The goal of the proposed research is to develop retrospective methods that can correct motion artifacts in fMRI to improve capability of functional localization. Subject motion encountered in fMRI results in two main effects. Rigid head motion causes misoriented slices at each repetitive excitation inducing subsequent changes in susceptibility-induced non-linear image distortion. The other source of localized artifacts in slice image is physiological brain motion during cardiac cycle from compression of ventricles and brain stem displacement. The effects of these motion artifacts are three dimensional (3D) non- linear image deformations, which would not be completely removed by rigid-body transform. This study utilizes a retrospective motion correction method to map each slice into volume (MSV), which is applied to multislice single shot Echo-planar image (EPI) data set. Individual slices are spatially repositioned into an anatomical reference volume space to accommodate out-of-plane and in-plane subject motion which include rigid body and thin-plate-spline (TPS) warping. The MSV technique is based on our automated spatial registration system that uses mutual information as a cost function to drive the registration without the need for user-supplied information or preprocessing. Random permutation test is used for significance testing, which offers advantages with respect to the protection against the type I error for dealing with data sets whose true distribution of the test statistics is unknown. The project will focus on (1) extension of the rigid body MSV method to provide an automated and efficient motion correction using 3D warping and to enhance accuracy of statistical signal analysis in fMRI, and (2) evaluation and validation of accuracy of the MSV registration methods. The MSV approach using a rigid body transformation has shown potential for effective motion correction in multislice fMRI time series. The proposed full development of automatic MSV to include warping will provide capability to correct non-linear distortions induced by rigid head motion and local physiological brain motion. The accuracy of the signal detection will be assessed using phantom experiments which provide the ground truth and the efficacy of the method will be continuously evaluated for clinical fMRI data sets throughout the development.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Diagnostic Imaging Study Section (DMG)
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Mclaughlin, Alan Charles
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University of Michigan Ann Arbor
Schools of Medicine
Ann Arbor
United States
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Bhagalia, Roshni; Fessler, Jeffrey A; Kim, Boklye (2009) Accelerated nonrigid intensity-based image registration using importance sampling. IEEE Trans Med Imaging 28:1208-16
Yeo, Desmond Teck Beng; Fessler, Jeffrey A; Kim, Boklye (2008) Motion robust magnetic susceptibility and field inhomogeneity estimation using regularized image restoration techniques for fMRI. Med Image Comput Comput Assist Interv 11:991-8
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Bhagalia, Roshni; Kim, Boklye (2008) Spin saturation artifact correction using slice-to-volume registration motion estimates for fMRI time series. Med Phys 35:424-34
Yeo, Desmond T B; Fessler, Jeffrey A; Kim, Boklye (2008) Concurrent correction of geometric distortion and motion using the map-slice-to-volume method in echo-planar imaging. Magn Reson Imaging 26:703-14
Yeo, Desmond T B; Chenevert, Thomas L; Fessler, Jeffrey A et al. (2007) Zero and first-order phase shift correction for field map estimation with dual-echo GRE using bipolar gradients. Magn Reson Imaging 25:1263-71
Yeo, Desmond T B; Bhagalia, Roshni R; Kim, Boklye (2006) Improved map-slice-to-volume motion correction with B0 inhomogeneity correction: validation of activation detection algorithms using ROC curve analyses. Med Image Comput Comput Assist Interv 9:276-83