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Vessel-based fast deformable registration with minimal strain energy

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Abstract

Purpose

Image registration for internal organs and soft tissues is considered extremely challenging due to organ shifts and tissue deformation caused by patients’ movements such as respiration and repositioning. In this paper, we propose a fast deformable image registration method. The purpose of our work is to greatly improve the registration time while maintaining the registration accuracy.

Methods

In this study, we formulate the deformable image registration problem as a quadratic optimization problem that minimizes strain energy subject to the constraints of 3D curves of blood vessel centerlines and point marks. The proposed method does not require iteration and is local minimum free. By using 2nd order B-splines to model the blood vessels in the moving image and a new transformation model, our method provides a closed-form solution that imitates the manner in which physical soft tissues deform, thus guarantees a physically consistent match.

Results

We have demonstrated the effectiveness of our deformable technique in registering MR images of the liver. Validation results show that we can achieve a target registration error (TRE) of 1.29 mm and an average centerline distance error (ACD) of 0.84 ± 0.55 mm.

Conclusions

This technique has the potential to significantly improve registration capabilities and the quality of intraoperative image guidance. To the best of our knowledge, this is the first time that a global analytical solution has been determined for the registration energy function with 3D curve constraints.

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References

  1. Sotiras A, Davatzikos C, Paragios N. Deformable medical image registration: a survey. IEEE T Med Imaging. 2013; 32(7): 1153–90.

    Article  Google Scholar 

  2. Pluim JPW, Maintz JBA, Viergever MA. Mutual-information based registration of medical images: a survey. IEEE T Med Imaging. 2003; 22(8):986–1004.

    Article  MATH  Google Scholar 

  3. Huang X, Ren J, Guiraudon G, Boughner D, Peters TM. Rapid dynamic image registration of the beating heart for diagnosis and surgical navigation. IEEE T Med Imaging. 2009; 28(11):1802–14.

    Article  Google Scholar 

  4. Reinertsen I, Descoteaux M, Siddiqi K, Collins DL. Validation of vessel-based registration for correction of brain shift. Med Image Anal. 2007; 11(4):374–88.

    Article  Google Scholar 

  5. Brock KK. Imaging and image-guided radiation therapy in liver cancer. Semin Radiat Oncol. 2011; 21(4):247–55.

    Article  MathSciNet  Google Scholar 

  6. Osorio EMV, Hoogeman MS, Romero AM, Wielopolski P, Zolnay A, Heijmen BJM. Accurate CT/MR vessel-guided nonrigid registration of largely deformed livers. Med Phys. 2012; 39(5):2463–77.

    Article  Google Scholar 

  7. Lange T, Papenberg N, Heldmann S, Modersitzki J, Fischer B, Lamecker H, Schlag PM. 3D ultrasound-CT registration of the liver using combined landmark-intensity information. Int J Comput Assist Radiol Surg. 2009; 4(1):79–88.

    Article  Google Scholar 

  8. Shusharina N, Sharp G. Analytic regularization for landmarkbased image registration. Phys Med Biol. 2012; 57(6):1477–98.

    Article  Google Scholar 

  9. Zitová B, Flusser J. Image registration methods: a survey. Image Vis Comput. 2003; 21(11):977–1000.

    Article  Google Scholar 

  10. Hill DL, Batchelor PG, Holden M, Hawkes DJ. Medical image registration. Phys Med Biol. 2001; 46(3):R1–45.

    Article  Google Scholar 

  11. Maintz JBA, Viergever MA. A survey of medical image registration. Med Image Anal. 1998; 2(1):1–36.

    Article  Google Scholar 

  12. Davis MH, Khotanzad A, Flamig DP, Harms SE. A physicsbased coordinate transformation for 3-D image matching. IEEE T Med Imaging. 1997; 16(3):317–28.

