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Matching Sparse Sets of Cardiac Image Cross-Sections Using Large Deformation Diffeomorphic Metric Mapping Algorithm | SpringerLink
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Matching Sparse Sets of Cardiac Image Cross-Sections Using Large Deformation Diffeomorphic Metric Mapping Algorithm

  • Conference paper
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges (STACOM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7085))

Abstract

The purpose of this study is to illustrate the application of large deformation diffeomorphic metric mapping to perform registration among sparsely sampled cardiac magnetic resonance imaging (MRI) data. To evaluate the performance of this method, we use two sets of data: 1) contours that are generated from sparsely sampled left ventricular sections and extracted from short axis cardiac MRI of patients with hypertrophic cardiomyopathy and 2) left ventricular surface mesh that is generated from higher resolution cardiac computed tomography image. We present two different discrepancy criteria, one based on a measure that is embedded in the dual of a reproducing kernel Hilbert space of functions for curves and the other is based on a geometric soft matching distance between a surface and a curve.

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References

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Author information

Authors and Affiliations

  1. Center for Imaging Sciences, Johns Hopkins University, USA

    Siamak Ardekani, Aastha Jain, Saurabh Jain, Michael I. Miller & Laurent Younes

  2. Medical Institutions, Johns Hopkins University, USA

    Theodore P. Abraham, Maria R. Abraham & Stefan Zimmerman

  3. Institute for Computational Medicine, Johns Hopkins University, USA

    Raimond L. Winslow, Michael I. Miller & Laurent Younes

Authors
  1. Siamak Ardekani
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  2. Aastha Jain
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  3. Saurabh Jain
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  4. Theodore P. Abraham
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  5. Maria R. Abraham
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  6. Stefan Zimmerman
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  7. Raimond L. Winslow
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  8. Michael I. Miller
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  9. Laurent Younes
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Editor information

Editors and Affiliations

  1. Department of Information and Communication Technologies, University of Pompeu Fabra, Center for ComputationalImaging and Simulation Technologies in Biomedicine (CISTIB), Plaça de la Mercè 10-12, 08002, Barcelona, Spain

    Oscar Camara

  2. Machine Learning and Perception, Microsoft Research, 7 J J Thomson Avenue, CB3 0FB, Cambridge, UK

    Ender Konukoglu

  3. Department of Medical Biophysics, Ontario Cancer Institute, princess Margaret Hospital, University of Toronto, Faculty of Medicien, 610 University Avenue, M5G 2M9, Toronto, ON, Canada

    Mihaela Pop

  4. Division of Imaging Sciences, King’s College London, St. Thomas Hospital, Lambeth Wing, SE1 7EH, London, UK

    Kawal Rhode

  5. Research Centre, Ascöepios Team, Inria Sophia Antipolis- Méditerranée, 2004 Route des Lucioles, BP 93, 06902, Sophia Antipolis Cedex, France

    Maxime Sermesant

  6. Auckland Bioengineering Institute, Medical and Health Sciences, University of Auckland, Grafton Campus, Building 502, Auckland, New Zealand

    Alistair Young

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© 2012 Springer-Verlag Berlin Heidelberg

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Ardekani, S. et al. (2012). Matching Sparse Sets of Cardiac Image Cross-Sections Using Large Deformation Diffeomorphic Metric Mapping Algorithm. In: Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2011. Lecture Notes in Computer Science, vol 7085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28326-0_24

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  • DOI: https://doi.org/10.1007/978-3-642-28326-0_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28325-3

  • Online ISBN: 978-3-642-28326-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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