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Proceedings Paper

Radiation dose reduction in digital breast tomosynthesis (DBT) by means of neural network convolution (NNC) deep learning
Author(s): Junchi Liu; Amin Zarshenas ; Syed Ammar Qadir; Limin Yang; Laurie Fajardo; Kenji Suzuki
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Paper Abstract

To reduce cumulative radiation exposure and lifetime risks for radiation-induced cancer from breast cancer screening, we developed neural network convolution (NNC) deep learning for radiation dose reduction in digital breast tomosynthesis (DBT). Our NNC deep learning employed patched-based neural network regression in a convolutional manner to convert lower-dose (LD) to higher-dose (HD) tomosynthesis images. We trained our NNC with quarter-dose (25% of the standard dose: 12 mAs at 32 kVp) raw-projection images and corresponding “teaching” higher-dose (HD) images (200% of the standard dose: 99 mAs at 32 kVp) of a breast cadaver phantom acquired with a DBT system (Selenia Dimensions, Hologic, Inc, Bedford, MA). Once trained, NNC no longer requires HD images. It converts new LD images to images that look like HD images; thus the term “virtual” HD (VHD) images. We reconstructed tomosynthesis slices on a research DBT system. To determine a dose reduction rate, we acquired 4 studies of another test phantom at 4 different radiation doses (1.35, 2.7, 4.04, and 5.39 mGy entrance dose). Structural SIMilarity (SSIM) index was used to evaluate the image quality. Our cadaver phantom experiment demonstrated up to 79% dose reduction. For further testing, we collected half-dose (50% of the standard dose: 32±14 mAs at 33±5 kVp) and full-dose (100% of the standard dose: 68±23 mAs at 33±5 kvp) images of 10 clinical cases with the DBT system at University of Iowa Hospitals and Clinics. Our NNC converted half-dose DBT images of the 10 clinical cases to VHD DBT images that were equivalent to full-dose DBT images, according our observer rating study of 10 breast radiologists. Thus, we achieved 50% dose reduction without sacrificing the image quality.

Paper Details

Date Published: 6 July 2018
PDF: 10 pages
Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 1071814 (6 July 2018); doi: 10.1117/12.2317789
Show Author Affiliations
Junchi Liu, Illinois Institute of Technology (United States)
Amin Zarshenas , Illinois Institute of Technology (United States)
Syed Ammar Qadir, Illinois Institute of Technology (United States)
Limin Yang, The Univ. of Iowa Hospitals and Clinics (United States)
Laurie Fajardo, The Univ. of Iowa (United States)
Kenji Suzuki, Illinois Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10718:
14th International Workshop on Breast Imaging (IWBI 2018)
Elizabeth A. Krupinski, Editor(s)

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