Improved optimization for the robust and accurate linear registration and motion correction of brain images.

Full 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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Author information

Author/s: Jenkinson, Mark (M); Bannister, Peter (P); Brady, Michael (M); Smith, Stephen (S);

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

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: NeuroImage (Neuroimage), published in United States. (Language: eng)

Reference: 2002-Oct; vol 17 (issue 2) : pp 825-41

Dates: Created 2002/10/14; Completed 2002/11/25; Revised 2006/11/15;

PMID: 12377157, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Humans
Image Interpretation, Computer-Assisted - methods
Linear Models
Models, Neurological
Motion
Photic Stimulation
Reproducibility of Results

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