International journal of radiation oncology, biology, physics · 2026
State-Aware Contactless Respiratory Volume Monitoring for Breath-Hold-Guided Radiation Therapy.
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Yang Jiahao, Li Zixin, Zhang Sai, Wang Hao, Zhang Heng, Zhang Yutao, Tu Xiaoshuang, Ni Xinye
Аннотация
PURPOSE: Respiratory motion increases setup uncertainty in thoracic and abdominal tumor radiation therapy and elevates dose exposure to organs at risk (OARs), such as the heart and lungs. Deep inspiration breath-hold (DIBH) can reduce OAR dose, but clinical deployment requires accurate, real-time, low-burden monitoring of breath-hold stability and tidal volume. This study proposes a contactless tidal volume-estimation system for DIBH-guided radiation therapy.
METHODS AND MATERIALS: A depth camera captured 3-dimensional thoracoabdominal surface motion to construct a surface-based volumetric surrogate. A state-aware, surrogate-guided time-series regression model incorporating dynamic-state gating, steady-state anchor modeling, adaptive baseline correction, and subject-specific calibration was developed for tidal-volume estimation. In this prospective model-development and pilot internal-validation study, synchronized depth-camera data and tidal-volume signals from an Active Breathing Coordinator (ABC) system were collected from 22 volunteers. ABC-derived tidal volume served as the reference signal, and performance was evaluated using subject-independent leave-one-subject-out cross-validation. Real-time respiratory feedback was provided through a head-mounted visualization interface.
RESULTS: Predicted tidal volume strongly correlated with the ABC reference signal (pooled r = 0.968; subject-level Fisher z-transformed mean r = 0.966; 95% CI, 0.954-0.975; P < .001), with an overall mean absolute error of 0.098 L. Phase-specific mean absolute error/mean absolute percentage error were 0.1059 L/17.27% during breath hold and 0.0967 L/16.68% during normal breathing. GPU-side algorithmic latency was 12.55 ms per frame.
CONCLUSION: The proposed contactless framework enables stable tidal-volume estimation and real-time visual feedback during DIBH while minimizing patient encumbrance, supporting low-burden respiratory monitoring for radiation therapy workflows.
METHODS AND MATERIALS: A depth camera captured 3-dimensional thoracoabdominal surface motion to construct a surface-based volumetric surrogate. A state-aware, surrogate-guided time-series regression model incorporating dynamic-state gating, steady-state anchor modeling, adaptive baseline correction, and subject-specific calibration was developed for tidal-volume estimation. In this prospective model-development and pilot internal-validation study, synchronized depth-camera data and tidal-volume signals from an Active Breathing Coordinator (ABC) system were collected from 22 volunteers. ABC-derived tidal volume served as the reference signal, and performance was evaluated using subject-independent leave-one-subject-out cross-validation. Real-time respiratory feedback was provided through a head-mounted visualization interface.
RESULTS: Predicted tidal volume strongly correlated with the ABC reference signal (pooled r = 0.968; subject-level Fisher z-transformed mean r = 0.966; 95% CI, 0.954-0.975; P < .001), with an overall mean absolute error of 0.098 L. Phase-specific mean absolute error/mean absolute percentage error were 0.1059 L/17.27% during breath hold and 0.0967 L/16.68% during normal breathing. GPU-side algorithmic latency was 12.55 ms per frame.
CONCLUSION: The proposed contactless framework enables stable tidal-volume estimation and real-time visual feedback during DIBH while minimizing patient encumbrance, supporting low-burden respiratory monitoring for radiation therapy workflows.