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Double noise filtering in DBT: pre- and post-reconstruction

Grant number: 16/09714-4
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): May 01, 2017
Effective date (End): November 30, 2018
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Daniele Cristina Scarparo
Home Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil


Digital Breast Tomosynthesis (DBT) is a new kind of imaging system for mammary structures, especially for breast cancer diagnostics purposes. This modality follows the same principle of Computed Tomography (CT). In this sense, the minimization of radiation needed to obtain a proper examination is a principle to be sought (ALARA). But to make this possible, noise reduction methods need to be applied, since that reducing radiation raises the noise level in the image. Traditionally, noise filtering has been applied in the projection domain, dealing with Poisson noise. However, recent studies have been successfully applied in the reconstructed image domain or in both stages (pre- and post-reconstruction). Thus, based on a recent study in CT, this work proposes performing a double noise filtering in DBT using (1) noise filtering methods in state-of-the-art for the pre-reconstruction stage, (2) reconstructing the image of these filtered projections and, subsequently, (3) using noise filtering methods (also in state-of-the-art) for Gaussian noise on the reconstructed image. Thus, we expect to obtain a better balance between detail preservation and noise reduction. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SCARPARO, DANIELE CRISTINA; PINHEIRO SALVADEO, DENIS HENRIQUE; GUIMARAES PEDRONETTE, DANIEL CARLOS; BARUFALDI, BRUNO; ARNOLD MAIDMENT, ANDREW DOUGLAS. Evaluation of denoising digital breast tomosynthesis data in both projection and image domains and a study of noise model on digital breast tomosynthesis image domain. JOURNAL OF MEDICAL IMAGING, v. 6, n. 3 JUL 2019. Web of Science Citations: 0.

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