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

Autor(es)

Scarparo, Daniele Cristina ^[1] ; Pinheiro Salvadeo, Denis Henrique ^{[2, 1]} ; Guimaraes Pedronette, Daniel Carlos ^[1] ; Barufaldi, Bruno ^[2] ; Arnold Maidment, Andrew Douglas ^[2]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF MEDICAL IMAGING; v. 6, n. 3 JUL 2019.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 16/09714-4 - Dupla filtragem de ruído em DBT: pré e pós-reconstrução
• Beneficiário: Daniele Cristina Scarparo
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 17/17811-2 - Reconstrução de Tomossíntese Mamária usando Modelos Markovianos Não Locais Esparsos (SNLMRFs)
• Beneficiário: Denis Henrique Pinheiro Salvadeo
• Modalidade de apoio: Bolsas no Exterior - Pesquisa
• Processo FAPESP: 17/25908-6 - Aprendizado fracamente supervisionado para análise de vídeos no domínio comprimido em tarefas de recuperação e classificação para alertas visuais
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE