通过全局与局部特征交互,有效去除CT图像中的环状伪影。
Ring Artifacts Correction Based on Global-Local Features Interaction Guidance in the Projection Domain
- 利用小波分解分离投影数据的全局低频与局部高频伪影。
- 引入VSS与Dense模块分别处理低频和高频成分,提升去伪影精度。
- 适合需要高精度CT重建的医学影像领域研究者使用。
CT成像中常见的环状伪影主要由探测器单元对X射线响应不一致引起,表现为投影数据中的条纹伪影。在圆形扫描模式下,这些伪影呈现为以旋转中心为中心的同心环,严重降低图像质量。在Radon变换域中,即使物体密度函数在某些区域分段不连续,其投影在角度方向仍近似连续,理想投影具有平滑的全局低频特性。实际扫描中,同一探测器单元在不同角度产生的局部扰动导致条纹伪影具有显著的高频局部性。现有方法通常将环状伪影建模为固定加性误差,忽略了探测器响应在实际扫描中的动态变化。我们的实验表明,探测器响应不一致性是投影值的函数,因此需在条纹伪影提取与校正过程中考虑全局与局部特征的交互。为此,我们提出一种基于投影域全局-局部特征交互引导的CT环状伪影校正方法。采用小波分解将投影数据分为低频子带(捕捉全局相关性)与高频子带(包含局部条纹伪影),并分别使用VSS块和Dense块进行处理。通过全局与局部特征的交互引导,显著提升伪影校正精度。大量实验表明,该方法在定量指标与视觉质量上均优于现有方法,验证了其鲁棒性与实用性。
原文摘要 · Abstract (English)
Ring artifacts are common artifacts in CT imaging, typically caused by inconsistent responses of detector units to X-rays, resulting in stripe artifacts in the projection data. Under circular scanning mode, such artifacts manifest as concentric rings radiating from the center of rotation, severely degrading image quality. In the Radon transform domain, even if the object's density function is piecewise discontinuous in certain regions, the projection images remain nearly continuous in the angular direction, making the ideal projections exhibit a smooth global low-frequency characteristic. In practical scanning, the local disturbances of the same detector unit at different scanning angles lead to a prominent high-frequency locality of stripe artifacts. Existing studies generally model ring artifacts disturbances as fixed additive errors, which overlooks the dynamic variation of detector responses during practical scanning. However, the degree of detector response inconsistency is a function of the projection values, as revealed in our experiments, thereby requiring consideration of the interaction between global and local features in the process of stripe artifacts extraction and correction. Therefore, we propose a CT ring artifacts correction method based on global and local features in the projection domain. We employ the VSS block and Dense block to respectively correct the low-frequency sub-band, which capture the global correlations of the projection, and the high-frequency sub-band, which contain local stripe artifacts after wavelet decomposition. Specifically, the accuracy of artifacts correction is enhanced by the interaction guidance between global and local features. Extensive experiments demonstrate that our method achieves superior performance in both quantitative metrics and visual quality, verifying its robustness and practical applicability.
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