Abstract: A clinical decision support system (CDSS) includes a CDSS module comprising a digital processing device configured to store patient data and a clinical guideline. The clinical guideline comprises connected nodes representing clinical workflow events, actions, or decisions, wherein contiguous groups of the nodes are grouped into care phases. The CDSS module is configured to associate patient data with patient care phases selected from the care phases of the clinical guideline, and determine patient care phase time intervals for the patient care phases based on acquisition date information for the patient data associated with the patient care phases. The selected patient care phase may be a care phase associated with a clinical guideline node associated with the new patient data, or may be a patient care phase that is consistent with the acquisition date for the new patient data.

Abstract: A system for displaying lung ventilation information, the system comprising an input (12) and a processing unit (15). The input being provided for receiving multiple CT images (71) of a lung, each CT image (71) corresponding to one phase of at least two different phases in a respiratory cycle. The processing unit (15) being configured to compare CT images (71) corresponding to different phases in the respiratory cycle for determining a deformation vector field for each phase, to generate for each phase a ventilation image (72) based on the corresponding deformation vector field, to spatially align the ventilation images (72), and to generate for at least one common position (62) in each one of the aligned ventilation images (72), a function (81) of a time course of a ventilation value for said common position (62), each ventilation value in the function (81) being based on the deformation vector fields corresponding to the aligned ventilation images (73).

Abstract: A data structure for use by a computer system for processing medical image data and representing at least one first region of a patient includes at least one computer-detected feature of interest. The data structure includes a first computer code that is executable to detect first data representing at least one second region included within a respective first region. At least one said feature of interest in said second region has a significant likelihood of representing a computer-detected false positive. The second computer code is executable to provide second data for enabling at least one said first region to be displayed on a display device, such that at least one said second region is displayed on the display apparatus differently from part of said first region not containing features of interest having a significant likelihood of representing computer-detected false positives.

Abstract: An apparatus produces image space data (35) indicative of the spatially varying strength of the vascular connections between locations in the image space and a lesion or other feature of interest. The data may be presented by way of a maximum intensity projection (MIP) display in which the brightness of the image represents the strength of the vascular connection.

Abstract: A hot spot detection system for automatically segmenting and quantifying hot spots in functional images includes a segmentation unit (76) to segment an anatomical image representation (72) into regions corresponding to anatomical structures of a subject. A hot spot detection unit (90) detects regions of high uptake from a functional second image representation (74). The regions of high tracer uptake indicate high metabolic activity which maybe caused by potentially hazardous tumor lesions or other malignant processes. However, a number of normally functioning organs uptake high amounts of imaging tracer, particularly FDG. Therefore, a suppression unit (102) suppresses regions of high tracer uptake in the functional second image representation based on the results of a classification unit (101). The classification unit classifies the regions of high tracer uptake according to their position relative to the anatomical structures segmented from the anatomical first image representation.

Abstract: A clinical decision support system (CDSS) includes a CDSS module (20) comprising a digital processing device (10) configured to store patient data (34) and a clinical guideline (22). The clinical guideline comprises connected nodes representing clinical workflow events, actions, or decisions, wherein contiguous groups of the nodes are grouped into care phases (40). The CDSS module is configured to associate patient data with patient care phases selected from the care phases of the clinical guideline, and determine patient care phase time intervals for the patient care phases based on acquisition date information for the patient data associated with the patient care phases. The selected patient care phase may be a care phase associated with a clinical guideline node associated with the new patient data, or may be a patient care phase that is consistent with the acquisition date for the new patient data.

Abstract: The invention relates to a system (100) for mapping a patient data structure (PD) for describing a patient's case into a guideline data structure (GD) for describing a medical guideline, the system comprising storage (170) for storing: a plurality of data items (DI1; DI2; . . . ; DI99); the patient data structure (PD) comprising data items (DI1; DI3; DI5; DI27; DI47, DI67, DI74) of the plurality of data items (DI1; DI2; . . .

Abstract: A method for splitting a dataset relating to an anatomical tree structure (12) comprises establishing a plurality of seed points (24) within the tree structure; establishing a length of a path (20) along the tree structure from each of the plurality of seed points (24) to each of a plurality of other points (14); establishing a Euclidean distance (26) from each of the plurality of seed points (24) to each of the plurality of other points (14); associating with the seed point (24) a measure representing a likelihood that the seed point is the root point in dependence on the established lengths (20) and distances (26); identifying the root point of the tree structure (12) as the seed point (24) associated with a maximum measure representing the likelihood that the respective seed point is the root point; and establishing the principal bifurcation point (64) in dependence on the root point.

Abstract: A hot spot detection system for automatically segmenting and quantifying hot spots in functional images includes a segmentation unit (76) to segment an anatomical image representation (72) into regions corresponding to anatomical structures of a subject. A hot spot detection unit (90) detects regions of high uptake from a functional second image representation (74). The regions of high tracer uptake indicate high metabolic activity which maybe caused by potentially hazardous tumor lesions or other malignant processes. However, a number of normally functioning organs uptake high amounts of imaging tracer, particularly FDG. Therefore, a suppression unit (102) suppresses regions of high tracer uptake in the functional second image representation based on the results of a classification unit (101). The classification unit classifies the regions of high tracer uptake according to their position relative to the anatomical structures segmented from the anatomical first image representation.

