Abstract: When adapting models of anatomical structures in a patient for diagnosis or therapeutic planning, an atlas (26) of predesigned anatomical structure models or image volumes can be accessed, and a segmentation of one or more such structures can be selected and overlaid on an a 3D image of corresponding structure(s) in a clinical image (52) of a patient. A user can click on an initially unapproved segmentation 5 landmark (72) on the patient image (52), reposition the unapproved landmark, and approve the repositioned landmark. Remaining unapproved landmarks (72) are then repositioned as a function of the position of the approved landmark (92) using one or more interpolation techniques to adapt the model to the patient image on the fly.

Abstract: A method includes generating a set of group-wise registered images from a time sequence of images based on a region of interest of a subject or object identified in at least one of the images, the image sequence, and a motion model indicative of an estimate of a motion of the subject or object during which the image sequence is acquired.

Abstract: When modeling anatomical structures in a patient for diagnosis or therapeutic planning, an atlas (26) of predesigned anatomical structure models can be accessed, and model of one or more such structures can be selected and overlaid on an a 3D image of corresponding structure(s) in a clinic image of a patient. A user can click and drag a cursor on the model to deform the model to align with the clinical image. Additionally, a processor (16) can generate a volumetric deformation function using splines, parametric techniques, or the like, and can deform the model to fit the image in real time, in response to user manipulation of the model.

Abstract: Interactive mesh deformation for in-plane 3D segmentation/delineation for radiation therapy planning done on a slice by slice basis of a region/a volume of interest (VOI, ROI). Segmentation starts by some automatic 3D algorithm approximating the organ surface roughly by some triangular surface mesh which mesh is afterwards manually refined by the user who deforms it to bring it closer to the region of interest. The deformation is an invertible, i.e. one-to-one, mapping avoiding self-intersections of the deformed mesh thereby preserving the topology of the anatomy. The deformation mapping involves a Gaussian function (Gaussian deformation kernel) restricting the deformation to a local region. The user picks with the pointer a start point on a selected image slice through the volume and moves it to some end point. The distance the mesh vertices move decreases exponentially with the distance to the start point.

Abstract: A method for segmenting image data includes identifying a 2D boundary start position corresponding to tissue of interest in a cross-section of volumetric image data, wherein the start position is identified by a current position of a graphical pointer with respect to the cross-section, generating a preview 2D boundary for the tissue of interest based on the start position, displaying the preview 2D boundary superimposed over the cross-section, and updating the displayed preview 2D boundary if the position of the graphical pointer changes with respect to the cross-section.

Abstract: A method and system are provided for visualizing a surgical path (31, 41, 51) for a surgical tool. The method comprises a step of receiving (21) anatomical information (14) about a position of at least one anatomical structure (32, 33, 34, 35) in a region to undergo surgery, geometric information (15) describing the surgical path (31, 41, 51) and at least one safety margin defining a minimal distance between the surgical tool and the anatomical structure (32, 33, 34, 35). The method further comprises defining (23) a critical segment (43, 44, 45) of the surgical path (31, 41, 51), in which critical segment (43, 44, 45) a distance to the anatomical structure (32, 33, 34, 35) is smaller than the safety margin. Then a graphical representation (30, 40, 50) of the surgical path (31, 41, 51) is provided (24), wherein the critical segment (43, 44, 45) is highlighted.

Abstract: A tracking system for a target anatomy of a patient can include a medical device having a body (281) having a distal end (290) and at least one channel (292) formed therein, where the body is adapted for insertion through an anatomy (105) to reach a target area (430); an accelerometer (185) connected to the body and positioned in proximity to the distal end; an imaging device (295) operably coupled with the body; and a light source (297) operably coupled with the body, where the accelerometer is in communication with a remote processor (120) for transmitting acceleration data thereto, where the imaging device is in communication with the remote processor for transmitting real-time images thereto, and where an orientation of the medical device with respect to the anatomy is determined by the processor based on the acceleration data.

Abstract: The invention relates to a device and a process, with which images of different imaging methods can be registered, for example preoperatively obtained 3D X-ray images (A) and intra operatively obtained ultrasound images (B). First transformed images (A?,B?) are then generated in a data processing device (10), which are aligned to each other with regard to the peculiarities of each imaging method. Particularly from the three dimensional CT-image (A), can be generated a two dimensional image (A?) which adheres to the characteristic means of representation of an ultrasound system, while shaded areas behind bones and/or gas-filled volumes can be blended out. With a feature-based registration of the transformed images (A?, B?) errors are avoided, which are traced back to artifacts and peculiarities of the respective imaging methods.

