Improvement of material decomposition and image quality in dual-energy radiography by reducing image noise

Abstract

Although digital radiography has been widely used for screening human anatomical structures in clinical situations, it has several limitations due to anatomical overlapping. To resolve this problem, dual-energy imaging techniques, which provide a method for decomposing overlying anatomical structures, have been suggested as alternative imaging techniques. Previous studies have reported several dual-energy techniques, each resulting in different image qualities. In this study, we compared three dual-energy techniques: simple log subtraction (SLS), simple smoothing of a high-energy image (SSH), and anti-correlated noise reduction (ACNR) with respect to material thickness quantification and image quality. To evaluate dual-energy radiography, we conducted Monte Carlo simulation and experimental phantom studies. The Geant 4 Application for Tomographic Emission (GATE) v 6.0 and tungsten anode spectral model using interpolation polynomials (TASMIP) codes were used for simulation studies and digital radiography, and human chest phantoms were used for experimental studies. The results of the simulation study showed improved image contrast-to-noise ratio (CNR) and coefficient of variation (COV) values and bone thickness estimation accuracy by applying the ACNR and SSH methods. Furthermore, the chest phantom images showed better image quality with the SSH and ACNR methods compared to the SLS method. In particular, the bone texture characteristics were well-described by applying the SSH and ACNR methods. In conclusion, the SSH and ACNR methods improved the accuracy of material quantification and image quality in dual-energy radiography compared to SLS. Our results can contribute to better diagnostic capabilities of dual-energy images and accurate material quantification in various clinical situations.