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.
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