用隐式神经表示修复CT扫描条纹伪影，提升图像质量。
Ring Artifacts Removal Based on Implicit Neural Representation of Sinogram Data
- 用隐式神经函数建模探测器响应，连续修正缺陷像素值。
- 在投影域无监督迭代优化，有效去除环状伪影。
- 适合医学CT重建中伪影修复，尤其对低质数据有效。
X射线探测器元素响应不一致会导致正弦图数据中出现条纹伪影，进而在重建的CT图像中表现为环状伪影，严重降低图像质量。本文提出一种正弦图数据条纹伪影校正方法。该方法利用隐式神经表示（INR）通过隐式连续函数修正缺陷像素的响应值，并同时学习正弦图数据在角度方向上的条纹特征。这两个组件结合在一个优化约束框架内，实现了投影域的无监督迭代伪影校正。实验结果表明，所提方法在去除环状伪影方面显著优于当前最先进技术，同时保持了CT图像的清晰度。
原文摘要 · Abstract (English)
Inconsistent responses of X-ray detector elements lead to stripe artifacts in the sinogram data, which manifest as ring artifacts in the reconstructed CT images, severely degrading image quality. This paper proposes a method for correcting stripe artifacts in the sinogram data. The proposed method leverages implicit neural representation (INR) to correct defective pixel response values using implicit continuous functions and simultaneously learns stripe features in the angular direction of the sinogram data. These two components 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 removing ring artifacts while maintaining the clarity of CT images.
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