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Use the force: deformation correction in robotic 3D ultrasound

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

Abstract

Purpose

Ultrasound acquisitions are typically affected by deformations due to the pressure applied onto the contact surface. While a certain amount of pressure is necessary to ensure good acoustic coupling and visibility of the anatomy under examination, the caused deformations hinder accurate localization and geometric analysis of anatomical structures. These complications have even greater impact in case of 3D ultrasound scans as they limit the correct reconstruction of acquired volumes.

Methods

In this work, we propose a method to estimate and correct the induced deformation based solely on the tracked ultrasound images and information about the applied force. This is achieved by modeling estimated displacement fields of individual image sequences using the measured force information. By representing the computed displacement fields using a graph-based approach, we are able to recover a deformation-less 3D volume.

Results

Validation is performed on 30 in vivo human datasets acquired using a robotic ultrasound framework. Compared to ground truth, the presented deformation correction shows errors of \(3.39 \, \pm \, 1.86\,\hbox {mm}\) for an applied force of 5 N at a penetration depth of 55 mm.

Conclusion

The proposed technique allows for the correction of deformations induced by the transducer pressure in entire 3D ultrasound volumes. Our technique does not require biomechanical models, patient-specific assumptions or information about the tissue properties; it can be employed based on the information from readily available robotic ultrasound platforms.

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Notes

  1. https://itk.org.

  2. http://www.ros.org.

  3. http://openigtlink.org/.

  4. http://campar.in.tum.de/files/virga/dataset.zip.

References

  1. Arya S, Nagarkatti DG, Dudhat SB, Nadkarni KS, Joshi MS, Shinde SR (2000) Soft tissue sarcomas: ultrasonographic evaluation of local recurrences. Clin Radiol 55(3):193–197

    Article  PubMed  CAS  Google Scholar 

  2. Boehler T, Peitgen HO (2008) Reducing motion artifacts in 3-D breast ultrasound using non-linear registration. In: International conference on medical image computing and computer-assisted intervention, pp 998–1005

  3. Burcher MR, Han L, Noble JA (2001) Deformation correction in ultrasound images using contact force measurements. In: IEEE workshop on mathematical methods in biomedical image analysis, pp 63–70

  4. Dahmani J, Petit Y, Laporte C (2017) Model-based correction of ultrasound image deformations due to probe pressure. In: Medical imaging 2017: image processing, vol 10133, p 101331D

  5. Elek R, Nagy TD, Nagy D, Takcs B, Galambos P, Rudas I, Haidegger T (2017) Robotic platforms for ultrasound diagnostics and treatment. In: IEEE international conference on systems, man, and cybernetics (SMC), pp 1752–1757

  6. Grimer RJ (2006) Size matters for sarcomas!. Ann R Coll Surg Engl 88(6):519–524

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hennersperger C, Baust M, Mateus D, Navab N (2015) Computational sonography. In: International conference on medical image computing and computer-assisted intervention, pp 459–466

  8. Hennersperger C, Fuerst B, Virga S, Zettinig O, Frisch B, Neff T, Navab N (2017) Towards MRI-based autonomous robotic us acquisitions: a first feasibility study. IEEE Trans Med Imaging 36(2):538–548

    Article  PubMed  Google Scholar 

  9. Hennersperger C, Mateus D, Baust M, Navab N (2014) A quadratic energy minimization framework for signal loss estimation from arbitrarily sampled ultrasound data. In: International conference on medical image computing and computer-assisted intervention, pp 373–380

  10. Morrison BA (2003) Soft tissue sarcomas of the extremities. In: Baylor University Medical Center. Proceedings, vol 16, p 285

  11. Natarajan S, Marks LS, Margolis DJ, Huang J, Macairan ML, Lieu P, Fenster A (2011) Clinical application of a 3D ultrasound-guided prostate biopsy system. In: Urologic oncology: seminars and original investigations, vol 29, pp 334–342

  12. Ophir J, Cespedes I, Ponnekanti H, Yazdi Y, Li X (1991) Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrason Imaging 13(2):111–134

    Article  PubMed  CAS  Google Scholar 

  13. Pfister K, Schierling W, Jung EM, Apfelbeck H, Hennersperger C, Kasprzak PM (2016) Standardized 2D ultrasound versus 3D/4D ultrasound and image fusion for measurement of aortic aneurysm diameter in follow-up after evar. Clin Hemorheol Microcircul 62(3):249–260

    Article  Google Scholar 

  14. Pheiffer TS, Miga MI (2015) Toward a generic real-time compression correction framework for tracked ultrasound. Int J Comput Assist Radiol Surg 10(11):1777–1792

    Article  PubMed  PubMed Central  Google Scholar 

  15. Rastrelli M, Tropea S, Basso U, Roma A, Maruzzo M, Rossi CR (2014) Soft tissue limb and trunk sarcomas: diagnosis, treatment and follow-up. Anticancer Res 34(10):5251–5262

    PubMed  CAS  Google Scholar 

  16. Sun SY, Anthony BW, Gilbertson MW (2010) Trajectory-based deformation correction in ultrasound images. In: Medical imaging 2010: ultrasonic imaging, tomography, and therapy, vol 7629, p 76290A

  17. Swerdlow DR, Cleary K, Wilson E, Azizi-Koutenaei B, Monfaredi R (2017) Robotic arm-assisted sonography: Review of technical developments and potential clinical applications. Am J Roentgenol 208(4):733–738

    Article  Google Scholar 

  18. Treece GM, Prager RW, Gee AH, Berman L (2002) Correction of probe pressure artifacts in freehand 3D ultrasound. Med Image Anal 6(3):199–214

    Article  PubMed  CAS  Google Scholar 

  19. Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1):S61–S72

    Article  PubMed  Google Scholar 

  20. Zikic D, Baust M, Kamen A, Navab NA (2011) general preconditioning scheme for difference measures in deformable registration. In: 2011 IEEE international conference on computer vision (ICCV), pp 49–56

  21. Schulte zu Berge C, Kapoor A, Navab N (2014) Orientation-driven ultrasound compounding using uncertainty information. In: International conference on information processing in computer-assisted interventions, pp 236–245

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Authors and Affiliations

  1. Technical University of Munich, Boltzmannstr. 3, 85748, Garching bei München, Germany

    Salvatore Virga, Rüdiger Göbl, Maximilian Baust, Nassir Navab & Christoph Hennersperger

  2. Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218, USA

    Nassir Navab

Authors
  1. Salvatore Virga
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  2. Rüdiger Göbl
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  3. Maximilian Baust
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  4. Nassir Navab
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  5. Christoph Hennersperger
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Corresponding author

Correspondence to Salvatore Virga.

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The authors declare to have no conflict of interest.

Human and animals rights

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. No animal experiments were performed in this study.

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Informed consent was obtained from all participants.

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Virga, S., Göbl, R., Baust, M. et al. Use the force: deformation correction in robotic 3D ultrasound. Int J CARS 13, 619–627 (2018). https://doi.org/10.1007/s11548-018-1716-8

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  • DOI: https://doi.org/10.1007/s11548-018-1716-8

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