2 March 2018 Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing
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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.
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Junchi Liu, Junchi Liu, Amin Zarshenas, Amin Zarshenas, Ammar Qadir, Ammar Qadir, Zheng Wei, Zheng Wei, Limin Yang, Limin Yang, Laurie Fajardo, Laurie Fajardo, Kenji Suzuki, Kenji Suzuki, } "Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740F (2 March 2018); doi: 10.1117/12.2293125; https://doi.org/10.1117/12.2293125
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