University of Washington

Florence H Sheehan

Robert M Haralick

Edward L Bolson

Paul D Sampson

Gregory L Zick

Hain-Ching H Liu

Huaichuan Jin

Fred L Bookstein

Jasjit Suri

Yong Shao

Malcolm E Legget

Chang-Kyu Lee

John A McDonald

A method for defining a three-dimensional surface of at least a portion of a patient's heart, based on data obtained by ultrasound imaging of the heart. The imaging is carried out over at least one cardiac cycle and provides a plurality of images in different image planes made with an ultrasound transducer at known positions and orientations. At least three anatomical landmarks in these images are manually identified in each of the images. An ultrasound mesh model in which a three-dimensional abstract mesh defining an archetype shape for a population of other hearts is developed. This abstract mesh has associated with it a covariance that defines the extent of variation of control vertices in the mesh for the population of training data used to derive the mesh model. The mesh model is rigidly aligned with the images of the patient's heart. Predicted images in planes corresponding to those of the images for the patient's heart and derived from the mesh model are compared corresponding images of the patient's heart. Control vertices are included with the abstract mesh for reiteratively adjusting a shape of the mesh model to optimize the fit of the predicted images to the observed images of the patient's heart. This adjustment and comparison continues until an acceptable fit is obtained, yielding an output that defines the shape of the endocardium and epicardium of the left ventricle or other portion of the patient's heart, in three dimensions, for use in determining cardiac parameters.

Automated delineation of heart contours from images using

A method is disclosed for reconstructing a surface of an object using three-dimensional imaging data. While the method can be applied to reconstruct the surface of almost any object represented by three-dimensional data obtained by some imaging modality, an example of the technique is disclosed that reconstructs the surface of a left ventricle. In the disclosed example, digital imaging data for a patient's heart are traced producing a data set of points that define the border and specific anatomic features of the left ventricle. An abstract model of a generalized left ventricle is generated that fits a wide range of sizes and shapes of this portion of the heart. The abstract model includes an abstract control mesh in which the anatomic features are labeled and sharp (edge) characteristics are identified. Coordinates are assigned to the abstract control mesh, producing an initial embedded mesh, which is then subdivided twice to increase its smoothness. The embedded subdivided mesh is rigidly aligned with the data set points of the patient's left ventricle, and in particular, with the anatomic features. Finally, the aligned subdivided mesh is optimally fit to the data set points and anatomic features, yielding the reconstructed surface of the organ. The reconstruction of a surface using medical imaging data is a particularly good exemplary application of the invention, because it shows that the method can be employed for reconstructing the surface of any three-dimensional object that is defined by relatively sparse and noisy data points.

Reconstruction of three-dimensional objects using labeled piecewise

An automated method for evaluating image data taken over a sequence of image frames to determine a contour of a left ventricle (LV). The sequence of image frames are converted to digital data that identify a gray scale value for each pixel in each image frame. An initial LV region is estimated based upon training data parameters derived from processing manually drawn contours of the LV in the hearts of other individuals, corresponding to the sequence of image frames being analyzed. Prior to producing the initial estimate, the location of the aortic valve in the sequence of image frames for the patient is determined to serve as a reference. The data derived frown the contours of the LVs for the other hearts are rotated and translated relative to this reference to insure that the training data information corresponds to the position and orientation of the LV being analyzed. In addition, the effects of tissue mass comprising a diaphragm adjacent to the inferior portion of the LV are compensated by masking out the diaphragm. Uncertainty of the contour of the LV in this portion due to the presence of the diaphragm is thereby minimized. The resulting automatically determined contours of each image frame can be displayed to enable a physician to more readily diagnose physiological defects of the heart.

