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Proceedings Paper

A model of respiratory airway motion for real-time tracking of an ultrathin bronchoscope
Author(s): Timothy D. Soper; David R. Haynor; Robb W. Glenny; Eric J. Seibel
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Paper Abstract

Deformable registration of chest CT scans taken of a subject at various phases of respiration provide a direct measure of the spatially varying displacements that occur in the lung due to breathing. This respiratory motion was studied as part of the development of a CT-based guidance system for a new electromagnetically tracked ultrathin bronchoscope. Fifteen scans of an anesthesized pig were acquired at five distinct lung pressures between full expiration to full inspiration. Deformation fields were computed by non-rigid registration using symmetric "demons" forces followed by Gaussian regularization in a multi-resolution framework. Variants of the registration scheme were tested including: initial histogram matching of input images, degree of field smoothing during regularization, and applying an adaptive smoothing method that weights elements of the smoothing kernel by the magnitude of the image gradient. Registration quality was quantified and compared using inverse and transitive consistency metrics. After optimizing the algorithm parameters, deformation fields were computed by registering each image in the set to a baseline image. Registration of the baseline image at full inspiration to an image at full expiration produced the maximum deformation. Two hypotheses were made: first, that each deformation could be modeled as a mathematical sub-multiple of the maximum deformation, and second, that the deformation scales linearly with respiratory pressure. The discrepancy between the deformation measured by image registration and that predicted by the linear model was 1.25 mm on average. At maximum deformation, this motion compensation constitutes an 87% reduction in respiration-induced localization error.

Paper Details

Date Published: 29 March 2007
PDF: 12 pages
Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 65110M (29 March 2007); doi: 10.1117/12.710150
Show Author Affiliations
Timothy D. Soper, Univ. of Washington (United States)
David R. Haynor, Univ. of Washington (United States)
Robb W. Glenny, Univ. of Washington (United States)
Eric J. Seibel, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 6511:
Medical Imaging 2007: Physiology, Function, and Structure from Medical Images
Armando Manduca; Xiaoping P. Hu, Editor(s)

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