Denoising digital breast tomosynthesis projections using convolutional neural networks - De Araújo, Darlan M.N. [UNESP]; Salvadeo, Denis H. P. [UNESP]; De Paula, Davi D. [UNESP] (SPIE, the international society for optics and photonics, 2021)
The Digital Breast Tomosynthesis (DBT) projections are obtained with low quality, being essential to use denoising methods to increase the quality of the projections. Currently, deep learning methods have become the state-of-art approach in denoising. Some papers have proposed to apply deep learning methods for denoising DBT projections, however, there is a lack of clarity in the results comparing with traditional methods. In this paper, we proposed to use a CNN to denoise DBT projections, and compare it with traditional denoising methods. The results shown that the CNN is superior quantitatively and qualitatively in comparison with the traditional methods.
Keywords: Convolutional neural networks; Deep learning; Denoising; Digital breast tomosynthesis