Abstract: A system and method for automatic contrast enhancement for contouring. The system and method including displaying a volumetric image slice to be analyzed, receiving a delineation of a target anatomic structure in the volumetric image slice, identifying a region of interest based upon an area being delineated in the volumetric image slice, analyzing voxel intensity values in the region of interest and determining an appropriate window-level setting based on the voxel intensity values.

Abstract: A system and method for segmenting an anatomical structure. The system and method initiating a segmentation algorithm, which produces a surface mesh of the anatomical structure from a series of volumetric images, the surface mesh formed of a plurality of polygons including vertices and edges, assigning a spring to each of the edges and a mass point to each of the vertices of the surface mesh, displaying a 2D reformatted view including a 2D view of the surface mesh and the anatomical structure, adding pull springs to the surface mesh, the pull springs added based upon a selected point on a surface of the surface mesh and moving a portion of the surface mesh via an interactive point.

Abstract: A method includes generating a set of group-wise registered images from a time sequence of images based on a region of interest of a subject or object identified in at least one of the images, the image sequence, and a motion model indicative of an estimate of a motion of the subject or object during which the image sequence is acquired.

Abstract: A therapy planner (16) 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 (20) 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 (18) 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 (30) 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: When performing model-based segmentation on a 3D patient image (80), metal artifacts in the patient image (80), caused by metal in the patient's body, are detected, and a metal artifact reduction technique is performed to reduce the artifact(s) by interpolation projection data in the region of the artifact(s). The interpolated data is used to generate an uncertainty map for artifact-affected voxels in the image, and a mesh model (78) is conformed to the image to facilitate segmentation thereof. Internal and external energies applied to push and pull the model (78) are weighted as a function of the uncertainty associated with one or more voxels in the image (80). Iteratively, mathematical representations of the energies and respective weights are solved to describe an updated model shape that more closely aligns to the image (80).

Abstract: A perfusion analysis system includes a perfusion modeller (120) and a user interface (122). The perfusion modeller (120) 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 (122) accepts an input indicative of a modification to the quantification of the one or more identified pathologies. In response, the perfusion modeller (120) 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: When modeling anatomical structures in a patient for diagnosis or therapeutic planning, an atlas (26) of segmented co-registered CT and MRI anatomical structure reference images can be accessed, and an image of one or more such structures can be selected and overlaid with an MRI image of corresponding structure(s) in a clinical image of a patient. A user can click and drag landmarks or segment edges on the reference MRI image to deform the reference MRI image to align with the patient MRI image. Registration of a landmark in the patient MRI image to the reference MRI image also registers the patient MRI image landmark with a corresponding landmark in the co-registered reference CT image, and electron density information from the reference CT image landmark is automatically attributed to the corresponding registered patient MRI landmark.

Abstract: When adapting models of anatomical structures in a patient for diagnosis or therapeutic planning, an atlas (26) of predesigned anatomical structure models or image volumes can be accessed, and a segmentation of one or more such structures can be selected and overlaid on an a 3D image of corresponding structure(s) in a clinical image (52) of a patient. A user can click on an initially unapproved segmentation 5 landmark (72) on the patient image (52), reposition the unapproved landmark, and approve the repositioned landmark. Remaining unapproved landmarks (72) are then repositioned as a function of the position of the approved landmark (92) using one or more interpolation techniques to adapt the model to the patient image on the fly.

Abstract: When modeling anatomical structures in a patient for diagnosis or therapeutic planning, an atlas (26) of predesigned anatomical structure models can be accessed, and model of one or more such structures can be selected and overlaid on an a 3D image of corresponding structure(s) in a clinic image of a patient. A user can click and drag a cursor on the model to deform the model to align with the clinical image. Additionally, a processor (16) can generate a volumetric deformation function using splines, parametric techniques, or the like, and can deform the model to fit the image in real time, in response to user manipulation of the model.

Abstract: This invention relates to a method and image processing apparatus for automatically correcting mis-orientation of medical images. One or more image processing software modules are used to extract (101) anatomical areas from the medical images. It is determined (103) whether the extracted anatomical areas correspond to reference anatomical areas, but the reference anatomical areas have associated thereto data indicating the orientation of the reference anatomical areas. If the extracted anatomical areas correspond with the reference anatomical areas, the true orientation of the extracted anatomical areas is determined (105) by realigning the medical image until the orientation of the extracted anatomical areas corresponds to the orientation of the reference anatomical areas.

Abstract: To permit gripping of tacky prepreg blanks with stepped surface in an apparatus for the transport of tacky prepreg blanks comprising a suction head, a suction head suspension, a suction pump and a drive generating the movement of the suction head in the vertical and horizontal direction, the suction head (1) comprises a plurality of individually sprung suction tubes (12).