Digital breast tomosynthesis (DBT) is an advanced three-dimensional screening modality for the early detection of breast cancer. DBT is able to reduce the problem of tissue overlap in standard two-dimensional mammograms, thus improving the sensitivity and specificity of cancer detection. Although DBT can improve diagnostic accuracy, it leads to higher radiation dose to patients compared to two-dimensional mammography. In this paper, we propose a novel radiation dose reduction technique that introduces multi-scale kernels to our original massive-training artificial neural network (MTANN) to reduce radiation dose substantially, while maintaining high image quality in DBT. After training our new MTANN with low-dose (LD) images and the corresponding "teaching" high-dose (HD) images, we can convert new LD images to "virtual" high-dose (VHD) images where noise and artifact in the LD images are significantly reduced. In VHD images, it is critical to preserve subtle structures and tiny patterns such as microcalcifications (MCs) which are essential for breast cancer diagnosis. We developed anatomical MTANN experts including an MC-specific expert with multi-scale kernels, which are combined by gating layers to generate whole VHD images. Our MTANN scheme was able to achieve a 79% dose reduction while preserving details of MCs. Experimental results demonstrated that our method achieved the highest performance among the best-known noise-reduction techniques and state-of-the-art deep-learning techniques.Clinical Relevance- Our method can decrease the dose radiation dose in DBT and maintain the image quality.