Validation of a hybrid Doppler ultrasound vessel-based registration algorithm for neurosurgery

  • Sean Jy-Shyang Chen
  • Ingerid Reinertsen
  • Pierrick Coupé
  • Charles X. B. Yan
  • Laurence Mercier
  • D. Rolando Del Maestro
  • D. Louis Collins
Original Article

Abstract

Purpose

We describe and validate a novel hybrid nonlinear vessel registration algorithm for intra-operative updating of preoperative magnetic resonance (MR) images using Doppler ultrasound (US) images acquired on the dura for the correction of brain-shift and registration inaccuracies. We also introduce an US vessel appearance simulator that generates vessel images similar in appearance to that acquired with US from MR angiography data.

Methods

Our registration uses the minimum amount of preprocessing to extract vessels from the raw volumetric images. This prevents the removal of important registration information and minimizes the introduction of artifacts that may affect robustness, while reducing the amount of extraneous information in the image to be processed, thus improving the convergence speed of the algorithm. We then completed 3 rounds of validation for our vessel registration method for robustness and accuracy using (i) a large number of synthetic trials generated with our US vessel simulator, (ii) US images acquired from a real physical phantom made from polyvinyl alcohol cryogel, and (iii) real clinical data gathered intra-operatively from 3 patients.

Results

Resulting target registration errors (TRE) of less than 2.5 mm are achieved in more than 90 % of the synthetic trials when the initial TREs are less than 20 mm. TREs of less than 2 mm were achieved when the technique was applied to the physical phantom, and TREs of less than 3 mm were achieved on clinical data.

Conclusions

These test trials show that the proposed algorithm is not only accurate but also highly robust to noise and missing vessel segments when working with US images acquired in a wide range of real-world conditions.

Keywords

Volumetric registration Brain-shift Doppler ultrasound Digital phantom Validation 

