Abstract: A system and method for detecting, diagnosing, and monitoring a disease and determining a disease signature including accessing patient deviation scores indicative of differences between patient data and reference data representative of a population segment, the patient deviation scores derived from longitudinal patient data such that the patient deviation scores include a plurality of sets of patient deviation scores, each set indicative of differences between patient data collected at a respective point in time and the reference data. The system and method also includes identifying a trend in the patient deviation scores for at least one clinical parameter, generating a report including a visual indication of the trend, and outputting the report. The report includes one or more views including Z, T, D, DT, and D feedback on T views, using image and non-image data.

Abstract: A system and method for detecting, diagnosing, and monitoring a disease and determining a disease signature including accessing patient deviation scores indicative of differences between patient data and reference data representative of a population segment, the patient deviation scores derived from longitudinal patient data such that the patient deviation scores include a plurality of sets of patient deviation scores, each set indicative of differences between patient data collected at a respective point in time and the reference data. The system and method also includes identifying a trend in the patient deviation scores for at least one clinical parameter, generating a report including a visual indication of the trend, and outputting the report. The report includes one or more views including Z, T, D, DT, and D feedback on T views, using image and non-image data.

Abstract: A system and method for detecting, diagnosing, and monitoring a disease and determining a disease signature including accessing patient deviation scores indicative of differences between patient data and reference data representative of a population segment, the patient deviation scores derived from longitudinal patient data such that the patient deviation scores include a plurality of sets of patient deviation scores, each set indicative of differences between patient data collected at a respective point in time and the reference data. The system and method also includes identifying a trend in the patient deviation scores for at least one clinical parameter, generating a report including a visual indication of the trend, and outputting the report. The report includes one or more views including Z, T, D, DT, and D feedback on T views, using image and non-image data.

Abstract: A method and system is provided for automatically processing a volumetric diagnostic image dataset. A first seed point is defined within a lesion. The lesion is within a first image dataset representative of a subject. A first boundary is defined in three dimensions within the first image dataset and the first seed point and the lesion are within the first boundary. At least one parameter is determined based on the first image dataset. A first segmentation algorithm is selected from a plurality of segmentation algorithms based on the at least one parameter, and the lesion is segmented using the first segmentation algorithm.

Abstract: Method and system for processing an object within a diagnostic image comprises segmenting a three dimensional (3D) object within a diagnostic image. A contour of the object is fitted with a 3D mesh comprising splines in at least first and second directions. The splines provide a plurality of editable control points, and the splines in the first direction intersect with the splines in the second direction at intersection points. A position of at least one control point on the 3D mesh is adjusted based on a user input.

Abstract: A CT system includes a scintillator array having a first length in an axial direction of the CT system, the scintillator array includes a plurality of scintillator cells along the first length. The CT system includes a controller configured to repeatedly position a subject fore and aft along the axial direction and over a second length of the CT system, the second length greater than the first length, energize an x-ray source to emit x-rays toward the subject while the subject is being repeatedly positioned, and obtain non-uniform projection data of the subject from the scintillator array, the non-uniform projection data comprising the x-rays received from the x-ray source while the subject is repeatedly positioned.

Abstract: Methods and systems for automatically generating a severity index for images of anatomical features of a patient are provided. In an exemplary embodiment, an image of an anatomical feature of a patient is compared with a normal, standardized image of the same anatomical feature. Based on this comparison, a deviation image for the anatomical feature is generated that represents the degree and manner the acquired image deviates from normal for that anatomical feature. The deviation image is automatically pattern matched against multiple images of known disease severity for the anatomical feature. Based on the automated pattern match, a known disease severity, such as in the form of a severity index, is provided as corresponding anatomical feature for the patient.

Abstract: A method and system for processing a cardiac image dataset acquired from an imaging system is provided. A location of a first potential blockage point within a first cardiac vessel is located. A functional impact of the first potential blockage point on the cardiac tissue is predicted, and a representation of the functional impact is provided.

Abstract: A method for managing data transmission between computing devices is disclosed. In one embodiment, the method includes monitoring a plurality of parameters of a computer network that includes a server and a client. The plurality of parameters may include a client resource parameter, a server resource parameter, and a network operating parameter. The disclosed method may also include automatically determining a desired compression level at which to send data to the client based at least in part on the client resource parameter, the server resource parameter, and the network operating parameter. Further, in one embodiment the method may include communicating the data from the server to the client at the desired compression level in response to a client request for the data. Various other methods, systems, and manufactures are also disclosed.

Abstract: A method for generating an image includes accessing data of a scan of an object, using at least one characteristic of the accessed data to delineate at least one item of interest in the data and generating a 3D visualization image wherein transparency levels for at least some pixels not representing the item of interest are set according to a first set of rules, and transparency levels for at least some pixels representing an interior portion of the item of interest are set according to a second set of rules different than the first set of rules, and at least some pixels representative of a transition area are set according to a third set of rules different than the first and second sets of rules.

