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Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features

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

Abstract

Purpose

To compensate for brain shift in image-guided neurosurgery, we propose a new non-rigid registration method that integrates surface and vessel/sulci feature to noninvasively track the brain surface.

Method

Textured brain surfaces were acquired using phase-shift three-dimensional (3D) shape measurement, which offers 2D image pixels and their corresponding 3D points directly. Measured brain surfaces were noninvasively tracked using the proposed method by minimizing a new energy function, which is a weighted combination of 3D point corresponding estimation and surface deformation constraints. Initially, the measured surfaces were divided into featured and non-featured parts using a Frangi filter. The corresponding feature/non-feature points between intraoperative brain surfaces were estimated using the closest point algorithm. Subsequently, smoothness and rigidity constraints were introduced in the energy function for a smooth surface deformation and local surface detail conservation, respectively. Our 3D shape measurement accuracy was evaluated using 20 spheres for bias and precision errors. In addition, the proposed method was evaluated based on root mean square error (RMSE) and target registration error (TRE) with five porcine brains for which deformations were produced by gravity and pushing with different displacements in both the vertical and horizontal directions.

Results

The minimum and maximum bias errors were 0.32 and 0.61 mm, respectively. The minimum and maximum precision errors were 0.025 and 0.30 mm, respectively. Quantitative validation with porcine brains showed that the average RMSE and TRE were 0.1 and 0.9 mm, respectively.

Conclusion

The proposed method appeared to be advantageous in integrating vessels/sulci feature, robust to changes in deformation magnitude and integrated feature numbers, and feasible in compensating for brain shift deformation in surgeries.

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References

  1. Ding S, Miga M, Noble JH, Cao A, Dumpuri P, Thompson RC, Dawant BM et al (2009) Semiautomatic registration of pre-and postbrain tumor resection laser range data: method and validation. IEEE Trans Biomed Eng 56(3):770–780

    Article  PubMed  Google Scholar 

  2. 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 MR imaging of brain shift. Neurosurgery 48(4):787–798

    CAS  PubMed  Google Scholar 

  3. Clatz O, Delingette H, Talos IF, Golby AJ, Kikinis R, Jolesz F, Ayache N, Warfield SK et al (2005) Robust nonrigid registration to capture brain shift from intraoperative MRI. IEEE Trans Med Imaging 24(11):1417–1427

    Article  PubMed  PubMed Central  Google Scholar 

  4. Letteboer MM, Willems PW, Viergever M, Niessen WJ et al (2005) Brain shift estimation in image-guided neurosurgery using 3-d ultrasound. IEEE Trans Biomed Eng 52(2):268–276

    Article  PubMed  Google Scholar 

  5. Reinertsen I, Descoteaux M, Siddiqi K, Collins DL (2007) Validation of vessel-based registration for correction of brain shift. Med Image Anal 11(4):374–388

    Article  CAS  PubMed  Google Scholar 

  6. Reinertsen I, Lindseth F, Unsgaard G, Collins DL (2007) Clinical validation of vessel-based registration for correction of brain-shift. Med Image Anal 11(6):673–684

    Article  CAS  PubMed  Google Scholar 

  7. Reinertsen I, Lindseth F, Askeland C, Iversen DH, Unsgård G (2014) Intra-operative correction of brain-shift. Acta neurochirurg 156(7):1301–1310

    Article  Google Scholar 

  8. Audette MA, Siddiqi K, Ferrie FP, Peters TM (2003) An integrated range-sensing, segmentation and registration framework for the characterization of intra-surgical brain deformations in image-guided surgery. Comput Vis Image Underst 89(2):226–251

    Article  Google Scholar 

  9. Sinha TK, Duay V, Dawant BM, Miga MI (2003) Cortical shift tracking using a laser range scanner and deformable registration methods. Med Image Comput Comput Assist Interv 2879:166–174

    PubMed  PubMed Central  Google Scholar 

  10. Miga M, Sinha TK, Cash DM, Galloway RL, Weil RJ (2003) Cortical surface registration for image-guided neurosurgery using laser-range scanning. IEEE Trans Med Imaging 22(8):973–985

    Article  PubMed  Google Scholar 

  11. Sinha TK, Dawant BM, Duay V, Cash DM, Weil RJ, Thompson RC, Weaver KD, Miga M (2005) A method to track cortical surface deformations using a laser range scanner. IEEE Trans Med Imaging 24(6):767–781

    Article  PubMed  Google Scholar 

  12. Cao A, Dumpuri P, Miga M (2006) Tracking cortical surface deformations based on vessel structure using a laser range scanner. In: 3rd IEEE international symposium on biomedical imaging: nano to macro, 2006, IEEE, pp 522–525

  13. 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: 29th annual international conference of the IEEE on engineering in Medicine and Biology Society, 2007, EMBS 2007, pp 848–851

  14. Cao A, Thompson R, Pa Dumpuri, Dawant B, Galloway R, Ding S, Miga M (2008) Laser range scanning for image-guided neurosurgery: investigation of image-to-physical space registrations. Med Phys 35(4):1593–1605

    Article  PubMed  PubMed Central  Google Scholar 

  15. Li P, Wang W, Song Z, An Y, Zhang C (2014) A framework for correcting brain retraction based on an extended finite element method using a laser range scanner. Int J Comput Assist Radiol Surg 9(4):669–681

