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Title: SU-G-JeP4-02: An Investigation of Respiratory Surrogate Motion Data Requirements for Multiple-Step Ahead Prediction

Abstract

Purpose: Respiratory-gated radiotherapy and dynamic tracking employ real-time imaging and surrogate motion-monitoring methods with tumor motion prediction in advance of real-time. This study investigated respiratory motion data length on prediction accuracy of tumor motion. Methods: Predictions generated from the algorithm are validated against a one-dimensional surrogate signal of amplitude versus time. Prediction consists of three major components: extracting top-ranked subcomponents from training data matching the last respiratory cycle; calculating weighting factors from best-matched subcomponents; fusing data proceeding best-matched subcomponents with respective weighting factors to form predictions. Predictions for one respiratory cycle (∼3-6seconds) were assessed using 351 patient data from the respiratory management device. Performance was evaluated for correlation coefficient and root mean square error (RMSE) between prediction and final respiratory cycle. Results: Respiratory prediction results fell into two classes, where best predictions for 70 cycles or less performed using relative prediction and greater than 70 cycles are predicted similarly using relative and derivative relative. For 70 respiratory cycles or less, the average correlation between prediction and final respiratory cycle was 0.9999±0.0001, 0.9999±0.0001, 0.9988±0.0003, 0.9985±0.0023, and 0.9981±0.0023 with RMSE values of 0.0091±0.0030, 0.0091±0.0030, 0.0305±0.0051, 0.0299±0.0259, and 0.0299±0.0259 for equal, relative, pattern, derivative equal and derivative relative weighting methods, respectively. Respectively, themore » total best prediction for each method was 37, 65, 20, 22, and 22. For data with greater than 70 cycles average correlation was 0.9999±0.0001, 0.9999±0.0001, 0.9988±0.0004, 0.9988±0.0020, and 0.9988±0.0020 with RMSE values of 0.0081±0.0031, 0.0082±0.0033, 0.0306±0.0056, 0.0218±0.0222, and 0.0218±0.0222 for equal, relative, pattern, derivative equal and derivative relative weighting methods, respectively. Respectively, the total best prediction for each method was 24, 44, 42, 30, and 45. Conclusion: The prediction algorithms are effective in estimating surrogate motion in advance. These results indicate an advantage in using relative prediction for shorter data and either relative or derivative relative prediction for longer data.« less

Authors:
 [1]; ;  [2]
  1. Montefiore Medical Center, Bronx, NY (United States)
  2. Duke University Medical Center, Durham, NC (United States)
Publication Date:
OSTI Identifier:
22649452
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; BIOMEDICAL RADIOGRAPHY; CORRELATIONS; FORECASTING; ONE-DIMENSIONAL CALCULATIONS; RADIOTHERAPY