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References

  1. 1.
    Arbel T, Morandi X, Comeau RM, Collins DL (2004) Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations. Comput Aided Surg 9(4): 123–136PubMedGoogle Scholar
  2. 2.
    Besl P, McKay H (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2): 239–256. doi:10.1109/34.121791 CrossRefGoogle Scholar
  3. 3.
    Bucholz R, Yeh D, Trobaugh J, McDurmont L, Sturm C, Baumann C, Henderson J, Levy A, Kessman P (1997) The correction of stereotactic inaccuracy caused by brain shift using an intraoperative ultrasound device. In: CVRMed-MRCAS’97, pp 459–466. doi:10.1007/BFb0029268
  4. 4.
    Collins D, Evans A (1997) ANIMAL: Validation and application of non-linear registration-based segmentation. IJPRAI 11(8): 1271–1294Google Scholar
  5. 5.
    Coupé P, Hellier P, Morandi X, Barillot C (2007) A probabilistic objective function for 3D rigid registration of intraoperative US and preoperative MR brain images. In: IEEE ISBI: Nano to Macro, pp 1320–1323Google Scholar
  6. 6.
    Danilchenko A, Fitzpatrick J (2011) General approach to first-order error prediction in rigid point registration. IEEE Trans Med Imaging 30(3): 679–693. doi:10.1109/TMI.2010.2091513 PubMedCrossRefGoogle Scholar
  7. 7.
    Descoteaux M, Collins L, Siddiqi K (2008) A multi-scale geometric flow for segmenting vasculature in mri: theory and validation. Med Image Anal 12(4): 497–513PubMedCrossRefGoogle Scholar
  8. 8.
    Ding S, Miga M, Thompson R, Dumpuri P, Cao A, Dawant B (2007) Estimation of intra-operative brain shift using a tracked laser range scanner. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th annual international conference of the IEEE, pp 848–851Google Scholar
  9. 9.
    Duchon J (1977) Splines minimizing rotation-invariant semi-norms in sobolev spaces. In: Schempp W, Zeller K (eds) Constructive theory of functions of several variables, Lecture notes in mathematics, vol 571. Springer, Berlin, pp 85–100. doi:10.1007/BFb0086566 CrossRefGoogle Scholar
  10. 10.
    Fieten L, Schmieder K, Engelhardt M, Pasalic L, Radermacher K, Heger S (2009) Fast and accurate registration of cranial ct images with a-mode ultrasound. Int J Comput Assist Radiol Surg 4: 225–237. doi:10.1007/s11548-009-0288-z PubMedCrossRefGoogle Scholar
  11. 11.
    Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: MICCAI 1998, pp 130–137Google Scholar
  12. 12.
    Gobbi DG, Comeau RM, Peters TM (1999) Ultrasound probe tracking for real-time ultrasound/mri overlay and visualization of brain shift. In: Proceedings of the second international conference on medical image computing and computer-assisted intervention, MICCAI ’99, pp 920–927. Springer, London, UK. http://dl.acm.org/citation.cfm?id=646922.709913
  13. 13.
    Haberland N, Ebmeier K, Hliscs R, Grunewald JP, Silbermann J, Steenbeck J, Nowak H, Kalff R (2000) Neuronavigation in surgery of intracranial and spinal tumors. J Cancer Res Clin Oncol 126(9): 529–541PubMedCrossRefGoogle Scholar
  14. 14.
    Hastreiter P, Rezk-Salama C, Soza G, Bauer M, Greiner G, Fahlbusch R, Ganslandt O, Nimsky C (2004) Strategies for brain shift evaluation. Med Image Anal 8(4): 447–464. doi:10.1016/j.media.2004.02.001 PubMedCrossRefGoogle Scholar
  15. 15.
    Hill D, Maurer C, Maciunas R, Barwise J, Fitzpatrick J, Wang M (1998) Measurement of intraoperative brain surface deformation under a craniotomy. Neurosurgery 43(3): 514–528PubMedCrossRefGoogle Scholar
  16. 16.
    Ji S, Wu Z, Hartov A, Roberts D, Paulsen K (2008) Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery. Med Phys 35: 4612–4624PubMedCrossRefGoogle Scholar
  17. 17.
    Jomier J, Aylward SR (2004) Rigid and deformable vasculature-to-image registration: a hierarchical approach. In: Barillot C, Haynor DR, Hellier P (eds) Medical image computing and computer-assisted intervention MICCAI 2004, Lecture notes in computer science, vol 3216. Springer, Berlin, pp 829–836CrossRefGoogle Scholar
  18. 18.
    Khan MF, Mewes K, Skrinjar O (2006) Brain shift analysis for deep brain stimulation surgery. In: IEEE ISBI: Nano to Macro pp 654–657Google Scholar
  19. 19.
    Lange T, Eulenstein S, Hünerbein M, Schlag PM (2003) Vessel-based non-rigid registration of MR/CT and 3D ultrasound for navigation in liver surgery. Comput Aided Surg 8(5): 228–240PubMedCrossRefGoogle Scholar
  20. 20.
    Leo WR (1994) Techniques for nuclear and particle physics experiments: a how-to approach. Springer, BerlinCrossRefGoogle Scholar
  21. 21.
    Letteboer MMJ, Willems PWA, Viergever MA, Niessen WJ (2005) Brain shift estimation in image-guided neurosurgery using 3-D ultrasound. IEEE Trans Biomed Eng 52(2): 268–276PubMedCrossRefGoogle Scholar
  22. 22.
    Maurer CR Jr, Hill D, Martin A, Liu H, McCue M, Rueckert D, Lloret D, Hall W, Maxwell R, Hawkes D, Truwit C (1998) Investigation of intraoperative brain deformation using a 1.5-t interventional mr system: preliminary results. IEEE Trans Med Imaging 17(5): 817–825. doi:10.1109/42.736050 PubMedCrossRefGoogle Scholar
  23. 23.
