Audiovisual biofeedback improves motion prediction accuracy

Purpose: The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients’ respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this...

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Veröffentlicht in:	Medical physics (Lancaster) Jg. 40; H. 4; S. 041705 - n/a
Hauptverfasser:	Pollock, Sean, Lee, Danny, Keall, Paul, Kim, Taeho
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States American Association of Physicists in Medicine 01.04.2013
Schlagworte:	Acoustic Stimulation - methods Algorithms Analysis of motion Approximations and expansions audiovisual biofeedback Biofeedback Biofeedback, Psychology - methods Biofeedback, Psychology - physiology biomedical MRI Cancer data acquisition Dosimetry feedback Feedback, Sensory - physiology Humans Interpolation Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging lung Magnetic resonance imaging mean square error methods Medical imaging Medical magnetic resonance imaging motion estimation motion management motion prediction Movement - physiology Numerical approximation and analysis Patient Positioning - methods Photic Stimulation - methods Pneumodyamics, respiration pneumodynamics radiation therapy Radiation Therapy Physics Radiotherapy, Conformal - methods Reproducibility of Results Respiratory Mechanics - physiology Sensitivity and Specificity system latency Tissues Ultrasonography motion prediction system latency motion management audiovisual biofeedback
ISSN:	0094-2405, 2473-4209, 2473-4209, 0094-2405
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Abstract	Purpose: The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients’ respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. Methods: An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student'st-test. Results: Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively. Conclusions: This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.
AbstractList	Purpose: The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients’ respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. Methods: An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Results: Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively. Conclusions: This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy. Purpose: The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients’ respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. Methods: An AV biofeedback system combined with real‐time respiratory data acquisition and MR images were implemented in this project. One‐dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student'st‐test. Results: Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively. Conclusions: This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy. The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction. An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test. Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively. This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy. The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction.PURPOSEThe accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction.An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test.METHODSAn AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test.Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively.RESULTSPrediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively.This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.CONCLUSIONSThis study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.
Author	Pollock, Sean Lee, Danny Kim, Taeho Keall, Paul
Author_xml	– sequence: 1 givenname: Sean surname: Pollock fullname: Pollock, Sean organization: Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia – sequence: 2 givenname: Danny surname: Lee fullname: Lee, Danny organization: Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia – sequence: 3 givenname: Paul surname: Keall fullname: Keall, Paul organization: Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia – sequence: 4 givenname: Taeho surname: Kim fullname: Kim, Taeho email: taeho.kim@sydney.edu.au organization: Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia
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Keywords	motion prediction system latency motion management audiovisual biofeedback
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Notes	taeho.kim@sydney.edu.au Author to whom correspondence should be addressed. Electronic mail Telephone: 61 2 9351 3385; Fax: 61 2 9351 4018. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author to whom correspondence should be addressed. Electronic mail: taeho.kim@sydney.edu.au; Telephone: 61 2 9351 3385; Fax: 61 2 9351 4018.
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Snippet	Purpose: The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities... The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in... Purpose: The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities...
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SubjectTerms	Acoustic Stimulation - methods Algorithms Analysis of motion Approximations and expansions audiovisual biofeedback Biofeedback Biofeedback, Psychology - methods Biofeedback, Psychology - physiology biomedical MRI Cancer data acquisition Dosimetry feedback Feedback, Sensory - physiology Humans Interpolation Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging lung Magnetic resonance imaging mean square error methods Medical imaging Medical magnetic resonance imaging motion estimation motion management motion prediction Movement - physiology Numerical approximation and analysis Patient Positioning - methods Photic Stimulation - methods Pneumodyamics, respiration pneumodynamics radiation therapy Radiation Therapy Physics Radiotherapy, Conformal - methods Reproducibility of Results Respiratory Mechanics - physiology Sensitivity and Specificity system latency Tissues Ultrasonography
Title	Audiovisual biofeedback improves motion prediction accuracy
URI	http://dx.doi.org/10.1118/1.4794497 https://onlinelibrary.wiley.com/doi/abs/10.1118%2F1.4794497 https://www.ncbi.nlm.nih.gov/pubmed/23556875 https://www.proquest.com/docview/1324391446 https://pubmed.ncbi.nlm.nih.gov/PMC3612118
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