Comparison of reconstruction algorithms for digital breast tomosynthesis

TL;DR

This paper systematically compares reconstruction algorithms for digital breast tomosynthesis, revealing that TV-minimization reduces artifacts and improves image quality over FBP and EM methods in pre-clinical evaluations.

Contribution

It provides a comparative analysis of FBP, EM, and TV-minimization algorithms for DBT, highlighting the advantages of TV-minimization in artifact reduction and image quality.

Findings

TV-minimization yields images with fewer artifacts.

FBP images are noisier and have lower resolution.

Iterative methods outperform FBP in image quality.

Abstract

Digital breast tomosynthesis (DBT) is an emerging modality for breast imaging. A typical tomosynthesis image is reconstructed from projection data acquired at a limited number of views over a limited angular range. In general, the quantitative accuracy of the image can be significantly compromised by severe artifacts and non-isotropic resolution resulting from the incomplete data. Nevertheless, it has been demonstrated that DBT may yield useful information for detection/classification tasks and thus is considered a promising breast imaging modality currently undergoing pre-clinical evaluation trials. The purpose of this work is to conduct a preliminary, but systematic, investigation and evaluation of the properties of reconstruction algorithms that have been proposed for DBT. We use a breast phantom designed for DBT evaluation to generate analytic projection data for a typical DBT configuration, which is currently undergoing pre-clinical evaluation. The reconstruction algorithms under comparison include (i) filtered backprojection (FBP), (ii) expectation maximization (EM), and (iii) TV-minimization algorithms. Results of our studies indicate that FBP reconstructed images are generally noisier and demonstrate lower in-depth resolution than those obtained through iterative reconstruction and that the TV-minimization reconstruction yield images with reduced artifacts as compared to that obtained with other algorithms under study.