Biomedical subjects

H S Stiehl

Publications and source records attributed to H S Stiehl.

4 recordsLinked to original sources

PubMed2002

Coupling of fluid and elastic models for biomechanical simulations of brain deformations using FEM.

In order to improve the accuracy of image-guided neurosurgery, different biomechanical models have been developed to correct preoperative images with respect to intraoperative changes like brain shift or tumor resection. All existing biomechanical models simulate different anatomical structures by using either appropriate boundary conditions or by spatially varying material parameter values, while assuming the same physical model for all anatomical structures. In general, this leads to physically implausible results, especially in the case of adjacent elastic and fluid structures. Therefore, we propose a new approach which allows to couple different physical models. In our case, we simulate rigid, elastic and fluid regions by using the appropriate physical description for each material, namely either the Navier equation or the Stokes equation. To solve the resulting differential equations, we derive a linear matrix system for each region by applying the finite element method (FEM). Thereafter, the linear matrix systems are linked together, ending up with one overall linear matrix system. Our new approach has been tested and compared to a purely linear elastic model using synthetic as well as tomographic images. It turns out from our experiments, that the integrated treatment of rigid, elastic and fluid regions improves the physical plausibility of the predicted deformation results as compared to a purely linear elastic model.

Biomechanical Phenomena↗

PubMed2002

Error analysis in cranial neuronavigation.

Neuronavigation systems are now an important component of many modern neurosurgical treatment strategies. Their support facilities intraoperative orientation and makes neurosurgical operations more precise and less traumatic. Computer-aided neurosurgery is definitively not a temporary fashionable phenomenon, the concept of neuronavigation is here to stay. This report summarizes a ten-years-long experience and presents an error analysis of 108 failures (12.4 %) in a total of 874 image-guided cranial neurosurgical procedures with an arm-linked (mechanical) system and two different infrared-light emitting (optical) systems. The application of neuronavigation incurs multiple reasons for pitfalls because of the complex man-machine interface. Principally, we have to differentiate two types of errors: "machine made errors" due to soft- or hardware failure and "man made errors" generally, due to inadequate handling of the navigation system. The error analysis demonstrated that the so-called human interface plays the main role causing a high error rate.

Bias↗

PubMed2001

Landmark-based elastic registration using approximating thin-plate splines.

We consider elastic image registration based on a set of corresponding anatomical point landmarks and approximating thin-plate splines. This approach is an extension of the original interpolating thin-plate spline approach and allows to take into account landmark localization errors. The extension is important for clinical applications since landmark extraction is always prone to error. Our approach is based on a minimizing functional and can cope with isotropic as well as anisotropic landmark errors. In particular, in the latter case it is possible to include different types of landmarks, e.g., unique point landmarks as well as arbitrary edge points. Also, the scheme is general with respect to the image dimension and the order of smoothness of the underlying functional. Optimal affine transformations as well as interpolating thin-plate splines are special cases of this scheme. To localize landmarks we use a semi-automatic approach which is based on three-dimensional (3-D) differential operators. Experimental results are presented for two-dimensional as well as 3-D tomographic images of the human brain.

Algorithms↗

PubMed1999

Biomechanical modeling of the human head for physically based, nonrigid image registration.

The accuracy of image-guided neurosurgery generally suffers from brain deformations due to intraoperative changes. These deformations cause significant changes of the anatomical geometry (organ shape and spatial interorgan relations), thus making intraoperative navigation based on preoperative images error prone. In order to improve the navigation accuracy, we developed a biomechanical model of the human head based on the finite element method, which can be employed for the correction of preoperative images to cope with the deformations occurring during surgical interventions. At the current stage of development, the two-dimensional (2-D) implementation of the model comprises two different materials, though the theory holds for the three-dimensional (3-D) case and is capable of dealing with an arbitrary number of different materials. For the correction of a preoperative image, a set of homologous landmarks must be specified which determine correspondences. These correspondences can be easily integrated into the model and are maintained throughout the computation of the deformation of the preoperative image. The necessary material parameter values have been determined through a comprehensive literature study. Our approach has been tested for the case of synthetic images and yields physically plausible deformation results. Additionally, we carried out registration experiments with a preoperative MR image of the human head and a corresponding postoperative image simulating an intraoperative image. We found that our approach yields good prediction results, even in the case when correspondences are given in a relatively small area of the image only.

Biomechanical Phenomena↗