In a retrospective study, a team from South Korea used a commercial vendor-agnostic deep-learning algorithm to reconstruct low-dose chest CT images from 100 patients with only a quarter of the standard low-dose CT exam reconstructed with a dedicated iterative reconstruction algorithm. They then compared the image quality and detectability of lung nodules on the quarter-dose images with the full low-dose images.
The researchers found no subjective difference in noise, spatial resolution, and overall image quality between the two types of images. There was also no difference in detection rates for Lung-RADS 3-4 nodules.
“Deep-learning reconstruction may further decrease radiation dose needed for lung nodule evaluation,” wrote presenter Dr. Gyeong Deok Jo of Seoul National University School of Medicine and colleagues.
Take in this scientific presentation on Thursday afternoon to get all the details.
