A projection-based sparse-view virtual monochromatic computed tomography method based on a compressed-sensing algorithm

Computed tomography (CT) images obtained at different monochromatic X-ray beam energies can be synthesized from conventional dual-energy CT scans. This approach to synthesizing monochromatic CT images is based on basis material decomposition and the knowledge of attenuation of basis materials. The main benefits of virtual monochromatic CT (VMCT) images include reduction of beam-hardening artifacts and provision of accurate atteuation measurements. Despite the VMCT's benefits, main concerns in the use of VMCT in clinics may be high radiation dose the patient is exposed to. In this study, we investigated a projection-based sparse-view VMCT method in an attempt to overcome these difficulties. We performed a computational simulation and evaluated the feasibility of using the VMCT method in sparse-view CT. Two polychromatic data sets of 90 projections, far less than what is required by the Nyquist sampling theory, were simulated at 80 kV(p) and 140 kV(p) and used to synthesize VMCT images at a monochromatic energy range of 40-140 keV. VMCT image characteristics were quantitatively evaluated in terms of intensity profile, the contrast-to-noise ratio, and the signal-to-noise ratio. Our results indicate that the CS-based algorithm produced high-quality sparse-view CT images, and thereby the proposed VMCT method yielded CT image results of improved beam-hardening artifacts and quantitative measurements.