@InProceedings{PaulaSalv:2021:BrToRe,
               author = "Paula, Davi Duarte de and Salvadeo, Denis Henrique Pinheiro",
          affiliation = "{S{\~a}o Paulo State University (Unesp) - Institute of 
                         Geosciences and Exact Sciences} and {S{\~a}o Paulo State 
                         University (Unesp) - Institute of Geosciences and Exact 
                         Sciences}",
                title = "Breast Tomosynthesis Reconstruction Using Artificial Neural 
                         Networks with Deep Learning",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Deep Learning, Breast Tomosynthesis, Image Reconstruction.",
             abstract = "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 adapt the BP 
                         layer for DBT reconstruction, making possible the use of FBP 
                         simulated as a ANN to reconstruct DBT images. For evaluation, 
                         Structural Similarity Index Measure (SSIM) and Peak 
                         Signal-to-Noise Ratio (PSNR) metrics were calculated to measure 
                         the improvement of the images made by the ANN, regarding a dataset 
                         containing 100 virtual breast phantoms to perform the experiments. 
                         We shown that making the Ram-Lak layer trainable, the 
                         reconstructed image can be improved in terms of noise reduction. 
                         And, considering an additional post-filtering step performed by 
                         Denoising Convolutional Neural Network (DnCNN), it shown 
                         comparable and superior results than a state-of-the-art DBT 
                         reconstruction method, averaging 37.644 dB and 0.869 values of 
                         PSNR and SSIM, respectively. Finally, this study enables 
                         additional proposals of ANN with Deep Learning models for DBT 
                         reconstruction and denoising.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45EHMTL",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EHMTL",
           targetfile = "artigo_final.pdf",
        urlaccessdate = "2024, May 06"
}