Objectives: The purpose of this study was to derive an optimum radiographic technique for computed radiography (CR) chest imaging using a digitally reconstructed radiograph computer simulator. The simulator is capable of producing CR chest radiographs of adults with various tube potentials, receptor doses and scatter rejection. Methods: Four experienced image evaluators graded images of average and obese adult patients at different potentials (average-sized, n550; obese, n520), receptor doses (n510) and scatter rejection techniques (average-sized, n520; obese, n520). The quality of the images was evaluated using visually graded analysis. The influence of rib contrast was also assessed. Results: For average-sized patients, image quality improved when tube potential was reduced compared with the reference (102 kVp). No scatter rejection was indicated. For obese patients, it has been shown that an antiscatter grid is indicated, and should be used in conjunction with as low a tube potential as possible (while allowing exposure times ,20 ms). It is also possible to reduce receptor air kerma by 50% without adversely influencing image quality. Rib contrast did not interfere at any tube potential. Conclusions: A virtual clinical trial has been performed with simulated chest CR images. Results indicate that low tube potentials (,102 kVp) are optimal for average and obese adults, the former acquired without scatter rejection, the latter with an antiscatter grid. Lower receptor (and therefore patient doses) than those used clinically are possible while maintaining adequate image quality.
The purpose of this study was to examine the use of three physical image quality metrics in the calibration of an automatic exposure control (AEC) device for chest radiography with a computed radiography (CR) imaging system. The metrics assessed were signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and mean effective noise equivalent quanta (eNEQm), all measured using a uniform chest phantom. Subsequent calibration curves were derived to ensure each metric was held constant across the tube voltage range. Each curve was assessed for its clinical appropriateness by generating computer simulated chest images with correct detector air kermas for each tube voltage, and grading these against reference images which were reconstructed at detector air kermas correct for the constant detector dose indicator (DDI) curve currently programmed into the AEC device. All simulated chest images contained clinically realistic projected anatomy and anatomical noise and were scored by experienced image evaluators. Constant DDI and CNR curves do not appear to provide optimized performance across the diagnostic energy range. Conversely, constant eNEQm and SNR do appear to provide optimized performance, with the latter being the preferred calibration metric given as it is easier to measure in practice. Medical physicists may use the SNR image quality metric described here when setting up and optimizing AEC devices for chest radiography CR systems with a degree of confidence that resulting clinical image quality will be adequate for the required clinical task. However, this must be done with close cooperation of expert image evaluators, to ensure appropriate levels of detector air kerma.
Validated with images containing realistic anatomical noise, it is possible to improve image quality by utilizing grids for chest radiography with CR systems without increasing patient exposure. Increasing tube mAs by an amount determined by the Bucky factor is not justified.
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