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PURPOSE Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, FFDM image has a different texture pattern from DBT. The difference of texture pattern might make the training of the neural network unstable and result in high intensity distortion, which make it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like a FFDM image in vision and preserves local structures such as both mass and MCs in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network (DCNN) to learn the transformation to generate SDM from DBT. METHOD To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded networks (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The mean-squared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate low-frequency SDM image. The gradient guided generative adversarial networks (GGGAN) objective function is used to train the second sub-network, termed high-frequency network, to generate full SDM image with textures similar to the FFDM image. RESULTS 1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or microcalcification (MC) clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generate full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio (PSNR) from 25.3dB to 27.9dB and improves the structural similarity (SSIM) from 0.703 to 0.724, and significantly increases the perceptual similarity. CONCLUSIONS The proposed method can stabilize the training and generate SDM images with lower intensity distortion and higher perceptual similarity. This article is protected by copyright. All rights reserved.