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TU‐C‐M100J‐08: Objective Characterization, Estimation and Prediction for Modeling Breathing‐Related Movement
Author(s) -
Ruan D,
Fessler J,
Balter J
Publication year - 2007
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2761339
Subject(s) - computer science , image warping , metric (unit) , hidden markov model , dynamic time warping , noise (video) , interpolation (computer graphics) , algorithm , artificial intelligence , pattern recognition (psychology) , mathematics , motion (physics) , operations management , economics , image (mathematics)
Purpose: To propose a hierarchical model for estimation, tracking, and prediction of respiratory tumor motion. To incorporate modeling on different scales: semi‐reproducibility globally and slow frequency/displacement variation locally. Method and Materials: The problem is formulated with a hierarchy of scales: On the finer scale, a databased approach is used to estimate the local variation of both displacement and frequency, utilizing classic control and chaos theory. A warping procedure is used to “counteract” local variation, resulting in a much more regular post‐warping signal. On the global level, the post‐warping (phase‐synchronized) signal is modeled as a noisy observation of an intrinsic periodic system, and the best periodic pattern is estimated within a nonparametric optimization setting. For tracking and prediction purposes, the locally estimated warping map (together with proper interpolation or extrapolation, whichever applies) is used to un‐warp the globally obtained periodic pattern. A recursive method is devised to further improve the efficiency for real‐time processing. Results: The obtained estimation/prediction signal demonstrates similar local variation as the raw observation, while semi‐periodicity is incorporated to decrease its noise sensitivity and enhance prediction accuracy. Verification using RPM data shows that the proposed method reduces 1‐period look‐ahead prediction error (RMSE) by more than 50% compared to perfect periodic modeling. Conventional local linear models generally fail in such long‐term prediction tasks. Conclusions: This work provides an infrastructure for incorporating information on different knowledge levels, and offers the flexibility to adaptively balance the roles of physical prior knowledge and data fidelity. The model‐based method on the global level incorporates the well‐recognized semi‐periodicity pattern of respiratory motion, overcoming the myopia of local state models. Data‐driven local phase map estimation used in warping and un‐warping fully utilizes the observation and enjoys the freedom of nonparametric setup. This work is sponsored by NIH P01‐CA59827.