A Smartphone-Based Motion Monitoring System for Surface Guided Radiation Therapy

Surface guided radiation therapy (SGRT) improves patient setup and motion monitoring, particularly for deep-inspiratory breath-hold (DIBH) maneuvers in left-sided breast cancer treatment. However, high costs and complexity limit widespread adoption, especially in low-resource settings. In this study, the purpose is to develop and validate a smartphone-based iOS SGRT application (iSGRT) leveraging Light-Detection-and-Ranging (LiDAR) sensors on smartphone-devices for accurate, low-cost surface tracking for radiation therapy.

iSGRT was developed in Xcode using Swift and Open3D, and captures 6-degrees-of-freedom (6DoF) motion for patient positioning and respiratory monitoring. Application was tested using the LiDAR camera on an Apple iPhone 15 Pro, with an Apple iPad Pro for remote monitoring. The system achieved a temporal resolution of ∼200 to 250 ms (4-5 Hz), comparable to clinical SGRT systems. Static accuracy was evaluated by comparing LiDAR-derived displacements with programmed couch movements on a Varian TrueBeam with a PerfectPitch 6DoF couch. Dynamic accuracy was assessed using a QUASAR respiratory motion phantom programmed with sinusoidal and patient-derived breathing waveforms. Motion tracking performance was analyzed using Pearson correlations and Bland-Altman agreement using GraphPad Prism. iSGRT was compared with SDX spirometry system in a healthy volunteer performing DIBH within the bore of a Varian Halcyon.

iSGRT demonstrated strong correlations with couch displacements across all translational (r²≥ 0.995) and rotational (r² ≥ 0.975) axes, with minimal biases (≤0.9 mm, ≤0.4°). Dynamic motion evaluation showed high agreement between the application and ground-truth phantom motion (r² ≥ 0.963), with minimal angular dependence on displacement accuracy (r² ≥ 0.950). Breath-hold duration was comparable in the healthy volunteer between systems (ΔDIBH = DIBH_SDX - DIBH_iSGRT = 33.34seconds - 33.31 seconds = 0.03 seconds).

Feasibility of an iOS smartphone-based SGRT application to provide real-time respiratory motion is demonstrated in this study as a viable alternative motion monitoring system. The iSGRT application's accuracy aligns with existing clinical SGRT systems while significantly reducing cost and complexity. This technology has the potential to expand SGRT accessibility, particularly in resource-limited settings.