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.

Design and Implementation of a Laparoscope Calibration Method for Augmented Reality Navigation

Intraoperative laparoscopic calibration remains a challenging task. In this work we present a new method and instrumentation for intraoperative camera calibration. Contrary to conventional calibration methods, the proposed technique allows intraoperative laparoscope calibration from single perspective observations, resulting in a standardized scheme for calibrating in a clinical scenario. Results show an average displacement error of 0.52 ± 0.19 mm, indicating sufficient accuracy for clinical use. Additionally, the proposed method is validated clinically by performing a calibration during the surgery.