Conference Paper

Early detection of rejection in cardiac MRI: a spectral graph approach.

Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA
DOI: 10.1109/ISBI.2006.1624865 Conference: Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 6-9 April 2006
Source: IEEE Xplore

ABSTRACT This paper develops an algorithm to detect abnormalities of small animals' transplanted hearts in MRI, at early stage of rejection when the hearts do not display prominent abnormal features. Existing detection methods require experts to manually identify these abnormal regions. This task is time consuming, and the detection criteria are operator dependent. We present a semi-automatic approach that needs experts to label only a small portion of the motion maps. Our algorithm begins with representing the left ventricular motions by a weighted graph that approximates the manifold where these motions lie. We compute the eigendecomposition of the Laplacian of the graph and use these as basis functions to represent the classifier. The experimental results with synthetic data and real cardiac MRI data demonstrate the application of our classifier to early detection of heart rejection

1 Bookmark
 · 
74 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we analyse mathematical properties of spatial optical-flow computation algorithm. First by numerical analysis, we derive the convergence property on variational optical-flow computation method used for cardiac motion detection. From the convergence property of the algorithm, we clarify the condition for the scheduling of the regularisation parameters. This condition shows that for the accurate and stable computation with scheduling the regularisation coefficients, we are required to control the sampling interval for numerical computation.
    Computer Analysis of Images and Patterns, 12th International Conference, CAIP 2007, Vienna, Austria, August 27-29, 2007, Proceedings; 01/2007
  • 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.
    09/2008; 27(8):1095-106. DOI:10.1109/TMI.2008.918329
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Graphs are canonical examples of high-dimensional non-Euclidean data sets, and are emerging as a common data structure in many fields. While there are many algorithms to analyze such data, a signal processing theory for evaluating these techniques akin to detection and estimation in the classical Euclidean setting remains to be developed. In this paper we show the conceptual advantages gained by formulating graph analysis problems in a signal processing framework by way of a practical example: detection of a subgraph embedded in a background graph. We describe an approach based on detection theory and provide empirical results indicating that the test statistic proposed has reasonable power to detect dense subgraphs in large random graphs.
    Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on; 04/2010

Full-text (5 Sources)

Download
43 Downloads
Available from
May 21, 2014