    Article  PubMed  Google Scholar 

  16. Sun H, Roberts DW, Farid H, Wu Z, Hartov A, Paulsen KD (2005) Cortical surface tracking using a stereoscopic operating microscope. Neurosurgery 56(1):86–97

    PubMed  Google Scholar 

  17. Sun H, Lunn KE, Farid H, Wu Z, Roberts DW, Hartov A, Paulsen KD (2005) Stereopsis-guided brain shift compensation. IEEE Trans Med Imaging 24(8):1039–1052

    Article  PubMed  Google Scholar 

  18. Ji S, Fan X, Roberts DW, Hartov A, Paulsen KD (2014) Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration. Med Image Anal 18(7):1169–1183

    Article  PubMed  PubMed Central  Google Scholar 

  19. Faria C, Sadowsky O, Bicho E, Ferrigno G, Joskowicz L, Shoham M, Vivanti R, De ME (2014) Validation of a stereo camera system to quantify brain deformation due to breathing and pulsatility. Med Phys 41(11):113502

    Article  PubMed  Google Scholar 

  20. Paul P, Morandi X, Jannin P (2009) A surface registration method for quantification of intraoperative brain deformations in image guided neurosurgery. IEEE Trans Inf Technol Biomed 13(6):976–983

    Article  PubMed  Google Scholar 

  21. 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):1201–1210

    Article  CAS  PubMed  Google Scholar 

  22. Sun H, Roberts DW, Hartov A, Rick KR, Paulsen KD (2003) Using cortical vessels for patient registration during image-guided neurosurgery: a phantom study. In: Medical imaging 2003, International Society for Optics and Photonics, pp 183–191

  23. DeLorenzo C, Papademetris X, Wu K, Vives KP, Spencer D, Duncan JS (2006) Nonrigid 3d brain registration using intensity/feature information. Med Image Comput Comput Assist Interv 2006:932–939

  24. DeLorenzo C, Papademetris X, Vives KP (2006) Combined feature/intensity-based brain shift compensation using stereo guidance. In: 3rd IEEE international symposium on biomedical imaging: nano to macro, 2006, IEEE, pp 335–338

  25. Marreiros F, Rossitti S, Wang C, Smedby Ö (2013) Non-rigid deformation pipeline for compensation of superficial brain shift. Med Image Comput Comput Assist Interv 2013:141–148

    Google Scholar 

  26. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. Med Image Comput Comput Assist Interv 98:130–137

    Google Scholar 

  27. Wen R, Chui CK, Ong SH, Lim KB, Chang SKY (2013) Projection-based visual guidance for robot-aided RF needle insertion. Int J Comput Assist Radiol Surg 8(6):1015–1025

    Article  PubMed  Google Scholar 

  28. Olesen OV, Paulsen RR, Højgaard L, Roed B, Larsen R (2012) Motion tracking for medical imaging: a nonvisible structured light tracking approach. IEEE Trans Med Imaging 31(1):79–87

    Article  PubMed  Google Scholar 

  29. Ding S, Miga MI, Thompson RC, Garg I, Dawant BM (2009) Automatic segmentation of cortical vessels in pre-and post-tumor resection laser range scan images. In: SPIE medical imaging, International Society for Optics and Photonics, pp 726104

  30. Marreiros F, Rossitti S, Gustafsson T, Carleberg P, Smedby Ö (2014) Multi-view 3D vessel tracking using near-infrared cameras. In: CARS 2013-computer assisted radiology and surgery, 27th international congress and exhibition, Heidelberg, Germany, June 26–29, 2013, Springer, pp S165

  31. Platenik L, Miga M, Roberts DW, Lunn KE, Kennedy FE, Hartov A, Paulsen KD (2002) In vivo quantification of retraction deformation modeling for updated image-guidance during neurosurgery. IEEE Trans Biomed Eng 49(8):823–835

    Article  PubMed  Google Scholar 

  32. Roberts DW, Hartov A, Kennedy FE, Miga MI, Paulsen KD (1998) Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery 43(4):749–758

    Article  CAS  PubMed  Google Scholar 

  33. Allen B, Curless B, Popović Z (2003) The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans Graphics 22:587–594

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Bioengineering, Graduate School of Engineering, University of Tokyo, Room 213A, Engineering Building #12, Yayoi 2-11-16, Bunkyo, Tokyo, 113-8656, Japan

    Jue Jiang, Yoshikazu Nakajima, Yoshio Sohma, Toki Saito, Taichi Kin, Horoshi Oyama & Nobuhito Saito

  2. Department of Clinical Information Engineering, Graduate School of Medicine, University of Tokyo, Tokyo, Japan

    Toki Saito & Horoshi Oyama

  3. Department of Neurosurgery, Graduate School of Medicine, University of Tokyo, Tokyo, Japan

    Taichi Kin & Nobuhito Saito

Authors
  1. Jue Jiang
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  2. Yoshikazu Nakajima
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  3. Yoshio Sohma
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  4. Toki Saito
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  5. Taichi Kin
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Correspondence to Jue Jiang.

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The authors declare that they have no conflict of interest.

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All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

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Jiang, J., Nakajima, Y., Sohma, Y. et al. Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features. Int J CARS 11, 1687–1701 (2016). https://doi.org/10.1007/s11548-016-1358-7

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  • DOI: https://doi.org/10.1007/s11548-016-1358-7

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