Technical Note: Evaluation of audiovisual biofeedback smartphone application for respiratory monitoring in radiation oncology

Abstract

Purpose Radiation dose delivered to targets located near the upper abdomen or thorax are significantly affected by respiratory motion, necessitating large margins, limiting dose escalation. Surrogate motion management devices, such as the Real‐time Position Management (RPM™) system (Varian Medical Systems, Palo Alto, CA), are commonly used to improve normal tissue sparing. Alternative to current solutions, we have developed and evaluated the feasibility of a real‐time position management system that leverages the motion data from the onboard hardware of Apple iOS devices to provide patients with visual coaching with the potential to improve the reproducibility of breathing as well as improve patient compliance and reduce treatment delivery time. Methods and materials The iOS application, coined the Instant Respiratory Feedback (IRF) system, was developed in Swift (Apple Inc., Cupertino, CA) using the Core‐Motion library and implemented on an Apple iPhone® devices. Operation requires an iPhone®, a three‐dimensional printed arm, and a radiolucent projector screen system for feedback. Direct comparison between IRF, which leverages sensor fusion data from the iPhone®, and RPM™, an optical‐based system, was performed on multiple respiratory motion phantoms and volunteers. The IRF system and RPM™ camera tracking marker were placed on the same location allowing for simultaneous data acquisition. The IRF surrogate measurement of displacement was compared to the signal trace acquired using RPM™ with univariate linear regressions and Bland‐Altman analysis. Results Periodic motion shows excellent agreement between both systems, and subject motion shows good agreement during regular and irregular breathing motion. Comparison of IRF and RPM™ show very similar signal traces that were significantly related across all phantoms, including those motion with different amplitude and frequency, and subjects’ waveforms (all r > 0.9, P < 0.0001). We demonstrate the feasibility of performing four‐dimensional cone beam computed tomography using IRF which provided similar image quality as RPM™ when reconstructing dynamic motion phantom images. Conclusions Feasibility of an iOS application to provide real‐time respiratory motion is demonstrated. This system generated comparable signal traces to a commercially available system and offers an alternative method to monitor respiratory motion.

Shallow kinetics induced by a metronome (SKIM): A novel contactless respiratory motion management

“…Capaldi et al introduced a mobile app that can provide real-time respiratory motion. 14 The respiratory motion data is acquired from an instant respiratory feedback (IRF) system placed in the same location as the real-time position management (RPM) system to trace a signal simultaneously. Sadeghi et al developed a capacitive-sensing monitoring system (CMS) for realtime monitoring of respiratory motion without contact with the patient and surrogating markers.…”

“…Capaldi et al. introduced a mobile app that can provide real‐time respiratory motion 14 . The respiratory motion data is acquired from an instant respiratory feedback (IRF) system placed in the same location as the real‐time position management (RPM) system to trace a signal simultaneously.…”