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A data-driven soft sensor for needle deflection in heterogeneous tissue using just-in-time modelling

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Abstract

Global modelling has traditionally been the approach taken to estimate needle deflection in soft tissue. In this paper, we propose a new method based on local data-driven modelling of needle deflection. External measurement of needle–tissue interactions is collected from several insertions in ex vivo tissue to form a cloud of data. Inputs to the system are the needle insertion depth, axial rotations, and the forces and torques measured at the needle base by a force sensor. When a new insertion is performed, the just-in-time learning method estimates the model outputs given the current inputs to the needle–tissue system and the historical database. The query is compared to every observation in the database and is given weights according to some similarity criteria. Only a subset of historical data that is most relevant to the query is selected and a local linear model is fit to the selected points to estimate the query output. The model outputs the 3D deflection of the needle tip and the needle insertion force. The proposed approach is validated in ex vivo multilayered biological tissue in different needle insertion scenarios. Experimental results in five different case studies indicate an accuracy in predicting needle deflection of 0.81 and 1.24 mm in the horizontal and vertical lanes, respectively, and an accuracy of 0.5 N in predicting the needle insertion force over 216 needle insertions.

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Acknowledgements

This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada under Grant CHRP 446520, the Canadian Institutes of Health Research (CIHR) under Grant CPG 127768, and Alberta Innovates—Health Solutions (AIHS) under Grant CRIO 201201232.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2V4, Canada

    Carlos Rossa, Thomas Lehmann & Mahdi Tavakoli

  2. Cross Cancer Institute and the Department of Oncology, University of Alberta, Edmonton, AB, T6G 1Z2, Canada

    Ronald Sloboda & Nawaid Usmani

Authors
  1. Carlos Rossa
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  2. Thomas Lehmann
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  3. Ronald Sloboda
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  4. Nawaid Usmani
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  5. Mahdi Tavakoli
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Corresponding author

Correspondence to Carlos Rossa.

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Rossa, C., Lehmann, T., Sloboda, R. et al. A data-driven soft sensor for needle deflection in heterogeneous tissue using just-in-time modelling. Med Biol Eng Comput 55, 1401–1414 (2017). https://doi.org/10.1007/s11517-016-1599-1

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  • DOI: https://doi.org/10.1007/s11517-016-1599-1

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