Optimal Sensor Placement for Predictive Cardiac Motion Modeling

  • Qian Wu
  • Adrian J. Chung
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


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 and in vivo results are provided, which demonstrate the potential clinical value of the technique.


Intensity Modulate Radiation Therapy Partial Little Square Regression Feature Subset Sequential Forward Selection Optimal Feature Subset 
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  1. 1.
    Keegan, J., Gatehouse, P.D., Yang, G.Z., Firmin, D.N.: Coronary Artery Motion with the Respiratory Cycle during Breath-holding and Free-breathing: Implications for Slice-followed Coronary Artery Imaging. Magn. Reson Med. 47, 476–481 (2002)CrossRefGoogle Scholar
  2. 2.
    Ablitt, N., Gao, J., Keegan, J., Stegger, L., Firmin, D.N., Yang, G.Z.: Predictive Cardiac Motion Modeling and Correction with Partial Least Squares Regression. IEEE Trans. Med. Imag. 23, 1315–1324 (2004)CrossRefGoogle Scholar
  3. 3.
    Leardi, R., González, A.L.: Genetic Algorithms Applied to Feature Selection in PLS Regression: How and When to Use Them. Chemometrics and Intellingent Laboratory Systems 41, 195–207 (1998)CrossRefGoogle Scholar
  4. 4.
    Robnik-Sikonja, M., Kononenko, I.: An Adaptation of Relief for Attribute Estimation in Regression. In: Fisher, D. (ed.) Machine Learning, Proceedings of 14th International Conference on Machine Learning ICML 1997, Nashville, TN (1997)Google Scholar
  5. 5.
    Narendra, P.M., Fukunaga, K.: A Branch and Bound Algorithm for Feature Subset Selection. IEEE Trans. Comput. 26, 917–922 (1977)MATHCrossRefGoogle Scholar
  6. 6.
    Whitney, A.W.: A Direct Method of Nonparametric Measurement Selection. IEEE Trans. Comput. 20, 1100–1103 (1971)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Marill, T., Green, D.M.: On the Effectiveness of Receptors in Recognition System. IEEE Trans. Inform. Theory 9, 11–17 (1963)CrossRefGoogle Scholar
  8. 8.
    Pudil, P., Novovicova, J., Kittler, J.: Floating Search Methods in Feature Selection. Pattern Recognition Letters 15, 1119–1125 (1994)CrossRefGoogle Scholar
  9. 9.
    Wold, H.: Soft Modeling by Latent Variables: the Nonlinear Iterative Partial Least Squares Approach. In: Gani, J. (ed.) Perspectives in Probability and Statistics, pp. 520–540. Academic Press, London (1975)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qian Wu
    • 1
  • Adrian J. Chung
    • 1
  • Guang-Zhong Yang
    • 1
  1. 1.Department of ComputingImperial College London 

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