A benchmark of denoising digital breast tomosynthesis in projection domain: neural network-based and traditional 
methods

Darlan M. Nakamura de Araújo; Denis H. P. Salvadeo; Davi D. de Paula

doi:10.1117/12.2611833

4 April 2022 A benchmark of denoising digital breast tomosynthesis in projection domain: neural network-based and traditional methods

Darlan M. Nakamura de Araújo, Denis H. P. Salvadeo, Davi D. de Paula

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203207 (2022) https://doi.org/10.1117/12.2611833
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Digital Breast Tomosynthesis (DBT) projections are acquired with a high level of noise, compared to Digital Mammography (DM) projections. Noise reduction in DBT projections is important because the projections are obtained with low radiation dose, elevating the noise level. In this way, noise reduction is essential to improve the quality of DBT exam. Recently, neural network based methods have been applied to denoise DBT projections, reaching remarkable results. Some papers have been published showing that these methods are able to overpass traditional methods’ results, but we could not find a paper comparing the different types of networks to denoise DBT projections. In this paper, we proposed an experiment to compare neural network based methods, with different architecture types, and traditional methods. We performed a comparison among five traditional non blind denoising methods and six neural network models. Considering both quantitative and qualitative analysis, we found that some neural network models achieve remarkable results, especially shallower models.

Conference Presentation

Citation Download Citation

Darlan M. Nakamura de Araújo, Denis H. P. Salvadeo, and Davi D. de Paula "A benchmark of denoising digital breast tomosynthesis in projection domain: neural network-based and traditional methods", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203207 (4 April 2022); https://doi.org/10.1117/12.2611833

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