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Brain, Body and Machine pp 33–43Cite as

Rigid Registration of 3D Ultrasound and MRI: Comparing Two Approaches on Nine Tumor Cases

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 83))

Abstract

We present a new technique for registering ultrasound and magnetic resonance (MR) images in the context of neurosurgery. It involves generating a pseudo-ultrasound (pseudo-US) from a segmented MR image and uses cross-correlation as the cost function to register with ultrasound. The algorithm’s performance is compared to a state-of-the-art technique that uses a median filtered MR images to register with a Gaussian-blurred ultrasound using normalized mutual information (NMI). The two methods are tested on nine tumor cases, including both high- and low-grade gliomas. The pseudo-US method yielded significantly better alignment average than that obtained by NMI (p = 0.0009).  If one case where NMI failed is excluded, the mean distance obtained by the pseudo-US approach (2.6 mm) is slightly lower than the one obtained by NMI (2.8mm), but not significantly so (p = 0.16).  We conclude that the pseudo-US method is more robust for these cases.

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References

  1. van Velthoven, V.: Intraoperative ultrasound imaging: comparison of pathomorphological findings in US versus CT, MRI and intraoperative findings. Acta Neurochir Suppl. 85, 95–99 (2003)

    Google Scholar 

  2. Woydt, M., et al.: Correlation of intra-operative ultrasound with histopathologic findings after tumour resection in supratentorial gliomas. A method to improve gross total tumour resection. Acta Neurochir (Wien) 138, 1391–1398 (1996)

    Google Scholar 

  3. LeRoux, P.D., et al.: A comparison between preoperative magnetic resonance and intraoperative ultrasound tumor volumes and margins. J. Clin. Ultrasound 22, 29–36 (1994)

    Article  Google Scholar 

  4. Erdogan, N., et al.: Ultrasound guidance in intracranial tumor resection: correlation with postoperative magnetic resonance findings. Acta Radiol. 46, 743–749 (2005)

    Article  Google Scholar 

  5. Tirakotai, W., et al.: A novel platform for image-guided ultrasound. Neurosurgery 58, 710–718 (2006) (discussion 710–718)

    Article  Google Scholar 

  6. Unsgaard, G., et al.: Ability of navigated 3D ultrasound to delineate gliomas and metastases–comparison of image interpretations with histopathology. Acta Neurochir (Wien) 147, 1259–1269 (2005) (discussion 1269)

    Article  Google Scholar 

  7. Unsgaard, G., et al.: Neuronavigation by intraoperative three-dimensional ultrasound: initial experience during brain tumor resection. Neurosurgery 50, 804–812 (2002) (discussion 812)

    Article  Google Scholar 

  8. Stummer, W., et al.: Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial. Lancet. Oncol. 7, 392–401 (2006)

    Article  Google Scholar 

  9. Hatiboglu, M.A., et al.: Impact of Intraoperative High-Field Magnetic Resonance Imaging Guidance on Glioma Surgery: A Prospective Volumetric Analysis. Neurosurgery 64, 1073–1081 (2009)

    Article  Google Scholar 

  10. Nimsky, C., et al.: Volumetric assessment of glioma removal by intraoperative high-field magnetic resonance imaging. Neurosurgery 55, 358–370 (2004) (discussion 370-371)

    Article  Google Scholar 

  11. Gerganov, V.M., et al.: Reliability of intraoperative high-resolution 2D ultrasound as an alternative to high-field strength MR imaging for tumor resection control: a prospective comparative study. J. Neurosurg. 111, 512–519 (2009)

    Article  Google Scholar 

  12. Bucholz, R.D., et al.: The correction of stereotactic inaccuracy caused by brain shift using an intraoperative ultrasound device. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 459–466. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  13. Roche, A., et al.: Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information. IEEE Trans. Med. Imaging 20, 1038–1049 (2001)

    Article  Google Scholar 

  14. Arbel, T., et al.: Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations. Comput. Aided Surg. 9, 123–136 (2004)

    Article  Google Scholar 

  15. Arbel, T., et al.: Automatic Non-linear MRI-Ultrasound Registration for the Correction of Intra-operative Brain Deformations. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 913–922. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  16. Ji, S., et al.: Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery. Med. Phys. 35, 4612–4624 (2008)

