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Proceedings Paper

A comparison of lung motion measured using implanted electromagnetic transponders and motion algorithmically predicted using external surrogates as an alternative to respiratory correlated CT imaging
Author(s): Kristen M. Lechleiter; Daniel A. Low; Amir Chaudhari; Wei Lu; James P. Hubenschmidt; Martin L. Mayse; Steven C. Dimmer; Jeffrey D. Bradley; Parag J. Parikh
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Paper Abstract

Three-dimensional volumetric imaging correlated with respiration (4DCT) typically utilizes external breathing surrogates and phase-based models to determine lung tissue motion. However, 4DCT requires time consuming post-processing and the relationship between external breathing surrogates and lung tissue motion is not clearly defined. This study compares algorithms using external respiratory motion surrogates as predictors of internal lung motion tracked in real-time by electromagnetic transponders (Calypso® Medical Technologies) implanted in a canine model. Simultaneous spirometry, bellows, and transponder positions measurements were acquired during free breathing and variable ventilation respiratory patterns. Functions of phase, amplitude, tidal volume, and airflow were examined by least-squares regression analysis to determine which algorithm provided the best estimate of internal motion. The cosine phase model performed the worst of all models analyzed (R2 = 31.6%, free breathing, and R2 = 14.9%, variable ventilation). All algorithms performed better during free breathing than during variable ventilation measurements. The 5D model of tidal volume and airflow predicted transponder location better than amplitude or either of the two phasebased models analyzed, with correlation coefficients of 66.1% and 64.4% for free breathing and variable ventilation respectively. Real-time implanted transponder based measurements provide a direct method for determining lung tissue location. Current phase-based or amplitude-based respiratory motion algorithms cannot as accurately predict lung tissue motion in an irregularly breathing subject as a model including tidal volume and airflow. Further work is necessary to quantify the long term stability of prediction capabilities using amplitude and phase based algorithms for multiple lung tumor positions over time.

Paper Details

Date Published: 29 March 2007
PDF: 11 pages
Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 65111P (29 March 2007); doi: 10.1117/12.710203
Show Author Affiliations
Kristen M. Lechleiter, Washington Univ. School of Medicine (United States)
Daniel A. Low, Washington Univ. School of Medicine (United States)
Amir Chaudhari, Washington Univ. School of Medicine (United States)
Wei Lu, Washington Univ. School of Medicine (United States)
James P. Hubenschmidt, Washington Univ. School of Medicine (United States)
Martin L. Mayse, Washington Univ. School of Medicine (United States)
Calypso Medical Technologies, Inc. (United States)
Steven C. Dimmer, Calypso Medical Technologies, Inc. (United States)
Jeffrey D. Bradley, Washington Univ. School of Medicine (United States)
Parag J. Parikh, Washington Univ. School of Medicine (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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