Deep learning based tomosynthesis denoising: a bias investigation across different breast types

High noise levels due to low X-ray dose are a challenge in digital breast tomosynthesis (DBT) reconstruction. Deep learning algorithms show promise in reducing this noise. However, these algorithms can be complex and biased toward certain patient groups if the training data are not representative. It is important to thoroughly evaluate deep learning-based denoising algorithms before they are applied in the medical field to ensure their effectiveness and fairness. In this work, we present a deep learning-based denoising algorithm and examine potential biases with respect to breast density, thickness, and noise level.

We use physics-driven data augmentation to generate low-dose images from full field digital mammography and train an encoder-decoder network. The rectified linear unit (ReLU)-loss, specifically designed for mammographic denoising, is utilized as the objective function. To evaluate our algorithm for potential biases, we tested it on both clinical and simulated data generated with the virtual imaging clinical trial for regulatory evaluation pipeline. Simulated data allowed us to generate X-ray dose distributions not present in clinical data, enabling us to separate the influence of breast types and X-ray dose on the denoising performance.

Our results show that the denoising performance is proportional to the noise level. We found a bias toward certain breast groups on simulated data; however, on clinical data, our algorithm denoises different breast types equally well with respect to structural similarity index.

We propose a robust deep learning-based denoising algorithm that reduces DBT projection noise levels and subject it to an extensive test that provides information about its strengths and weaknesses.