Numerical assessment of shearlet-based regularization in ROI tomography

TL;DR

This paper introduces a shearlet-based regularization method for small-region CT reconstruction, demonstrating stability and insensitivity to ROI location in fan beam CT, addressing the ill-posedness of the inverse problem.

Contribution

It presents a novel convex optimization approach using shearlets for ROI CT reconstruction, with an iterative SGP scheme, improving stability for small and arbitrarily located ROIs.

Findings

Stable reconstruction for small ROIs

Insensitivity to ROI location

Effective in fan beam CT

Abstract

When it comes to computed tomography (CT), the possibility to reconstruct a small region-of-interest (ROI) using truncated projection data is particularly appealing due to its potential to lower radiation exposure and reduce the scanning time. However, ROI reconstruction from truncated projections is an ill-posed inverse problem, with the ill-posedness becoming more severe when the ROI size is getting smaller. To address this problem, both ad hoc analytic formulas and iterative numerical schemes have been proposed in the literature. In this paper, we introduce a novel approach for ROI CT reconstruction, formulated as a convex optimization problem with a regularized term based on shearlets. Our numerical implementation consists of an iterative scheme based on the scaled gradient projection (SGP) method and is tested in the context of fan beam CT. Our results show that this approach is essentially insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.