Huanmei Wu

Online event-driven subsequence matching over financial data streams

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 representation of the data stream features high sensitivity and accuracy. The similarity definition is based on a permutation followed by a metric distance function, which provides the similarity search with flexibility, sensitivity and scalability. Also, the metric-based indexing methods can be applied for speed-up. To reduce the system burden, the event-driven similarity search is performed only when there is a potential event. The query sequence is the most recent subsequence of piecewise data representation of the incoming stream which is automatically generated by the system. The retrieved results can be analyzed in different ways according to the requirements of specific applications. This paper discusses an application for future data movement prediction based on statistical information. Experiments on real stock data are performed. The correctness of trend predictions is used to evaluate the performance of subsequence similarity matching.

A finite state model for respiratory motion analysis in image guided radiation therapy

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 natural understanding of breathing stages. In this model, a regular breathing cycle is represented by three line segments, exhale, end-of-exhale and inhale, while abnormal breathing is represented by an irregular breathing state. In addition, we describe an on-line implementation of this model in one dimension. We found this model can accurately characterize a wide variety of patient breathing patterns. This model was used to describe the respiratory motion for 23 patients with peak-to-peak motion greater than 7 mm. The average root mean square error over all patients was less than 1 mm and no patient has an error worse than 1.5 mm. Our model provides a convenient tool to quantify respiratory motion characteristics, such as patterns of frequency changes and amplitude changes, and can be applied to internal or external motion, including internal tumour position, abdominal surface, diaphragm, spirometry and other surrogates.

Subsequence matching on structured time series data

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 time series directly in the matching process. This idea is applied to the problem domain of respiratory motion data in cancer radiation treatment. We propose a comprehensive solution for analysis, clustering, and online prediction of respiratory motion using subsequence similarity matching. In this system, a motion signal is captured in real time as a data stream, and is analyzed immediately for treatment and also saved in a database for future study. A piecewise linear representation of the signal is generated from a finite state model, and is used as a query for subsequence matching. To ensure that the query subsequence is representative, we introduce the concept of subsequence stability, which can be used to dynamically adjust the query subsequence length. To satisfy the special needs of similarity matching over breathing patterns, a new subsequence similarity measure is introduced. This new measure uses a weighted L1 distance function to capture the relative importance of each source stream, amplitude, frequency, and proximity in time. From the subsequence similarity measure, stream and patient similarity can be defined, which are then used for offline and online applications. The matching results are analyzed and applied for motion prediction and correlation discovery. While our system has been customized for use in radiation therapy, our approach to time series modeling is general enough for application domains with structured time series data.

Correlation and prediction uncertainties in the CyberKnife Synchrony respiratory tracking system

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 uncertainty in radiation delivery can be calculated. Modeler points indicate the tracked position of the tumor and Predictor points predict the position about 115 ms in the future. The discrepancy between Predictor points and their corresponding Modeler points was analyzed for 100 treatment model data sets from 23 de-identified lung patients. The treatment margins were determined in each anatomic direction to cover an arbitrary volume of the GTV, derived from the Modeler points, when the radiation is targeted at the Predictor points. Each treatment model had about 30 min of motion data, of which about 10 min constituted treatment time; only these 10 min were used in the analysis. The frequencies of margin sizes were analyzed and truncated Gaussian normal functions were fit to each directions distribution. The standard deviation of each Gaussian distribution was then used to describe the necessary margin expansions in each signed dimension in order to achieve the desired coverage. In this study, 95% modeler point coverage was compared to 99% modeler coverage. Two other error sources were investigated: the correlation error and the targeting error. These were added to the prediction error to give an aggregate error for the CyberKnife during treatment of lung tumors. RESULTS Considering the magnitude of 2sigma from the mean of the Gaussian in each signed dimension, the margin expansions needed for 95% modeler point coverage were 1.2 mm in the lateral (LAT) direction and 1.7 mm in the anterior-posterior (AP) direction. For the superior-inferior (SI) direction, the fit was poor; but empirically, the expansions were 3.5 mm. For 99% modeler point coverage, the AP margin was 3.6 mm and the lateral margin was 2.9 mm. The SI margins for 99% modeler point coverage were highly variable. The aggregate error at 95% was 6.9 mm in the SI direction, 4.6 mm in the AP direction, and 3.5 in the lateral direction. CONCLUSIONS The Predictor points follow the Modeler points closely. Margins were found in each clinical direction that would provide 95% modeler point coverage for 95% of the models reviewed in this study. Similar margins were found in two clinical directions for 99% modeler point coverage in 95% of models. These results can offer guidance in the selection of CTV margins for treatment with the CyberKnife.

