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Radiation dose reduction in computed radiography (CR) mammography through image processing

Grant number: 18/19888-5
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): January 01, 2019
Effective date (End): December 31, 2021
Field of knowledge:Engineering - Biomedical Engineering - Bioengineering
Principal Investigator:Paulo Mazzoncini de Azevedo Marques
Grantee:Lucas Rodrigues Borges
Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
Associated research grant:14/50889-7 - National Institute of Science and Technology Medicine Assisted by Scientific Computing (INCT-MACC), AP.TEM


X-ray mammography remains as the gold standard for breast cancer screening and diagnosis around the world. Even though recent developments in the field of breast imaging allowed the clinical use of systems based on digital radiography (DR) and digital breast tomosynthesis (DBT), the brazilian public health system still relies on the CR technology for breast imaging. Recent works have raised concerns about image quality and excessive radiation dose in breast images acquired by CR systems in brazilian health centers. In this scenario, the goal of this project is to propose a restoration algorithm capable of recovering the quality of breast images acquired by CR systems at low radiation doses to achieve equivalent or superior quality compared to images acquired using the current protocols. To reach this goal, we will propose a dedicated noise model to describe the degradation found in breast CR images. Furthermore, new variance stabilizing tools will be created to allow the use of state-of-the-art denoising techniques in CR breast images. An image bleding technique will be created to minimize signal smearing and blur, which naturally arises from the filtering process. The final validation of this work will be conducted on patient clinical data from the Clinics Hospital, at the School of Medicine of Ribeirão Preto, with the assistance of radiologists.

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Scientific publications (6)
(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.; CARON, RENATO F.; DE AZEVEDO-MARQUES, PAULO M.; VIEIRA, MARCELO A. C.; BOSMANS, H; MARSHALL, N; VANONGEVAL, C. Effect of denoising on the localization of microcalcification clusters in digital mammography. 15TH INTERNATIONAL WORKSHOP ON BREAST IMAGING (IWBI2020), v. 11513, p. 7-pg., . (16/25750-0, 18/19888-5)
VIMIEIRO, RODRIGO B.; BORGES, LUCAS R.; BARUFALDI, BRUNO; MAIDMENT, ANDREW D. A.; WANG, GE; VIEIRA, MARCELO A. C.; ZHAO, W; YU, L. Assessment of training strategies for convolutional neural network to restore low-dose digital breast tomosynthesis projections. MEDICAL IMAGING 2022: PHYSICS OF MEDICAL IMAGING, v. 12031, p. 10-pg., . (16/25750-0, 18/19888-5)
COSTA, ARTHUR C.; OLIVEIRA, HELDER C. R.; BORGES, LUCAS R.; VIEIRA, MARCELO A. C.; BOSMANS, H; MARSHALL, N; VANONGEVAL, C. Transfer Learning in Deep Convolutional Neural Networks for Detection of Architectural Distortion in Digital Mammography. 15TH INTERNATIONAL WORKSHOP ON BREAST IMAGING (IWBI2020), v. 11513, p. 8-pg., . (15/20812-5, 18/19888-5)
COSTA, ARTHUR C.; BARUFALDI, BRUNO; BORGES, LUCAS R.; BIEHL, MICHAEL; MAIDMENT, ANDREW D. A.; VIEIRA, MARCELO A. C.; SCHMIDT, TG; CHEN, GH; BOSMANS, H. Analysis of feature relevance using an image quality index applied to digital mammography. MEDICAL IMAGING 2019: PHYSICS OF MEDICAL IMAGING, v. 10948, p. 10-pg., . (18/19888-5)
BORGES, LUCAS R.; BROCHI, MARCO A. C.; XU, ZHONGWEI; FOI, ALESSANDRO; VIEIRA, MARCELO A. C.; AZEVEDO-MARQUES, PAULO M.. Noise modeling and variance stabilization of a computed radiography (CR) mammography system subject to fixed-pattern noise. Physics in Medicine and Biology, v. 65, n. 22, . (14/50889-7, 18/19888-5)
BORGES, LUCAS R.; DE AZEVEDO MARQUES, PAULO M.; VIEIRA, MARCELO A. C.; NISHIKAWA, RM; SAMUELSON, FW. A 2-AFC study to validate artificially inserted microcalcification clusters in digital mammography. MEDICAL IMAGING 2019: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, v. 10952, p. 7-pg., . (16/25750-0, 18/19888-5)

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