Scale-Space Anisotropic Total Variation for Limited Angle Tomography

TL;DR

This paper introduces the scale-space anisotropic total variation (ssaTV) algorithms to effectively reduce streak artifacts at multiple scales in limited angle tomography, outperforming existing methods like wTV.

Contribution

The paper proposes two novel ssaTV algorithms that adapt TV regularization across multiple scales to better eliminate streak artifacts in tomography.

Findings

ssaTV algorithms outperform wTV in streak reduction

Both ssaTV implementations effectively handle multi-scale streak artifacts

Numerical and clinical data validate improved performance of ssaTV methods

Abstract

This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at eliminating large ones since total variation (TV) regularization is scale-dependent and may regard these streaks as homogeneous areas. Hence, the main purpose of this paper is to reduce streak artifacts at various scales. We propose the scale-space anisotropic total variation (ssaTV) algorithm in two different implementations. The first implementation (ssaTV-1) utilizes an anisotropic gradient-like operator which uses 2s neighboring pixels along the streaks' normal direction at each scale s. The second implementation (ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations, similarly oriented along the streaks' normal direction, to apply TV regularization at various scales. Experiments on numerical and clinical data demonstrate that both ssaTV algorithms reduce streak artifacts more effectively and efficiently than wTV, particularly when using multiple scales.