Digital Breast Tomosynthesis (DBT) is a new imaging modality that enhances breast cancer detection by having better resolution along the depth axis compared to digital mammography. However, it is challenging to obtain a correct 3D model of the breast from DBT imaging and standard reconstruction methods due to the limited number of views acquired. The reconstructions are also marred by dominant artifacts that make tissue identification difficult. To improve DBT reconstruction, Learned Experts Assessment-Based Reconstruction Network (LEARN) deep learning (DL) models are investigated in this work. Originally designed to enhance CT imaging with sparse data, we have redesigned and adjusted the LEARN algorithm to combat challenges of DBT reconstruction of 2D slices of digital breast phantoms.