Multimodality Data Integration in Epilepsy

Muzik, Otto; Chugani, Diane C.; Zou, Guangyu; Hua, Jing; Lu, Yi; Lu, Shiyong; Asano, Eishi; Chugani, Harry T.

doi:https://doi.org/10.1155/2007/13963

International Journal of Biomedical Imaging

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Multimodal Imaging and Hybrid Scanners

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Research Article | Open Access

Volume 2007 | Article ID 013963 | https://doi.org/10.1155/2007/13963

Multimodality Data Integration in Epilepsy

Otto Muzik,^1,2Diane C. Chugani,¹Guangyu Zou,³Jing Hua,³Yi Lu,³Shiyong Lu,³Eishi Asano,⁴and Harry T. Chugani⁴

Academic Editor: Haim Azhari

Received13 Sept 2006

Accepted08 Feb 2007

Published23 Apr 2007

Abstract

An important goal of software development in the medical field is the design of methods which are able to integrate information obtained from various imaging and nonimaging modalities into a cohesive framework in order to understand the results of qualitatively different measurements in a larger context. Moreover, it is essential to assess the various features of the data quantitatively so that relationships in anatomical and functional domains between complementing modalities can be expressed mathematically. This paper presents a clinically feasible software environment for the quantitative assessment of the relationship among biochemical functions as assessed by PET imaging and electrophysiological parameters derived from intracranial EEG. Based on the developed software tools, quantitative results obtained from individual modalities can be merged into a data structure allowing a consistent framework for advanced data mining techniques and 3D visualization. Moreover, an effort was made to derive quantitative variables (such as the spatial proximity index, SPI) characterizing the relationship between complementing modalities on a more generic level as a prerequisite for efficient data mining strategies. We describe the implementation of this software environment in twelve children (mean age 5.2±4.3 years) with medically intractable partial epilepsy who underwent both high-resolution structural MR and functional PET imaging. Our experiments demonstrate that our approach will lead to a better understanding of the mechanisms of epileptogenesis and might ultimately have an impact on treatment. Moreover, our software environment holds promise to be useful in many other neurological disorders, where integration of multimodality data is crucial for a better understanding of the underlying disease mechanisms.

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Copyright

Copyright © 2007 Otto Muzik et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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