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Positron emission tomography (PET) is one of the most advanced imaging diagnostic devices in the medical field, playing a crucial role in tumor diagnosis and treatment. However, patient motion during scanning can lead to motion artifacts, which affect diagnostic accuracy. This study aims to develop a head motion monitoring system to identify and select images with excessive motion and corresponding periods. The system, based on an RGB-D structured-light camera, implements facial feature point detection, 3D information acquisition, and head motion monitoring, along with a user interaction software. Through phantom experiments and volunteer experiments, the system’s performance was tested under various conditions, including stillness, pitch movement, yaw movement, and comprehensive movement. Experimental results show that the system’s translational error is less than 2.5 mm, rotational error is less than 2.0°, and it can output motion monitoring results within 10 s after the PET scanning, meeting clinical accuracy requirements and showing significant potential for clinical application.
