Abstract
Registration of whole-body radiographic images is an important task in analysis of the disease progression and assessment of responses to therapies. Numerous registration algorithms have been successfully used in applications where differences between source and target images are relatively small. However, registration of whole-body CT scans remains extremely challenging for such algorithms as it requires taking large deformations of body organs and articulated skeletal motions into account. For registration problems involving large differences between source and target images, registration using biomechanical models has been recommended in the literature. Therefore, in this study, we propose a patient-specific nonlinear finite element model to predict the movements and deformations of body organs for the whole-body CT image registration. We conducted a verification example in which a patient-specific torso model was implemented using a suite of nonlinear finite element algorithms we previously developed, verified and successfully used in neuroimaging registration. When defining the patient-specific geometry for the generation of computational grid for our model, we abandoned the time-consuming hard segmentation of radiographic images typically used in patient-specific biomechanical modelling to divide the body into non-overlapping constituents with different material properties. Instead, an automated Fuzzy C-Means (FCM) algorithm for tissue classification was applied to assign the constitutive properties at finite element mesh integration points. The loading was defined as a prescribed displacement of the vertebrae (treated as articulated rigid bodies) between the two CT images. Contours of the abdominal organs obtained by warping the source image using the deformation field within the body predicted using our patient-specific finite element model differed by only up to only two voxels from the actual organs’ contours in the target image. These results can be regarded as encouraging step in confirming feasibility of conducting accurate registration of whole-body CT images using nonlinear finite element models without the necessity for time-consuming image segmentation when building patient-specific finite element meshes.
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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) and Australian Research Council (Discovery Grants No. DP1092893 and DP120100402) 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 National Centre 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 Centres for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics.
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Li, M. et al. (2014). Whole-Body Image Registration Using Patient-Specific Nonlinear Finite Element Model. In: Doyle, B., Miller, K., Wittek, A., Nielsen, P. (eds) Computational Biomechanics for Medicine. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0745-8_9
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DOI: https://doi.org/10.1007/978-1-4939-0745-8_9
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