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

Daniele Cristina Scarparo; Denis Henrique Pinheiro Salvadeo; Daniel Carlos Guimarães Pedronette; Bruno Barufaldi; Andrew D. A. Maidment

doi:10.1117/1.JMI.6.3.031410

20 February 2019 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

Daniele Cristina Scarparo, Denis Henrique Pinheiro Salvadeo, Daniel Carlos Guimarães Pedronette, Bruno Barufaldi, Andrew D. A. Maidment

Author Affiliations +

Funded by: São Paulo Research Foundation (FAPESP), Brazilian National Council for Scientific and Technological Development - CNPq

Journal of Medical Imaging, Vol. 6, Issue 3, 031410 (February 2019). https://doi.org/10.1117/1.JMI.6.3.031410

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.

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Daniele Cristina Scarparo, Denis Henrique Pinheiro Salvadeo, Daniel Carlos Guimarães Pedronette, Bruno Barufaldi, and Andrew D. A. Maidment "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 6(3), 031410 (20 February 2019). https://doi.org/10.1117/1.JMI.6.3.031410

Received: 1 October 2018; Accepted: 29 January 2019; Published: 20 February 2019

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