  Computed tomography (CT) is increasingly being used for cancer screening,
such as early detection of lung cancer. However, CT studies have varying pixel
spacing due to differences in acquisition parameters. Thick slice CTs have
lower resolution, hindering tasks such as nodule characterization during
computer-aided detection due to partial volume effect. In this study, we
propose a novel 3D enhancement convolutional neural network (3DECNN) to improve
the spatial resolution of CT studies that were acquired using lower
resolution/slice thicknesses to higher resolutions. Using a subset of the LIDC
dataset consisting of 20,672 CT slices from 100 scans, we simulated lower
resolution/thick section scans then attempted to reconstruct the original
images using our 3DECNN network. A significant improvement in PSNR (29.3087dB
vs. 28.8769dB, p-value < 2.2e-16) and SSIM (0.8529dB vs. 0.8449dB, p-value <
2.2e-16) compared to other state-of-art deep learning methods is observed.
