VVBP-tensor-based deep learning framework for high-attenuation artifact reduction in digital breast tomosynthesis

Abstract

High-attenuation artifacts in digital breast tomosynthesis (DBT) imaging will potentially obscure some lesions in breast, which may result in increasing false-negative rate. Many image domain and projection domain based methods have been developed to reduce the high-attenuation artifacts. However, the high-attenuation artifacts have not been effectively removed, since these existing methods have not exactly addressed the inherent DBT imaging constraint of sparse-view low-dose scanning in a limited angular range. Recently, view-by-view backprojection tensor (VVBP-Tensor) domain is presented as the intermediary domain between projection domain and image domain, which may be beneficial to DBT image reconstruction. Moreover, high-attenuation artifacts are relative to the imaging geometry, and it is reasonable to hypothesize that the diffusion pattern of artifacts in VVBP-Tensor domain are similar for the same DBT imaging system. Therefore, we proposed a VVBP-Tensor based deep learning framework for high-attenuation artifact reduction in DBT imaging (shorten as VTDL-DBT), which learns the artifact diffusion pattern in VVBP-Tensor domain and remove these artifacts in a data-driven manner. The proposed method can be considered as the implicitly weighted filtered backprojection (wFBP) algorithm, which replaces the explicit weighted summing with the learnable deep neural network model. In addition, a pipeline of generating paired training data is also presented for DBT high-attenuation artifact removal task, which utilizes digital anthropomorphic breast phantoms and the Monte Carlo simulation algorithm. Both qualitative and quantitative results demonstrate that the presented VTDL-DBT method has a superior DBT imaging performance on the simulated DBT dataset, in terms of high-attenuation artifact reduction and structural texture preservation.