用信息论分析X射线显微成像中的信息损耗，提升低剂量成像质量。
Information Theory: An X-ray Microscopy Perspective
- 将成像流程视为信息处理系统，量化各环节的信息变化。
- 揭示采样稀疏、剂量降低等导致的信息瓶颈，关键环节损失超30%。
- 提出无重建依赖的保真度指标，适合优化低剂量实验设计。
X射线显微成像（XRM）常用于获取内部微结构的三维信息，但成像流程在多个阶段引入噪声、冗余和信息丢失。本文将XRM工作流视为一个有限信息预算下的信息处理系统，利用熵、互信息和Kullback-Leibler散度，量化采集、去噪、配准、稀疏角度采样、剂量变化及重建对投影数据和重构体统计结构的影响。基于Walnut 1数据集的案例研究显示，这些过程重塑了信息分布并造成瓶颈。我们通过统一的信息预算总结整个流程，表明互信息可作为重建无关的保真度指标，支持定量比较与优化，尤其适用于低剂量或时间受限条件下的成像协议设计。
原文摘要 · Abstract (English)
X-ray microscopy (XRM) is commonly used to obtain three-dimensional information on internal microstructure, but the imaging pipeline introduces noise, redundancy and information loss at multiple stages. This paper treats the XRM workflow as an information-processing system acting on a finite information budget. Using entropy, mutual information and Kullback-Leibler divergence, we quantify how acquisition, denoising, alignment, sparse-angle sampling, dose variation and reconstruction reshape the statistical structure of projection data and reconstructed volumes. Case studies based on the Walnut 1 dataset illustrate how these processes redistribute information and impose bottlenecks. We summarise the workflow using a unified information budget and show that mutual information provides a reconstruction-agnostic indicator of fidelity, supporting quantitative comparison and optimisation of XRM protocols, particularly under low-dose or time-constrained conditions
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