Audiovisual biofeedback improves motion prediction accuracy

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'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.

Feasibility of predicting tumor motion using online data acquired during treatment and a generalized neural network optimized with offline patient tumor trajectories

“…With an average RMSE of 0.67 ± 0.36 mm for a prediction horizon of 665 ms, our results are within the range of accuracies found elsewhere such as a RMSE of 1.2 ± 0.9 mm for a system latency of 600 ms, 0.97 mm for a latency of 400 ms and 0.9 mm for system latency of 520 ms . Our results are also within the range of accuracies when compared with the extensive list of prediction studies surveyed in Ref.…”

“…In the current work, the prediction horizon was fixed at five EPID image samples (665 ms). This value is in between the range of latencies (50 to 1400 ms) observed in some image‐guided adaptive radiotherapy systems . It is well‐known that prediction accuracy deteriorates as the prediction horizon increases …”

“…In most of these studies, system latency due to image acquisition, image saving and reading, tumor detection and aperture adjustment has been identified as one of the challenges for real‐time adaptive treatment . Latencies ranging from 50 to 1400 ms have been reported . Since most systems are unable to respond instantaneously, the prediction of the position of the tumor, ahead of its current location, is needed if image‐guided adaptive radiotherapy treatment is to be successful.…”

A Time-Varying Seasonal Autoregressive Model-Based Prediction of Respiratory Motion for Tumor following Radiotherapy

“…It has also been reported that audiovisual biofeedback can make breathing pattern stable and improve accuracy of KDE-based prediction [22, 23]. As shown in the results, TVSAR is superior to KDE for respiratory motion with relatively regular pattern.…”

Amplitude gating for a coached breathing approach in respiratory gated 10 MV flattening filter‐free VMAT delivery

“…During recording of the BIBOP trace, the patients were asked to hold their breath for approximately 5 to 10 s at end of exhale, inhale once and repeat the 5 to 10 s hold at each exhale. A screen was mounted on the simulation couch to display the breathing pattern to the patients in real time to improve breathing reproducibility 17 , 18 , 19 . A 4D CT of the patient under free‐breathing conditions was also obtained at this time.…”