Skip to main content

Advertisement

SpringerLink
Log in

Context region discovery for automatic motion compensation in fluoroscopy

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Image-based tracking for motion compensation is an important topic in image-guided interventions, as it enables physicians to operate in a less complex space. In this paper, we propose an automatic motion compensation scheme to boost image guidence power in transcatheter aortic valve implantation (TAVI).

Methods

The proposed tracking algorithm automatically discovers reliable regions that correlate strongly with the target. These discovered regions can assist to estimate target motion under severe occlusion, even if target tracker fails.

Results

We evaluate the proposed method for pigtail tracking during TAVI. We obtain significant improvement (12 %) over the baseline in a clinical dataset. Calcification regions are automatically discovered during tracking, which would aid TAVI processes.

Conclusion

In this work, we open a new paradigm to provide dynamic real-time guidance for TAVI without user interventions, specially in case of severe occlusion where conventional tracking methods are challenged.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Aregger F, Wenaweser P, Hellige GJ, Kadner A, Carrel T, Windecker S, Frey FJ (2009) Risk of acute kidney injury in patients with severe aortic valve stenosis undergoing transcatheter valve replacement. Nephrol Dial Transplant 24:2175–2179

    Article  PubMed  Google Scholar 

  2. Chapelle O, Schlkopf B, Zien A (2010) Semi-supervised learning. MIT Press, Cambridge

    Google Scholar 

  3. Chen T, Wang Y, Durlak P, Comaniciu D (2012) Real time assistance for stent positioning and assessment by self-initialized tracking. In: Proceedings of the MICCAI, pp 405–413

  4. Dinh TB, Vo N, Medioni G (2011) Context tracker: exploring supporters and distracters in unconstrained environments. In: Proceedings of the CVPR, pp 1177–1184

  5. Elhmidi Y, Bleiziffer S, Deutsch MA, Krane M, Mazzitelli D, Lange R, Piazza N (2014) Acute kidney injury after transcatheter aortic valve implantation: incidence, predictors and impact on mortality. Arch Cardiovasc Dis 107:133–139

    Article  PubMed  Google Scholar 

  6. Elqursh A, Elgammal A (2013) Online motion segmentation using dynamic label propagation. In: Proceedings of the ICCV

  7. Ernst J, Singh MK, Ramesh V (2012) Discrete texture traces: topological representation of geometric context. In: IEEE conference on computer vision and pattern recognition (CVPR), 2012, pp 422–429. IEEE

  8. Grabner H, Matas J, Gool LV, Cattin P (2010) Tracking the invisible: learning where the object might be. In: Proceedings of the CVPR, pp 1285–1292

  9. Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection. In: IEEE transactions on pattern analysis and machine intelligence

  10. Karar M, John M, Holzhey D, Falk V, Mohr FW, Burgert O (2011) Model-updated image-guided minimally invasive off-pump transcatheter aortic valve implantation. Proc MICCAI 6891:275–282

    Google Scholar 

  11. Lin T, Cerviño L, Tang X, Vasconcelos N, Jiang S (2009) Fluoroscopic tumor tracking for image-guided lung cancer radiotherapy. Phys Med Biol 54(4):981

    Article  PubMed  Google Scholar 

  12. Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. IJCAI 81:674–679

    Google Scholar 

  13. Nguyen D, Garreau M, Auffret V, Breton HL, Verhoye J, Haigron P (2013) Intraoperative tracking of aortic valve plane. In: IEEE engineering in medicine and biology society, pp 4378–4381

  14. Shi J, Tomasi C (1994) Good features to track. Proc CVPR 14(1):593–600

    Google Scholar 

  15. Tzoumas S, Wang P, Zheng Y, John M, Comaniciu D (2012) Robust pigtail catheter tip detection in fluoroscopy. In: Proceedings of the SPIE 8316

  16. Wang P, Zheng Y, John M, Comaniciu D (2012) Catheter tracking via online learning for dynamic motion compensation in transcatheter aortic valve implantation. In: Proceedings of the MICCAI

  17. Wijesinghe N, Masson JB, Nietlispach F, Tay E, Gurvitch R, Blumenfeld A, Brada R, Wood DA, Webb JG (2010) A novel real-time image processor to facilitate transcatheter aortic valve implantation. The Paieon’s C-THV system. J Am Coll Cardiol 55(10):A147–E1384

    Google Scholar 

  18. Yang M, Wu Y, Hua G (2008) Context-aware visual tracking. Proc CVPR 31(7):1195–1209

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Northwestern University, Evanston, IL, USA

    Yin Xia & Ying Wu

  2. University of Central Florida, Orlando, FL, USA

    Sarfaraz Hussein

  3. Siemens Corporate Research, Princeton, NJ, USA

    Vivek Singh & Terrence Chen

  4. Siemens AG, Healthcare Sector, Forchheim, Germany

    Matthias John

Authors
  1. Yin Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarfaraz Hussein
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vivek Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthias John
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ying Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Terrence Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yin Xia.

Ethics declarations

Conflict of interest

Author Terrence Chen has received research grants from Siemens medical solutions, Inc.

Human participants and/or animals

For this type of study formal consent is not required.

Additional information

Disclaimer The outlined concepts are not commercially available. Due to regulatory reasons its future availability cannot be guaranteed.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, Y., Hussein, S., Singh, V. et al. Context region discovery for automatic motion compensation in fluoroscopy. Int J CARS 11, 977–985 (2016). https://doi.org/10.1007/s11548-016-1362-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-016-1362-y

Keywords

Search

Navigation