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'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.
Authors: Lawrence B Marks; Soren M Bentzen; Joseph O Deasy; Feng-Ming Spring Kong; Jeffrey D Bradley; Ivan S Vogelius; Issam El Naqa; Jessica L Hubbs; Joos V Lebesque; Robert D Timmerman; Mary K Martel; Andrew Jackson Journal: Int J Radiat Oncol Biol Phys Date: 2010-03-01 Impact factor: 7.038
Authors: Paul J Keall; Amit Sawant; Byungchul Cho; Dan Ruan; Junqing Wu; Per Poulsen; Jay Petersen; Laurence J Newell; Herbert Cattell; Stine Korreman Journal: Int J Radiat Oncol Biol Phys Date: 2010-07-07 Impact factor: 7.038
Authors: Mitchell Machtay; Kyounghwa Bae; Benjamin Movsas; Rebecca Paulus; Elizabeth M Gore; Ritsuko Komaki; Kathy Albain; William T Sause; Walter J Curran Journal: Int J Radiat Oncol Biol Phys Date: 2010-10-25 Impact factor: 7.038
Authors: Sean Pollock; Ricky O'Brien; Kuldeep Makhija; Fiona Hegi-Johnson; Jane Ludbrook; Angela Rezo; Regina Tse; Thomas Eade; Roland Yeghiaian-Alvandi; Val Gebski; Paul J Keall Journal: BMC Cancer Date: 2015-07-18 Impact factor: 4.430