    Article  Google Scholar 

  13. Meinguet J. Multivariate interpolation at arbitrary points made simple. J Appl Math Phys. 1979; 30(2):292–304.

    Article  MathSciNet  MATH  Google Scholar 

  14. Bookstein FL. Principal warps: thin-plate splines and the decomposition of deformations. IEEE T Pattern Anal Mach Intell. 1989; 11(6):567–85.

    Article  MATH  Google Scholar 

  15. Gobbi DG, Comeau RM, Peters TM. Ultrasound/MRI overlay with image warping for neurosurgery. Conf Proc Med Image Comput Comput-Assist Interv. 2000; 1:106–14.

    Google Scholar 

  16. Murphy K, van Ginneken B, Reinhardt JM, Kabus S, Ding K, Deng X, Cao K, Du K, Christensen GE, Garcia V, Vercauteren T, Ayache N, Commowick O, Malandain G, Glocker B, Paragios N, Navab N, Gorbunova V, Sporring J, de Bruijne M, Han X, Heinrich MP, Schnabel JA, Jenkinson M, Lorenz C, Modat M, McClelland JR, Ourselin S, Muenzing SE, Viergever MA, De Nigris D, Collins DL, Arbel T, Peroni M, Li R, Sharp GC, Schmidt-Richberg A, Ehrhardt J, Werner R, Smeets D, Loeckx D, Song G, Tustison N, Avants B, Gee JC, Staring M, Klein S, Stoel BC, Urschler M, Werlberger M, Vandemeulebroucke J, Rit S, Sarrut D, Pluim JP. Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE T Med Imaging. 2011; 30(11):1901–20.

    Article  Google Scholar 

  17. Baumhauer M, Feuerstein M, Meinzer HP, Rassweiler J. Navigation in endoscopic soft tissue surgery: perspectives and limitations. J Endourol. 2008; 22(4):751–66.

    Article  Google Scholar 

  18. Huang X, Abdalbari A, Zaheer S, Looi T, Ren J, Drake J. 3D curve constrained deformable registration using a neuro-fuzzy transformation model. Conf Proc IEEE Eng Med Biol Soc. 2012; 1:5294–7.

    Google Scholar 

  19. Commowick O, Arsigny V, Isambert A, Costa J, Dhermain F, Bidault F, Bondiau PY, Ayache N, Malandain G. An efficient locally affine framework for the smooth registration of anatomical structures. Med Image Anal. 2008; 12(4):427–41.

    Article  Google Scholar 

  20. Ciarlet PG. Mathermatical Elasticity, Vol I. North-holland, Amsterdam, Newyork: Oxford; 1988.

    Google Scholar 

  21. Bower AF. Applied Mechanics of Solids. 1st ed. CRC Press; 2009.

    Google Scholar 

  22. Fitzpatrick JM, West JB, Maurer CR Jr. Predicting error in rigidbody point-based registration. IEEE T Med Imaging. 1998; 17(5):694–702.

    Article  Google Scholar 

  23. Huang X, Babyn PS, Looi T, Kim PCW. A novel hybrid model for deformable image registration in abdominal procedures. Proc SPIE 7964 Med Imaging; 2011. doi: 10.1117/12.878068.

    Google Scholar 

  24. 3D Slicer. http://www.slicer.org/. Accessed 2 Mar 2016.

  25. VMTK. http://www.vmtk.org/. Accessed 2 Mar 2016.

  26. Huang X, Zaheer S, Abdalbari A, Looi T, Ren J, Drake J. Extraction of liver vessel centerlines under guidance of patientspecific models. Conf Proc IEEE Eng Med Biol Soc. 2012; 1:2347–50.

    Google Scholar 

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Correspondence to Jing Ren.

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Huang, X., Ren, J., Abdalbari, A. et al. Vessel-based fast deformable registration with minimal strain energy. Biomed. Eng. Lett. 6, 47–55 (2016). https://doi.org/10.1007/s13534-016-0213-7

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  • DOI: https://doi.org/10.1007/s13534-016-0213-7

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