The system comprises an input port (IN) for receiving i) image data comprising a range of intensity values converted from signals acquired by an imaging apparatus in respect of an imaged object, and ii) a definition of a first transfer function configured to map a data interval within said range of said intensity values to an image interval of image values. A transition region identifier (TRI) identifies from among intensity values outside said data interval, one or more transition intensity values representative of a transition in composition and/or configuration of said object or of a transition in respect of a physical property in relation to said object. A transfer function generator (TFG) generates for said intensity values outside said data interval a second transfer function. The second transfer function is non-linear and has a respective gradient that is locally maximal around said transition intensity values.

Abstract: A method for processing image data includes obtaining a first set of 3D volumetric image data. The 3D volumetric image data includes a volume of voxels. Each voxel has an intensity. The method further includes obtaining a local voxel noise estimate for each of the voxels of the volume. The method further includes processing the volume of voxels based at least on the intensity of the voxels and the local voxel noise estimates of the voxels. An image data processor (124) includes a computer processor that at least one of: generate a 2D direct volume rendering from first 3D volumetric image data based on voxel intensity and individual local voxel noise estimates of the first 3D volumetric image data, or registers second 3D volumetric image data and first 3D volumetric image data based at least one individual local voxel noise estimates of second and first 3D volumetric image data sets.

Abstract: A system and method are provided for displaying medical images. A first viewport is generated which shows a part of a first medical image which shows a region of interest. A second viewport is generated which shows a part of a second medical image which shows a corresponding region of interest, e.g., representing a same anatomical structure or a same type of anatomical structure. In order to establish this ‘synchronized’ display of regions of interest, a displacement field is estimated between the first medical image and the second medical image. However, the displacement field is not used to deform the second medical image. Rather, the displacement field is used to identify the corresponding region of interest and thereby which part of the second medical image is to be shown. It is thus avoided that the second medical image itself is deformed, which would typically also deform the region of interest and thereby impair its assessment.

Abstract: A system and method is provided for visualizing a volumetric image of an anatomical structure. Using a first view of the volumetric image showing a non-orthogonal cross-section of a surface of the anatomical structure, a local orientation of the surface within the volumetric image is determined, namely by analyzing the image data of the volumetric image. Having determined the local orientation of the surface, a second view is generated of the volumetric image, the second view being geometrically defined by a viewing plane intersecting the surface of the anatomical structure in the volumetric image orthogonally. Accordingly, the surface is shown in a sharper manner in the second view than would typically be the case in the first view. Advantageously, the user can manually define or correct a delineation of the outline of the anatomical structure in a more precise manner.

Abstract: A method displays spectral image data reconstructed from spectral projection data with a first reconstruction algorithm and segmented image data reconstructed from the same spectral projection data with a different reconstruction algorithm, which is different from the first reconstruction algorithm. The method includes reconstructing spectral projection data with the first reconstruction algorithm, which generates the spectral image data and displaying the spectral image data. The method further includes reconstructing the spectral projection data with the different reconstruction algorithm, which generates segmentation image data, segmenting the segmentation image data, which produces the segmented image data, and displaying the segmented image data.

Abstract: A method for processing image data includes obtaining a first set of 3D volumetric image data. The 3D volumetric image data includes a volume of voxels. Each voxel has an intensity. The method further includes obtaining a local voxel noise estimate for each of the voxels of the volume. The method further includes processing the volume of voxels based at least on the intensity of the voxels and the local voxel noise estimates of the voxels. An image data processor (124) includes a computer processor that at least one of: generate a 2D direct volume rendering from first 3D volumetric image data based on voxel intensity and individual local voxel noise estimates of the first 3D volumetric image data, or registers second 3D volumetric image data and first 3D volumetric image data based at least one individual local voxel noise estimates of second and first 3D volumetric image data sets.

Abstract: A method, system and program product are provided for planning an intervention procedure in a body lumen. A CT scan of the body lumen is performed. A virtual rendering is created of the inside of the body lumen corresponding to an interventional camera image. Then a virtual tape corresponding to a planned path for the intervention procedure is projected onto a wall of the body lumen. The virtual tape is projected onto the lumen wall, which is relatively distant from the camera point on the virtual rendering, so the tape does not appear to oscillate like a central thread. Also, since the virtual tape is located on the lumen wall, it does not occlude the center of the lumen, allowing a user to better visualize the lumen during planning, during fly through, and even during an actual intervention.

Abstract: A method and a device for analyzing a region of interest in an object is proposed. The method comprises: (a) providing measurement data by a differential phase contrast X-ray imaging system, and (b) analyzing characteristics of the object in the region of interest. Therein, the measurement data comprise a 2-dimensional or 3-dimensional set of pixels wherein for each pixel the measurement data comprises three types of image data spatially aligned with each other, including (i) absorption representing image data A, (ii) differential phase contrast representing image data D, and (iii) coherence representing image data C. The analyzing step is based, for each pixel, on a combination of at least two of information comprised in the absorption representing image data A and information comprised in the differential phase contrast representing image data D and information comprised in the coherence representing image data C.