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 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: 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: A comprehensive strategy is used to determine valid reference time-concentration curves (TCCs) from image data. The image data corresponds to a series of image scans acquired over time for an area of interest of a patient to which a contrast agent was previously administered. The image scans are initially registered to a common coordinate system. Then, observed potential reference TCCs in the image scans are compared to modeled reference TCCs to determine if the potential reference TCCs are plausible reference TCCs. Thereafter, any plausible reference TCCs are evaluated to determine if they contain residual, isolated motion artifacts. If a plausible reference TCC does not include any motion artifacts, the plausible reference TCC is considered a valid reference TCC. If a plausible reference TCC is determined to include motion artifacts, the plausible reference TCC is modified to a valid reference TCC by removing the motion artifacts, or otherwise the plausible reference TCC is rejected.

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: 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 for generating a slicing scheme for slicing a specimen is disclosed. A parameter unit (1) is arranged for determining at least one parameter of a lesion in a specimen, based on an image dataset (14) representing at least part of the specimen. A slicing scheme unit (2) is arranged for determining a slicing scheme (15) for pathologic examination of the specimen, based on the at least one parameter. A specimen preparation unit (3) is arranged for determining a slicing preparation protocol, based on the image dataset, wherein the slicing preparation protocol comprises a representation of preparation steps relating to the specimen. A segmentation unit (5) is arranged for segmenting the lesion in the image dataset (14), wherein the parameter unit (1) is arranged for determining the at least one parameter, based on the segmented lesion.

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 system for associating acquired images with objects is disclosed. It comprises an image selector (1) for selecting a stored image from a database (5) comprising a plurality of stored images, the database (5) comprising an association (21) between the stored image (6) and an object of interest; an image scanner (2) for acquiring a new image (9) comprising a representation (10) of at least part of the object of interest, during an imaging session; a user interface (3) for enabling a user, during the imaging session, to indicate that the new image (9) is to be associated with the object of interest; and an associating subsystem (4) for creating an association (23) between the new image (9) and the object of interest in the database. The user interface (3) is arranged for enabling a user to select the object of interest from a plurality of objects of interest associated with the stored image (6).

Abstract: Image processing apparatus (100) for creating an overlaid presentation of a first input image (101) and a second input image (102) in an output image (108), the first input image comprising input values, the output image comprising vectors of output values, the vectors of output values representing colors of the output image, and the apparatus comprising an input (110) for obtaining the first input put image and the second input image, a rendering unit (140) configured for rendering the first input image in the output image by using a first mapping function for representing the input values in the vectors of output values, a predictor (120) configured for predicting the second input image from the first input image for obtaining a predicted second input image (104), a residual calculator (130) configured for calculating a residual image (106) from the second input image and the predicted second input image, the residual image comprising residual values representing prediction errors of the predicted second in

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 system (100) for processing a medical image (102), the system being arranged for establishing a region of interest in the medical image, and the system comprising segmentation means (120) for applying a plurality of different segmentation methods (124) to the region of interest for obtaining an associated plurality of segmentation results (122), visualization means (140) for simultaneously displaying the plurality of segmentation results to a user, and a user input (160) for receiving from the user a selection command (162) indicative of a selection of one of the plurality of segmentation results for establishing an associated one of the plurality of different segmentation methods as a selected segmentation method (164).

Abstract: A system for generating a slicing scheme for slicing a specimen is disclosed. A parameter unit (1) is arranged for determining at least one parameter of a lesion in a specimen, based on an image dataset (14) representing at least part of the specimen. A slicing scheme unit (2) is arranged for determining a slicing scheme (15) for pathologic examination of the specimen, based on the at least one parameter. A specimen preparation unit (3) is arranged for determining a slicing preparation protocol, based on the image dataset, wherein the slicing preparation protocol comprises a representation of preparation steps relating to the specimen. A segmentation unit (5) is arranged for segmenting the lesion in the image dataset (14), wherein the parameter unit (1) is arranged for determining the at least one parameter, based on the segmented lesion.

Abstract: Image processing apparatus (100) for creating an overlaid presentation of a first input image (101) and a second input image (102) in an output image (108), the first input image comprising input values, the output image comprising vectors of output values, the vectors of output values representing colors of the output image, and the apparatus comprising an input (110) for obtaining the first input put image and the second input image, a rendering unit (140) configured for rendering the first input image in the output image by using a first mapping function for representing the input values in the vectors of output values, a predictor (120) configured for predicting the second input image from the first input image for obtaining a predicted second input image (104), a residual calculator (130) configured for calculating a residual image (106) from the second input image and the predicted second input image, the residual image comprising residual values representing prediction errors of the predicted second in

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: A system for handling a specimen image is disclosed. An image input (1) is configured to receive a body image representing at least a portion of a living body and a specimen image representing a dissected portion of the living body. An alignment unit (2) is configured to compute an alignment of the specimen image with the body image, based on image content of the specimen image and/or the body image. A border detector (3) is operatively connected to the alignment unit (2) and configured to detect at least part of an outer border of the dissected portion in the specimen image.

Abstract: A system for associating acquired images with objects is disclosed. It comprises an image selector (1) for selecting a stored image from a database (5) comprising a plurality of stored images, the database (5) comprising an association (21) between the stored image (6) and an object of interest; an image scanner (2) for acquiring a new image (9) comprising a representation (10) of at least part of the object of interest, during an imaging session; a user interface (3) for enabling a user, during the imaging session, to indicate that the new image (9) is to be associated with the object of interest; and an associating subsystem (4) for creating an association (23) between the new image (9) and the object of interest in the database. The user interface (3) is arranged for enabling a user to select the object of interest from a plurality of objects of interest associated with the stored image (6).

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 comprehensive strategy is used to determine valid reference time-concentration curves (TCCs) from image data. The image data corresponds to a series of image scans acquired over time for an area of interest of a patient to which a contrast agent was previously administered. The image scans are initially registered to a common coordinate system. Then, observed potential reference TCCs in the image scans are compared to modeled reference TCCs to determine if the potential reference TCCs are plausible reference TCCs. Thereafter, any plausible reference TCCs are evaluated to determine if they contain residual, isolated motion artifacts. If a plausible reference TCC does not include any motion artifacts, the plausible reference TCC is considered a valid reference TCC. If a plausible reference TCC is determined to include motion artifacts, the plausible reference TCC is modified to a valid reference TCC by removing the motion artifacts, or otherwise the plausible reference TCC is rejected.

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.