CAPTURE-GAN: Conditional Attribute Preservation through Unveiling Realistic GAN for artifact removal in dual-energy CT imaging

This study addresses the challenge of detecting bone marrow edema (BME) using dual-energy CT (DECT), a task complicated by the lower contrast DECT offers compared to MRI and the presence of artifacts inherent in the image formation process. Despite the advancements in AI-based solutions for image enhancement, achieving an artifact-free outcome in DECT remains difficult due to the impracticality of obtaining paired ground-truth and artifact-containing images for supervised learning. To overcome this, we explore unsupervised techniques such as CycleGAN and AttGAN for artifact removal, which, while effective in other domains, face challenges in DECT due to the similarity between artifact and pathological patterns. Our contribution, the Conditional Attribute Preservation through Unveiling Realistic GAN (CAPTURE-GAN), innovatively combines a generative model with conditional constraints through masking and classification models to not only minimize artifacts but also preserve the pathology of BME and the anatomical integrity of bone. By incorporating bone priors into CycleGAN and adding a disease classification network, CAPTURE-GAN significantly improves the specificity and sensitivity of BME detection in DECT imaging. Our approach demonstrates a substantial enhancement in generating artifact-free images, ensuring that critical diagnostic patterns are not obscured, thereby advancing the potential for DECT in diagnosing and localizing lesions accurately.