Gain and offset calibration reduces variation in exposure‐dependent SNR among systems with identical digital flat‐panel detectors

Purpose: The conditions under which vendor performance criteria for digital radiography systems are obtained do not adequately simulate the conditions of actual clinical imaging with respect to radiographic technique factors, scatter production, and scatter control. Therefore, the relationship between performance under ideal conditions and performance in clinical practice remains unclear. Using data from a large complement of systems in clinical use, the authors sought to develop a method to establish expected performance criteria for digital flat‐panel radiography systems with respect to signal‐to‐noise ratio (SNR) versus detector exposure under clinical conditions for thoracic imaging. Methods: The authors made radiographic exposures of a patient‐equivalent chest phantom at 125 kVp and 180 cm source‐to‐image distance. The mAs value was modified to produce exposures above and below the mAs delivered by automatic exposure control. Exposures measured free‐in‐air were corrected to the imaging plane by the inverse square law, by the attenuation factor of the phantom, and by the Bucky factor of the grid for the phantom, geometry, and kilovolt peak. SNR was evaluated as the ratio of the mean to the standard deviation (SD) of a region of interest automatically selected in the center of each unprocessed image. Data were acquired from 18 systems, 14 of which were tested both before and after gain and offset calibration. SNR as a function of detector exposure was interpolated using a double logarithmic function to stratify the data into groups of 0.2, 0.5, 1.0, 2.0, and 5.0 mR exposure (1.8, 4.5, 9.0, 18, and 45 μ Gy air KERMA) to the detector. Results: The mean SNR at each exposure interval after calibration exhibited linear dependence on the mean SNR before calibration ( r 2  = 0.9999). The dependence was greater than unity ( m  = 1.101 ± 0.006), and the difference from unity was statistically significant ( p  < 0.005). The SD of mean SNR after calibration also exhibited linear dependence on the SD of the mean SNR before calibration ( r 2  = 0.9997). This dependence was less than unity ( m  = 0.822 ± 0.008), and the difference from unity was also statistically significant ( p  < 0.005). Systems were separated into two groups: systems with a precalibration SNR higher than the median SNR ( N  = 7), and those with a precalibration SNR lower than the median SNR ( N  = 7). Posthoc analysis was performed to correct for expanded false positive results. After calibration, the authors noted differences in mean SNR within both high and low groups, but these differences were not statistically significant at the 0.05 level. SNR data from four additional systems and one system from those previously tested after replacement of its detector were compared to the 95% confidence intervals (CI) calculated from the postcalibration SNR data. The comparison indicated that four of these five systems were consistent with the CI derived from the previously tested 14 systems after calibration. Two systems from the paired group that remained outside the CI were studied further. One system was remedied with a grid replacement. The nonconformant behavior of the other system was corrected by replacing the image receptor. Conclusions: Exposure‐dependent SNR measurements under conditions simulating thoracic imaging allowed us to develop criteria for digital flat‐panel imaging systems from a single manufacturer. These measurements were useful in identifying systems with discrepant performance, including one with a defective grid, one with a defective detector, and one that had not been calibrated for gain and offset. The authors also found that the gain and offset calibration reduces variation in exposure‐dependent SNR performance among the systems.