Radiation dose reduction in digital breast tomosynthesis (DBT) by means of neural network convolution (NNC) deep learning

Junchi Liu; Amin Zarshenas; Syed Ammar Qadir; Limin Yang; Laurie Fajardo; Kenji Suzuki

doi:10.1117/12.2317789

6 July 2018 Radiation dose reduction in digital breast tomosynthesis (DBT) by means of neural network convolution (NNC) deep learning

Junchi Liu, Amin Zarshenas , Syed Ammar Qadir, Limin Yang, Laurie Fajardo, Kenji Suzuki

Author Affiliations +

Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 1071814 (2018) https://doi.org/10.1117/12.2317789
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States

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.

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Junchi Liu, Amin Zarshenas , Syed Ammar Qadir, Limin Yang, Laurie Fajardo, and Kenji Suzuki "Radiation dose reduction in digital breast tomosynthesis (DBT) by means of neural network convolution (NNC) deep learning ", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 1071814 (6 July 2018); https://doi.org/10.1117/12.2317789

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