Recovering unprocessed digital mammograms from processed mammograms for quantitative analysis

In many mammography facilities only the processed mammograms are preserved to reduce the space requirement and cost of digital archiving. The original unprocessed "raw" mammograms are preferred for quantitative analysis, since they more faithfully represent the x-ray transmission pattern and thus the breast composition. We present the results of a machine learning algorithm that attempts to restore a raw mammogram from its processed version. In this study, 2776 paired sets of the two image types were obtained, corresponding to 635 patients. The machine learning model used was based on a U-Net with attention gates on the long skip connections. A two-pass learning approach was used. The first pass used a mean-squared error loss function with focus on the periphery of the breast, with 5 epochs and a learning rate of 10(-5) to settle the network weights quickly. In a second pass, a perceptual loss function, based on features extracted from a pretrained VGG16 neural net, was used with 15 epochs and a 10(-6) learning rate. When tested on central ROIs, the mean relative absolute difference ( MRAD) and structural similarity index (SSIM) between the original and restored raw images were 0.04 and 0.98, respectively. On the complete (but downsampled) images, MRAD and SSIM were 0.10 and 0.99, respectively. Lesion detectability and cancer masking potential were also measured on the original and restored raw images, showing Pearson correlations of 0.89 in both cases. The algorithm shows potential for using the restored raw images from processed images for the purposes of quantitative analysis. Future work will extend the approach to higher resolution images to preserve detail and more efficient network architectures to reduce memory requirements.