A patient-specific deep learning framework for 3D motion estimation and volumetric imaging during lung cancer radiotherapy

. Respiration introduces a constant source of irregular motion that poses a significant challenge for the precise irradiation of thoracic and abdominal cancers. Current real-time motion management strategies require dedicated systems that are not available in most radiotherapy centers. We sought to develop a system that estimates and visualises the impact of respiratory motion in 3D given the 2D images acquired on a standard linear accelerator. . In this paper we introduce , a patient-specific deep learning framework that achieves 3D motion estimation and volumetric imaging using the data and resources available in standard clinical settings. Here we perform a simulation study of this framework using imaging data from two lung cancer patients. . Using 2D images as input and 3D-3D registrations as ground-truth, was able to continuously predict 3D tumor motion with mean errors of 0.1 ± 0.5, -0.6 ± 0.8, and 0.0 ± 0.2 mm along the left-right, superior-inferior, and anterior-posterior axes respectively. also predicted 3D thoracoabdominal motion with mean errors of -0.1 ± 0.3, -0.1 ± 0.6, and -0.2 ± 0.2 mm respectively. Moreover, volumetric imaging was achieved with mean average error 0.0003, root-mean-squared error 0.0007, structural similarity 1.0 and peak-signal-to-noise ratio 65.8. . The results of this study demonstrate the possibility of achieving 3D motion estimation and volumetric imaging during lung cancer treatments on a standard linear accelerator.