Image reconstruction from projections of digital breast tomosynthesis using deep learning - De Paula, Davi D. [UNESP]; Salvadeo, Denis H. P. [UNESP]; De Araújo, Darlan M. N. [UNESP] (SPIE, the international society for optics and photonics, 2021)
The Filtered Backprojection (FBP) algorithm for Computed Tomography (CT) reconstruction can be mapped entire in an Artificial Neural Network (ANN), with the backprojection (BP) operation simulated analytically in a layer and the Ram-Lak filter simulated as a convolutional layer. Thus, this work adapts the BP layer for Digital Breast Tomosynthesis (DBT) reconstruction, making possible the use of FBP simulated as an ANN to reconstruct DBT images. We showed that making the Ram-Lak layer trainable, the reconstructed image can be improved in terms of noise reduction. Finally, this study enables additional proposals of ANN with Deep Learning models for DBT reconstruction and denoising.
Keywords: Deep learning; Digital breast tomosynthesis; Noise reduction; Tomographic reconstruction