WE-G-213CD-07: Enhancing Respiratory Motion Prediction Accuracy Using Audiovisual (AV) Biofeedback

Medical Physics
S PollockT Kim

Abstract

Prediction of respiratory-related tumor motion is hampered by irregularities present in the patient breathing patterns. Audiovisual (AV) biofeedback reduces irregularities, thereby producing a less complex breathing pattern. The aim of this project is to improve respiratory motion prediction accuracy using an AV biofeedback system. An AV biofeedback system combined with real-time MRI was implemented in this project (4 human subjects across 5 studies (one subject had both an initial and follow-up study)). The AV biofeedback system consists of external marker positioned on the abdomen of human subjects, being tracked using an RPM system (Real-time Position Management, Varian) to guide the subject's breathing. Acquired respiratory data has been used as input for motion prediction through a dynamic multi-leaf collimator (DMLC) simulator developed by Prof. Keall. The prediction algorithm utilized was a kernel density estimation-based real-time prediction algorithm. A variety of prediction parameters were tested to determine optimum prediction performance. Prediction parameters adjusted were the delay time (DT) and training examples (TE); the parameters tested here were: DT/TE = 2500/1500, 2500/100, 1000/250, 500/250; Given that the ...Continue Reading

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