Enhancing Precision and Interpretability of CT Image Reconstruction via Self-supervised Adaptive Domain Transformation

This paper addresses the challenges faced in computed tomography (CT) reconstruction, including artifacts and poor image quality due to imaging imperfections like under-sampling. While deep learning-based CT reconstruction models have emerged to combat these issues, they often lack precision and interpretability when employing end-to-end neural networks to directly transform signals from sinograms into CT images. Additionally, conventional supervised methods for guiding network training face hurdles in obtaining supervision information from CT images, particularly in biomedical imaging contexts. To surmount these limitations, we introduce a novel self-supervised CT reconstruction approach employing an Adaptive Domain Transformation (ADT) module. The ADT module reverses the sinogram signals and translates them into tomographic insights through a Tomographic Inverse Transform component, followed by a Tomographic Attention component that adeptly leverages this information to reconstruct CT image intensities. Further quality enhancement is achieved with a refinement network, and notably, the Radon Transform is used to process the reconstructed CT images, generating simulated sinograms for loss calculation without the need for extra supervision from CT images. Empirical evidence supports the efficacy of our approach, showcasing tangible enhancements in both the reconstruction of anatomical structures and the mitigation of artifacts.