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Spectral analysis of the tremor motion for needle detection in curvilinear ultrasound via spatiotemporal linear sampling

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

Abstract

Purpose

This paper presents a new approach to detect a standard handheld needle in ultrasound-guided interventions.

Methods

Our proposal is to use natural hand tremor, which causes minute displacement of the needle, to detect the needle in ultrasound B-mode images. Subtle displacements arising from tremor motion have a periodic pattern which is usually imperceptible to the naked eye in the B-mode image. We use these displacement measurements in a spatiotemporal framework to detect linear structures with periodic pattern among a sequence of frames. The needle trajectory is estimated as a linear path in the image having maximum spectral correlation with the time trace of displacement due to tremor. A coarse estimation process is followed by a fine estimation step, where the motion pattern is analyzed along spatiotemporal linear paths with various angles originating from the estimated puncture site, within the trajectory channel. Spectral coherency is derived for each sample path versus the reference path, and the needle trajectory is identified as the mean of the sample paths with the maximum coherence within the tremor frequency range.

Results

To evaluate the detection accuracy, we tested the method in vivo on porcine tissue, where the needle was inserted into the biceps femoris muscle. To understand whether tremor itself affects needle position, the maximum angular change due to tremor was calculated: mean, standard deviation (SD) and root-mean-square (RMS) measurement of \(0.43^\circ , 0.23^\circ \) and \(0.48^\circ \). The accuracy of the needle trajectory was calculated by comparing to an expert manual segmentation, averaged over the captured data and presented in mean, SD and RMS error of \(2.83^\circ , 1.64^\circ \) and \(3.23^\circ \), respectively.

Conclusion

Results demonstrate that natural tremor motion creates minute coherent motion along the needle, which could be used to localize the needle trajectory within the acceptable accuracy. This method is suitable for standard needles used clinically.

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References

  1. Adebar TK, Fletcher AE, Okamura AM (2014) 3D ultrasound-guided robotic needle steering in biological tissue. IEEE Trans Biomed Eng 61(12):2899–2910

    Article  PubMed  Google Scholar 

  2. Beigi P, Salcudean T, Rohling RN, Lessoway VA, Ng GC (2015) Needle detection in ultrasound using the spectral properties of the displacement field: a feasibility study. In: SPIE Medical Imaging

  3. Beigi P, Salcudean T, Rohling RN, Lessoway VA, Ng GC (2015) Needle trajectory and tip localization in 3D ultrasound using a moving stylus. Ultrasound Med Biol 41(7):2057–2070

    Article  PubMed  Google Scholar 

  4. Boctor EM, Choti MA, Burdette EC, Webster RJ (2008) Three-dimensional ultrasound-guided robotic needle placement: an experimental evaluation. Int J Med Robot Comput Assist Surg 4(2):180–91

    Article  Google Scholar 

  5. Cheung S, Rohling R (2004) Enhancement of needle visibility in ultrasound-guided percutaneous procedures. Ultrasound Med Biol 30(5):617–624

    Article  PubMed  Google Scholar 

  6. Chin KJ, Perlas A, Chan VWS, Brull R (2008) Needle visualization in ultrasound-guided regional anesthesia: challenges and solutions. Reg Anesth Pain Med 33(6):532–544

    PubMed  Google Scholar 

  7. Draper KJ, Blake CC, Gowman L, Downey DB, Fenster A (2000) An algorithm for automatic needle localization in ultrasound-guided breast biopsies. Med Phys 27(8):1971–1979

    Article  CAS  PubMed  Google Scholar 

  8. Harmat A, Rohling RN, Salcudean SE (2006) Needle tip localization using stylet vibration. Ultrasound Med Biol 32(9):1339–1348

    Article  PubMed  Google Scholar 

  9. Hatt CR, Ng G, Parthasarathy V (2015) Enhanced needle localization in ultrasound using beam steering and learning-based segmentation. Comput Med Imaging Graph 41:46–54

  10. Hebard S, Hocking G (2011) Echogenic technology can improve needle visibility during ultrasound-guided regional anesthesia. Reg Anesth Pain Med 36(2):185–189

    Article  PubMed  Google Scholar 

  11. Horn B, Schunck B (1981) Determining optical flow. Artif Intell 17:185–203

    Article  Google Scholar 

  12. Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: 7th International joint conference on artificial intelligence 81, 674–679

  13. Matalon TA, Silver B (1990) US guidance of interventional procedures. Radiology 174(1):43–47

    Article  CAS  PubMed  Google Scholar 

  14. Rosenfeld A (1970) A nonlinear edge detection technique. Proc IEEE lett 58(5):814–816

    Article  Google Scholar 

  15. Stiles RN (1976) Frequency and displacement amplitude relations for normal hand tremor. J Appl Physiol 40(1):44–54

    CAS  PubMed  Google Scholar 

  16. Sviggum HP, Ahn K, Dilger JA, Smith HM (2013) Needle echogenicity in sonographically guided regional anesthesia: blinded comparison of 4 enhanced needles and validation of visual criteria for evaluation. J Ultrasound Med 32(1):143–148

    PubMed  Google Scholar 

  17. Tikhonov AN (1963) On the solution of ill-posed problems and the method of regularization. Doklady Akademii Nauk SSSR 151:501–504

    Google Scholar 

  18. Uhercik M, Kybic J, Cachard C, Liebgott H (2009) Line filtering for detection of microtools in 3d ultrasound data. In: IEEE international ultrasonics symposium, pp. 594–597

  19. White LB, Boashash B (1990) Cross spectral analysis of non-stationary processes. IEEE Trans Inf Theory 36(4):830–835

    Article  Google Scholar 

  20. Wu Q, Yuchi M, Ding M (2014) Phase grouping-based needle segmentation in 3D trans-rectal ultrasound-guided prostate trans-perineal therapy. Ultrasound Med Biol 40(4):804–816

    Article  Google Scholar 

Download references

Acknowledgments

This work is jointly funded by a Collaborative Health Research Grant (CHRPJ 365561-09) sponsored by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Institutes of Health Research (CIHR). Thanks to Philips Ultrasound for supplying the ultrasound machine and research interface.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, University of British Columbia, Vancouver, BC, Canada

    Parmida Beigi, Robert Rohling & Septimiu E. Salcudean

  2. Mechanical Engineering Department, University of British Columbia, Vancouver, BC, Canada

    Robert Rohling

  3. Philips Ultrasound, Bothell, WA, USA

    Gary C. Ng

Authors
  1. Parmida Beigi
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  2. Robert Rohling
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  3. Septimiu E. Salcudean
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  4. Gary C. Ng
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Corresponding author

Correspondence to Parmida Beigi.

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The authors declare that they have no conflict of interest.

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All applicable international, national and/or institutional guidelines for the care and use of animals were followed.

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Beigi, P., Rohling, R., Salcudean, S.E. et al. Spectral analysis of the tremor motion for needle detection in curvilinear ultrasound via spatiotemporal linear sampling. Int J CARS 11, 1183–1192 (2016). https://doi.org/10.1007/s11548-016-1402-7

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  • DOI: https://doi.org/10.1007/s11548-016-1402-7

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