Improved Methods for the Registration and Motion Correction of Brain Images
FMRIB Technical Report TR02MJ1
(A related paper has been accepted by Neuroimage)
Mark Jenkinson, Peter Bannister, Michael Brady and Stephen Smith
1: Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB),
Department of Clinical Neurology, University of Oxford, John Radcliffe Hospital,
Headley Way, Headington, Oxford, UK
2: Medical Vision Laboratory, Department of Engineering Science,
University of Oxford, Oxford, UK
Corresponding author is Mark Jenkinson:
mark@fmrib.ox.ac.uk
Linear registration and motion correction are important components of
structural and functional brain image analysis. Most modern methods
optimise some intensity-based cost function to determine the best
registration. To date, little attention has been focused on the
optimisation method itself, even though the success of most
registration methods hinges on the quality of this optimisation. This
paper examines the optimisation process in detail and demonstrates
that the commonly used multi-resolution local optimisation 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 optimisation method.
This new optimisation method is specifically designed for registering
whole brain images. It substantially reduces the likelihood of
producing mis-registrations 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.