Dynamic CT reconstruction by smoothed rank minimization

Abstract

We address the problem of dynamic CT reconstruction from parsimoniously sampled sinograms. In this paper we propose a novel approach to solve the aforesaid problem by modeling the dynamic CT sequence as a lowrank matrix. This dynamic CT matrix is formed by stacking each frame as a column of the matrix. As these images are temporally correlated, the dynamic CT matrix would therefore be of low-rank as its columns are not independent. We exploit the low-rank information to reconstruct the CT matrix from its parsimoniously sampled sinograms. Mathematically this is a low-rank matrix recovery problem, and we propose a novel algorithm to solve it. Our proposed method reduces the reconstruction error by 50% or more when compared to previous recovery techniques.