    Article  Google Scholar 

  17. Coupe, P., et al.: A Probabilistic Objective Function For 3d Rigid Registration Of Intraoperative Us And Preoperative MR Brain Images. Presented at the ISBI (2007)

    Google Scholar 

  18. Reinertsen, I., et al.: Validation of vessel-based registration for correction of brain shift. Med. Image Anal. 11, 374–388 (2007)

    Article  Google Scholar 

  19. Solberg, O.V., et al.: Freehand 3d Ultrasound Reconstruction Algorithms-a Review. Ultrasound Med. Biol., May 17 (2007)

    Google Scholar 

  20. Coupe, P., et al.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imaging 27, 425–441 (2008)

    Article  Google Scholar 

  21. Zijdenbos, A.P., et al.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13, 716–724 (1994)

    Article  Google Scholar 

  22. Janke, A.L., et al.: MNI and Talairach space: Everything you wanted to know but were afraid to ask. In: HMB, Florence, Italy (2006)

    Google Scholar 

  23. MacDonald, D., et al.: Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage 12, 340–356 (2000)

    Article  Google Scholar 

  24. Frangi, A.F., et al.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L., et al. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)

    Google Scholar 

  25. Bezdek, J.C., et al.: Review of MR image segmentation techniques using pattern recognition. Med. Phys. 20, 1033–1048 (1993)

    Article  Google Scholar 

  26. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys. Man. Cyber. 9, 62–66 (1979)

    Article  Google Scholar 

  27. Jannin, P., et al.: Validation of medical image processing in image-guided therapy. IEEE Trans. Med. Imaging 21, 1445–1449 (2002)

    Article  Google Scholar 

  28. Reinertsen, I., et al.: Clinical validation of vessel-based registration for correction of brain-shift. Medical Image Analysis 11, 673–684 (2007)

    Article  Google Scholar 

  29. Lindseth, F., et al.: Image fusion of Ultrasound and MRI as an Aid for assessing Anatomical Shifts and improving Overview and Interpretation in Ultrasound Guided Neurosurgery. In: CARS 2001, pp. 247–252 (2001)

    Google Scholar 

  30. Hartov, A., et al.: A comparative analysis of coregistered ultrasound and magnetic resonance imaging in neurosurgery. Neurosurgery 62, 91–99 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada

    Laurence Mercier, Vladimir Fonov & D. Louis Collins

  2. Brain Tumour Research Center, McGill University, Montreal, Canada

    Rolando F. Del Maestro & Kevin Petrecca

  3. Dept. Of Health Science and Technology, Aalborg University, Denmark

    Lasse R. Østergaard

Authors
  1. Laurence Mercier
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  2. Vladimir Fonov
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  3. Rolando F. Del Maestro
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  4. Kevin Petrecca
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  5. Lasse R. Østergaard
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  6. D. Louis Collins
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Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering & Centre for Intelligent Machines, McGill University, 817 Sherbrooke St. W., H3A 2K6, Montreal, Quebec, Canada

    Jorge Angeles

  2. Department of Electrical and Computer Engineering & Centre for Intelligent Machines, McGill University, 3480 University Street, H3A 2A7, Montreal, Quebec, Canada

    Benoit Boulet  & James J. Clark  & 

  3. Department of Mechanical Engineering & Centre for Intelligent Machines, McGill University, 817 Sherbrooke St.West, H3A 2K6, Montreal, Quebec, Canada

    József Kövecses

  4. School of Computer Science & Centre for Intelligent Machines, McGill University, 3480 University Street, H3A 2A7, Montreal, QC, Canada

    Kaleem Siddiqi

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© 2010 Springer-Verlag Berlin Heidelberg

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Mercier, L., Fonov, V., Del Maestro, R.F., Petrecca, K., Østergaard, L.R., Collins, D.L. (2010). Rigid Registration of 3D Ultrasound and MRI: Comparing Two Approaches on Nine Tumor Cases. In: Angeles, J., Boulet, B., Clark, J.J., Kövecses, J., Siddiqi, K. (eds) Brain, Body and Machine. Advances in Intelligent and Soft Computing, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16259-6_3

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  • DOI: https://doi.org/10.1007/978-3-642-16259-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16258-9

  • Online ISBN: 978-3-642-16259-6

  • eBook Packages: EngineeringEngineering (R0)

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