Abstract: In one embodiment, a method of detecting centerline of a vessel is provided. The method comprises steps of acquiring a 3D image volume, initializing a centerline, initializing a Kalman filter, predicting a next center point using the Kalman filter, checking validity of the prediction made using the Kalman filter, performing template matching, updating the Kalman filter based on the template matching and repeating the steps of predicting, checking, performing and updating for a predetermined number of times. Methods of automatic vessel segmentation and temporal tracking of the segmented vessel is further described with reference to the method of detecting centerline.

Abstract: In one embodiment, a method of detecting centerline of a vessel is provided. The method comprises steps of acquiring a 3D image volume, initializing a centerline, initializing a Kalman filter, predicting a next center point using the Kalman filter, checking validity of the prediction made using the Kalman filter, performing template matching, updating the Kalman filter based on the template matching and repeating the steps of predicting, checking, performing, and updating for a predetermined number of times. Methods of automatic vessel segmentation and temporal tracking of the segmented vessel is further described with reference to the method of detecting centerline.

Abstract: In one embodiment, a method of detecting centerline of a vessel is provided. The method comprises steps of acquiring a 3D image volume, initializing a centerline, initializing a Kalman filter, predicting a next center point using the Kalman filter, checking validity of the prediction made using the Kalman filter, performing template matching, updating the Kalman filter based on the template matching and repeating the steps of predicting, checking, performing, and updating for a predetermined number of times. Methods of automatic vessel segmentation and temporal tracking of the segmented vessel is further described with reference to the method of detecting centerline.

Abstract: In one embodiment, a method of detecting centerline of a vessel is provided. The method comprises steps of acquiring a 3D image volume, initializing a centerline, initializing a Kalman filter, predicting a next center point using the Kalman filter, checking validity of the prediction made using the Kalman filter, performing template matching, updating the Kalman filter based on the template matching and repeating the steps of predicting, checking, performing and updating for a predetermined number of times. Methods of automatic vessel segmentation and temporal tracking of the segmented vessel is further described with reference to the method of detecting centerline.

Abstract: In one embodiment, a method of detecting centerline of a vessel is provided. The method comprises steps of acquiring a 3D image volume, initializing a centerline, initializing a Kalman filter, predicting a next center point using the Kalman filter, checking validity of the prediction made using the Kalman filter, performing template matching, updating the Kalman filter based on the template matching and repeating the steps of predicting, checking, performing and updating for a predetermined number of times. Methods of automatic vessel segmentation and temporal tracking of the segmented vessel is further described with reference to the method of detecting centerline.

Abstract: In one embodiment, a method of detecting centerline of a vessel is provided. The method comprises steps of acquiring a 3D image volume, initializing a centerline, initializing a Kalman filter, predicting a next center point using the Kalman filter, checking validity of the prediction made using the Kalman filter, performing template matching, updating the Kalman filter based on the template matching and repeating the steps of predicting, checking, performing and updating for a predetermined number of times. Methods of automatic vessel segmentation and temporal tracking of the segmented vessel is further described with reference to the method of detecting centerline.

Abstract: A technique is provided for sharing clinical protocols for diagnostic imaging systems. The clinical protocols generally represent operational parameters, such as configuration data and procedures, which are clinically developed for a particular imaging diagnosis. An interface or access point, such as a network accessible database or website, is provided to facilitate the exchange of these clinical protocols between clinicians. The present technique also may facilitate the formation of new clinical protocols and/or the integration of new clinical protocols into various diagnostic imaging systems. Accordingly, clinicians can electronically exchange and configure a variety of imaging protocols for potentially greater quality in the particular imaging diagnosis.

Abstract: A computer-aided detection system for evaluating tissue based on a series of timed images acquired both before and after a contrast agent is administered performs a temporal analysis of the tissue and then a spatial analysis in which the tissue is categorized. After the temporal and spatial analysis is performed, the results can be displayed. The displayed results can include both tissue characterization results, underlying curves used to determine the characterization, and confidence data regarding how good the characterization is expected to be. The confidence data can be provided, for example, by using variations in color schemes, displaying numerical confidence levels, or providing graphical features such as piecewise linear models.

Abstract: A technique is provided for sharing clinical protocols for diagnostic imaging systems. The clinical protocols generally represent operational parameters, such as configuration data and procedures, which are clinically developed for a particular imaging diagnosis. An interface or access point, such as a network accessible database or website, is provided to facilitate the exchange of these clinical protocols between clinicians. The present technique also may facilitate the formation of new clinical protocols and/or the integration of new clinical protocols into various diagnostic imaging systems. Accordingly, clinicians can electronically exchange and configure a variety of imaging protocols for potentially greater quality in the particular imaging diagnosis.

Abstract: The present invention is a method and apparatus for semi-automatically registering low signal to noise ratio relaxation-time images, with particular application to tracking motion in moving organs and quantifying myocardial perfusion reserve. For each image acquired in a time series, a corresponding high signal to noise ratio image is extrapolated from the collected data. The high signal to noise ratio images are registered to track motion, and the resulting registration data is copied onto the corresponding relaxation time image.

Abstract: The present invention is a method and apparatus for semi-automatically registering low signal to noise ratio relaxation-time images, with particular application to tracking motion in moving organs and quantifying myocardial perfusion reserve. For each image acquired in a time series, a corresponding high signal to noise ratio image is extrapolated from the collected data. The high signal to noise ratio images are registered to track motion, and the resulting registration data is copied onto the corresponding relaxation time image.