Imposing noise correlation fidelity on digital breast tomosynthesis restoration through deep learning techniques

Rodrigo B. Vimieiro; Lucas R. Borges; Ge Wang; Marcelo A. C. Vieira

doi:10.1117/12.2626634

13 July 2022 Imposing noise correlation fidelity on digital breast tomosynthesis restoration through deep learning techniques

Rodrigo B. Vimieiro, Lucas R. Borges, Ge Wang, Marcelo A. C. Vieira

Author Affiliations +

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122861C (2022) https://doi.org/10.1117/12.2626634
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

Digital breast tomosynthesis (DBT) is an important imaging modality for breast cancer screening. The morphology of breast masses and the shape of the microcalcifications are important factors to detect and determine the malignancy of breast cancer. Recently, convolutional neural networks (CNNs) have been used for denoising in medical imaging and have shown potential to improve the performance of radiologists. However, they can impose noise spatial correlation in the restoration process. Noise correlation can negatively impact radiologists’ performance, creating image signals that can resemble breast lesions. In this work, we propose a deep CNN that restores low-dose DBT projections by partially filtering out the noise, but imposes fidelity of the noise correlation between the original and restored images, avoiding artifacts that may resemble signs of breast cancer. The combination of a loss function that calculates the difference in the power spectra (PS) of the input and output images and another one that seeks image visual perception is proposed. We compared the performance of the proposed neural network with traditional denoising methods that do not consider the noise correlation in the restoration process and found superior results in terms of PS for our approach.

Citation Download Citation

Rodrigo B. Vimieiro, Lucas R. Borges, Ge Wang, and Marcelo A. C. Vieira "Imposing noise correlation fidelity on digital breast tomosynthesis restoration through deep learning techniques", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122861C (13 July 2022); https://doi.org/10.1117/12.2626634

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Picosecond phenomena

Digital breast tomosynthesis

Image restoration

Breast

Visualization

Breast cancer

Image quality

Show All Keywords

Keywords/Phrases

Search In:

Publication Years