Abstract: To produce a black body angiographic image representation of a subject (42), an imaging scanner (10) acquires imaging data that includes black blood vascular contrast. A reconstruction processor (38) reconstructs a gray scale image representation (100) from the imaging data. A post-acquisition processor (46) transforms (130) the image representation (100) into a pre-processed image representation (132). The processor (46) assigns (134) each image element into one of a plurality of classes including a black class corresponding to imaged vascular structures, bone, and air, and a gray class corresponding to other non-vascular tissues. The processor (46) averages (320) intensities of the image elements of the gray class to obtain a mean gray intensity value (322), and replaces (328) the values of image elements comprising non-vascular structures of the black class with the mean gray intensity value.

Abstract: A magnetic resonance angiographic method includes acquiring (70) high resolution volume image data comprising data (74) corresponding to a plurality of high resolution image slices, and acquiring (72) data corresponding to at least one vessel identification image slice (76), said acquired data having selectively enhanced contrast for one of arteries and veins. A high resolution volume image representation (80) is reconstructed from the acquired high resolution volume image data (74). At least one vessel identification slice image representation (82) is reconstructed from the acquired data corresponding to at least one vessel identification image slice (76). At least one of an artery starting point (86) and a vein starting point (88) is identified based on the vessel identification slice image representation (82).

Abstract: A two-dimensional slice formed of pixels (376) is extracted from the angiographic image (76) after enhancing the vessel edges by second order spatial differentiation (78). Imaged vascular structures in the slice are located (388) and flood-filled (384). The edges of the filled regions are iteratively eroded to identify vessel centers (402). The extracting, locating, flood-filling, and eroding is repeated (408) for a plurality of slices to generate a plurality of vessel centers (84) that are representative of the vascular system. A vessel center (88) is selected, and a corresponding vessel direction (92) and orthogonal plane (94) are found. The vessel boundaries (710) in the orthogonal plane (94) are identified by iteratively propagating (704) a closed geometric contour arranged about the vessel center (88). The selecting, finding, and estimating are repeated for the plurality of vessel centers (84). The estimated vessel boundaries (710) are interpolated (770) to form a vascular tree (780).

Abstract: An imaging system and method enables 3-D direct segmentation from a series of spatially offset 2-D image slices in a volume scan. The algorithm first smoothes and preserves the interface edges of the image volume using Bottom-Hat gray scale morphological transform followed by 3-D segmentation using fast 3-D level sets by preserving topology constraints, for example, cortical thickness in a brain volume. The method inputs opposite polarity spheres (contracting and expanding spheres) which morph into shapes within the volume using a surface propagation technique. The speed of propagation is controlled by the likelihood statistical component derived under constraints. During the propagation polygonalization extracts the zero-level surface set. The field distribution is computed using the improved shortest distance method or polyline distance method. The morphing algorithm then morphs the input concentric spheres into interface surfaces such as WM-GM and GM-CSF with cortical constraint.

Abstract: A method of digital imaging includes receiving image data and fitting a curve to boundaries within the image data. The curve is fit to the boundaries within the image data by extracting a region of interest from the image data and computing a signed distance transform in a narrow band within the region of interest. Finite difference equations including various variables are solved to determine a rate at which the distance transform changes. The distance transform is then diffused at that rate. The technique is based on region-based diffusion propagation, pixel classification, and mathematical morphology. The method is implemented to run in the narrow band of the region of interest specified by the user and the computations are implemented using a fast marching method in the narrow band. While idealized for distinguishing segments of white matter, gray matter, and cerebral spinal fluid in the brain, the algorithm can applied to find contours in any digital image.

Abstract: A medical imaging system includes an imaging device for producing temporally spaced image representations, a memory for storing the image representations, and a processor. The processor aligns the image representations by iteratively deriving a transform between progressively smaller sub-sets of pixels in temporally adjacent images until the sub-sets converge, and, applies the transform to the temporally adjacent images. The processor further sorts selected pixels in a region of interest spatially within each of the image representations for further processing by classifying pixels in the images according to a characteristic indicative of an absorption rate of the contrast agent.

Abstract: A magnetic resonance angiographic method includes acquiring (70) high resolution volume image data comprising data (74) corresponding to a plurality of high resolution image slices, and acquiring (72) data corresponding to at least one vessel identification image slice (76), said acquired data having selectively enhanced contrast for one of arteries and veins. A high resolution volume image representation (80) is reconstructed from the acquired high resolution volume image data (74). At least one vessel identification slice image representation (82) is reconstructed from the acquired data corresponding to at least one vessel identification image slice (76). At least one of an artery starting point (86) and a vein starting point (88) is identified based on the vessel identification slice image representation (82).

Abstract: A medical imaging display system includes a memory (40) for storing first image data representative of a region of interest. The memory (40) stores image data generated by medical imaging devices such as magnetic resonance devices (20), computed tomography devices (22), nuclear imaging devices (26,28,30), and ultrasound devices. Typically, image data from these devices is obtained some time prior to a surgical event and users may access this data in planning for the surgical event. A processor (42), in data communication with the memory, is organized under a component object modeling architecture. The processor (42) is connected to a user interface (10) for providing user requests to the processor. Thus, in response to user action via the user interface (10), the processor (42) determines an object (54) adapted to act on the request, selects a handle (60) for the determined object and, employs the object via the handle to act on the request.

Abstract: A magnetic resonance imaging method includes acquiring a baseline magnetic resonance image of a region of interest in the absence of a contrast agent and simulating an increase in image intensity of a subregion of interest within the region of interest which is subject to increased image intensity in the presence of a contrast agent. The magnetic resonance k-space signal intensity is correlated with contrast agent concentration in the subregion and a contrast agent is administered to the subject. As k-space data for the region of interest is acquired, the signal intensity is monitored to derive contrast agent concentration information. When the peak contrast agent concentration is detected from the monitored k-space data signal intensity, the phase encoding is adjusted so that k-space data with zero phase encoding is acquired. In a further aspect, a magnetic resonance imaging apparatus is also provided.

Abstract: In magnetic resonance angiography (MRA), the MRA data (40) is smoothed and converted into an isotropic format (52). A binary surface fitting mask (56) that differentiates vascular regions from surrounding tissue is generated from the isotropic MRA data. Vascular starting points (60) are identified based on the binary surface fitting mask. The vascular system corresponding to each starting point is tracked (62). The tracked vascular system is graphically displayed (68). Preferably, the arteries and the veins in the binary surface fitting mask data are differentiated (58) based on anatomical constraints. The tracking (62) preferably includes estimating an oblique plane that is orthogonal to the vessel (204), determining the vessel edges in the oblique plane (212), and determining an estimated vessel center in the oblique plane (216). The vessel edges are preferably determined by determining a raw vessel edge (208), and refining the raw vessel edge to obtain a refined vessel edge representation (212).

Abstract: A magnetic resonance imaging method includes acquiring a baseline magnetic resonance image of a region of interest in the absence of a contrast agent and simulating an increase in image intensity of a subregion of interest within the region of interest which is subject to increased image intensity in the presence of a contrast agent. The magnetic resonance k-space signal intensity is correlated with contrast agent concentration in the subregion and a contrast agent is administered to the subject. As k-space data for the region of interest is acquired, the signal intensity is monitored to derive contrast agent concentration information. When the peak contrast agent concentration is detected from the monitored k-space data signal intensity, the phase encoding is adjusted so that k-space data with zero phase encoding is acquired. In a further aspect, a magnetic resonance imaging apparatus is also provided.