Estimation of 3D left ventricular deformation from echocardiography

Papademetris, Xenophon; Sinusas, Albert J.; Dione, Donald P.; Duncan, James S.

doi:10.1016/s1361-8415(00)00022-0

Cited by 179 publications

(122 citation statements)

References 37 publications

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“…The deformation fields, as well as the derived motion parameters such as myocardial strain, can be determined with accuracy [5,6]. The second category of methods uses segmentation of the myocardial wall, followed by geometrical and mechanical modeling using active contours or surfaces to extract the displacement field and to perform the motion analysis [5,7,8]. For matching two contours or surfaces, curvatures are frequently used to establish initial sparse correspondences, followed by the dense correspondence interpolation in other myocardial positions by regularization or mechanical modeling [5,9].…”

Section: Introductionmentioning

confidence: 99%

Consistent Estimation of Cardiac Motions by 4D Image Registration

Shen

Sundar

Xue

et al. 2005

Lecture Notes in Computer Science

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Abstract. A 4D image registration method is proposed for consistent estimation of cardiac motion from MR image sequences. Under this 4D registration framework, all 3D cardiac images taken at different time-points are registered simultaneously, and motion estimated is enforced to be spatiotemporally smooth, thereby overcoming potential limitations of some methods that typically estimate cardiac deformation sequentially from one frame to another, instead of treating the entire set of images as a 4D volume. To facilitate our image matching process, an attribute vector is designed for each point in the image to include intensity, boundary and geometric moment invariants (GMIs). Hierarchical registration of two image sequences is achieved by using the most distinctive points for initial registration of two sequences and gradually adding less-distinctive points for refinement of registration. Experimental results on real data demonstrate good performance of the proposed method in registering cardiac images and estimating motions from cardiac image sequences.

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Section: Introductionmentioning

confidence: 99%

Consistent Estimation of Cardiac Motions by 4D Image Registration

Shen

Sundar

Xue

et al. 2005

Lecture Notes in Computer Science

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“…In (Papademetris et al, 2001), biomedical models are used for heart motion analysis. The segmentation stage is semi-automatic and simplified by using opened chest dog heart images.…”

Section: Introductionmentioning

confidence: 99%

Anisotropic filtering for model-based segmentation of 4D cylindrical echocardiographic images

Montagnat

Sermesant

Delingette

et al. 2003

Pattern Recognition Letters

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This paper presents a 4D (3D þ time) echocardiographic image anisotropic filtering and a 3D model-based segmentation system. To improve the extraction of left ventricle boundaries, we rely on two preprocessing stages. First, we apply an anisotropic filter that reduces image noise. This 4D filter takes into account the spatial and temporal nature of echocardiographic images. Second, we adapt the usual gradient filter estimation to the cylindrical geometry of the 3D ultrasound images. The reconstruction of the endocardium takes place by deforming a deformable simplex mesh having an a priori knowledge of left ventricle shape and that is guided by a region-based data attraction force. The external force formulation improves the segmentation robustness against noise and outliers. We illustrate our method by showing experimental results on very challenging sparse and noisy ultrasound images of the heart and by computing quantitative measurements of the left ventricle volume.

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“…There are some other methods, which have been put on much research effort. For example, Pa- * University of Tsukuba, Graduate School of System and Information Engineering, 1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8573 pademetris et al proposed a method (12) by which myocardial boundaries are extracted from B-mode images. Then the myocardial strain tensors are calculated by applying the finite element method (FEM).…”

Section: Introductionmentioning

confidence: 99%