  This paper presents an iterative inversion algorithm for computed tomography
image reconstruction that performs well in terms of accuracy and speed using
limited data. The computational method combines an image domain technique and
statistical reconstruction by using an initial filtered back projection
reconstruction to create a binary edge mask, which is then used in an
l2-regularized reconstruction. Both theoretical and empirical results are
offered to support the algorithm. While in this paper a simple forward model is
used and physical edges are used as the sparse feature, the proposed method is
flexible and can accommodate any forward model and sparsifying transform.
