Digital Breast Tomosynthesis Reconstruction with Deep Neural Network for Improved Contrast and In-Depth Resolution

Digital breast tomosynthesis (DBT) provides 3D reconstruction which reduces the superposition and overlapping of breast tissues compared to mammography, leading to increased sensitivity and specificity. However, due to the limited angular sampling, DBT images are still accompanied with severe artifacts and limited in-depth resolution. In this paper, we proposed a deep learning-based DBT reconstruction method to mitigate the limited angular artifacts and improve in-depth resolution. An unroll-type neural network was used with decoupled training for each unroll to reduce training-time computational cost. A novel region of interest loss on inserted microcalcifications was further proposed to improve the spatial resolution and contrast of the microcalcifications. The network was trained and tested on 176 realistic breast phantoms, and improved in-plane contrast (3.17 versus 0.43, p<0.01) and in-depth resolution (1.19 mm versus 4.96 mm, p <0.01) was demonstrated by the proposed method compare to iterative reconstruction.