Citation Formats

Zawisza, I, Ren, L, and Yin, F. SU-G-JeP4-02: An Investigation of Respiratory Surrogate Motion Data Requirements for Multiple-Step Ahead Prediction. United States: N. p., 2016. Web. doi:10.1118/1.4957112.
Zawisza, I, Ren, L, & Yin, F. SU-G-JeP4-02: An Investigation of Respiratory Surrogate Motion Data Requirements for Multiple-Step Ahead Prediction. United States. doi:10.1118/1.4957112.
Zawisza, I, Ren, L, and Yin, F. Wed . "SU-G-JeP4-02: An Investigation of Respiratory Surrogate Motion Data Requirements for Multiple-Step Ahead Prediction". United States. doi:10.1118/1.4957112.
@article{osti_22649452,
title = {SU-G-JeP4-02: An Investigation of Respiratory Surrogate Motion Data Requirements for Multiple-Step Ahead Prediction},
author = {Zawisza, I and Ren, L and Yin, F},
abstractNote = {Purpose: Respiratory-gated radiotherapy and dynamic tracking employ real-time imaging and surrogate motion-monitoring methods with tumor motion prediction in advance of real-time. This study investigated respiratory motion data length on prediction accuracy of tumor motion. Methods: Predictions generated from the algorithm are validated against a one-dimensional surrogate signal of amplitude versus time. Prediction consists of three major components: extracting top-ranked subcomponents from training data matching the last respiratory cycle; calculating weighting factors from best-matched subcomponents; fusing data proceeding best-matched subcomponents with respective weighting factors to form predictions. Predictions for one respiratory cycle (∼3-6seconds) were assessed using 351 patient data from the respiratory management device. Performance was evaluated for correlation coefficient and root mean square error (RMSE) between prediction and final respiratory cycle. Results: Respiratory prediction results fell into two classes, where best predictions for 70 cycles or less performed using relative prediction and greater than 70 cycles are predicted similarly using relative and derivative relative. For 70 respiratory cycles or less, the average correlation between prediction and final respiratory cycle was 0.9999±0.0001, 0.9999±0.0001, 0.9988±0.0003, 0.9985±0.0023, and 0.9981±0.0023 with RMSE values of 0.0091±0.0030, 0.0091±0.0030, 0.0305±0.0051, 0.0299±0.0259, and 0.0299±0.0259 for equal, relative, pattern, derivative equal and derivative relative weighting methods, respectively. Respectively, the total best prediction for each method was 37, 65, 20, 22, and 22. For data with greater than 70 cycles average correlation was 0.9999±0.0001, 0.9999±0.0001, 0.9988±0.0004, 0.9988±0.0020, and 0.9988±0.0020 with RMSE values of 0.0081±0.0031, 0.0082±0.0033, 0.0306±0.0056, 0.0218±0.0222, and 0.0218±0.0222 for equal, relative, pattern, derivative equal and derivative relative weighting methods, respectively. Respectively, the total best prediction for each method was 24, 44, 42, 30, and 45. Conclusion: The prediction algorithms are effective in estimating surrogate motion in advance. These results indicate an advantage in using relative prediction for shorter data and either relative or derivative relative prediction for longer data.},
doi = {10.1118/1.4957112},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Purpose: To assure that tumor motion is within the radiation field during high-dose and high-precision radiosurgery, real-time imaging and surrogate monitoring are employed. These methods are useful in providing real-time tumor/surrogate motion but no future information is available. In order to anticipate future tumor/surrogate motion and track target location precisely, an algorithm is developed and investigated for estimating surrogate motion multiple-steps ahead. Methods: The study utilized a one-dimensional surrogate motion signal divided into three components: (a) training component containing the primary data including the first frame to the beginning of the input subsequence; (b) input subsequence component of the surrogatemore » signal used as input to the prediction algorithm: (c) output subsequence component is the remaining signal used as the known output of the prediction algorithm for validation. The prediction algorithm consists of three major steps: (1) extracting subsequences from training component which best-match the input subsequence according to given criterion; (2) calculating weighting factors from these best-matched subsequence; (3) collecting the proceeding parts of the subsequences and combining them together with assigned weighting factors to form output. The prediction algorithm was examined for several patients, and its performance is assessed based on the correlation between prediction and known output. Results: Respiratory motion data was collected for 20 patients using the RPM system. The output subsequence is the last 50 samples (∼2 seconds) of a surrogate signal, and the input subsequence was 100 (∼3 seconds) frames prior to the output subsequence. Based on the analysis of correlation coefficient between predicted and known output subsequence, the average correlation is 0.9644±0.0394 and 0.9789±0.0239 for equal-weighting and relative-weighting strategies, respectively. Conclusion: Preliminary results indicate that the prediction algorithm is effective in estimating surrogate motion multiple-steps in advance. Relative-weighting method shows better prediction accuracy than equal-weighting method. More parameters of this algorithm are under investigation.« less
  • Purpose: The implementation and realization of automatic anomaly detection of respiratory motion is a very important technique to prevent accidental damage during radiation therapy. Here, we propose an automatic anomaly detection method using singular value decomposition analysis. Methods: The anomaly detection procedure consists of four parts:1) measurement of normal respiratory motion data of a patient2) calculation of a trajectory matrix representing normal time-series feature3) real-time monitoring and calculation of a trajectory matrix of real-time data.4) calculation of an anomaly score from the similarity of the two feature matrices. Patient motion was observed by a marker-less tracking system using a depthmore » camera. Results: Two types of motion e.g. cough and sudden stop of breathing were successfully detected in our real-time application. Conclusion: Automatic anomaly detection of respiratory motion using singular spectrum analysis was successful in the cough and sudden stop of breathing. The clinical use of this algorithm will be very hopeful. This work was supported by JSPS KAKENHI Grant Number 15K08703.« less
