Abstract: Certain embodiments of the present invention provide a method and apparatus for identifying and segmenting vascular structure in an image including: receiving at least one image including a vascular network; identifying at least one seed point corresponding to the vascular network; identifying automatically at least a portion of the vascular network to form an original vascular identification based at least in part on the at least one seed point; and allowing a dynamic user interaction with the vascular identification to form an iterative vascular identification. In an embodiment, the iterative vascular identification is formable in real-time. In an embodiment, the iterative vascular identification is displayable in real-time. In an embodiment, the iterative vascular identification is formable without re-identifying substantially unaltered portions of the vascular identification.

Abstract: Certain embodiments of the present invention provide a method and apparatus for identifying vascular structure in an image including: receiving at least one image including a vascular network; identifying at least one seed point corresponding to the vascular network; identifying automatically at least a portion of the vascular network to form an original vascular identification based at least in part on the at least one seed point; and allowing a dynamic user interaction with the vascular identification to form an iterative vascular identification. In an embodiment, the iterative vascular identification is formable in real-time. In an embodiment, the iterative vascular identification is displayable in real-time. In an embodiment, the iterative vascular identification is formable without re-identifying substantially unaltered portions of the vascular identification.

Abstract: Certain embodiments of the present invention provide a method for identifying vascular structure in an image including: receiving at least one image including a vascular network; identifying at least one seed point corresponding to the vascular network; identifying automatically at least a portion of the vascular network to form an original vascular identification based at least in part on the at least one seed point; and allowing a dynamic user interaction with the vascular identification to form an iterative vascular identification. In an embodiment, the iterative vascular identification is formable in real-time. In an embodiment, the iterative vascular identification is displayable in real-time. In an embodiment, the iterative vascular identification is formable without re-identifying substantially unaltered portions of the vascular identification.

Abstract: A method includes receiving a multi-phase axial cardiac dataset, receiving a selection of a phase from a user, when the received selection is systole, generating an endocardial volume of a left ventricle at an end systole phase without further user intervention, and when the received selection is diastole, generating an endocardial volume of the left ventricle at an end diastole phase without further user intervention.

Abstract: A method for selecting and/or labeling a vessel image includes operating an imaging system to obtain reconstructed image data including a volume having vessels therein. A bone removal routine is then applied to the reconstructed image data to produce a bone-free image. An initial point on a main vessel in the bone-free image is located, and bifurcation points and branches departing from the main vessel are identified. An adjacency graph of each branch coming from the main vessel is built. The method includes either or both of selecting and displaying a best or at least a favorable path through the vessels in accordance with predetermined criteria using the adjacency branch, or labeling and displaying branches of the main vessel.

Abstract: Systems, methods and computer products for automatically extracting automatically cardiac calcifications and obtaining a centerline in the tracking. Exemplary embodiments include a method of cardiac diagnostics, the method including obtaining coronary tree segmentation to obtain cardiac volume information, splitting the volume into portions to obtain an adjacency graph, computing a mean of a sub-volume of the volume, obtaining gray value segmentation of the sub-volume, defining a centerline of a blood vessel that avoids calcifications within the blood vessel and detecting an actual centerline of the blood vessel and enhancing lumen visualization of the blood vessel.

Abstract: Certain embodiments of the present invention provide a method for identifying vascular structure in an image including: receiving at least one image including a vascular network; identifying at least one seed point corresponding to the vascular network; identifying automatically at least a portion of the vascular network to form an original vascular identification based at least in part on the at least one seed point; and allowing a dynamic user interaction with the vascular identification to form an iterative vascular identification. In an embodiment, the iterative vascular identification is formable in real-time. In an embodiment, the iterative vascular identification is displayable in real-time. In an embodiment, the iterative vascular identification is formable without re-identifying substantially unaltered portions of the vascular identification.

Abstract: A method for selecting and/or labeling a vessel image includes operating an imaging system to obtain reconstructed image data including a volume having vessels therein. A bone removal routine is then applied to the reconstructed image data to produce a bone-free image. An initial point on a main vessel in the bone-free image is located, and bifurcation points and branches departing from the main vessel are identified. An adjacency graph of each branch coming from the main vessel is built. The method includes either or both of selecting and displaying a best or at least a favorable path through the vessels in accordance with predetermined criteria using the adjacency branch, or labeling and displaying branches of the main vessel.

Abstract: An automatic segmentation of the left or right cavities of the heart muscle is carried out. Once it is decided which cavities have to be isolated, three steps are implemented. A first step is performed to determine which is the volume comprising the cardiac cavities in a volume image resulting from an examination. This first step comprises a thresholding operation and an erosion, and then the determining of the greatest connected component. In a second step, an identification) is made of the left and right cavities. In a third step, a precise reconstruction is made of the contours of the left or right cavities for which it is sought to make the segmentation. The reconstruction is done by the watershed algorithm.

Abstract: A method includes receiving a multi-phase axial cardiac dataset, receiving a selection of a phase from a user, when the received selection is systole, generating an endocardial volume of a left ventricle at an end systole phase without further user intervention, and when the received selection is diastole, generating an endocardial volume of the left ventricle at an end diastole phase without further user intervention.

Abstract: A method for generating views of a heart along anatomically useful planes includes receiving a cardiac 3D dataset and calculating at least one of a short axis and a long axis without user intervention.