Abstract: A process for purpose-specific enhancement filtering of anatomical data includes acts of selecting at least one specific purpose from a predetermined list of a plurality of analytical tools for anatomical data analysis and setting specific parameter values for the specific purpose. The process also includes acts of enhancing the anatomical data using a three-dimensional, segmentation-based filter, using the specific parameter values, to provide enhanced anatomical data and using the enhanced anatomical data for the selected specific purpose.

Abstract: A CT system includes a scintillator array having a first length in an axial direction of the CT system, the scintillator array includes a plurality of scintillator cells along the first length. The CT system includes a controller configured to repeatedly position a subject fore and aft along the axial direction and over a second length of the CT system, the second length greater than the first length, energize an x-ray source to emit x-rays toward the subject while the subject is being repeatedly positioned, and obtain non-uniform projection data of the subject from the scintillator array, the non-uniform projection data comprising the x-rays received from the x-ray source while the subject is repeatedly positioned.

Abstract: A method for managing medical image data transmission between computing devices is disclosed. In one embodiment, the method includes monitoring a plurality of parameters of a computer network that includes a server and a client. The plurality of parameters may include a client resource parameter, a server resource parameter, and a network operating parameter. The disclosed method may also include automatically determining a desired compression level at which to send medical image data to the client based at least in part on the client resource parameter, the server resource parameter, and the network operating parameter. Further, in one embodiment the method may include communicating the medical image data from the server to the client at the desired compression level in response to a client request for the image data. Various other methods, systems, and manufactures are also disclosed.

Abstract: Methods and systems for processing image data are described. One method includes identifying image data corresponding to an imaged trachea and identifying image data corresponding to imaged lungs. The method further includes separating the image data corresponding to the imaged trachea from the imaged data corresponding to the imaged lungs.

Abstract: A method for acquiring an image on an imaging system includes accessing at least first image data from a first imaging system, processing the first image data in accordance with a CAD algorithm, acquiring at least second image data based upon results of the CAD algorithm and processing the second image data in accordance with the CAD algorithm to confirm the results of the CAD algorithm regarding the first image data.

Abstract: A computer-implemented method for quantifying an object in a larger structure using a reconstructed image obtained from an imaging apparatus includes identifying an object boundary in a region of interest of the image and a region of exclusion, and determining a mean value of intensity in a neighborhood of the object to estimate a background value, excluding the region of exclusion. The method further includes determining an interior region, a transition region, and a background region of the object using the object boundary and a transfer function of the imaging apparatus, and determining and displaying a measure of object quantification utilizing intensities of at least one of the determined interior region or the transition region a method for quantifying an object in a larger structure using a reconstructed image obtained from an imaging apparatus.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes receiving image data from an imaging system and organizing the image data into multiple objects of interest. The method may also include identifying source-invariant features of the multiple objects of interest and classifying the multiple objects of interest via a learning algorithm into categories based, at least in part, on the identified source-invariant features. Further, the method may include outputting a report based at least in part on data derived from the classification of one or more of the multiple objects of interest. Additional methods, systems, and devices are also disclosed.

Abstract: A method for managing medical image data transmission between computing devices is disclosed. In one embodiment, the method includes monitoring a plurality of parameters of a computer network that includes a server and a client. The plurality of parameters may include a client resource parameter, a server resource parameter, and a network operating parameter. The disclosed method may also include automatically determining a desired compression level at which to send medical image data to the client based at least in part on the client resource parameter, the server resource parameter, and the network operating parameter. Further, in one embodiment the method may include communicating the medical image data from the server to the client at the desired compression level in response to a client request for the image data. Various other methods, systems, and manufactures are also disclosed.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing patient deviation data of a structural difference between a patient anatomical feature and a standardized anatomical feature. The method may also include comparing the patient deviation data to reference deviation data sets representative of multiple disease types. Each reference deviation data set may be representative of an expected deviation from the standardized anatomical feature for a particular disease type. The method may further include automatically identifying one or more potential patient disease types based at least in part on the comparison. Additional methods, systems, and manufactures are also disclosed.

Abstract: A data processing technique is provided. In one embodiment, a computer-implemented method includes accessing reference deviation maps for a plurality of disease types. The reference deviation maps may include subsets of maps associated with severity levels of respective disease types, and a disease severity score may be associated with each severity level. The method may include selecting patient severity levels for multiple disease types based on the subsets of reference deviation maps. Also, the method may include automatically calculating a combined patient disease severity score based at least in part on the disease severity scores associated with the selected patient severity levels, and may include outputting a report based at least in part on the combined patient disease severity score. Additional methods, systems, and manufactures are also disclosed.