STAN-CT: Standardizing CT Image using Generative Adversarial Networks

Computed Tomography (CT) plays an important role in lung malignancy diagnostics, therapy assessment, and facilitating precision medicine delivery. However, the use of personalized imaging protocols poses a challenge in large-scale cross-center CT image radiomic studies. We present an end-to-end solution called STAN-CT for CT image standardization and normalization, which effectively reduces discrepancies in image features caused by using different imaging protocols or using different CT scanners with the same imaging protocol. STAN-CT consists oftwo components: 1)a Generative Adversarial Networks (GAN) model where a latent-feature-based loss function is adopted to learn the data distribution of standard images within a few rounds of generator training, and 2) an automatic DICOM reconstruction pipeline with systematic image quality control that ensures the generation ofhigh-quality standard DICOM images. Experimental results indicate that the training efficiency and model performance of STAN-CT have been significantly improved compared to the state-of-the-art CT image standardization and normalization algorithms.