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An improved needle steering model with online parameter estimator

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose: Needle steering can improve targeting accuracy and guide the tip to areas that are currently not accessible. This paper proposes and validates a steering model to be capable of predicting the system dynamics during needle-tissue contact procedure.

Methods: The spring-beam-damper needle steering model we proposed has been extended with depth-varying mean parameters considering the tissue inhomogeneity. Local polynomial approximations in finite depth segments were adopted to estimate the unknown depth-varying mean parameters. Based on this approach, an online parameter estimator has been designed using modified least square method with forgetting factor to estimate the parameters using the online measured dataset.

Results: Extensive experiments have been carried out in various phantoms to validate the improved needle steering model based on online experiment data. Results have shown that the model can track the needle tip trajectory with satisfactory accuracy even in the presence of large disturbances and noises. The convergence rate and estimation accuracy can be greatly improved when a needle is suitably supported.

Conclusion: The model can give satisfactory prediction if the input data are properly collected to avoid the sensor noises.

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Authors and Affiliations

  1. Schools of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Kai Guo Yan & Wan Sing Ng

  2. Department of Radiation Oncology, Thomas Jefferson University Hospital, Philadelphia, PA, USA

    Tarun Podder & Yan Yu

  3. Department of Surgery, National University Hospital, Singapore, Singapore

    Di Xiao

  4. Computer Integrated Manufacturing Lab, California State University, Sacramento, CA, USA

    Tien-I Liu

  5. Department of Urology, Singapore General Hospital, Singapore, Singapore

    CWS Cheng

Authors
  1. Kai Guo Yan
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  2. Tarun Podder
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  3. Di Xiao
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  4. Yan Yu
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  5. Tien-I Liu
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  6. CWS Cheng
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  7. Wan Sing Ng
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Corresponding author

Correspondence to Kai Guo Yan.

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Yan, K.G., Podder, T., Xiao, D. et al. An improved needle steering model with online parameter estimator. Int J CARS 1, 205–212 (2006). https://doi.org/10.1007/s11548-006-0058-0

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  • DOI: https://doi.org/10.1007/s11548-006-0058-0

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