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Proposal of dose reduction in digital mammography by quantum noise filtering using advanced image-processing techniques

Grant number: 13/18915-5
Support type:Regular Research Grants
Duration: February 01, 2014 - January 31, 2016
Field of knowledge:Engineering - Biomedical Engineering - Medical Engineering
Principal Investigator:Marcelo Andrade da Costa Vieira
Grantee:Marcelo Andrade da Costa Vieira
Home Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Assoc. researchers: Andrew Douglas Arnold Maidment ; Homero Schiabel ; Nelson Delfino d'Ávila Mascarenhas ; Predrag Bakic


Digital mammography is considered the standard tool for early breast cancer screening in women over 40. However, the radiation dose received by the breast during screening mammography may induce cancer in some women, which adds to recent discussions about the risks and benefits of breast cancer screening. Efforts to reduce the radiation dose in mammography examinations are of great interest, due to the large number of asymptomatic women who are screened throughout the world each year. The radiation dose, on the other hand, directly influences mammographic image quality, as well as the performance of radiologists; a decrease in the dose leads to an increased quantum noise level, which may significantly degrade the image quality and the efficacy of the examination. Recent studies have shown that the quantum noise has a greater effect than the spatial resolution in the detection and classification of mammographic lesions by radiologists. Our proposed project is aimed at addressing this significant and timely clinical problem by the use of denoising techniques, which could allow for radiation dose reduction while keeping the image quality acceptable. Thus, the objective of this work is to study how denoising techniques can be adapted for filtering the quantum noise due to the reduced radiation dose in digital mammography. In addition, we will select the appropriate quantitative measures of image quality to calculate the percentage of dose reduction that, in combination with denoising techniques, would produce the same quality of images as those acquired with full radiation dose. This project will be developed in collaboration with the X-Ray Physics Laboratory of the University of Pennsylvania, which will support the use of their anthropomorphic breast software phantom for preclinical assessment of the performance of denoising techniques. Clinical evaluation of the most promising techniques will be performed using anonymized, previously acquired clinical images available in our image database and from the Hospital of the University of Pennsylvania. (AU)

Scientific publications (4)
(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)
BORGES, LUCAS R.; AZZARI, LUCIO; BAKIC, PREDRAG R.; MAIDMENT, ANDREW D. A.; VIEIRA, MARCELO A. C.; FOI, ALESSANDRO. Restoration of low-dose digital breast tomosynthesis. MEASUREMENT SCIENCE & TECHNOLOGY, v. 29, n. 6 JUN 2018. Web of Science Citations: 6.
BINDILATTI, ANDRE A.; VIEIRA, MARCELO A. C.; MASCARENHAS, NELSON D. A. Poisson Wiener filtering with non-local weighted parameter estimation using stochastic distances. Signal Processing, v. 144, p. 68-76, MAR 2018. Web of Science Citations: 9.
BORGES, LUCAS RODRIGUES; DA COSTA VIEIRA, MARCELO ANDRADE; FOI, ALESSANDRO. Unbiased Injection of Signal-Dependent Noise in Variance-Stabilized Range. IEEE SIGNAL PROCESSING LETTERS, v. 23, n. 10, p. 1494-1498, OCT 2016. Web of Science Citations: 1.
BORGES, LUCAS R.; DE OLIVEIRA, HELDER C. R.; NUNES, POLYANA F.; BAKIC, PREDRAG R.; MAIDMENT, ANDREW D. A.; VIEIRA, MARCELO A. C. Method for simulating dose reduction in digital mammography using the Anscombe transformation. Medical Physics, v. 43, n. 6, 1 JUN 2016. Web of Science Citations: 8.

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