Automatic calibration of an arbitrarily‐set near‐infrared camera for patient surface respiratory monitoring

摘要

Purpose A patient's respiratory monitoring is one of the key techniques in radiotherapy for a moving target. Generally, such monitoring systems are permanently set to a fixed geometry during the installation. This study aims to enable a temporary setup of such a monitoring system by developing a fast method to automatically calibrate the geometrical position by a quick measurement of calibration markers. Methods One calibration marker was placed on the isocenter and the other six markers were placed at positions 5‐cm apart from the isocenter to the left, right, anterior, posterior, superior, and inferior directions. A near‐infrared ( NIR ) camera ( NIC ) [Kinect v2 (Microsoft Corp.)] was arbitrarily set with ten different angles around the calibration phantom with a fixed tilting‐down angle at approximately 45° in a linear accelerator treatment vault. The three‐dimensional (3D) coordinates in the camera (Cam) coordinate system ( CS ; x and y are the horizontal and vertical coordinates of the image, respectively, and z is a coordinate along the NIR time‐of‐flight) were taken for 1?min with 30 frames per second. The data corresponding to the measurement times of 1, 3, 10, 30, and 60?s were created to mimic various measurement times. These data were used to calculate the initial matrix elements, which included six parameters of the pitching, yawing, and rolling angles; horizontal two‐dimensional translation in the treatment room; and the source‐to‐axis distance of NIC , for a conversion from the Cam CS to the treatment room CS for which the origin was defined at the isocenter (Iso coordinate). The six parameters were then optimized to minimize the displacements of the calculated marker coordinates from the actual positions in the Iso CS . The 3D positional accuracy and angular accuracy of the conversion were evaluated. The random error of the Iso coordinates was analyzed through a relation with the angle of each measurement setup. Results Three angles of NIC and relative translation vectors were successfully calculated from the measurement data of the calibration markers. The achieved spatial and angular accuracies were 0.02?mm and 1.6°, respectively, after the optimization. Among the mimicked measurement times investigated in this study, both spatial and angular accuracies had no dependence on the measurement time. The average random error of a static marker was 0.46?mm after the optimization. Conclusion We developed an automatic method to calibrate the 3D patient surface monitoring system. The procedure developed in this study enabled a quick calibration of NIC , which can be easily repeated multiple times for a frequent and quick setup of the monitoring system.