Few-View Projection Reconstruction With an Iterative Reconstruction-Reprojection Algorithm and TV Constraint

Abstract

In applications of tomographic imaging, insufficient data problems can take various forms, such as few-view projection imaging which enables rapid scanning with lower X-ray dose. In this work, an iterative reconstruction-reprojection (IRR) algorithm with total variation (TV) constraint is developed for few-view projections. The IRR algorithm is used to estimate the missing projection data by iterative extrapolation between projection and image space. TV minimization is a popular image restoration method with edge preserving. In recent studies, it has been successfully used for reconstructing images from sparse samplings, such as few-view projections. Our method is derived from this work. The combination of IRR and TV achieves both estimation in projection space and regularization in image space, which accelerates the convergence of the iterations. To improve the quality of the image reconstructed from few-view fan-beam projections, a short-scan type IRR is also approached to reduce the redundancy of projection data. An improved weighting function is proposed for few-view short-scan projection reconstruction by the filtered backprojection (FBP) algorithm. Numerical simulations show that the IRR-TV algorithm is effective for the few-view problem of reconstructing sparse-gradient images.