Modelling of soft tissue deformation for laparoscopic surgery simulation.

Virtual reality based surgical simulator systems offer a very elegant solution to the development of endoscopic surgical trainers. While the graphical performance of commercial systems already makes PC-based simulators viable, the real-time simulation of soft tissue deformation is still the major obstacle in developing simulators for soft-tissue surgery. The goal of the present work is to develop a framework for the full-scale, real-time, finite element simulation of elastic tissue deformation in complex systems such as the human abdomen. The key for such a development is the proper formulation of the model, the development of scalable parallel solution algorithms, and special-purpose parallel hardware. The developed techniques will be used for the implementation of a gynecological laparoscopic VR-trainer system.

Parametric estimate of intensity inhomogeneities applied to MRI.

This paper presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. We present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.

[Automatic measurement of dye filling of simultaneous digital ICG- and fluorescein angiography sequences]. / Automatische Berechnung der Kontrastmittelfüllung simultaner digitaler ICG- und Fluoreszeinangiographiesequenzen.

BACKGROUND: The ICG filling is supposed to be faster than Fluorescein filling. Interestingly the filling characteristics of these dyes were never correlated directly using precise quantitative methods. Since ICG and Fluorescein are injected as a mixture, the simultaneous 2-channel angiography provides a suitable method to correlate the filling characteristics of the dyes. MATERIAL AND METHODS: The simultaneous ICG and Fluorescein angiograms were recorded with a Rodenstock Scanning Laser Ophthalmoscope. The angiographic images were digitized real-time with a graphic workstation. Filling characteristics of the two dyes was calculated after off-line eye tracking in different regions of interests (ROIs) on the central retina. RESULTS: The Fluorescein filling was faster than the ICG filling in 56.5% of our patients. In 26% of our patients was a mixed filling detectable. Depending on the position of the ROIs the Fluorescein or ICG filling was faster. In only 17.5% of our cases was the ICG filling faster than the Fluorescein filling. CONCLUSION: Our results show that Fluorescein filling in more than 50% of the cases is faster than ICG filling and only a minority of the patients has a faster ICG filling. According to our experience the filling pattern of the two dyes is individual, there is no rule of thumb for the filling.

Virtual reality based surgery simulation for endoscopic gynaecology.

Virtual reality (VR) based surgical simulator systems offer very elegant possibilities to both enrich and enhance traditional education in endoscopic surgery. However, while a wide range of VR simulator systems have been proposed and realized in the past few years, most of these systems are far from able to provide a reasonably realistic surgical environment. We explore the basic approaches to the current limits of realism and ultimately seek to extend these based on our description and analysis of the most important components of a VR-based endoscopic simulator. The feasibility of the proposed techniques is demonstrated on a first modular prototype system implementing the basic algorithms for VR-training in gynaecologic laparoscopy.

Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models.

This paper describes a new model-based segmentation technique combining desirable properties of physical models (snakes), shape representation by Fourier parametrization, and modelling of natural shape variability. Flexible parametric shape models are represented by a parameter vector describing the mean contour and by a set of eigenmodes of the parameters characterizing the shape variation. Usually the segmentation process is divided into an initial placement of the mean model and an elastic deformation restricted to the model variability. This, however leads to a separation of biological variation due to a global similarity transform from small-scale shape changes originating from elastic deformations of the normalized model contours only. The performance can be considerably improved by building shape models normalized with respect to a small set of stable landmarks (AC-PC in our application) and by explaining the remaining variability among a series of images with the model flexibility. This way the image interpretation is solved by a new coarse-to-fine segmentation procedure based on the set of deformation eigenmodes, making a separate initialization step unnecessary. Although straightforward, the extension to 3-D is severely impeded by difficulties arising during the generation of a proper surface parametrization for arbitrary objects with spherical topology. We apply a newly developed surface parametrization which achieves a uniform mapping between object surface and parameter space. The 3-D procedure is demonstrated by segmenting deep structures of the human brain from MR volume data.