Registration of Electrophysiological and MRI Data Sets and Evaluation

D. Kozinska (Italy), F. Carducci (Poland), and K. Nowinski (Italy)


Medical Imaging, Multimodality Registration, Alignment


We developed a new technique of fully automatic alignment of brain data acquired with scalp sensors (e.g. EEG, MEG) with MRI head volume. The method basing on geometrical features (points extracted from objects) combines matching on 3D geometrical moments, that performs the initial alignment, and 3D distance based alignment that provides the final tuning. To reduce errors of digitization of the head surface points we introduced weights to compute geometrical moments and removing outliers procedure to eliminate incorrectly digitized points. The method was tested on simulated and real data. The simulations demonstrated that about 800 1000 points digitized from the head surface is enough to obtain the average map error between 0.7 mm and 2.1 mm. The average distance error was then less than 1 mm. Tests on real data gave the average distance error between 2.1 mm and 2.5 mm. To demonstrate the usefulness of the method it was applied to study spatially enhanced human somatosensory evoked potentials (SEPs). The developed technique demonstrates accuracy, robustness and speed sufficient for the routine use in clinical environment

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