Respiratory Motion Estimation with Hybrid Implementation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Suk Jin Lee , Yuichi Motai

ABSTRACT

The extended Kalman filter (EKF) can be used for the purpose of training nonlinear neural networks to perform desired input–output mappings. To improve the computational requirements of the EKF, Puskorius et al. proposed the decoupled extended Kalman filter (DEKF) as a practical remedy for the proper management of computational resources. This approach, however, sacrifices computational accuracy of estimates because it ignores the interactions between the estimates of mutually exclusive weights. To overcome such a limitation, therefore, we proposed hybrid implementation based on EKF (HEKF) for respiratory motion estimate, which uses the channel number for the mutually exclusive groups and the coupling technique to compensate the computational accuracy. Moreover, the authors restricted to a DEKF algorithm for which the weights connecting inputs to a node are grouped together. If there are multiple input training sequences with respect to time stamp, the complexity can increase by the power of input channel number. To improve the computational complexity, we split the complicated neural network into a couple of the simple neural networks to adjust separate input channels. The experiment results validated that the prediction overshoot of the proposed HEKF was improved by 62.95 % in the average prediction overshoot values. The proposed HEKF showed the better performance by 52.40 % improvement in the average of the prediction time horizon. We have evaluated that a proposed HEKF can outperform DEKF by comparing the prediction overshoot values, the performance of tracking estimation value and the normalized root mean squared error (NRMSE). More... »

PAGES

67-89

References to SciGraph publications

  • 2008-06. Predicting respiratory motion signals for image-guided radiotherapy using multi-step linear methods (MULIN) in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • Book

    TITLE

    Prediction and Classification of Respiratory Motion

    ISBN

    978-3-642-41508-1
    978-3-642-41509-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-41509-8_4

    DOI

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    DIMENSIONS

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