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.

Original publication

DOI

10.1006/nimg.2002.1132

Type

Journal article

Journal

NeuroImage

Publication Date

10/2002

Volume

17

Pages

825 - 841

Addresses

Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Headington, United Kingdom.

Keywords

Brain, Humans, Image Interpretation, Computer-Assisted, Acoustic Stimulation, Data Interpretation, Statistical, Linear Models, Reproducibility of Results, Photic Stimulation, Algorithms, Motion, Fuzzy Logic, Models, Neurological, Computer Simulation