%0 Journal Article %1 noKey %A Schwartz, D. %A Lemoine, D. %A Poiseau, E. %A Barillot, C. %D 1996 %I Kluwer Academic Publishers-Human Sciences Press %J Brain Topography %K eeg mri registration %N 2 %P 101-116 %R 10.1007/BF01200710 %T Registration of MEG/EEG data with 3D MRI: Methodology and precision issues %U http://dx.doi.org/10.1007/BF01200710 %V 9 %X Mapping neuro-physiological functions to high resolution MRI is an effective means to evaluate localization reconstructions and to exhibit the spatio-temporal aspects of dynamic functional processes. The registration step needed between MEG/EEG and MRI is a source of error which, for the worst cases may be greater than errors related to the localization algorithms. Several registration methods can be used: those based on fiducial markers and those based on surface matching. The aim of this paper is to propose a fully automatic surface matching method and to discuss its extended theoretical and experimental evaluation. The registration procedure matches the skin surface, segmented from MRI, and a digitized description of the head performed with a 3D tracker during the MEG/EEG examination. The registration uncertainties at the edges of the MRI volume were estimated to be between 2 and 3 mm. In comparison with commonly used manual methods the improvement in accuracy is significant. Registration uncertainties are smaller than the localization uncertainties usually observed. By minimizing manual intervention, the reliability of the registration process is increased and the accuracy is stabilized. With this automatic registration method the fusion of MEG/EEG localizations with MRI anatomical data gives highly significant information. Finally the accuracy obtained allows the use of complex anatomical constraints in the localization process without introducing large modelling errors.

@article{noKey, abstract = {Mapping neuro-physiological functions to high resolution MRI is an effective means to evaluate localization reconstructions and to exhibit the spatio-temporal aspects of dynamic functional processes. The registration step needed between MEG/EEG and MRI is a source of error which, for the worst cases may be greater than errors related to the localization algorithms. Several registration methods can be used: those based on fiducial markers and those based on surface matching. The aim of this paper is to propose a fully automatic surface matching method and to discuss its extended theoretical and experimental evaluation. The registration procedure matches the skin surface, segmented from MRI, and a digitized description of the head performed with a 3D tracker during the MEG/EEG examination. The registration uncertainties at the edges of the MRI volume were estimated to be between 2 and 3 mm. In comparison with commonly used manual methods the improvement in accuracy is significant. Registration uncertainties are smaller than the localization uncertainties usually observed. By minimizing manual intervention, the reliability of the registration process is increased and the accuracy is stabilized. With this automatic registration method the fusion of MEG/EEG localizations with MRI anatomical data gives highly significant information. Finally the accuracy obtained allows the use of complex anatomical constraints in the localization process without introducing large modelling errors.}, added-at = {2014-02-23T20:45:41.000+0100}, author = {Schwartz, D. and Lemoine, D. and Poiseau, E. and Barillot, C.}, biburl = {https://www.bibsonomy.org/bibtex/29776177006addb0afcfe0d835257cf92/agredain}, description = {Registration of MEG/EEG data with 3D MRI: Methodology and precision issues - Springer}, doi = {10.1007/BF01200710}, interhash = {8e8428ce859a9a3063f665bb12c49c6d}, intrahash = {9776177006addb0afcfe0d835257cf92}, issn = {0896-0267}, journal = {Brain Topography}, keywords = {eeg mri registration}, language = {English}, number = 2, pages = {101-116}, publisher = {Kluwer Academic Publishers-Human Sciences Press}, timestamp = {2014-02-23T20:45:41.000+0100}, title = {Registration of MEG/EEG data with 3D MRI: Methodology and precision issues}, url = {http://dx.doi.org/10.1007/BF01200710}, volume = 9, year = 1996 }