Noise-reduction approaches to single-shot dual-energy imaging with a multilayer detector

Abstract

The single-shot dual-energy (DE) method using a sandwich-like multilayer detector generally suffers from noise in the resultant DE images due to the poor quantum noise in images obtained from the rear detector layer and its amplification due to the subtraction operation in DE reconstruction. The use of interlayer metal filter can further increase noise in DE images by increasing the rear-detector quantum noise. We have adopted several noise-reduction algorithms to the single-shot DE reconstruction, which included the Gaussian-filtering noise reduction (GNR), the median-filtering noise reduction (MNR), and the anti-correlated noise reduction (ANR). We assessed the effectiveness of noise-reduction methods by investigating noise and dose-normalized contrast-to-noise ratio for a mouse-mimicking phantom consisting of aluminum bone and polyurethane soft tissues. Noise-power spectrum was also measured to investigate correlation noise. Overall, the ANR showed the best performance. At some extreme imaging technique conditions, such as a lower tube voltages and a larger spectral energy separation using thick copper sheets between the front and rear detector layers, the MNR outperformed the ANR. The performance of GNR was nearly similar to that of MNR. The results were well reflected into demonstration bone images obtained for a postmortem mouse. Although the ANR was effective to reduce noise in the single-shot DE imaging using the sandwich detector, multivariate optimization of ANR parameters with respect to imaging tasks and imaging techniques is remained as a future study.