Abstract
Most intensity-based fMRI registration methods do not account for the fact that the volumes being aligned may differ: one may have blood oxygen level dependent (BOLD) contrast while the other does not. Especially in least-squares registration, this can result in motion parameter errors that are correlated to the stimulus. An iterative technique to overcome this activation bias is proposed and analyzed. The method, using mostly off-the-shelf software, is able to find the least-squares solution to both the registration and activation detection problems simultaneously. The resulting motion parameters and activation maps are considerably more accurate, yielding two-thirds fewer false-positive and one-third fewer false-negative activations.
Chapter PDF
Similar content being viewed by others
References
Ramsey, N.F., van den Brink, J.S., van Muiswindle, M.M.C., Folkers, P.J.M., Moonen, C.T.W.: Phase navigator correction in 3D fMRI improves detection of brain activation: Qualitative assessment with a graded motor activation procedure. NeuroImage 8, 240–248 (1998)
Hajnal, J.V., Myers, R., Oatridge, A., Schwieso, J.E., Young, I.R., Bydder, G.M.: Artifacts due to stimulus correlated motion in functional imaging of the brain. Magn. Reson. Med. 31, 283–291 (1994)
Mathiak, K., Posse, S.: Evaluation of motion and realignment for functional magnetic resonance imaging in real time. Magn. Reson. Med. 45, 167–171 (2001)
Morgan, V.L., Pickens, D.R., Hartman, S.L., Price, R.R.: Comparison of functional MRI image realignment tools using a computer-generated phantom. Magn. Reson. Med. 46, 510–514 (2001)
Freire, L., Mangin, J.F.: Motion correction algorithms of the brain mapping community create spurious functional activations. In: Davis (ed.) Proc. Info. Proc. Med. Imag., pp. 246–258 (2001)
Freire, L., Mangin, J.F.: Two-stage alignment of fMRI time series using the experiment profile to discard activation-related bias. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 663–670. Springer, Heidelberg (2002)
Orchard, J., Greif, C., Golub, G., Bjornson, B., Atkins, M.S.: Simultaneous registration and activation detection for fMRI. IEEE Trans. Med. Imag (2003) (in press)
Cox, R.W.: AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research 29, 162–173 (1996)
Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7, 308–313 (1965)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1988)
Eddy, W.F., Fitzgerald, M., Noll, D.C.: Improved image registration using fourier interpolation. Magn. Reson. Med. 36, 923–931 (1996)
Cox, R.W., Jesmanowicz, A.: Real-time 3D image registration for functional MRI. Magn. Reson. Med. 42, 1014–1018 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Orchard, J., Atkins, M.S. (2003). Iterating Registration and Activation Detection to Overcome Activation Bias in fMRI Motion Estimates. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39903-2_108
Download citation
DOI: https://doi.org/10.1007/978-3-540-39903-2_108
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20464-0
Online ISBN: 978-3-540-39903-2
eBook Packages: Springer Book Archive