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Subsequence similarity matching in time series databases is an important research area for many applications. This paper presents a new approximate approach for automatic online subsequence similarity matching over massive data streams. With a simultaneous on-line segmentation and pruning algorithm over the incoming stream, the resulting piecewise linear(More)
Subsequence matching in time series databases is a useful technique, with applications in pattern matching, prediction, and rule discovery. Internal structure within the time series data can be used to improve these tasks, and provide important insight into the problem domain. This paper introduces our research effort in using the internal structure of a(More)
Tumors, especially in the thorax and abdomen, are subject to respiratory motion, and understanding the structure of respiratory motion is a key component to the management and control of disease in these sites. We have applied statistical analysis and correlation discovery methods to analyze and mine tumor respiratory motion based on a finite state model of(More)
Precise localization of mobile tumor positions in real time is critical to the success of gated radiotherapy. Tumor positions are usually derived from either internal or external surrogates. Fluoroscopic gating based on internal surrogates, such as implanted fiducial markers, is accurate however requiring a large amount of imaging dose. Gating based on(More)
Effective image guided radiation treatment of a moving tumour requires adequate information on respiratory motion characteristics. For margin expansion, beam tracking and respiratory gating, the tumour motion must be quantified for pretreatment planning and monitored on-line. We propose a finite state model for respiratory motion analysis that captures our(More)
PURPOSE The CyberKnife uses an online prediction model to improve radiation delivery when treating lung tumors. This study evaluates the prediction model used by the CyberKnife radiation therapy system in terms of treatment margins about the gross tumor volume (GTV). METHODS From the data log files produced by the CyberKnife synchrony model, the(More)
Characterizing respiratory-induced tumour motion is an important step in the effective image-guided radiation treatment of moving tumours, especially for tumours in the lung and lower abdomen. This study characterized tumour motion based on a piecewise linear model representing tumour motion at defined stages of the breathing cycle. Lung tumour locations(More)
PURPOSE To analyze and evaluate the necessity and use of dynamic gating techniques for compensation of baseline shift during respiratory-gated radiation therapy of lung tumors. METHODS Motion tracking data from 30 lung tumors over 592 treatment fractions were analyzed for baseline shift. The finite state model (FSM) was used to identify the end-of-exhale(More)
Tumor motion caused by patient breathing creates challenges for accurate radiation dose delivery to a tumor while sparing healthy tissues. Image-guided radiation therapy (IGRT) helps, but there's a lag time between tumor position acquisition and dose delivered to that position. An efficient and accurate predictive model is thus an essential requirement for(More)
This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those(More)