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

翻译：X射线显微学（XRM）常用于获取内部微观结构的三维信息，但成像流程在多个阶段引入了噪声、冗余和信息损失。本文将XRM工作流视为一个作用于有限信息预算的信息处理系统。利用熵、互信息和Kullback-Leibler散度，我们量化了采集、去噪、配准、稀疏角度采样、剂量变化和重建过程如何重塑投影数据与重建体数据的统计结构。基于Walnut 1数据集的案例研究阐明了这些过程如何重新分配信息并形成瓶颈。我们使用统一的信息预算框架总结工作流，并证明互信息可作为与重建方法无关的保真度指标，为XRM方案（尤其在低剂量或时间受限条件下）的定量比较与优化提供支持。