Inconsistent responses of X-ray detector elements lead to stripe artifacts within the sinogram data, which subsequently manifest as ring artifacts in the reconstructed computed tomography (CT) images, severely degrading image quality. This paper presents a novel method for correcting stripe artifacts in the sinogram data by separating the sinogram into an Ideal Sinogram (IS) and Stripe Artifacts (SA), with both components parameterized through Implicit Neural Representations (INR). The proposed method leverages INR to correct defective pixel response values using implicit continuous functions while simultaneously learning stripe features in the angular direction of the sinogram data. These two components, IS and SA, are combined within an optimization constraint framework, achieving unsupervised iterative correction of stripe artifacts in the projection domain. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art techniques in effectively removing ring artifacts while maintaining the clarity and fidelity of CT images, thereby enhancing the overall diagnostic quality of CT imaging.

Accurate and efficient vertebrae localization and detection in X-ray images are essential for diagnosing and treating spinal diseases. However, most existing methods struggle with the complexity of spine X-ray images, yielding inaccurate results due to insufficient utilization of spinal anatomy information and neglect of individual vertebra characteristics. In this paper, we propose an innovative Vertebrae Localization and Detection Network (VLD-Net) to accurately assist physicians in diagnosing spine-related diseases from X-ray images. Our VLD-Net, for the first time, defines vertebrae localization as a top-bottom sequential decision-making process, employing deep reinforcement learning (DRL) to fully leverage the anatomical information of the spine. Simultaneously, it also prioritizes the distinct characteristics of each vertebra for accurate detection. Specifically, VLD-Net combines three key components: 1) An advanced vertebrae localization module based on DRL is proposed, effectively leveraging anatomical information of the spine. 2) A novel adaptive exploration mechanism is coined to understand the behavior of the DRL agent during training, pinpointing how to effectively achieve the trade-off between exploration and exploitation. 3) An innovative vertebra-focused module is proposed to accurately detect vertebral landmarks, using the attention region of each vertebra as input to enhance focus on the target and reduce interference from surrounding tissue. Extensive experiments on two public spine datasets demonstrate that the VLD-Net outperforms the state-of-the-art methods in accuracy and robustness.