Gating based on internal/external signals with dynamic correlation updates.

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 external surrogates, such as patient abdominal surface motion, is non-invasive however less accurate due to the uncertainty in the correlation between tumor location and external surrogates. To address these complications, we propose to investigate an approach based on hybrid gating with dynamic internal/external correlation updates. In this approach, the external signal is acquired at high frequency (such as 30 Hz) while the internal signal is sparsely acquired (such as 0.5 Hz or less). The internal signal is used to validate and update the internal/external correlation during treatment. Tumor positions are derived from the external signal based on the newly updated correlation. Two dynamic correlation updating algorithms are introduced. One is based on the motion amplitude and the other is based on the motion phase. Nine patients with synchronized internal/external motion signals are simulated retrospectively to evaluate the effectiveness of hybrid gating. The influences of different clinical conditions on hybrid gating, such as the size of gating windows, the optimal timing for internal signal acquisition and the acquisition frequency are investigated. The results demonstrate that dynamically updating the internal/external correlation in or around the gating window will reduce false positive with relatively diminished treatment efficiency. This improvement will benefit patients with mobile tumors, especially greater for early stage lung cancers, for which the tumors are less attached or freely floating in the lung.

Statistical analysis and correlation discovery of tumor respiratory motion

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 tumor motion. Aggregates (such as minimum, maximum, average and mean), histograms, percentages, linear regression and multi-round statistical analysis have been explored. The results have been represented in various formats, including tables, graphs and text description. Different graphs, for example scatter plots, clustered column figures, 100% stacked column figures and box-whisker plots, have been applied to highlight different aspects of the results. The internal tumor motion from 42 lung tumors, 30 of which have motion larger than 5 mm, has been analyzed. Results for both inter-patient and intra-patient motion characteristics, such as duration and travel distance patterns, are reported. New knowledge of patient-specific tumor motion characteristics have been discovered, such as expected correlations between properties. The discovered tumor motion characteristics will be utilized in different aspects of image-guided radiation treatment, including treatment planning, online tumor motion prediction and real-time radiation dose delivery.

Survey: Real-Time Tumor Motion Prediction for Image-Guided Radiation Treatment

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 theres 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 IGRT success.

Dynamic gating window for compensation of baseline shift in respiratory-gated radiation therapy

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 (EOE) breathing phase throughout each treatment fraction. Using duty cycle as an evaluation metric, several methods of end-of-exhale dynamic gating were compared: An a posteriori ideal gating window, a predictive trend-line-based gating window, and a predictive weighted point-based gating window. These methods were evaluated for each of several gating window types: Superior/inferior (SI) gating, anterior/posterior beam, lateral beam, and 3D gating. RESULTS In the absence of dynamic gating techniques, SI gating gave a 39.6% duty cycle. The ideal SI gating window yielded a 41.5% duty cycle. The weight-based method of dynamic SI gating yielded a duty cycle of 36.2%. The trend-line-based method yielded a duty cycle of 34.0%. CONCLUSIONS Dynamic gating was not broadly beneficial due to a breakdown of the FSMs ability to identify the EOE phase. When the EOE phase was well defined, dynamic gating showed an improvement over static-window gating.

A state-based probabilistic model for tumor respiratory motion prediction

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 sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more general HMM-type predictive models. RMS errors for the time average model approach the theoretical limit of the HMM, and predicted state sequences are well correlated with sequences known to fit the data.

Site-specific volumetric analysis of lung tumour motion

The treatment of lung cancer with radiation therapy is hindered by respiratory motion. Real-time adjustments to compensate for this motion are hampered by mechanical system latencies and imaging-rate restrictions. To better understand tumour motion behaviour for adaptive image-guided radiation therapy of lung cancer, the volume of a tumours motion space was investigated. Motion data were collected by tracking an implanted fiducial using fluoroscopy at 30 Hz during treatment sessions. A total of 637 treatment fractions from 31 tumours were used in this study. For each fraction, data points collected from three consecutive breathing cycles were used to identify instantaneous tumour location. A convex hull was created over these data points, defining the tumour motion envelope. The study sought a correlation between the tumour location in the lung and the convex hulls volume and shape. It was found that tumours located in the upper apex had smaller motion envelopes (<50 mm(3)), whereas tumours located near the chest wall or diaphragm had larger envelopes (>70 mm(3)). Tumours attached to fixed anatomical structures had small motion spaces. Three general shapes described the tumour motion envelopes: 50% of motion envelopes enclosed largely 1D oscillation, 38% enclosed an ellipsoid path, 6% enclosed an arced path and 6% were of hybrid shape. This location-space correlation suggests it may be useful in developing a predictive model, but more work needs to be done to verify it.