Prof. Peter Gemmar Profile

Prof. Peter Gemmar

at Hochschule Trier

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Publications (2)

Proceedings Article | 3 March 2017 Paper

Integration of sparse electrophysiological measurements with preoperative MRI using 3D surface estimation in deep brain stimulation surgery

Andreas Husch, Peter Gemmar, Johan Thunberg, Frank Hertel

Proceedings Volume 10135, 101350H (2017) https://doi.org/10.1117/12.2255894

KEYWORDS: Electrodes, Data integration, Data modeling, Brain stimulation, Surgery, Brain, Magnetic resonance imaging, Distance measurement, 3D modeling, Electrophysiology, 3D acquisition, 3D metrology

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Intraoperative microelectrode recordings (MER) have been used for several decades to guide neurosurgeons during the implantation of Deep Brain Stimulation (DBS) electrodes, especially when targeting the subthalamic nucleus (STN) to suppress the symptoms of Parkinson’s Disease. The standard approach is to use an array of up to five MER electrodes in a fixed configuration. Interpretation of the recorded signals yields a spatially very sparse set of information about the morphology of the respective brain structures in the targeted area. However, no aid is currently available for surgeons to intraoperatively integrate this information with other data available on the patient’s individual morphology (e.g. MR imaging data used for surgical planning). This integration might allow surgeons to better determine the most probable position of the electrodes within the target structure during surgery. This paper suggests a method for reconstructing a surface patch from the sparse MER dataset utilizing additional a priori knowledge about the geometrical configuration of the measurement electrodes. The conventional representation of MER measurements as intervals of target region/non-target region is therefore transformed into an equivalent boundary set representation, allowing ecient point-based calculations. Subsequently, the problem is to integrate the resulting patch with a preoperative model of the target structure, which can be formulated as registration problem minimizing a distance measure between the two surfaces. When restricting this registration procedure to translations, which is reasonable given certain geometric considerations, the problem can be solved globally by employing an exhaustive search with arbitrary precision in polynomial time. The proposed method is demonstrated using bilateral STN/Substantia Nigra segmentation data from preoperative MRIs of 17 Patients with simulated MER electrode placement. When using simulated data of heavily perturbed electrodes and subsequent MER measurements, our optimization resulted in an improvement of the electrode position within 1 mm of the ground truth in 80.29% of the cases.

Proceedings Article | 21 March 2016 Paper

Fast correspondences for statistical shape models of brain structures

Florian Bernard, Nikos Vlassis, Peter Gemmar, Andreas Husch, Johan Thunberg, Jorge Goncalves, Frank Hertel

Proceedings Volume 9784, 97840R (2016) https://doi.org/10.1117/12.2206024

KEYWORDS: Shape analysis, Statistical modeling, Brain, Neuroimaging, Modeling, Image segmentation, Model-based design, Image analysis, Matrices, Francium, Systems modeling, Image processing, Visualization

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