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Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing
Author(s): Junchi Liu; Amin Zarshenas; Ammar Qadir; Zheng Wei; 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 a deep-learning-based supervised image-processing technique called neural network convolution (NNC) for radiation dose reduction in DBT. NNC 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, CA). 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. For testing, we collected half-dose (50% of the standard dose: 32±14 mAs at 33±5 kVp) and full-dose (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. NNC converted half-dose DBT images of 10 clinical cases to VHD DBT images that were equivalent to full dose DBT images. Our cadaver phantom experiment demonstrated 79% dose reduction.

Paper Details

Date Published: 2 March 2018
PDF: 9 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740F (2 March 2018); doi: 10.1117/12.2293125
Show Author Affiliations
Junchi Liu, Illinois Institute of Technology (United States)
Amin Zarshenas, Illinois Institute of Technology (United States)
Ammar Qadir, Illinois Institute of Technology (United States)
Zheng Wei, Illinois Institute of Technology (United States)
Limin Yang, Univ. of Iowa Hospitals and Clinics (United States)
Laurie Fajardo, The Univ. of Utah (United States)
Kenji Suzuki, Illinois Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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