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Biomechanical Model as a Registration Tool for Image-Guided Neurosurgery: Evaluation Against BSpline Registration

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Abstract

In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific biomechanical model against registration using a BSpline-based free form deformation algorithm. Unlike the BSpline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm.

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Notes

  1. IRB approval was acquired for the use of the anonymised retrospective image database for this study.

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Acknowledgments

The first author is a recipient of the SIRF scholarship and acknowledges the financial support of the University of Western Australia. The financial support of National Health and Medical Research Council (Grant No. APP1006031) is gratefully acknowledged. This investigation was also supported in part by NIH grants R01 EB008015 and R01 LM010033, and by a research grant from the Children’s Hospital Boston Translational Research Program. In addition, the authors also gratefully acknowledge the financial support of Neuroimage Analysis Center (NIH P41 EB015902), National Center for Image Guided Therapy (NIH U41RR019703) and the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics.

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Authors and Affiliations

  1. Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia

    Ahmed Mostayed, Revanth Reddy Garlapati, Grand Roman Joldes, Adam Wittek, Aditi Roy & Karol Miller

  2. Surgical Planning Laboratory, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Ron Kikinis

  3. Computational Radiology Laboratory, Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Simon K. Warfield

  4. Institute of Mechanics and Advanced Materials, Cardiff School of Engineering, Cardiff University, Wales, UK

    Karol Miller

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  1. Ahmed Mostayed
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Correspondence to Karol Miller.

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Associate Editor Joel D. Stitzel oversaw the review of this article.

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Mostayed, A., Garlapati, R.R., Joldes, G.R. et al. Biomechanical Model as a Registration Tool for Image-Guided Neurosurgery: Evaluation Against BSpline Registration. Ann Biomed Eng 41, 2409–2425 (2013). https://doi.org/10.1007/s10439-013-0838-y

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