Biomedical subjects

Grzegorz Soza

Publications and source records attributed to Grzegorz Soza.

3 recordsLinked to original sources

PubMed2004

Strategies for brain shift evaluation.

For the analysis of the brain shift phenomenon different strategies were applied. In 32 glioma cases pre- and intraoperative MR datasets were acquired in order to evaluate the maximum displacement of the brain surface and the deep tumor margin. After rigid registration using the software of the neuronavigation system, a direct comparison was made with 2D- and 3D visualizations. As a result, a great variability of the brain shift was observed ranging up to 24 mm for cortical displacement and exceeding 3 mm for the deep tumor margin in 66% of all cases. Following intraoperative imaging the neuronavigation system was updated in eight cases providing reliable guidance. For a more comprehensive analysis a voxel-based nonlinear registration was applied. Aiming at improved speed of alignment we performed all interpolation operations with 3D texture mapping based on OpenGL functions supported in graphics hardware. Further acceleration was achieved with an adaptive refinement of the underlying control point grid focusing on the main deformation areas. For a quick overview the registered datasets were evaluated with different 3D visualization approaches. Finally, the results were compared to the initial measurements contributing to a better understanding of the brain shift phenomenon. Overall, the experiments clearly demonstrate that deformations of the brain surface and deeper brain structures are uncorrelated.

Brain Neoplasms↗

PubMed2003

Remote computing environment compensating for brain shift.

OBJECTIVE: Anatomical and functional image data become invalid during an operation due to brain shift. Compensation is achieved by using intraoperative imaging to update anatomical information. To accelerate the registration and visualization of pre- and intraoperative image data, the presented work focuses on remote computing capabilities. The underlying framework efficiently combines local desktop computers and remote high-end graphics workstations exploiting expensive hardware. METHODS: By performing all computations on the remote computer, the MR volumes are rigidly aligned via voxel-based registration. Using graphics hardware for acceleration, all interpolation operations are performed with 3D texture-mapping hardware. A new approach then transforms functional markers from preoperative measurements to the intraoperative situation using an automatic tracking algorithm to identify corresponding sulci. Communicating Java viewers are suggested for analyzing the results interactively on a local computer, with all calculations being performed exclusively on the remote computer. RESULTS: The suggested approach was successfully applied in 5 cases using MR data containing functional markers of MEG and fMRI measurements identifying eloquent brain areas. Remote large-scale graphics hardware was thereby efficiently made available for fast registration and interactive direct volume rendering in neurosurgery. CONCLUSION: Overall, the presented framework demonstrates efficient access of expensive high-end hardware remotely controlled by thin clients, and further emphasizes the need to compensate for brain shift in functional neuronavigation.

Brain↗

PubMed2003

Fast and adaptive finite element approach for modeling brain shift.

OBJECTIVE: In this paper we introduce a finite element-based strategy for simulation of brain deformation occurring during neurosurgery. The phenomenon, known as brain shift, causes a decrease in the accuracy of neuronavigation systems that rely on preoperatively acquired data. This can be compensated for with a computational model of the brain deformation process. By applying model calculations to preoperative images, an update within the operating room can be performed. METHODS: One of the crucial concerns in the context of developing a physical-based model is the choice of governing equations describing the physics of the phenomenon. In this work, deformation of brain tissue is expressed in terms of a 3D consolidation model for a linearly elastic and porous fluid. The next crucial issue is ensuring stable calculations within the chosen model. For this purpose, we developed a special technique for generating the underlying geometry for the simulation. With this technique an unstructured grid consisting of regular tetrahedra is created, whereupon time-dependent finite element simulation is performed in an adaptive manner. RESULTS: We applied our algorithm to preoperative MR scans and investigated the value of the method. Due to the adaptivity of the method, only 5-10% of the computing time was needed as compared to traditional finite element approaches based on a uniformly subdivided grid. The results of the experiments were compared to the corresponding intraoperative MR scans. A close match between the computed deformation of the brain and the displacement resulting from the intraoperative data was observed. CONCLUSION: A model-based approach for the simulation of brain shift is presented. In this computational model the brain tissue is described as an elastic and porous material using Biot consolidation theory. Validating experiments conducted with MR data provided promising results.

Algorithms↗