Iterative 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 low-contrast 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.