The Hitchhiker's Guide to Differential Dynamic Microscopy

TL;DR

This paper provides a comprehensive tutorial on Differential Dynamic Microscopy (DDM), including experimental procedures and introduces fastDDM, an open-source software that significantly accelerates data analysis, making DDM more accessible and efficient.

Contribution

It offers a detailed guide for conducting DDM experiments and introduces fastDDM, a software that drastically reduces analysis time for large datasets, broadening DDM's usability.

Findings

fastDDM reduces analysis time by up to four orders of magnitude

Enables high-throughput DDM data processing

Broadens accessibility of DDM across scientific disciplines

Abstract

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.