    Miga M, Paulsen K, Hoopes P, Kennedy F Jr, Hartov A, Roberts D (2000) In vivo quantification of a homogeneous brain deformation model for updating preoperative images during surgery. IEEE Trans Biomed Eng 47(2): 266–273PubMedCrossRefGoogle Scholar
  24. 24.
    Nabavi A, McL Black P, Gering DT, Westin CF, Mehta V, Pergolizzi RSJ, Ferrant M, Warfield SK, Hata N, Schwartz RB, Wells WMI, Kikinis R, Jolesz FA (2001) Serial intraoperative magnetic resonance imaging of brain shift. Neurosurgery 48(4): 787–798PubMedGoogle Scholar
  25. 25.
    Nakajima S, Atsumi H, Kikinis R, Moriarty TM, Metcalf DC, Jolesz FA, Black PM (1997) Use of cortical surface vessel registration for image-guided neurosurgery. Neurosurgery 40(6): 1201–1210PubMedCrossRefGoogle Scholar
  26. 26.
    Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313. doi:10.1093/comjnl/7.4.308. http://comjnl.oxfordjournals.org/content/7/4/308.abstract Google Scholar
  27. 27.
    Nimsky OG, Hastreiter P, Fahlbusch R (2001) Intraoperative compensation for brain shift. Surg Neurol 10: 357–365CrossRefGoogle Scholar
  28. 28.
    Penney GP, Blackall JM, Hamady MS, Sabharwal T, Adam A, Hawkes DJ (2004) Registration of freehand 3d ultrasound and magnetic resonance liver images. Med Image Anal 8(1): 81– 91PubMedCrossRefGoogle Scholar
  29. 29.
    Perlin K (2002) Improving noise. In: SIGGRAPH ’02: Proceedings of the 29th annual conference on computer graphics and interactive techniques, pp 681–682. ACM, New York. doi:10.1145/566570.566636
  30. 30.
    Pizurica A, Philips W (2006) Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising. IEEE Trans Image Process 15(3): 654–665. doi:10.1109/TIP.2005.863698 PubMedCrossRefGoogle Scholar
  31. 31.
    Pluim J, Maintz J, Viergever M (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22(8): 986–1004. doi:10.1109/TMI.2003.815867 PubMedCrossRefGoogle Scholar
  32. 32.
    Reinertsen I, Collins DL (2006) A realistic phantom for brain-shift simulations. Med Phys 33(9): 3234–3240PubMedCrossRefGoogle Scholar
  33. 33.
    Reinertsen I, Descoteaux M, Siddiqi K, Collins D (2007) Validation of vessel-based registration for correction of brain shift. Med Image Anal 11(4): 374–388. doi:10.1016/j.media.2007.04.002 PubMedCrossRefGoogle Scholar
  34. 34.
    Reinertsen I, Lindseth F, Unsgaard G, Collins D (2007) Clinical validation of vessel-based registration for correction of brain-shift. Med Image Anal 11(6): 673–684PubMedCrossRefGoogle Scholar
  35. 35.
    Reinges MHT, Nguyen HH, Krings T, Htter BO, Rohde V, Gilsbach JM (2004) Course of brain shift during microsurgical resection of supratentorial cerebral lesions: limits of conventional neuronavigation. Acta Neurochirurgica 146(4): 369–377. doi:10.1007/s00701-003-0204-1 PubMedCrossRefGoogle Scholar
  36. 36.
    Roberts D, Miga M, Hartov A, Eisner S, Lemery J, Kennedy F, Paulsen K (1998) Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery 43(5): 749–760PubMedCrossRefGoogle Scholar
  37. 37.
    Roche A, Pennec X, Malandain G, Ayache N (2001) Rigid registration of 3-D ultrasound with mr images: a new approach combining intensity and gradient information. IEEE TMI 20(10): 1038–1049. doi:10.1109/42.959301 Google Scholar
  38. 38.
    Shamir R, Joskowicz L, Spektor S, Shoshan Y (2009) Localization and registration accuracy in image guided neurosurgery: a clinical study. Int J Comput Assist Radiol Surg 4: 45–52. doi:10.1007/s11548-008-0268-8 PubMedCrossRefGoogle Scholar
  39. 39.
    Shamir RR, Joskowicz L (2011) Geometrical analysis of registration errors in point-based rigid-body registration using invariants. Med Image Anal 15(1):85–95. doi:10.1016/j.media.2010.07.010. http://www.sciencedirect.com/science/article/pii/S1361841510001027
  40. 40.
    Sherebrin S, Fenster A, Rankin RN, Spence D (1996) Freehand three-dimensional ultrasound: implementation and applications. In: Proceeding of SPIE 2708, 296Google Scholar
  41. 41.
    Solberg OV, Lindseth F, Torp H, Blake RE, Hernes TAN (2007) Freehand 3d ultrasound reconstruction algorithms–a review. Ultrasound Med Biol 33(7): 991–1009. doi:10.1016/j.ultrasmedbio.2007.02.015 PubMedCrossRefGoogle Scholar
  42. 42.
    Sugahara T, Korogi Y, Hirai T, Shigematu Y, Ushio Y, Takahashi M (1998) Contrast-enhanced t1-weighted three-dimensional gradient-echo mr imaging of the whole spine for intradural tumor dissemination. Am J Neuroradiol 19(9):1773–1779. http://www.ajnr.org/content/19/9/1773.abstract Google Scholar

Copyright information

© CARS 2012

Authors and Affiliations

  • Sean Jy-Shyang Chen
    • 1
  • Ingerid Reinertsen
    • 2
  • Pierrick Coupé
    • 1
    • 3
  • Charles X. B. Yan
    • 1
  • Laurence Mercier
    • 1
  • D. Rolando Del Maestro
    • 4
  • D. Louis Collins
    • 1
  1. 1.McConnell Brain Imaging Centre, Montreal Neurological InstituteMcGill UniversityMontrealCanada
  2. 2.SINTEF Health Research and National Centre for 3D Ultrasound in SurgerySt. Olav University HospitalTrondheimNorway
  3. 3.CNRS, UMR 5800Université BordeauxTalence CedexFrance
  4. 4.Department of Neurology and Neurosurgery, Montreal Neurological InstituteMcGill UniversityMontrealCanada

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