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We aimed to optimize the exposure conditions in the acquisition of soft-tissue images using dual-energy subtraction chest radiography with a direct-conversion flat-panel detector system. Two separate chest images were acquired at high- and low-energy exposures with standard or thick chest phantoms. The high-energy exposure was fixed at 120 kVp with the use of an auto-exposure control technique. For the low-energy exposure, the tube voltages and entrance surface doses ranged 40-80 kVp and 20-100 % of the dose required for high-energy exposure, respectively. Further, a repetitive processing algorithm was used for reduction of the image noise generated by the subtraction process. Seven radiology technicians ranked soft-tissue images, and these results were analyzed using the normalized-rank method. Images acquired at 60 kVp were of acceptable quality regardless of the entrance surface dose and phantom size. Using a repetitive processing algorithm, the minimum acceptable doses were reduced from 75 to 40 % for the standard phantom and to 50 % for the thick phantom. We determined that the optimum low-energy exposure was 60 kVp at 50 % of the dose required for the high-energy exposure. This allowed the simultaneous acquisition of standard radiographs and soft-tissue images at 1.5 times the dose required for a standard radiograph, which is significantly lower than the values reported previously.

SummaryIterative reconstruction techniques, such as adaptive statistical iterative reconstruction (ASiR), improve the contrast-to-noise ratio of computed tomography (CT) images; however, underlying anatomical structures may nevertheless hamper detectability of low-contrast areas in clinical situations, despite using such a technique. We therefore conducted a phantom study to investigate the efficacy of ASiR in improving the detectability of lowcontrast areas in the presence of brain anatomical structures. We developed dedicated head phantoms simulating hyperacute cerebral infarction and confirmed that their CT numbers were sufficiently reproducible and that observer performance in detecting low-contrast areas using these phantoms more closely resembled that in clinical situations than that using a simple phantom. The efficacy of ASiR in improving low-contrast detectability was evaluated via receiver operating characteristics analysis. The mean area under the curve (AUC) values at ASiR blend rates of 0%, 30%, 60%, and 100% were 0. 57, 0.57, 0.59, and 0.59 at 200 mA; 0.83, 0.84, 0.84, and 0.90 at 500 mA; and 0.79, 0.77, 0.76, and 0.79 at 800 mA, respectively. No significant differences were noted in AUC values among ASiR blend rates at any mA setting, suggesting that ASiR does not improve the detectability of subtle low-contrast lesions seen in hyperacute cerebral infarction in clinical situations.

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