Dual-energy CT (DECT) imaging has gained a lot of attenuation because of its capability to discriminate materials. We proposes a flexible DECT scan strategy which can be realized on a system with general X-ray sources and detectors. In order to lower dose and scanning time, our DECT acquires two projections data sets on two arcs of limited-angular coverage (one for each energy) respectively. Meanwhile, a certain number of rays from two data sets form conjugate sampling pairs. Our reconstruction method for such a DECT scan mainly tackles the consequent limited-angle problem. Using the idea of artificial neural network, we excavate the connection between projections at two different energies by constructing a relationship between the linear attenuation coefficient of the high energy and that of the low one. We use this relationship to cross-estimate missing projections and reconstruct attenuation images from an augmented data set including projections at views covered by itself (projections collected in scanning) and by the other energy (projections estimated) for each energy respectively. Validated by our numerical experiment on a dental phantom with rather complex structures, our DECT is effective in recovering small structures in severe limited-angle situations. This DECT scanning strategy can much broaden DECT design in reality.
In this article, we present an easy-to-implement Multi-energy CT scanning strategy and a corresponding reconstruction method, which facilitate spectral CT imaging by improving the data efficiency the number-of-energy- channel fold without introducing visible limited-angle artifacts caused by reducing projection views. Leveraging the structure coherence at different energies, we first pre-reconstruct a prior structure information image using projection data from all energy channels. Then, we perform a k-means clustering on the prior image to generate a sparse dictionary representation for the image, which severs as a structure information constraint. We com- bine this constraint with conventional compressed sensing method and proposed a new model which we referred as Joint Clustering Prior and Sparsity Regularization (CPSR). CPSR is a convex problem and we solve it by Alternating Direction Method of Multipliers (ADMM).
We verify our CPSR reconstruction method with a numerical simulation experiment. A dental phantom with complicate structures of teeth and soft tissues is used. X-ray beams from three spectra of different peak energies (120kVp, 90kVp, 60kVp) irradiate the phantom to form tri-energy projections. Projection data covering only 75◦ from each energy spectrum are collected for reconstruction. Independent reconstruction for each energy will cause severe limited-angle artifacts even with the help of compressed sensing approaches. Our CPSR provides us with images free of the limited-angle artifact. All edge details are well preserved in our experimental study.