Deep Convolutional Neural Network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images.

ABSTRACT: Digital breast tomosynthesis (DBT) is a quasi-three-dimensional imaging modality that can reduce false negatives and false positives in mass lesion detection caused by overlapping breast tissue in conventional two-dimensional (2D) mammography. The patient dose of a DBT scan is similar to that of a single 2D mammogram, while acquisition of each projection view adds detector readout noise. The noise is propagated to the reconstructed DBT volume, possibly obscuring subtle signs of breast cancer such as microcalcifications (MCs). This study developed a deep convolutional neural network (DCNN) framework for denoising DBT images with a focus on improving the conspicuity of MCs as well as preserving the ill-defined margins of spiculated masses and normal tissue textures. We trained the DCNN using

SUBMITTER: Gao M 

PROVIDER: S-EPMC8274391 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature