RESUMEN
PURPOSE: This study proposes a system that can simulate head radiography by combining a technique for estimating human posture from moving images (hereafter referred to as "pose estimation technique") and use of two cameras capable of acquiring RGB images to determine body position during positioning. METHODS: The angles of the median sagittal plane (MS), axial plane (AX), and orbitomeatal baseline (OM) were obtained using the pose estimation technique from frontal and lateral images captured after positioning. The resulting radiographs were displayed according to the results. RESULTS: The head tilt during positioning could be determined based on the coordinate data of feature points acquired using the pose estimation technique. In an imaging experiment using a simulated human patient, errors increased as head tilt increased; however, the mean error values in each axis were 0.9° for MS, 0.8° for AX, and 1.5°for OM, when the patient was correctly positioned. CONCLUSION: The pose estimation technique can assist in evaluating positioning accuracy in radiography and is expected to be used as a potential simulator system.
RESUMEN
During the radiographic examination of the chest and bones in hospitals, communicating and maintaining posture is difficult for some patients, and movement before or during X-ray irradiation may necessitate re-exposure owing to body wobbling movements or breathing movements. To prevent the need for re-exposure during radiography and to determine the exposure timing, a body movement detection system that considers breathing movements was developed in this study. The posture of a patient was monitored using an RGB camera. The acquired video data was analyzed to detect body movement using either an inter-frame difference method or an optical flow estimation method. The performance of the system was evaluated by detecting the body and breathing movements during positioning. Consequently, the inter-frame difference method detected 179.8-1222.2 pixels during body movements, and the optical flow estimation method confirmed that the feature points moved by 5.5-26.6 mm (4.2-20.3 pixels). When detecting breathing movements, 82-585 pixels were detected by the inter-frame difference method, and the optical flow estimation method showed that the feature points moved by 5.2 mm (2-4 pixels). Therefore, the proposed method can detect body movements during radiography to prevent re-exposure due to body wobble and breathing movements. For healthcare providers, it will lead to reduce not only concerns about patient exposure but also unnecessary radiographic workload.
RESUMEN
PURPOSE: In this study, we propose a system that combines a depth camera with a deep learning model for estimating the human skeleton and a depth camera to estimate the shooting part to be radiographed and to acquire the thickness of the subject, thereby providing optimized X-ray imaging conditions. METHODS: We propose a system that provides optimized X-ray imaging conditions by estimating the shooting part and measuring the thickness of the subject using an RGB camera and a depth camera. The system uses OpenPose, a posture estimation library, to estimate the shooting part. RESULTS: The recognition rate of the shooting part was 15.38% for the depth camera and 84.62% for the RGB camera at a distance of 100 cm, and 42.31% for the depth camera and 100% for the RGB camera at a distance of 120 cm. The measurement accuracy of the subject thickness was within ±10 mm except for a few cases, indicating that the X-ray imaging conditions were optimized for the subject thickness. CONCLUSION: The implementation of this system in an X-ray system is expected to enable automatic setting of X-ray imaging conditions. The system is also useful in preventing increased exposure dose due to excessive dose or decreased image quality due to insufficient dose caused by incorrect setting of X-ray imaging conditions.
