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

Mark Jenkinson, Peter Bannister, Michael Brady, Stephen Smith

Research output: Contribution to journalArticlepeer-review

6758 Citations (Scopus)


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 languageEnglish
Pages (from-to)825-841
Number of pages17
Issue number2
Publication statusPublished or Issued - 2002
Externally publishedYes


  • Accuracy
  • Affine transformation
  • Global optimization
  • Motion correction
  • Multimodal registration
  • Multiresolution search
  • Robustness

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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