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

Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia.
Annals of Biomedical Engineering (Impact Factor: 3.2). 06/2013; 41(11). DOI: 10.1007/s10439-013-0838-y
Source: PubMed


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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Available from: Revanth Reddy Garlapati, Sep 20, 2014
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    • "The efficiency and effectiveness of this method has been verified through application in the studies on computation of brain deformation for neuroimage registration (Joldes et al., 2009b; Wittek et al., 2010). Although no commonly accepted specific guidelines regarding the required quality of hexahedral meshes in biomechanics are available, several authors have formulated their experience-based recommendations (Ito et al., 2009; Mostayed et al., 2013; Shepherd and Johnson, 2009; Yang and King, 2011). Following Ito et al. (2009), Shepherd and Johnson (2009) and Yang and King (2011), we used element Jacobian and warpage to assess mesh quality. "
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    • "Landmarks can be defined as distinct anatomical points in the body or as edges which can be used as feature points [1]. Landmark detection in medical imaging is focused on three approaches: (a) machine learning algorithms [2] [3]; (b) image intensity models [4]; and (c) invariant geometric measures such as curvatures and extreme points based on their shapes [5]. "
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    • "Additionally, the selection of the image features, the computation of the correspondences, and the volumetric brain deformations are all performed in parallel. Mostayed et al. (2013) presented a biomechanical-based registration method which does not require an intra-operative (intraop ) MRI to update the pre-op MRI, but only some sparse intra-op data points. The method was compared to a BSpline algorithm (Rueckert et al., 1999) and was qualitatively and quantitatively evaluated in 13 clinical cases. "
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