The main purpose of this work is to study the ability of denoising algorithms to reduce the radiation dose in Digital Breast Tomosynthesis (DBT) examinations. Clinical use of DBT is normally performed in “combo-mode”, in which, in addition to DBT projections, a 2D mammogram is taken with the standard radiation dose. As a result, patients have been exposed to radiation doses higher than used in digital mammography. Thus, efforts to reduce the radiation dose in DBT examinations are of great interest. However, a decrease in dose leads to an increased quantum noise level, and related decrease in image quality. This work is aimed at addressing this problem by the use of denoising techniques, which could allow for dose reduction while keeping the image quality acceptable. We have studied two “state of the art” denoising techniques for filtering the quantum noise due to the reduced dose in DBT projections: Non-local Means (NLM) and Block-matching 3D (BM3D). We acquired DBT projections at different dose levels of an anthropomorphic physical breast phantom with inserted simulated microcalcifications. Then, we found the optimal filtering parameters where the denoising algorithms are capable of recovering the quality from the DBT images acquired with the standard radiation dose. Results using objective image quality assessment metrics showed that BM3D algorithm achieved better noise adjustment (mean difference in peak signal to noise ratio < 0.1dB) and less blurring (mean difference in image sharpness ~ 6%) than the NLM for the projections acquired with lower radiation doses.