Abstract: An oncology monitoring system comprises: an image analysis module (42, 44) configured to perform an oncological monitoring operation based on images of a subject, for example acquired by positron emission tomography (PET) and computed tomography (CT); and a clinical guideline support module (10). The clinical guideline support module is configured to: display a graphical flow diagram (GFD) of a clinical therapy protocol for treating the subject comprising graphical blocks (B0, B1 B2, B3, B4, B5, B21, B211, B22, B221, B222, B223, B23, B231, B232) representing therapeutic or monitoring operations of the clinical therapy protocol including at least one monitoring operation performed by the image analysis module; annotate a graphical block of the graphical flow diagram with subject-specific information pertaining to a therapeutic or monitoring operation represented by the graphical block; and display an annotation (POP) of a graphical block (B211) responsive to selection of the graphical block by a user.

Abstract: A clinical decision support (CDS) system comprises a patient treatment histories database (10, 32) and a patient case navigation tool (10, 30) operative to select a patient treatment history from the patient treatment histories database and to display a flowchart representation (50) of at least a portion of the selected patient treatment history. Optionally, the navigation tool (10, 30) is further operative to selectively display a flowchart representation (64, 66) of a portion or all of a patient nonspecific treatment guideline not coinciding with the selected patient treatment history. Optionally, the CDS system further comprises a patient records query engine (10, 40) operative to receive a query and apply same against the patient treatment histories database to retrieve query results, the navigation tool (10, 30) being further operative to generate a query responsive to user input and to display query results retrieved for the query.

Abstract: A system and method for segmenting an image of an organ. The system and method including selecting a surface model of the organ, selecting a plurality of points on a surface of an image of the organ and transforming the surface model to the plurality of points on the image.

Abstract: A system (500) for visualizing a vascular structure represented by a three-dimensional angiography dataset is disclosed. Respective voxel values are associated with respective voxels. The dataset represents a vascular structure. The system comprises means (502) for establishing respective filling values; means (504) for identifying respective minimum filling values; means (506) for computing respective deficiency values; and an output (514) for providing a visualization in dependence on the deficiency values. A respective filling value is indicative of an amount of blood flow at the respective position in the vascular structure. A respective minimum filling value is a minimum of the filling values associated with the positions upstream of the respective position. A respective deficiency value is indicative of a difference between the filling value associated with the respective position and the minimum filling value associated with the respective position.

Abstract: A system for displaying lung ventilation information, the system comprising an input (12) and a processing unit (15). The input being provided for receiving multiple CT images (71) of a lung, each CT image (71) corresponding to one phase of at least two different phases in a respiratory cycle. The processing unit (15) being configured to compare CT images (71) corresponding to different phases in the respiratory cycle for determining a deformation vector field for each phase, to generate for each phase a ventilation image (72) based on the corresponding deformation vector field, to spatially align the ventilation images (72), and to generate for at least one common position (62) in each one of the aligned ventilation images (72), a function (81) of a time course of a ventilation value for said common position (62), each ventilation value in the function (81) being based on the deformation vector fields corresponding to the aligned ventilation images (73).

Abstract: A therapy treatment response simulator includes a modeler (202) that generates a model of a structure of an object or subject based on information about the object or subject and a predictor (204) that generates a prediction indicative of how the structure is likely to respond to treatment based on the model and a therapy treatment plan. In another aspect, a system includes performing a patient state determining in silico simulation for a patient using a candidate set of parameters corresponding to another patient and producing a first signal indicative of a predicted state of the patient, and generating a second signal indicative of whether the candidate set of parameters are suitable for the patient based on a known state of the patient.

Abstract: When treating a patient, clinical decision support system (CDSS) guidelines are employed to assist a physician in generating a treatment plan. These plans are generated using both imaging and non-imaging data. To accomplish this, the CDSS is interfaced with imaging systems (CADx, CAD, PACS etc.). A data-mining operation is performed to identify relevant patients with similar attributes such as diagnosis, medical history, treatment, etc from imaging and non-imaging data. Natural language processing is employed to extract and encode relevant non-imaging (textual) data from relevant patients' records. Additionally, an image of a current patient is compared to reference images in a patient database to identify relevant patients. Relevant patients are then identified to a user, and the user selects a relevant patient to view detailed information related to medical history, treatment, guidelines, efficacy, and the like.

Abstract: An apparatus produces image space data (35) indicative of the spatially varying strength of the vascular connections between locations in the image space and a lesion or other feature of interest. The data may be presented by way of a maximum intensity projection (MIP) display in which the brightness of the image represents the strength of the vascular connection.

Abstract: A method includes registering a first sub-portion of a first image with a corresponding second sub-portion of a second image, and registering the second sub-portion of the second image with a corresponding third sub-portion of the first image. The first sub-portion encompasses a first object of interest, and the third sub-portion encompasses a third object of interest. The method further includes reducing a size of the first sub-portion when the first and third objects of interest are substantially similar. The method further includes repeating the steps of registering the first sub-portion, registering the second sub-portion, and reducing the size of the first sub-portion until the first and third objects are not substantially similar.

Abstract: The present invention describes a way to quantify the trapped-air disease and how to allow efficient user interaction for inspection via a graphical user interface. The results of the invention may also be used for rapid and accurate diagnosis of trapped air disease. An apparatus, graphical user interface, computer-readable medium and use are also provided.

Abstract: A system (500) for visualizing a vascular structure represented by a three-dimensional angiography dataset is disclosed. Respective voxel values are associated with respective voxels. The dataset represents a vascular structure. The system comprises means (502) for establishing respective filling values; means (504) for identifying respective minimum filling values; means (506) for computing respective deficiency values; and an output (514) for providing a visualization in dependence on the deficiency values. A respective filling value is indicative of an amount of blood flow at the respective position in the vascular structure. A respective minimum filling value is a minimum of the filling values associated with the positions upstream of the respective position. A respective deficiency value is indicative of a difference between the filling value associated with the respective position and the minimum filling value associated with the respective position.