  • Purpose: Average or maximum intensity projection (AIP or MIP) images derived from 4DCT images are often used as a reference image for target alignment when free breathing Cone-beam CT (FBCBCT) is used for positioning a moving target at treatment. This method can be highly accurate if the patient has stable respiratory motion. However, a patient’s breathing pattern often varies irregularly. The purpose of this study is to investigate the effect of irregular respiration on the positioning accuracy of a moving target with FBCBCT. Methods: Eight patients’ respiratory motion curves were selected to drive a Quasar phantom with embedded cubic andmore » spherical targets. A 4DCT of the moving phantom was acquired on a CT scanner (Philips Brilliance 16) equipped with a Varian RPM system. The phase binned 4DCT images and the corresponding MIP and AIP images were transferred into Eclipse for analysis. CBCTs of the phantom driven by the same breathing curves were acquired on a Varian TrueBeam and fused such that the zero positions of moving targets are the same on both CBCT and AIP images. The sphere and cube volumes and centrioid differences (alignment error) determined by MIP, AIP and FBCBCT images were compared. Results: Compared to the volume determined by FBCBCT, the volumes of cube and sphere in MIP images were 22.4%±8.8% and 34.2%±6.2% larger while the volumes in AIP images were 7.1%±6.2% and 2.7%±15.3% larger, respectively. The alignment errors for the cube and sphere with center-center matches between MIP and FBCBCT were 3.5±3.1mm and 3.2±2.3mm, and the alignment errors between AIP and FBCBCT were 2.1±2.6mm and 2.1±1.7mm, respectively. Conclusion: AIP images appear to be superior reference images than MIP images. However, irregular respiratory motions could compromise the positioning accuracy of a moving target if the target center-center match is used to align FBCBCT and AIP images.« less
  • Purpose: The purpose of this study was to evaluate the interfractional and intrafractional motion of liver tumors in stereotactic body radiation therapy (SBRT), based on four-dimensional cone-beam computed tomography using fiducial markers. (4D-CBCT). Methods: Seven patients with liver tumors were treated by SBRT with abdominal compression (AC) in five fractions with image guidance based on 4D-CBCT. The 4D-CBCT studies were performed to determine the individualized internal margin for the planning simulation. The interfractional and intrafractional changes of liver tumor motion for all patients was measured, based on the planning simulation 4D-CBCT, pre-SBRT 4D-CBCT, and post-SBRT 4D-CBCT. The interfractional motion changemore » was calculated from the difference in liver tumor amplitude on pre-SBRT 4D-CBCT relative to that of the planning simulation 4D-CBCT for each fraction. The intrafractional motion change was calculated from the difference between the liver tumor amplitudes of the pre- and post-SBRT 4D-CBCT for each fraction. Significant interfractional and intrafractional changes in liver tumor motion were defined as a change ≥3 mm. Statistical analysis was performed using the Pearson correlation. Results: The values of the mean amplitude of liver tumor, as indicated by planning simulation 4D-CBCT, were 1.6 ± 0.8 mm, 1.6 ± 0.9 mm, and 4.9 ± 2.2 mm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. Pearson correlation coefficients between the liver tumor amplitudes, based on planning simulation 4D-CBCT, and pre-SBRT 4D-CBCT during fraction treatment in the LR, AP, and SI directions were 0.6, 0.7, and 0.8, respectively. Interfractional and intrafractional motion changes of ≥3 mm occurred in 23% and 3% of treatment fractions, respectively. Conclusion: The interfractional and intrafractional changes of liver tumor motion were small in most patients who received liver SBRT with AC. In addition, planning simulation 4D-CBCT was useful for representing liver tumor movement in patients undergoing SBRT. This work was supported by JSPS KAKENHI Grant Number 26861004.« less
  • Purpose: Surface guided radiation therapy (SGRT) uses stereoscopic video images in combination with patterns projected onto the patient’s surface to dynamically capture and reconstruct a 3D surface map. In this work, we used a C-RAD Catalyst HD system (C-RAD) to evaluate intrafraction motion in the delivery of lung SBRT. Methods: The surface acquired from the 4DCT images from our preliminary cohort of eight lung cancer patients treated with SBRT were matched to the surface images acquired prior to each treatment. Additionally, a CBCT image set was acquired. A linear regression model was established between the external and internal motion ofmore » tumor during pretreatment and used to predict the CBCT deviation during treatment. The shifts determined from CBCT and the shifts from surface map imaging were compared and assessed using Bland-Altman method. For intrafraction motion, we assessed the percentage of mean errors that fell outside of the threshold of 2 mm, 3 mm, and 5 mm along the translational directions. The required PTV margin was quantified over the course of treatment. The correlation between intrafraction treatment time and mean error of 3D displacement was evaluated using the Pearson coefficient, r Results: A total of 7971 data points were analyzed. Deviations of 2mm, 3mm, and 5mm were observed less than 7%, 2 %, and 0 % of the time along the translational direction. CBCT and Catalyst showed close agreement during patient positioning. Furthermore, the calculated PTV margins were less than our clinical tolerance of 5 mm. Using the Pearson coefficient r,the mean error of 3D displacement showed significant correlation with treatment time (r=0.69, p= 0.000002). Conclusion: SGRT can be used to ensure accurate patient positioning during treatment without an additional delivery of dose to the patient. This study shows that importance of treatment time as a consideration during the treatment planning process.« less