标题： 《微分动态显微镜的指南》 显示英文标题

标题： The Hitchhiker's Guide to Differential Dynamic Microscopy

摘要： 在过去近二十年中，差分动态显微镜（DDM）已成为从时间序列显微图像数据中提取动态关联函数的标准技术，其应用范围涵盖胶体悬浮液、聚合物溶液、活性流体和生物系统。 在最常见的实现方式中，DDM分析使用配备数字相机的传统显微镜获取的图像序列，生成的时间和波矢量分辨信息类似于多角度动态光散射（DLS）获得的信息。 随着应用范围的扩大和用户群体的多样化，降低执行DDM的技术门槛已成为一个核心目标。 在本文教程中，我们提供了一个逐步指南，指导如何进行DDM实验——从计划和采集到数据分析——并介绍了开源软件包fastDDM，该软件包旨在高效处理大型图像数据集。 fastDDM采用优化的并行算法，在典型数据集（例如10,000帧）上将分析时间缩短多达四个数量级，从而实现了高通量工作流程，并使DDM在各学科中更加普及。 显示英文摘要

摘要： Over nearly two decades, Differential Dynamic Microscopy (DDM) has become a standard technique for extracting dynamic correlation functions from time-lapse microscopy data, with applications spanning colloidal suspensions, polymer solutions, active fluids, and biological systems. In its most common implementation, DDM analyzes image sequences acquired with a conventional microscope equipped with a digital camera, yielding time- and wavevector-resolved information analogous to that obtained in multi-angle Dynamic Light Scattering (DLS). With a widening array of applications and a growing, heterogeneous user base, lowering the technical barrier to performing DDM has become a central objective. In this tutorial article, we provide a step-by-step guide to conducting DDM experiments -- from planning and acquisition to data analysis -- and introduce the open-source software package fastDDM, designed to efficiently process large image datasets. fastDDM employs optimized, parallel algorithms that reduce analysis times by up to four orders of magnitude on typical datasets (e.g., 10,000 frames), thereby enabling high-throughput workflows and making DDM more broadly accessible across disciplines.