Computed Tomography Image Enhancement using 3D Convolutional Neural Network
This work addresses the issue of varying CT image quality for medical professionals in cancer screening, but it is incremental as it builds on existing deep learning approaches for image enhancement.
The study tackled the problem of low-resolution CT scans hindering cancer detection by proposing a 3D convolutional neural network to enhance spatial resolution, resulting in significant improvements in PSNR (29.3087dB vs. 28.8769dB) and SSIM (0.8529 vs. 0.8449) compared to state-of-the-art methods.
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.