2025/07/07 by Enrico Lattuada, Lattuada, Enrico, Fabian Krautgasser +7 · 1 voice · 3 citations
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Advanced Electron Microscopy Techniques and Applications #Biological Physics (physics.bio-ph) #Data Analysis #FOS: Physical sciences #Optics (physics.optics) #Soft Condensed Matter (cond-mat.soft) #Statistics and Probability (physics.data-an) #cond-mat.soft #physics.bio-ph #physics.data-an #physics.optics
paper · pdf · doi:10.48550/arxiv.2507.05058
openalex publication_date 2025/07/07 · arxiv published 2025/07/07 · openalex created_date 2025/10/20 · arxiv updated 2025/11/10 · openalex updated_date 2026/08/01
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.