A directional total variation minimization algorithm for isotropic resolution in digital breast tomosynthesis

TL;DR

This paper introduces a directional total variation minimization algorithm for digital breast tomosynthesis that effectively reduces depth blur in contrast-enhanced imaging, improving image clarity for ICA detection.

Contribution

The paper presents a novel directional TV minimization algorithm tailored for dual-energy DBT, enhancing contrast and depth resolution over traditional methods.

Findings

Directional TV reduces depth blur in ICA imaging

Algorithm outperforms filtered back-projection in clarity

Effective in structured breast phantom experiments

Abstract

An optimization-based image reconstruction algorithm is developed for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm minimizes directional total variation (TV) with a data discrepancy and non-negativity constraints. Iodinated contrast agent (ICA) imaging is performed by reconstructing images from dual-energy DBT data followed by weighted subtraction. Physical DBT data is acquired with a Siemens Mammomat scanner of a structured breast phantom with ICA inserts. Results are shown for both directional TV minimization and filtered back-projection for reference. It is seen that directional TV is able to substantially reduce depth blur for the ICA objects.