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

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.