基于混合散射估计和体素自适应束硬化校正的联合解耦迭代CBCT重建

中文摘要 AI 辅助

锥束计算机断层扫描（CBCT）受到散射和束硬化伪影的根本挑战，它们分别源于X射线散射和X射线光谱的多色性。这两种伪影在重建图像中复杂耦合，具有相似的条纹和杯状特征，严重影响高精度CBCT成像。本文提出了一个基于多色Polyquant衰减模型的物理驱动迭代框架，通过在散射估计和相对电子密度（RED）重建之间建立优化循环来解耦这些伪影。开发了一种混合散射估计策略，其中基于多色物理模型解析导出一阶散射分量以保留高频结构信息，而通过对象自适应卷积模块有效估计更平滑的多次散射分量。随后，对于束硬化校正，引入了体素自适应更新机制，通过求解线性化、散射校正的多色方程来导出最佳权重，无需手动参数调整即可直接进行RED细化。通过对生物医学体模的综合研究，利用蒙特卡罗模拟和物理实验对该方法进行了验证。代表性结果表明，该方法优于现有技术，对于人体头部体模，平均相对误差从11.96%降至1.27%，对于物理阴阳体模，从12.55%降至5.46%。

英文摘要

Cone-beam computed tomography (CBCT) is fundamentally challenged by scatter and beam hardening artifacts, which originate from X-ray scattering and the polychromatic nature of the X-ray spectrum, respectively. These two types of artifacts are intricately coupled in reconstructed images and manifest with similar streaking and cupping features, severely compromising high-precision CBCT imaging. This paper proposes a physics-driven iterative framework rooted in the polychromatic Polyquant attenuation model, which decouples these artifacts by establishing an optimization loop between scatter estimation and relative electron density (RED) reconstruction. We develop a hybrid strategy for scatter estimation, in which the first-order scattering component is analytically derived based on a polychromatic physical model to preserve high-frequency structural information, whereas the smoother multiple scattering component is efficiently estimated via an object-adaptive convolution module. Subsequently, for beam-hardening correction, we introduce a voxel-adaptive update mechanism that solves linearized, scatter-corrected polychromatic equations to derive optimal weights, enabling direct RED refinement without manual parameter tuning. The proposed method was validated through comprehensive studies on biomedical phantoms, utilizing both Monte Carlo simulations and physical experiments. Representative results demonstrate that the proposed method outperforms state-of-the-art techniques, with the mean relative error decreased from 11.96\% to 1.27\% for the anthropomorphic head phantom and from 12.55\% to 5.46\% for the physical Yin-Yang phantom.

发表机构

• College of Mathematics Science, Inner Mongolia Normal University（内蒙古师范大学数学科学学院）
• School of Mathematical Sciences, Capital Normal University（首都师范大学数学科学学院）
• CREATIS (CNRS UMR 5220, Inserm U1294), INSA-Lyon, Université Claude Bernard Lyon 1（里昂INSA大学、克莱贝尔伯纳德里昂第一大学CREATIS实验室（CNRS UMR 5220, Inserm U1294））
• School of Data Science and Information Technology, China Women’s University（中华女子学院数据科学与信息技术学院）
• College of Computer Science (College of Software), Inner Mongolia University（内蒙古大学计算机学院（软件学院））

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