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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)

Abstract

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.

Keywords

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.

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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

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