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This paper addresses ring and streak artifacts in CT images caused by defective detectors by reformulating ring artifact reduction (RAR) as an inverse problem solved with an unrolled network that incorporates both non-ideal detector responses and CT geometry. The method leverages synthetic data generated from natural images to capture the correlation of ring artifacts between sinogram and image domains, eliminating the need for real clinical training data. Results demonstrate that the proposed SynthRAR method, trained solely on synthetic data, outperforms existing state-of-the-art RAR techniques across various scanning geometries and anatomical regions.
Train a CT artifact reduction model on purely synthetic data and still beat the state-of-the-art.
Defective and inconsistent responses in CT detectors can cause ring and streak artifacts in the reconstructed images, making them unusable for clinical purposes. In recent years, several ring artifact reduction solutions have been proposed in the image domain or in the sinogram domain using supervised deep learning methods. However, these methods require dedicated datasets for training, leading to a high data collection cost. Furthermore, existing approaches focus exclusively on either image-space or sinogram-space correction, neglecting the intrinsic correlations from the forward operation of the CT geometry. Based on the theoretical analysis of non-ideal CT detector responses, the RAR problem is reformulated as an inverse problem by using an unrolled network, which considers non-ideal response together with linear forward-projection with CT geometry. Additionally, the intrinsic correlations of ring artifacts between the sinogram and image domains are leveraged through synthetic data derived from natural images, enabling the trained model to correct artifacts without requiring real-world clinical data. Extensive evaluations on diverse scanning geometries and anatomical regions demonstrate that the model trained on synthetic data consistently outperforms existing state-of-the-art methods.