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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.
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