Suppressing noise correlation in digital breast tomosynthesis using convolutional neural network and virtual clinical trials

It is well-known that x-ray systems featuring indirect detectors are affected by noise spatial correlation. In the case of digital breast tomosynthesis (DBT), this phenomenon might affect the perception of small details in the image, such as microcalcifications. In this work, we propose the use of a deep convolutional neural network (CNN) to restore DBT projections degraded with correlated noise using the framework of a cycle generative adversarial network (cycle-GAN). To generate pairs of images for the training procedure, we used a virtual clinical trial (VCT) system. Two approaches were evaluated: in the first one, the network was trained to perform noise decorrelation by changing the frequency-dependency of the noise in the input image, but keeping the other characteristics. In the second approach, the network was trained to perform denoising and decorrelation, with the objective of generating an image with frequency-independent (white) noise and with characteristics equivalent to an acquisition with a radiation exposure four times greater than the input image. We tested the network with virtual and clinical images and we found that in both training approaches the model successfully corrected the power spectrum of the input images.