Method for determining the contour of an in vivo organ using multiple

A method for imaging and three-dimensional modeling portions of the heart, particularly the left ventricular endocardial and epicardial surfaces, using imaging data. Preferably, a transesophageal ultrasound probe is inserted into an esophagus of a patient to provide multiple plane imaging data at end systole and end diastole during a cardiac cycle. The image planes are then traced along the boundaries of the epicardial and endocardial surfaces to produce sets of data points, which are further processed and expanded through interpolation. These data points are used for modeling the endocardial and epicardial surface at the end systole and end diastole extremes of the cardiac cycle. A center surface is constructed between an inner and outer surface of the modeled surfaces of the left ventricle and either an average template of tiled sections is mapped onto this center surface, or a mesh of triangular tiled sections representing the inner surface is projected on the center surface to define corresponding numbered tiled sections on the center surface. The tiled sections of the center surface are then projected onto the inner and outer surfaces, defining triangular prisms. By determining the volume of the triangular prisms and an average area for their ends, the range of movement, which is equal to the volume divided by the average area, is determined. Similarly, for changes in wall thickness between the endocardial and epicardial surfaces at end diastole and end systole, the thickening of the cardiac wall is determined.

Determining cardiac wall thickness and motion by imaging and

A method for automatically evaluating image data taking over a sequence of image frames to determine a contour of a left ventricle (LV). The sequence of image frames are converted to digital data that identify a gray scale value for each pixel in each image frame. Using probability, an initial LV region is estimated based upon previously determined a priori parameters that are derived from processing manually drawn contours of the LV in other hearts, for the sequence of image frames being analyzed. The initial estimate of the LV region is refined using motion constraints, which are also derived from training data determined from the manually-drawn contours of other hearts. An LV region is estimated and its coordinates are extracted for use in applying global shape constraints that provide a more accurate LV boundary estimate for the region around an apex of the left ventricle. Finally, the estimated LV boundary estimate is tested for consistency, yielding an accepted estimate, or warning that the estimate is unreliable. The resulting automatically determined contours of each image frame can be displayed almost immediately after the image data are produced so that a physician can more readily diagnose physiological defects of the heart.

A method and system for identifying end systole and end diastole frames within an angiography sequence. A plurality of images produced during an angiography sequence are digitized, producing digital image data in which gray scale values for each of the pixels in the images are represented. The digital image data are input to a computer (48) to determine the frames in which the coronary arteries are most visible. The coronary arteries are made visible in the images by injecting a radio-opaque contrast substance into the arteries. The frames that occur a end diastole are preferred for diagnostic analysis because the arteries are distended, spread apart from each other, and moving very slowly. To identify such frames for further analysis, the total length of edges within a centered window covering approximately one-fourth of each image is determined. The edges represent spatial transitions between relatively light and dark areas in the image that occur across the borders of the coronary arteries. At end diastole during the cardiac cycle, the total length of the coronary arteries within the window is substantially less than at end systole, when the heart has contracted to a minimum volume. Frames in which a local maximum total edge length is observed thus depict the arteries at end systole. Similarly, frames having a local minimum total edge length show the arteries at end diastole. Either a spatial method or a discrete cosine transform (DCT) method is used for determining the total edge length in each frame.

Automatic indexing of cine-angiograms

A method for imaging and three-dimensional modeling portions of the heart, particularly, the left ventricular endocardial and epicardial surfaces, using imaging data. Preferably, a transesophageal ultrasound probe is inserted into an esophagus of a patient to provide multiple plane imaging data at end systole and end diastole during a cardiac cycle. The image planes are then traced along the boundaries of the epicardial and endocardial surfaces to produce sets of data points, which are further processed and expanded through interpolation. These data points are used for modeling the endocardial and epicardial surface at the end systole and end diastole extremes of the cardiac cycle. A center surface is constructed between an inner and outer surface of the modeled surfaces of the left ventricle and an average template of tiled sections is mapped onto this center surface. The mapped tiled sections are then projected onto the inner and outer surfaces, defining triangular prisms. By determining the volume of the triangular prisms and an average area for their ends, the range of movement, which is equal to the volume divided by the average area, is determined. Similarly, for changes in wall thickness between the endocardial and epicardial surfaces at end diastole and end systole, the thickening of the cardiac wall is determined.

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Mercer Island, WA, United States of America

Florence H Sheehan