Abstract: In a diagnostic imaging system (10), a user interface (82) facilitates viewing of 4D kinematic data sets. A set of reference points is selected in a first 3D image to designate an anatomical component. An algorithm (104) calculates a propagation of the selected reference points from the first 3D image into other 3D images. Transforms which describe the propagation of the reference points between 3D images are defined. An aligning algorithm (112) applies inverse of the transforms to the 3D images to define a series of frames for the video processor (120) to display, in which frames the designated anatomical component defined by the reference points in each of the 3D images remains fixed while the other portions of the anatomical region of interest move relative to the fixed designated anatomical component.

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: The apparatus comprises an input for receiving a suitable source image data of an object. The core of the apparatus is formed by a control unit 4 arranged to load image data from the input and determine a spatial position and orientation of a portion of the object and to automatically calculate actual parameters of the imaging geometry based on said position and orientation and using default parameters if the imaging geometry, selected by the control unit in accordance with the portion of the object. The apparatus according to the invention comprises a recognition module arranged to determine a spatial position and orientation of the portion of the object with respect to a coordinate system of an imaging apparatus conceived to use the actual parameters of the imaging geometry provided by the apparatus.

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: To improve the scanning effect, a scanning method is provided, which comprises the steps of performing at least one of an nCT scan and a CTA scan on an object so as to obtain a set of images; detecting characteristics of a region of interest based on the set of images; and performing a CTP scan on the region of interest by adopting the characteristics to obtain a CTP image. By deriving the characteristics of the region of interest, e.g. a lesion or an area covering the lesion, before performing the CTP scan, and by adapting the subsequent CTP scan based on the characteristics of the region of interest, the drawback introduced by a limited scan area of the CTP scanner is mitigated, or even overcome.

Abstract: The present invention relates to analysis of image data, e.g. brain image data, where regions of interest are identified in patient specific image data based on non-image data. The brain image data is analyzed by correlating non-image data (20) in the form of data indicative of a neurological deficit and an object model (21) to identify one or more regions of interest (22) in the brain model, mapping the brain model to patient specific brain image data to obtain target image data (24), and identifying the one or more regions of interest in the target image data.

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: The invention relates to a system (100) arranged to delineate the acute intracerebral haematoma in non-contrasted CT images in two stages. The first stage, performed by the extraction unit (110), employs an analysis of gray values of the image data in order to extract the candidate region. The candidate region may comprise both an acute haematoma and other regions having similar gray values, e.g., regions resulting from partial volume effects at the interface of the bony structures of the skull and the brain. The novel second stage, performed by the classification unit (120), analyzes spatial features of the candidate region such as, for example, the size, shape, and connectedness to the skull bone of the candidate region. Using spatial features of the candidate region improves the correctness of classification of the candidate region as a true or false acute haematoma.

Abstract: A scanner (10) is used to provide images for automated diagnoses of neurodegenerative diseases, such as Alzheimers disease. The images are registered (90) to a template (78). The aligned image is analyzed (60) in relation to reference image data (76, 80) which has been registered to the template which is contained in a knowledge maintenance engine (70) for similar patterns of hypo-intensity that would indicate (in the case of an FDG tracer) reduced glucose uptake in the brain. The most appropriate reference images for the analysis of the present study are chosen by a filter (74). The present study is then given a dementia score (84) as a diagnostic feature vector that indicates to a clinician the type and severity of the ailment based on the analysis. The scanner (10) can produce PET or other metabolic and MR images for diagnosis. The MR can be used to measure blood flow rate into the brain. From the blood flow rate and the metabolic image, tracer, e.g.

Abstract: The apparatus (1) comprises an input (2) for receiving a suitable source image data of an object. The core of the apparatus (1) is formed by a control unit (4) which is arranged to load image data from the input (2) and determine a spatial position and orientation of a portion of the object and to automatically calculate actual parameters (4a) of the imaging geometry based on said position and orientation and using default parameters if the imaging geometry, selected by the control unit (4) in accordance with the portion of the object. The apparatus (1) further comprises a storage unit (8) arranged to store at least one set of default parameters of the imaging geometry, which may be representative of an imaging protocol. The working memory (6) typically holds the (parts of) image data being processed and the instructions for the suitable image processing means used for processing those parts of the image data.

Abstract: The invention relates to a method, a system and a computer program for dynamic imaging of a moving object. First, motion between the elements of common portions of images Im(t), Im+1(t) is computed. At step 1 motion compensation the said elements is performed, using a suitable transformation. Assuming that Im+1 is the image that has to be transformed, its motion from m to m+1 is compensated applying the inverse motion estimation Formula (I) resulting in the reformatted image Formula (II) at position m. At step 2 grey value interpolation is performed, based on image Im and the transformed image I?m+1 resulting in j interpolated images Formula (III) with o<i?j. At step 3 spatial interpolation is carried out yielding a series of images for dynamic imaging of the moving object. The spatial interpolation is calculated placing the images Formula (III) at position i resulting in j images Formula (IV) with 0<i?j and the transformation's weighting factor w=i/j.

Abstract: In a diagnostic imaging system (10), a user interface (82) facilitates viewing of 4D kinematic data sets. A set of reference points is selected in a first 3D image to designate an anatomical component. An algorithm (104) calculates a propagation of the selected reference points from the first 3D image into other 3D images. Transforms which describe the propagation of the reference points between 3D images are defined. An aligning algorithm (112) applies inverse of the transforms to the 3D images to define a series of frames for the video processor (120) to display, in which frames the designated anatomical component defined by the reference points in each of the 3D images remains fixed while the other portions of the anatomical region of interest move relative to the fixed designated anatomical component.

Abstract: A potential region of interest segmentation device segments an image data into regions of potential interest. From the regions of potential interest, an identifying device identifies a region of interest including an object of interest based on a set of rules. A recognizing device differentiates among the identified objects of interest and selects at least one particular object of interest.