Advertisement

Hybrid 2D–3D ultrasound registration for navigated prostate biopsy

  • Sonia-Yuki Selmi
  • Emmanuel Promayon
  • Jocelyne Troccaz
Original Article
  • 94 Downloads

Abstract

Purpose

We present a hybrid 2D–3D ultrasound (US) rigid registration method for navigated prostate biopsy that enables continuous localization of the biopsy trajectory during the exam.

Methods

Current clinical computer-assisted biopsy systems use either sensor-based or image-based approaches. We combine the advantages of both in order to obtain an accurate and real-time navigation based only on an approximate localization of the US probe. Starting with features extracted in both 2D and 3D images, our method introduces a variant of the iterative closest point (ICP) algorithm. Among other differences to ICP, a combination of both the euclidean distance of feature positions and the similarity distance of feature descriptors is used to find matches between 2D and 3D features. The evaluation of the method is twofold. First, an analysis of variance on input parameters is conducted to estimate the sensitivity of our method to their initialization. Second, for a selected set of their values, the target registration error (TRE) was calculated on 29,760 (resp. 4000) registrations in two different experiments. It was obtained using manually identified anatomical fiducials.

Results

For 160 US volumes, from 20 patients, recorded during routine biopsy procedures performed in two hospitals by six operators, the mean TRE was \(3.91\pm 3.22\) mm (resp. \(4.37\pm 2.62\) mm).

Conclusion

This work allows envisioning further developments for prostate navigation and their clinical transfer.

Keywords

2D–3D registration Prostate biopsy Ultrasound 

Notes

Compliance with ethical standards

Ethical statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Iremashvili V, Pelaez L, Jorda M, Manoharan M, Arianayagam M, Rosenberg D, Soloway M (2012) Prostate sampling by 12-core biopsy: comparison of the biopsy results with tumor location in prostatectomy specimens. Urology 79(1):37–42CrossRefPubMedGoogle Scholar
  2. 2.
    Xu S, Kruecker J, Turkbey B, Glossop N, Singh A, Choyke P, Pinto P, Wood B (2008) Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies. Aided Surg 13(5):255–264CrossRefGoogle Scholar
  3. 3.
    Bax J, Cool D, Gardi L, Knight K, Smith D, Montreuil J, Sherebrin S, Romagnoli C, Fenster A (2008) Mechanically assisted 3D ultrasound guided prostate biopsy system. Med Phys 35(12):5397–5410CrossRefPubMedGoogle Scholar
  4. 4.
    Baumann M, Mozer P, Daanen V, Troccaz J (2011) Prostate biopsy tracking with deformation estimation. Med Image Anal 16:562–576CrossRefPubMedGoogle Scholar
  5. 5.
    De Silva T, Fenster A, Cool D, Gardi L, Romagnoli C, Samarabandu J, Ward A (2013) 2D–3D rigid registration to compensate for prostate motion during 3D TRUS-guided biopsy. Med Phys 40(2):022904CrossRefPubMedGoogle Scholar
  6. 6.
    De Silva T, Cool DW, Yuan J, Romagnoli C, Samarabandu J, Fenster A, Ward AD (2017) Aaron robust 2D–3D registration optimization for motion compensation during 3D TRUS-guided biopsy using learned prostate motion data. IEEE Trans Med Imaging 36(10):2010–2020CrossRefPubMedGoogle Scholar
  7. 7.
    Gillies DJ, Gardi L, Zhao R, Fenster A (2017) Optimization of real-time rigid registration motion compensation for prostate biopsies using 2D/3D ultrasound. In: SPIE medical imaging, pp 101351F–101351FGoogle Scholar
  8. 8.
    Khallaghi S, Sanchez C, Nouranian S, Sojoudi S, Chang S, Abdi H, Machan L, Harris A, Black P, Gleave M, Goldenberg L, Fels S, Abolmaesumi P (2015) A 2D–3D registration framework for freehand TRUS-guided prostate biopsy. MICCAI 9350:272–279Google Scholar
  9. 9.
    Selmi S-Y, Promayon E, Troccaz J (2016) 3D–2D ultrasound feature-based registration for navigated prostate biopsy: a feasibility study. In: 38th annual international conference on IEEE engineering in medicine and biology society, p 41094112Google Scholar
  10. 10.
    Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–100CrossRefGoogle Scholar
  11. 11.
    Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-D point sets. IEEE Trans Pattern Anal Mach Intell PAMI 9(5):698–700CrossRefGoogle Scholar
  12. 12.
    Harris C, Stephens M (1988) A combined corner and edge detector, pp 147–151Google Scholar
  13. 13.
    Cornud F, Brolis L, Delongchamps NB, Portalez D, Malavaud B, Renard-Penna R, Mozer P (2013) TRUS-MRI image registration: a paradigm shift in the diagnosis of significant prostate cancer. Abdom Imaging 38(6):1447–1463CrossRefPubMedGoogle Scholar
  14. 14.
    Fouard C, Deram A, Keraval Y, Promayon E (2012) CamiTK: a modular framework integrating visualization, image processing and biomechanical modeling. In: Payan Y (ed) Soft tissue biomechanical modeling for computer assisted surgery. Springer, Berlin, pp 323–354CrossRefGoogle Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  • Sonia-Yuki Selmi
    • 1
  • Emmanuel Promayon
    • 1
  • Jocelyne Troccaz
    • 1
  1. 1.Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC-IMAGGrenobleFrance

Personalised recommendations