Advertisement

Flexible needle and patient tracking using fractional scanning in interventional CT procedures

  • Guy MedanEmail author
  • Leo Joskowicz
Original Article
  • 18 Downloads

Abstract

Purpose

We present a new method for flexible needle and patient localization in interventional CT procedures based on fractional CT scanning. Our method accurately localizes the trajectory of a flexible needle to which a spherical marker is attached at a known distance from the tip with respect to a baseline scan of patient in the CT scanner coordinate frame.

Methods

The localization is achieved with a significantly lower dose compared to a full scan using sparse view angle sampling and without reconstructing the CT image of the repeat scan. Our method starts by performing rigid registration between the patient and the baseline scan in 3D Radon space computed from the sparse projection data. It then computes 2D projection difference images in which the flexible needle and the spherical marker appear as prominent features. Their 3D spatial locations are then automatically extracted from the projection images to accurately trace the flexible needle trajectory. To validate our method, we conducted registration and needle trajectory localization experiments in seven abdomen phantom scans using a short and a long flexible needle.

Results

Our experimental results yield a mean needle trajectory localization error of 0.7 ± 0.2 mm and a mean tip localization error of 2.4 ± 0.9 mm with a \(\times \)7.5 radiation dose reduction with respect to a full CT scan.

Conclusions

The significant radiation dose reduction enables more frequent needle trajectory localization during the needle insertion for a similar total dose, or a reduced total dose for the same localization frequency.

Keywords

Interventional CT Fractional CT scanning Reduced dose CT scanning Flexible needle tracking Radon space registration 

