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

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.

Patient-specific biomechanical model as whole-body CT image registration tool

“…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.…”

“…This ensures plausibility and robustness of the predicted deformations. In particular, patient-specific biomechanical modelling has been successfully used in numerous studies on computing the brain deformations for neuroimage registration (Garlapati et al, 2014; Hu et al, 2007; Ji et al, 2009; Mostayed et al, 2013; Wittek et al, 2010; Xu and Nowinski, 2001). …”

“…Challenges to overcome when applying biomechanical modelling for medical image registration include quick and reliable generation of patient-specific computational models, automatic segmentation of radiographic images and efficient solution of the models (Miller, 2011; Miller et al, 2010; Mostayed et al, 2013). To facilitate rapid generation of patient-specific biomechanical models for whole-body CT image registration, we abandon time-consuming image segmentation that divides the problem domain into non-overlapping constituents with different material properties.…”

Towards measuring neuroimage misalignment

“…The system will create an augmented reality visualisation of the intra-operative configuration of the patient’s brain merged with high resolution pre-operative imaging data, including diffusion tensor imaging (DTI) and functional MR imaging (fMRI), in order to better localise the tumour and critical healthy tissues. We accomplish this by registering high quality pre-operative neuroimages onto the current, intra-operative configuration of the patient’s brain; however, we do not use an intra-operative image as a target (2, 3)(Fig. 1).…”

Efficient Inverse Isoparametric Mapping Algorithm for Whole-Body Computed Tomography Registration Using Deformations Predicted by Nonlinear Finite Element Modeling

“…In the past nonrigid registration of CTs (and other radiographic image modalities) relied solely on image processing methods that predict the deformation field within the human body organs/tissues without taking into account the principles of mechanics governing deformations of such organs/tissues [2,4]. Such methods do not ensure plausibility of the predicted deformations and their accuracy tends to decrease when the differences between the source and target images become large due to articulated motion of the body segments and soft tissue deformations [4][5][6]. Therefore, biomechanical models, in which predicting the organs/tissue deformation is treated as a computational problem of solid mechanics, have been introduced [7][8][9][10][11].…”