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Hierarchical adaptive local affine registration for fast and robust respiratory motion estimation

Research output: Contribution to journalArticle

Christian Buerger, Tobias Schaeffter, Andrew P. King

Original languageEnglish
Pages (from-to)551 - 564
Number of pages14
JournalMEDICAL IMAGE ANALYSIS
Volume15
Issue number4
DOIs
StatePublished - Aug 2011

Documents

  • Hierarchical adaptive local affine registration for fast and robust respiratory motion estimation

    main.pdf, 1 MB, application/pdf

    21/07/2015

    Accepted author manuscript

    NOTICE: this is the author’s version of a work that was accepted for publication in Medical Image Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication.

King's Authors

Abstract

Non-rigid image registration techniques are commonly used to estimate complex tissue deformations in medical imaging. A range of non-rigid registration algorithms have been proposed, but they typically have high computational complexity. To reduce this complexity, combinations of multiple less complex deformations have been proposed such as hierarchical techniques which successively split the non-rigid registration problem into multiple locally rigid or affine components. However, to date the splitting has been regular and the underlying image content has not been considered in the splitting process. This can lead to errors and artefacts in the resulting motion fields. In this paper, we propose three novel adaptive splitting techniques, an image-based, a similarity-based, and a motion-based technique within a hierarchical framework which attempt to process regions of similar motion and/or image structure in single registration components. We evaluate our technique on free-breathing whole-chest 3D MRI data from 10 volunteers and two publicly available CT datasets. We demonstrate a reduction in registration error of up to 49.1% over a non-adaptive technique and compare our results with a commonly used free-form registration algorithm.

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