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Proceedings Paper

Assessing breathing motion by shape matching of lung and diaphragm surfaces
Author(s): Martin Urschler; Horst Bischof
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Paper Abstract

Studying complex thorax breating motion is an important research topic for accurate fusion of functional and anatomical data, radiotherapy planning or reduction of breathing motion artifacts. We investigate segmented CT lung, airway and diaphragm surfaces at several different breathing states between Functional Residual and Total Lung Capacity. In general, it is hard to robustly derive corresponding shape features like curvature maxima from lung and diaphragm surfaces since diaphragm and rib cage muscles tend to deform the elastic lung tissue such that e.g. ridges might disappear. A novel registration method based on the shape context approach for shape matching is presented where we extend shape context to 3D surfaces. The shape context approach was reported as a promising method for matching 2D shapes without relying on extracted shape features. We use the point correspondences for a non-rigid thin-plate-spline registration to get deformation fields that describe the movement of lung and diaphragm. Our validation consists of experiments on phantom and real sheep thorax data sets. Phantom experiments make use of shapes that are manipulated with known transformations that simulate breathing behaviour. Real thorax data experiments use a data set showing lungs and diaphragm at 5 distinct breathing states, where we compare subsets of the data sets and qualitatively and quantitatively asses the registration performance by using manually identified corresponding landmarks.

Paper Details

Date Published: 14 April 2005
PDF: 13 pages
Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); doi: 10.1117/12.595687
Show Author Affiliations
Martin Urschler, Graz Univ. of Technology (Austria)
Horst Bischof, Graz Univ. of Technology (Austria)


Published in SPIE Proceedings Vol. 5746:
Medical Imaging 2005: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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