Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images

Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Headington, United Kingdom.
NeuroImage (Impact Factor: 6.36). 11/2002; 17(2):825-41. DOI: 10.1016/S1053-8119(02)91132-8
Source: PubMed

ABSTRACT Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.

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    • "The fMRI data were processed through typical preprocessing procedures using SPM8 software (Wellcome Department of Cognitive Neurology, London, UK) [37] [38], including the first five volumes of each run discarded, slice scan time correction, head motion correction [37], normalized images with a BOLD EPI template in the Montreal Neurological Institute (MNI) atlas space, and spatial smoothing with Gaussian kernel of 8 mm fullwidth half-maximum (FWHM). Temporal filtering (bandpass ) was then performed between 0.01 and 0.08 Hz. "
    Neural Plasticity 10/2015; · 3.58 Impact Factor
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    • "Preprocessing included motion correction (Jenkinson et al., 2002), non-brain removal (Smith, 2002), spatial smoothing using a Gaussian kernel of full width of half maximum (FWHM) = 6 mm, and highpass temporal filtering with a 90 s window. Registration from fMRI to structural space was carried out using FLIRT (Jenkinson et al., 2002), and fMRI data were warped to MNI space via the high-resolution structural volume using FNIRT ( In a lower-level general linear model (GLM) analysis for each run (0-back and 2-back), the onset and duration of the on-blocks were modeled with the off-blocks as implicit baseline. "
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