Image analysis – Applications – 3-d or stereo imaging analysis
Reexamination Certificate
2000-02-18
2004-06-29
Ahmed, Samir (Department: 3737)
Image analysis
Applications
3-d or stereo imaging analysis
C382S128000
Reexamination Certificate
active
06757423
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to methods for determining indicators of tissue motion from imaging data obtained from imaged tissue. In particular, this invention relates to methods for determining a displacement vector field and other indicators of tissue motion from tagged magnetic resonance imaging (MRI) data obtained from imaging a left ventricle of a heart.
2. Description of the Prior Art
Noninvasive imaging techniques for assessing the dynamic behavior of the human heart are invaluable in the diagnosis of ischemic heart disease, as abnormalities in the myocardial motion sensitively reflect deficits in blood perfusion. MRI is a noninvasive imaging technique that provides superb anatomic information with excellent spatial resolution and soft tissue contrast. Conventional Magnetic Resonance (MR) studies of the heart provide accurate measures of global myocardial function, chamber volume, ejection fraction, and regional wall motions. In MR tagging, the magnetization property of selective material points in the myocardium are altered in order to create tagged patterns within a deforming body such as the heart muscle. The resulting pattern defines a time-varying curvilinear coordinate system on the underlying tissue. During tissue contractions, the grid patterns move, allowing for visual tracking of the grid intersections over time. The intrinsic high spatial and temporal resolutions of such myocardial analysis schemes provide unsurpassed information about local contraction and deformation in the myocardium which can be used to derive local strain and deformation indices from different regions.
Previous research in analysis of tagged images includes work by Young, Kraitchman, Dougherty, and Axel who adopted an analysis system for tagged images based on snakes. Once the tag positions on the myocardium are found, coordinates of these points in deformed images are determined within a volumetric finite element model fitted to endocardial and epicardial contours. The work of Park, Metaxas, and Axel considers geometric primitives which are generalization of volumetric ellipsoids, through use of parameter functions which allow for spatial variations of aspect ratios of the model along the long axis (LA) of the LV. This model is specially useful for computing the twisting motion of the heart. Prince and McVeigh, and Gupta and Prince developed optical flow based approaches for analysis of tagged MR images. The approach of Guttman, Prince, and McVeigh for analysis of radial tagged images is to use a graph-search technique that determines the optimal inner and outer boundaries of the myocardium as well as tag lines by finding points one after the other in a sequence, using initial search starting points on the determined LV boundaries.
Denney and McVeigh presented a technique for reconstructing a three-dimensional myocardial strain map using the discrete model-free (DMF) algorithm, which decomposes the myocardial volume into a finely spaced mesh of points and reconstructs a three-dimensional displacement and strain fields based on local incompressibility and first order displacement smoothness. Moulton, et al., developed a method for approximating continuous smooth distributions of finite strains in the left ventricle from the deformations of MRI tissue tags. A 3-D displacement field on tag surfaces is extracted using sets of MR images and employing spline surface interpolation, followed by a global polynomial fit for determining 3-D displacements, and regional strains between the reference and deformed states.
O'Dell, et al., used images from three sequences of parallel tags from segmented k-space imaging obtained at different times. The least squares fitting of a truncated power series in the prolate spheroidal coordinate system on the whole of the myocardium is performed in order to measure dense displacements.
However, there is a need for a method which uses all of the extracted tag information. There is also a need for a method which performs B-spline interpolation over 3-D space and time simultaneously. There is also a need for a method which uses polynomial basis functions with local support for better interpolation properties as opposed to approaches which make use of global polynomial fits. There is also a need for a method which accurately captures the movement of each myocardial point over time. In addition, there is a need for a method which can obtain the shape of the left-ventricle at any time instant. There is also a need for a method which provides for fast computation of tag surfaces intersecting individual slices. There is also a need for a method which leads to an easy implementation algorithm for computing 3-D material points. There is also a need for a method which allows reconstruction of dense deformations between two arbitrary frames in a sequence of tagged images. There is also a need for a method which can obtain the change of strain over time at all myocardial points.
SUMMARY OF THE INVENTION
It is an object of this invention to provide a method of processing images generated from MRI data of moving tissue which method provides a less subjective assessment.
It is an object of this invention to provide a method of processing images generated from tagged MRI data of moving tissue which method, in the case of tagged MRI, provides visualization and measurement of motion at points between tag intersections.
It is an object of this invention to provide a method of processing images generated from tagged MRI data of moving tissue which method is fast and efficient.
It is an object of this invention to provide a method of processing images generated from tagged MRI data of moving tissue which method uses all of the available tag information in reconstructing dense motion fields.
It is an object of this invention to provide a method of processing images generated from tagged MRI data of moving tissue which method provides a way of extracting positions of non-invasively placed beads with MRI in the tissue.
It is an object of this invention to provide a method of processing images generated from tagged MRI data of moving tissue which method, in the case of motion of cardiac tissue, reveals motion of the beads for all time points imaged during the cardiac cycle.
It is also an object of this invention to provide such methods for use in fitting knot planes to tag planes and, once fit, for doing B-spline summations at various time intervals beginning with the initial interval at the onset of tags as indicated by an electrocardiogram for recovering cardiac tissue motion.
It is also an object of this invention to provide such methods for use in assessing left ventricular function from MRI and for use in other applications where one can image moving tissue in-vivo with tagged MRI.
It is also an object of this invention to provide such methods for use in assessing left ventricular function from tagged MRI.
It is also an object of the invention to provide a 4-D model that achieves in a single step 3-D material point localization and displacement reconstruction.
It is also an object of the invention to provide a mechanism for in-vivo measurement of tissue motion and strain.
In one form, the invention comprises a method for tracking motion of tissue in three or more dimensions by obtaining a model from imaging data from which a grid of control points may be defined. The imaging data has tag planes. The method comprises the steps of:
calculating knot planes from the grid of control points of the imaging data;
fitting the knot planes to the tag planes to obtain the model of the tissue; and
representing motion of tissue in three dimensions with the model of the tissue.
In another form, the invention comprises a method for representing indicators of tissue motion with a B-spline model from imaging data. The imaging data has tag planes. The method comprises the steps of:
defining displacement vectors corresponding to the tag planes;
fitting the B-spline model to the defined displacement vectors; and
deriving indicators of tissue
Ahmed Samir
Barnes-Jewish Hospital
Sonnenschein Nath & Rosenthal LLP
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