Reducing high-density object artifacts with iterative image reconstruction in digital tomosynthesis

Abstract

In digital tomosynthesis, high-density object artifacts such as ripples and undershoots can show up in the reconstructed image in conjunction with a limited angle problem and may hinder an accurate diagnosis. In this study, we propose an iterative image reconstruction method for reducing such artifacts by use of a voting strategy with a data fidelity term that involves derivative data. It has been confirmed that the voting strategy can help reduce high-density object artifacts in the algebraic iterative reconstruction framework for tomosyntheis and more importantly shown that its contribution greatly improves when the derivative data term is jointly used in the cost function. For evaluation, the CIRS breast phantom and a forearm phantom with metal implants were scanned using a prototype digital breast tomosynthesis system and a chest digital tomosynthesis system, respectively.