Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks

Background The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel choice is important for quantitative CT‐based assessment as kernel differences can lead to substantial shifts in measurements unrelated to underlying anatomical structures. Purpose In this study, we investigate kernel harmonization in a multi‐vendor low‐dose CT lung cancer screening cohort and evaluate our approach's validity in quantitative CT‐based assessments. Methods Using the National Lung Screening Trial, we identified CT scan pairs of the same sessions with one reconstructed from a soft tissue kernel and one from a hard kernel. In total, 1000 pairs of five different paired kernel types (200 each) were identified. We adopt the pix2pix architecture to train models for kernel conversion. Each model was trained on 100 pairs and evaluated on 100 withheld pairs. A total of 10 models were implemented. We evaluated the efficacy of kernel conversion based on image similarity metrics including root mean squared error (RMSE), peak signal‐to‐noise ratio (PSNR), and structural similarity index measure (SSIM) as well as the capability of the models to reduce measurement shifts in quantitative emphysema and body composition measurements. Additionally, we study the reproducibility of standard radiomic features for all kernel pairs before and after harmonization. Results Our approach effectively converts CT images from one kernel to another in all paired kernel types, as indicated by the reduction in RMSE (p