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| Title: Spatio-temporal deep learning models for tip force estimation during needle insertion. |
| Written by: N. Gessert and T. Priegnitz and T. Saathoff and S.-T. Antoni and D. Meyer and M. F. Hamann and K.-P. Jünemann and C. Otte and A. Schlaefer |
| in: <em>International Journal of Computer Assisted Radiology and Surgery</em>. May (2019). |
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| DOI: 10.1007/s11548-019-02006-z |
| URL: https://doi.org/10.1007/s11548-019-02006-z |
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Abstract: Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and needle deflection which in turn may lead to misplacement or injury. Hence, a number of approaches to estimate the forces at the needle have been proposed. Yet, integrating sensors into the needle tip is challenging and a careful calibration is required to obtain good force estimates.