Abstract
We describe a new model which is able to model accurately the characteristics of subject motion, a dominant artefact in Functional Magnetic Resonance Images. Using the model, which is based on specific knowledge regarding the nature of the image acquisition, it is possible to correct for this motion which would otherwise render activation detection on the images invalid. We also present an initial implementation based on the model and are able to demonstrate that the corrections available under this new scheme are significantly more accurate than existing approaches to the problem of subject motion, enabling a far more accurate analysis of the patterns of brain activation which these images seek to capture.
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© 2004 Springer-Verlag Berlin Heidelberg
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Bannister, P.R., Brady, J.M., Jenkinson, M. (2004). TIGER – A New Model for Spatio-temporal Realignment of FMRI Data. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds) Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. MMBIA CVAMIA 2004 2004. Lecture Notes in Computer Science, vol 3117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27816-0_25
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DOI: https://doi.org/10.1007/978-3-540-27816-0_25
Publisher Name: Springer, Berlin, Heidelberg
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