Reference : Integration of sparse electrophysiological measurements with preoperative MRI using 3...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/29958
Integration of sparse electrophysiological measurements with preoperative MRI using 3D surface estimation in deep brain stimulation surgery
English
Husch, Andreas mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Gemmar, Peter mailto [> >]
Thunberg, Johan mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Hertel, Frank mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
14-Feb-2017
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
Webster, Robert
Fei, Baowei
SPIE
10135-16
Yes
No
International
SPIE Medical Imaging
11-02-2017 to 16-02-2017
SPIE
Orlando
United States
[en] deep brain stimulation ; data integration ; microelectrode recording
[en] 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 spatiallyvery 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 efficient 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 measuremen
Luxembourg Centre for Systems Biomedicine (LCSB): Systems Control (Goncalves Group)
Fonds National de la Recherche - FnR
Researchers ; Professionals
http://hdl.handle.net/10993/29958

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