Clinical introduction of image lag correction for a cone beam CT system
Purpose: Image lag in the flat-panel detector used for Linac integrated cone beam computed tomography (CBCT) has a degrading effect on CBCT image quality. The most prominent visible artifact is the presence of bright semicircular structure in the transverse view of the scans, known also as radar artifact. Several correction strategies have been proposed, but until now the clinical introduction of such corrections remains unreported. In November 2013, the authors have clinically implemented a previously proposed image lag correction on all of their machines at their main site in Amsterdam. The purpose of this study was to retrospectively evaluate the effect of the correction on the quality of CBCT images and evaluate the required calibration frequency. Methods: Image lag was measured in five clinical CBCT systems (Elekta Synergy 4.6) using an in-house developed beam interrupting device that stops the x-ray beam midway through the data acquisition of an unattenuated beam for calibration. A triple exponential falling edge response was fitted to the measured data and used to correct image lag from projection images with an infinite response. This filter, including an extrapolation for saturated pixels, was incorporated in the authors’ in-house developed clinical CBCT reconstruction software. To investigate the short-term stability of the lag and associated parameters, a series of five image lag measurement over a period of three months was performed. For quantitative analysis, the authors have retrospectively selected ten patients treated in the pelvic region. The apparent contrast was quantified in polar coordinates for scans reconstructed using the parameters obtained from different dates with and without saturation handling. Results: Visually, the radar artifact was minimal in scans reconstructed using image lag correction especially when saturation handling was used. In patient imaging, there was a significant reduction of the apparent contrast from 43 ± 16.7 to 15.5 ± 11.9 HU without the saturation handling and to 9.6 ± 12.1 HU with the saturation handling, depending on the date of the calibration. The image lag correction parameters were stable over a period of 3 months. The computational load was increased by approximately 10%, not endangering the fast in-line reconstruction. Conclusions: The lag correction was successfully implemented clinically and removed most image lag artifacts thus improving the image quality. Image lag correction parameters were stable for 3 months indicating low frequency of calibration requirements.