  Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D
images of a human chest for pulmonary disease screening, with 2D X-ray
projections taken within an extremely limited range of angles. However, under
the limited angle scenario, DCT contains strong artifacts caused by the
presence of ribs, jamming the imaging quality of the lung area. Recently, great
progress has been achieved for rib suppression in a single X-ray image, to
reveal a clearer lung texture. We firstly extend the rib suppression problem to
the 3D case at the software level. We propose a $\textbf{T}$omosynthesis
$\textbf{RI}$b Su$\textbf{P}$pression and $\textbf{L}$ung
$\textbf{E}$nhancement $\textbf{Net}$work (TRIPLE-Net) to model the 3D rib
component and provide a rib-free DCT. TRIPLE-Net takes the advantages from both
2D and 3D domains, which model the ribs in DCT with the exact FBP procedure and
3D depth information, respectively. The experiments on simulated datasets and
clinical data have shown the effectiveness of TRIPLE-Net to preserve lung
details as well as improve the imaging quality of pulmonary diseases. Finally,
an expert user study confirms our findings.
