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Optimal Sensor Placement for Predictive Cardiac Motion Modeling

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Abstract

Subject-specific physiological motion modeling combined with low-dimensional real-time sensing can provide effective prediction of acyclic tissue deformation particularly due to respiration. However, real-time sensing signals used for predictive motion modeling can be strongly coupled with each other but poorly correlated with respiratory induced cardiac deformation. This paper explores a systematic framework based on sequential feature selection for optimal sensor placement so as to achieve maximal model sensitivity and prediction accuracy in response to the entire range of tissue deformation. The proposed framework effectively resolves the problem encountered by traditional regression methods in that the latent variables from both the input and output of the regression model are used to establish their inner relationships. Detailed numerical analysis andin vivo results are provided, which demonstrate the potential clinical value of the technique.

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Author information

Authors and Affiliations

  1. Department of Computing, Imperial College London,  

    Qian Wu, Adrian J. Chung & Guang-Zhong Yang

Authors
  1. Qian Wu

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  2. Adrian J. Chung

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  3. Guang-Zhong Yang

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Editor information

Editors and Affiliations

  1. Department of Informatics and Mathematical Modelling, Technical University of Denmark, Denmark

    Rasmus Larsen

  2. Nordic Bioscience, Herlev, Denmark

    Mads Nielsen

  3. Department of Computer Science, University of Copenhagen, Denmark

    Jon Sporring

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© 2006 Springer-Verlag Berlin Heidelberg

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Wu, Q., Chung, A.J., Yang, GZ. (2006). Optimal Sensor Placement for Predictive Cardiac Motion Modeling. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866763_63

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