Statistical reconstruction using dual formulation of subband-wise total variation regularization (SDST) for limited angle tomography

In this paper, a novel reconstruction algorithm for limited angle tomography using total variation (TV) regularization is presented. Inspired by duality-based TV minimization in denoising and deblurring applications, we derived a TV regularized statistical reconstruction algorithm composed of relatively simple and structured operations such as discrete gradient and divergence calculations, which presents an effective way to introduce TV regularization to the statistical reconstruction. In initial tests with real data from a digital breast tomosynthesis system, the proposed algorithm showed reliable reconstructions for low dose conditions.