Improved Map-Slice-to-Volume Motion Correction with B0 Inhomogeneity Correction: Validation of Activation Detection Algorithms Using ROC Curve Analyses

  • Desmond T. B. Yeo
  • Roshni R. Bhagalia
  • Boklye Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


Head motion is a significant source of error in fMRI activation detection and a common approach is to apply 3D volumetric rigid body motion correction techniques. However, in 2D multislice fMRI, each slice may have a distinct set of motion parameters due to inter-slice motion. Here, we apply an automated mutual information based slice-to-volume rigid body registration technique on time series data synthesized from a T2 MRI brain dataset with simulated motion, functional activation, noise and geometric distortion. The map-slice-to-volume (MSV) technique was previously applied to patient data without ground truths for motion and activation regions. In this study, the activation images and area under the receiver operating characteristic curves for various time series datasets indicate that the MSV registration improves the activation detection capability when compared to results obtained from Statistical Parametric Mapping (SPM). The effect of temporal median filtering of motion parameters on activation detection performance was also investigated.


Receiver Operating Characteristic Curve Activation Detection Motion Correction Statistical Parametric Mapping Geometric Distortion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Friston, K.J., Ashburner, J., Frith, C.D., Poine, J.B., Heather, J.D., Frackowiak, R.S.J.: Spatial registration and normalization of images. Hum. Brain Map. 2, 165–189 (1995)CrossRefGoogle Scholar
  2. 2.
    Jiang, A.P., Kennedy, D.N., Baker, J.R., Weisskoff, R., Tootell, R.B.H., Woods, R.P., Benson, R.R., Kwong, K.K., Brady, T.J., Rosen, B.R., Beliveau, J.W.: Motion detection and correction in functional MR imaging. Hum. Brain Map. 3, 224–235 (1995)CrossRefGoogle Scholar
  3. 3.
    Kim, B., Boes, J.L., Bland, P.H., Chenevert, T.L., Meyer, C.R.: Motion correction in fMRI via registration of individual slices into an anatomical volume. Magn. Reson. Med. 41, 964–972 (1999)CrossRefGoogle Scholar
  4. 4.
    Sutton, B.P., Noll, D.C., Fessler, J.A.: Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities. IEEE Trans. Med. Imag. 22, 178–188 (2003)CrossRefGoogle Scholar
  5. 5.
    Yeo, D.T.B., Fessler, J.A., Kim, B.: Concurrent geometric distortion correction in mapping slice-to-volume (MSV) motion correction of fMRI time series. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 752–760. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Good, P.: Permutation tests. Springer, New York (1994)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Desmond T. B. Yeo
    • 1
    • 2
  • Roshni R. Bhagalia
    • 1
    • 2
  • Boklye Kim
    • 1
  1. 1.Department of RadiologyUniversity of Michigan Medical SchoolUSA
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of MichiganUSA

Personalised recommendations