Model-based multi-constrained integration of invasive electrophysiology with other modalities

Bidaut, L. M. (2001) Model-based multi-constrained integration of invasive electrophysiology with other modalities. In: Conference of Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures, 18 - 20 February 2001, San Diego, CA; United States.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

Following recent developments, most brain imaging modalities (MR, CT, SPECT, PET) can nowadays be registered and integrated in a manner almost simple enough for routine use. By design though, these modalities are still not able to match the principles and near real-time capabilities of the much simpler (but of lower spatial resolution) EEG, thus the need to integrate it as well, along with - for some patients - the more accurate invasive electrophysiology measurements taken directly in contact with brain structures. A standard control CT (or MR) is routinely performed after the implantation of invasive electrodes. After registration with the other modalities, the initial estimates of the electrodes' locations extracted from the CT (or MR) are iteratively improved by using a geometrical model of the etectrodes' arrangement (grids, strips, etc.) and other optional constraints (morphology, etc.). Unlike the direct 3D pointing of each electrode in the surgical suite - which can still act as a complementary approach this technique estimates the most likely location of the electrodes during monitoring and can also deal with non critical arrangements (internal strips, depth electrodes, etc.). Although not always applicable to normal volunteers because of its invasive components, this integration further opens the door towards an improved understanding of a very complex biological system.

Additional Information:Conference Code:58381
Keywords:Brain, Electrodes, Electroencephalography, Medical imaging, Neurophysiology, Brain imaging, Electrophysiology
Subjects:F Physical Sciences > F350 Medical Physics
Divisions:College of Science > School of Computer Science
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ID Code:24157
Deposited On:25 Oct 2016 17:48

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