发表机构
Capital Normal University; Inner Mongolia University（首都师范大学; 内蒙古大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TRACE是一种两阶段无监督正弦图分解方法，通过固定偏差加低阶DCT分量建模条纹，并利用角度梯度软正交约束，在实测PCD-CT数据上有效校正环状伪影并保持图像细节。
AI 中文摘要
探测器响应不均匀性会在光子计数探测器计算机断层扫描（PCD-CT）中引入系统性投影误差和环状伪影。在实测的PCD-CT数据中，残余条纹幅度随投影角度缓慢变化，固定偏差模型无法充分捕捉这种变化。我们提出TRACE，一种两阶段无监督正弦图分解方法，用于估计和校正这些与响应相关的误差。TRACE将条纹表示为固定偏差加上低阶离散余弦变换（DCT）分量，使用少量系数描述每个探测器元件处的角度变化。一个可学习的分析-合成架构表示理想投影，而两阶段优化将理想投影与固定条纹及动态条纹分离。角度梯度软正交约束抑制共享DCT梯度子空间内的相关变化，减少物体结构泄漏到伪影估计中。所有参数直接在实测正弦图上优化，无需成对训练数据。在实测QRM小鼠体模和猪蹄数据上的实验表明，TRACE抑制环状伪影并改善图像均匀性，同时保持边缘锐度、软组织纹理和小梁细节。
英文摘要
Detector response nonuniformity introduces systematic projection errors and ring artifacts in photon-counting detector computed tomography (PCD-CT). In measured PCD-CT data, residual stripe amplitudes vary slowly with projection angle, which fixed-bias models cannot adequately capture. We propose TRACE, a two-stage unsupervised sinogram decomposition method for estimating and correcting these response-related errors. TRACE represents stripes as a fixed bias plus low-order discrete cosine transform (DCT) components, using a small number of coefficients to describe angular variations at each detector element. A learnable analysis--synthesis architecture represents the ideal projections, while two-stage optimization separates them from fixed and then dynamic stripes. An angular-gradient soft orthogonality constraint suppresses correlated variations within the shared DCT gradient subspace, reducing the leakage of object structures into the artifact estimate. All parameters are optimized directly on the measured sinogram without paired training data. Experiments on measured QRM mouse phantom and porcine trotter data show that TRACE suppresses ring artifacts and improves image uniformity while preserving edge sharpness, soft-tissue texture, and trabecular detail.
