A hand-eye calibration method for augmented reality applied to computer-assisted orthopedic surgery.

BACKGROUND: Augmented reality (AR) allows the surgeon to represent holographic patient-specific anatomical information and surgical instruments in the physical world. To correctly superimpose virtual and physical objects, a hand-eye (HE) calibration method for mapping the virtual and physical spaces was proposed. METHODS: Mathematical relationships between the virtual camera and the physical space were derived. Finally, the accuracy and robustness of the proposed HE calibration method were qualitatively and quantitatively evaluated. RESULTS: The proposed calibration method allows us to determine an optimal invariant spatiotemporal mapping between the virtual camera and the physical space. CONCLUSION: Qualitatively and quantitatively reliable and accurate estimates for the physical-virtual mapping transformation were verified. Consequently, imaging data and surgical instruments holograms can be precisely represented in the physical space.

Directional entropy based model for diffusivity-driven tumor growth.

In this work, we present and investigate a multiscale model to simulate 3D growth of glioblastomas (GBMs) that incorporates features of the tumor microenvironment and derives macroscopic growth laws from microscopic tissue structure information. We propose a normalized version of the Shannon entropy as an alternative measure of the directional anisotropy for an estimation of the diffusivity tensor in cases where the latter is unknown. In our formulation, the tumor aggressiveness and morphological behavior is tissue-type dependent, i.e. alterations in white and gray matter regions (which can e.g. be induced by normal aging in healthy individuals or neurodegenerative diseases) affect both tumor growth rates and their morphology. The feasibility of this new conceptual approach is supported by previous observations that the fractal dimension, which correlates with the Shannon entropy we calculate, is a quantitative parameter that characterizes the variability of brain tissue, thus, justifying the further evaluation of this new conceptual approach.

An image-based method to automatically propagate bony landmarks: application to computational spine biomechanics.

In attempts to elucidate the underlying mechanisms of spinal injuries and spinal deformities, several experimental and numerical studies have been conducted to understand the biomechanical behavior of the spine. However, numerical biomechanical studies suffer from uncertainties associated with hard- and soft-tissue anatomies. Currently, these parameters are identified manually on each mesh model prior to simulations. The determination of soft connective tissues on finite element meshes can be a tedious procedure, which limits the number of models used in the numerical studies to a few instances. In order to address these limitations, an image-based method for automatic morphing of soft connective tissues has been proposed. Results showed that the proposed method is capable to accurately determine the spatial locations of predetermined bony landmarks. The present method can be used to automatically generate patient-specific models, which may be helpful in designing studies involving a large number of instances and to understand the mechanical behavior of biomechanical structures across a given population.