A Couch Mounted Smartphone-based Motion Monitoring System for Radiation Therapy

Abstract

PurposeSurface-guided radiation-therapy (SGRT) systems are being adopted into clinical practice for patient setup and motion monitoring. However, commercial systems remain cost prohibitive to resource-limited clinics around the world. Our aim is to develop and validate a smartphone-based application using LiDAR cameras (such as on recent Apple iOS devices) for facilitating SGRT in low-resource centers. The proposed SGRT application was tested at multiple institutions, and validated using phantoms and volunteers against various commercial systems to demonstrate feasibility. Methods and MaterialsAn iOS application was developed in Xcode and written in Swift using the Augmented-Reality (AR) Kit and implemented on an Apple iPhone 13 Pro with a built-in LiDAR camera. The application contains multiple features: 1) visualization of both the camera and depth video feeds (at a ∼60Hz sample-frequency), 2) region-of-interest (ROI) selection over the patient's anatomy where motion is measured, 3) chart displaying the average motion over time in the ROI, and 4) saving/exporting the motion traces and surface map over the ROI for further analysis. The iOS application was tested to evaluate depth measurement accuracy for: 1) different angled surfaces, 2) different field-of-views over different distances, and 3) similarity to a commercially available SGRT systems (Vision RT AlignRT® and Varian IDENTIFY™) with motion phantoms and healthy volunteers across three institutions. Measurements were analyzed using linear-regressions and Bland-Altman analysis. ResultsCompared to the clinical system measurements (reference), the iOS application showed excellent agreement for depth (r=1.000,p<0.0001; bias=-0.07±0.24cm) and angle (r=1.000,p<0.0001; bias=0.02±0.69°) measurements. For free-breathing traces, the iOS application was significantly correlated to phantom motion (institute 1: r=0.99,p<0.0001; bias=-0.003±0.03cm; institute 2: r=0.98,p<0.0001; bias=-0.001±0.10cm; institute 3: r=0.97,p<0.0001; bias=0.04±0.06cm) and healthy volunteer motion (institute 1: r=0.98,p<0.0001; bias=-0.008±0.06cm; institute 2: r=0.99,p<0.0001; bias=-0.007±0.12cm; institute 3: r=0.99,p<0.0001; bias=-0.001±0.04cm). ConclusionThe proposed approach using a smartphone-based application provides a low-cost platform that could improve access to surface-guided radiation therapy accounting for motion.