Abstract:Objective: Tracking tumors and surrounding tissues in real-time is critical for reducing errors and uncertainties during radiotherapy. Existing methods are either limited by the linear representation or scale poorly with the volume resolution. To address both issues, we propose a novel coordinate-based neural network representation of lung motion to predict the instantaneous 3D volume at arbitrary spatial resolution from various surrogates: patient surface, fiducial marker, and single kV projection. Approach:… Show more
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