Notes

Acknowledgements

We thank Eyal Lin and Ronen Shter of GE Healthcare Israel for the CT scans and for their valuable assistance.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Gupta R, Walsh C, Wang IS, Kachelrieß M, Kuntz J, Bartling S (2014) CT-guided interventions: current practice and future directions. In: Jolesz FA (ed) Intraoperative imaging and image-guided therapy. Springer, New York, pp 173–191Google Scholar
  2. 2.
    Boas FE, Fleischmann D (2012) CT artifacts: causes and reduction techniques. Imaging Med 4(2):229–240CrossRefGoogle Scholar
  3. 3.
    Mettler FA Jr, Wiest PW, Locken JA, Kelsey CA (2000) CT scanning: patterns of use and dose. J Radiol Prot 20(4):353CrossRefGoogle Scholar
  4. 4.
    Chodick G, Ronckers CM, Shalev V, Ron E et al (2007) Excess lifetime cancer mortality risk attributable to radiation exposure from computed tomography examinations in children. IMAJ-RAMAT GAN- 9(8):584Google Scholar
  5. 5.
    Miller DL, Vañó E, Bartal G, Balter S, Dixon R, Padovani R, Schueler B, Cardella JF, De Baère T (2010) Occupational radiation protection in interventional radiology: a joint guideline of the Cardiovascular and Interventional Radiology Society of Europe and the Society of Interventional Radiology. Cardiovasc Interv Radiol 33(2):230–239CrossRefGoogle Scholar
  6. 6.
    Sarti M, Brehmer WP, Gay SB (2012) Low-dose techniques in CT-guided interventions. Radiographics 32(4):1109–1119CrossRefGoogle Scholar
  7. 7.
    Stoeckelhuber BM, Leibecke T, Schulz E, Melchert UH, Bergmann-Koester CU, Helmberger T, Gellissen J (2005) Radiation dose to the radiologist’s hand during continuous ct fluoroscopy-guided interventions. Cardiovasc Interv Radiol 28(5):589–594CrossRefGoogle Scholar
  8. 8.
    Sheafor DH, Paulson EK, Kliewer MA, DeLong DM, Nelson RC (2000) Comparison of sonographic and CT guidance techniques: does CT fluoroscopy decrease procedure time? Am J Roentgenol 174(4):939–942CrossRefGoogle Scholar
  9. 9.
    Orth RC, Wallace MJ, Kuo MD of the Society of Interventional Radiology TAC et al (2008) C-arm cone-beam CT: general principles and technical considerations for use in interventional radiology. J Vasc Interv Radiol 19(6):814–820CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Manduca A, Yu L, Trzasko JD, Khaylova N, Kofler JM, McCollough CM, Fletcher JG (2009) Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med Phys 36(11):4911–4919CrossRefGoogle Scholar
  12. 12.
    Zhang Y, Wang Y, Zhang W, Lin F, Pu Y, Zhou J (2016) Statistical iterative reconstruction using adaptive fractional order regularization. Biomed Opt Express 7(3):1015–1029CrossRefGoogle Scholar
  13. 13.
    Kim K, Ye JC, Worstell W, Ouyang J, Rakvongthai Y, El Fakhri G, Li Q (2015) Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty. IEEE Trans Med Imaging 34(3):748–760CrossRefGoogle Scholar
  14. 14.
    Niu S, Gao Y, Bian Z, Huang J, Chen W, Yu G, Liang Z, Ma J (2014) Sparse-view X-ray CT reconstruction via total generalized variation regularization. Phys Med Biol 59(12):2997CrossRefGoogle Scholar
  15. 15.
    Liu Y, Liang Z, Ma J, Lu H, Wang K, Zhang H, Moore W (2014) Total variation-stokes strategy for sparse-view X-ray CT image reconstruction. IEEE Trans Med Imaging 33(3):749–763CrossRefGoogle Scholar
  16. 16.
    Medan G, Shamul N, Joskowicz L (2017) Sparse 3D Radon space rigid registration of CT scans: method and validation study. IEEE Trans Med Imaging 36(2):497–506CrossRefGoogle Scholar
  17. 17.
    Medan G, Joskowicz L (2017) Reduced-dose imageless needle and patient tracking in interventional CT procedures. IEEE Trans Med Imaging 36(12):2449–2456CrossRefGoogle Scholar
  18. 18.
    Wu G, Li X, Lehocky CA, Riviere CN (2013) Automatic steering of manually inserted needles. In: IEEE international conference on systems, man, and cybernetics (SMC), 2013. IEEE, pp 1488–1493Google Scholar
  19. 19.
    Engh JA, Minhas DS, Kondziolka D, Riviere CN (2010) Percutaneous intracerebral navigation by duty-cycled spinning of flexible bevel-tipped needles. Neurosurgery 67(4):1117–1123CrossRefGoogle Scholar
  20. 20.
    Ben-David E, Shochat M, Roth I, Nissenbaum I, Sosna J, Goldberg SN (2018) Evaluation of a CT-guided robotic system for precise percutaneous needle insertion. J Vasc Interv Radiol 29:1440–1446CrossRefGoogle Scholar
  21. 21.
    Glozman D, Shoham M (2007) Image-guided robotic flexible needle steering. IEEE Trans Robot 23(3):459–467CrossRefGoogle Scholar
  22. 22.
    Vrooijink GJ, Abayazid M, Misra S (2013) Real-time three-dimensional flexible needle tracking using two-dimensional ultrasound. In: IEEE international conference on robotics and automation (ICRA), 2013. IEEE, pp 1688–1693Google Scholar
  23. 23.
    Marinetto E, Uneri A, De Silva T, Reaungamornrat S, Zbijewski W, Sisniega A, Vogt S, Kleinszig G, Pascau J, Siewerdsen J (2017) Integration of free-hand 3D ultrasound and mobile C-arm cone-beam CT: Feasibility and characterization for real-time guidance of needle insertion. Comput Med Imaging Graph 58:13–22CrossRefGoogle Scholar
  24. 24.
    Huo B, Zhao X, Han J, Xu W (2015) Shape reconstruction and attitude estimation of bevel-tip needle via CT-guidance. In: IEEE international conference on robotics and biomimetics (ROBIO), 2015. IEEE, pp 620–625Google Scholar
  25. 25.
    Yaniv Z, Cheng P, Wilson E, Popa T, Lindisch D, Campos-Nanez E, Abeledo H, Watson V, Cleary K, Banovac F (2010) Needle-based interventions with the image-guided surgery toolkit (IGSTK): from phantoms to clinical trials. IEEE Trans Biomed Eng 57(4):922–933CrossRefGoogle Scholar
  26. 26.
    Cong W, Yang J, Ai D, Chen Y, Liu Y, Wang Y (2015) Quantitative analysis of deformable model-based 3-D reconstruction of coronary artery from multiple angiograms. IEEE Trans Biomed Eng 62(8):2079–2090CrossRefGoogle Scholar
  27. 27.
    Khan M (2012) A new method for video data compression by quadratic Bézier curve fitting. Signal Image Video Process 6(1):19–24CrossRefGoogle Scholar
  28. 28.
    Adelman Z (2018) Reduced-dose region-of-interest image reconstruction in repeat ct scanning. Master Thesis, The Hebrew University of Jerusalem, Dec 2018Google Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.CASMIP Laboratory, School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael

Personalised recommendations