Conference Paper

Reconstruction of 3D Dense Cardiac Motion From Tagged MR Sequences.

Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
DOI: 10.1109/ISBI.2004.1398679 Conference: Proceedings of the 2004 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 15-18 April 2004
Source: IEEE Xplore

ABSTRACT This paper develops an energy minimization algorithm to reconstruct the 3D motion of transplanted hearts of small animals (rats) from tagged magnetic resonance (MR) sequences. We describe the heart by a layered aggregate of thin oriented elastic fibers. We use the orientation of myocardial fibers to develop a local dense motion of the heart. This dense model is fit to the tagged MRI data by minimizing an energy functional with two terms: the first term is the external energy, derived from matching the image intensities on the fibers across two consecutive frames; the second term is the fibers' internal energy, derived from biomechanics analysis. This paper illustrates the application of the approach to a set of cardiac MR sequences containing four slices of a transplanted rat heart.

0 Bookmarks
 · 
41 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The overall objective of this project is to develop advanced techniques for scene analysis. Specifically, the problems of image registration, tracking, and change detection are considered. In this project, the following new techniques have been developed: 1) robust feature-based algorithm for object tracking, 2) motion-segmentation-based technique for change detection, 3) a target detection algorithm that consists of image differencing, maximum-margin classifier, and diversity combining, 4) a rotation-invariant transform for change detection, 5) a depth-based image registration algorithm, 6) an image registration algorithm that leverages wavelet, 7) a machine learning algorithm to automatically recover 3D surface from sparse 3D points, 8) an automatic surface fitting method for 3D reconstruction from 2D video sequence, and 9) a depth-based image registration method via geometric segmentation.
    05/2010;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: D reconstruction is a major problem in computer vision. This paper considers the problem of reconstructing 3D structures, given a 2D video sequence. This problem is challenging since it is difficult to identify the trajectory of each object point/pixel over time. Traditional stereo D reconstruction methods and volumetric D reconstruction methods suffer from the blank wall problem, and the estimated dense depth map is not smooth, resulting in loss of actual geometric structures such as planes. To retain geometric structures embedded in the 3D scene, this paper proposes a novel surface fitting approach for 3D dense reconstruction. Specifically, we develop an expanded deterministic annealing algorithm to decompose D point cloud to multiple geometric structures, and estimate the parameters of each geometric structure. In this paper, we only consider plane structure, but our methodology can be extended to other parametric geometric structures such as spheres, cylinders, and cones. The experimental results show that the new approach is able to segment D point cloud into appropriate geometric structures and generate accurate 3D dense depth map.
    J. Visual Communication and Image Representation. 01/2011; 22:421-431.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Contrast-enhanced magnetic resonance imaging (MRI) is useful to study the infiltration of cells in vivo. This research adopts ultrasmall superparamagnetic iron oxide (USPIO) particles as contrast agents. USPIO particles administered intravenously can be endocytosed by circulating immune cells, in particular, macrophages. Hence, macrophages are labeled with USPIO particles. When a transplanted heart undergoes rejection, immune cells will infiltrate the allograft. Imaged by T(2)(*)-weighted MRI, USPIO-labeled macrophages display dark pixel intensities. Detecting these labeled cells in the image facilitates the identification of acute heart rejection. This paper develops a classifier to detect the presence of USPIO-labeled macrophages in the myocardium in the framework of spectral graph theory. First, we describe a USPIO-enhanced heart image with a graph. Classification becomes equivalent to partitioning the graph into two disjoint subgraphs. We use the Cheeger constant of the graph as an objective functional to derive the classifier. We represent the classifier as a linear combination of basis functions given from the spectral analysis of the graph Laplacian. Minimization of the Cheeger constant based functional leads to the optimal classifier. Experimental results and comparisons with other methods suggest the feasibility of our approach to study the rejection of hearts imaged by USPIO-enhanced MRI.
    IEEE transactions on medical imaging. 09/2008; 27(8):1095-106.

Full-text (3 Sources)

View
18 Downloads
Available from
Jun 4, 2014