title: Hierarchical adaptive local affine registration for fast and robust respiratory motion estimation.
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.
authors: Division Imaging Sciences & Biomedical Engineering, King's College London, 4th Floor Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK. christian.buerger@kcl.ac.uk
authors: Christian
authors: C
authors: Buerger
authors: Tobias
authors: T
authors: Schaeffter
authors: Andrew P
authors: AP
authors: King
mesh_terms: D016477:Artifacts; D006801:Humans; D008168:Lung / Q000033:anatomy & histology / Q000000981:diagnostic imaging / Q000502:physiology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015656:Respiratory Mechanics; D055425:Respiratory-Gated Imaging Techniques / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*