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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Scarparo, Daniele Cristina [1] ; Pinheiro Salvadeo, Denis Henrique [2, 1] ; Guimaraes Pedronette, Daniel Carlos [1] ; Barufaldi, Bruno [2] ; Arnold Maidment, Andrew Douglas [2]
Total Authors: 5
Affiliation:
[1] Sao Paulo State Univ, UNESP, Inst Geosci & Exact Sci, Rio Claro, SP - Brazil
[2] Univ Penn, Hosp Univ Penn, Dept Radiol, Philadelphia, PA 19104 - USA
Total Affiliations: 2
Document type: Journal article
Source: JOURNAL OF MEDICAL IMAGING; v. 6, n. 3 JUL 2019.
Web of Science Citations: 0
Abstract

Digital breast tomosynthesis (DBT) is an imaging technique created to visualize 3-D mammary structures for the purpose of diagnosing breast cancer. This imaging technique is based on the principle of computed tomography. Due to the use of a dangerous ionizing radiation, the ``as low as reasonably achievable{''} (ALARA) principle should be respected, aiming at minimizing the radiation dose to obtain an adequate examination. Thus, a noise filtering method is a fundamental step to achieve the ALARA principle, as the noise level of the image increases as the radiation dose is reduced, making it difficult to analyze the image. In our work, a double denoising approach for DBT is proposed, filtering in both projection (prereconstruction) and image (postreconstruction) domains. First, in the prefiltering step, methods were used for filtering the Poisson noise. To reconstruct the DBT projections, we used the filtered backprojection algorithm. Then, in the postfiltering step, methods were used for filtering Gaussian noise. Experiments were performed on simulated data generated by open virtual clinical trials (OpenVCT) software and on a physical phantom, using several combinations of methods in each domain. Our results showed that double filtering (i.e., in both domains) is not superior to filtering in projection domain only. By investigating the possible reason to explain these results, it was found that the noise model in DBT image domain could be better modeled by a Burr distribution than a Gaussian distribution. Finally, this important contribution can open a research direction in the DBT denoising problem. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) (AU)

FAPESP's process: 16/09714-4 - Double noise filtering in DBT: pre- and post-reconstruction
Grantee:Daniele Cristina Scarparo
Support type: Scholarships in Brazil - Scientific Initiation
FAPESP's process: 17/17811-2 - Digital Breast Tomosynthesis (DBT) reconstruction using sparse non-local Markov Random Field (SNLMRFs) models
Grantee:Denis Henrique Pinheiro Salvadeo
Support type: Scholarships abroad - Research
FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
Grantee:João Paulo Papa
Support type: Research Grants - Research Partnership for Technological Innovation - PITE