We investigate the feasibility of dual-energy method for image contrast enhancement in small animal studies using a low kV X-ray radiographic system. A robust method for X-ray spectrum estimation from transmission measurements, based on expectation-maximization (EM) method, is applied to an X-ray specimen radiographic system for dual energy imaging of a mouse. From transmission measurements of two known attenuators at two different X-ray tube voltages, the X-ray energy spectra are reconstructed using the EM-based method. From the spectra information thus obtained, the transmission data for bone and soft tissue in terms of various thicknesses are generated. Two polynomial functions of transmission data are then sought for to fit the inverted thicknesses of bone and soft-tissue. Scatters in cone-beam projection data acquired at two X-ray energies were corrected. From the scatter-corrected data, a bone thickness map is separated from a soft-tissue thickness map by use of the polynomial functions.

There has been a renewed interest in algorithm development for image reconstruction from highly incomplete data in computed tomography (CT). Such algorithms may lead to reduced imaging dose and time, and to the design of innovative configurations tailored to specific imaging tasks. In recent years, a carbon-nanotube (CNT)-based field-emission x-ray source has been developed, which offers easy electronic control of radiation and thus can be an ideal candidate for gated imaging. We have recently proposed algorithms for image reconstruction from fan- and cone-beam data collected at highly sparse angular views through minimization of the total-variation (TV) of the image subject to the condition that the estimated data are consistent with the measured data. In this work, we investigate and demonstrate the application of the TV-minimization algorithm to reconstructing images from mouse data acquired with a CNT-based CT scanner at a number of views much lower than what is used in conventional CT imaging. The results demonstrate that the TV-minimization algorithm can yield images with quality comparable to those obtained from a large number of views by use of the conventional algorithms. The significance of the work may lie in that the substantial reduction of projection views promised by the TV-minimization algorithm can be exploited for reducing imaging dose and time or for improving temporal resolution in tasks such as dynamic imaging.