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Related Experiment Video

Updated: Jun 12, 2025

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

Convolutional neural networks applied to differential dynamic microscopy reduces noise when quantifying heterogeneous

Gildardo Martinez¹, Justin Siu¹, Steven Dang¹

¹Department of Physics and Biophysics, University of San Diego, San Diego, CA 92110, USA. rmcgorty@sandiego.edu.

|September 24, 2024

Summary

This study introduces a machine learning method to reduce noise in differential dynamic microscopy (DDM) analysis, enabling accurate motion quantification even with limited imaging frames or rapidly changing dynamics.

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Area of Science:

Soft matter physics
Biophysics
Materials science

Background:

Differential dynamic microscopy (DDM) requires extensive imaging data for accurate motion quantification in soft matter.
Short or dynamic imaging sequences yield noisy and unreliable DDM results, limiting applications.

Purpose of the Study:

To develop and validate a machine learning-based denoising method for DDM.
To improve DDM accuracy with limited frames and capture fast-evolving dynamics.

Main Methods:

A convolutional neural network encoder-decoder (CNN-ED) model was employed to denoise the intermediate scattering function computed via DDM.
The method was tested on colloidal particle suspensions undergoing gelation and exhibiting viscosity gradients.

Main Results:

The CNN-ED model effectively reduced noise in DDM analysis, even with limited imaging frames.
Accurate quantification of particle diffusivity was achieved during fluid gelation and across viscosity gradients.
The denoising approach successfully captured time-varying and spatially varying particle dynamics.

Conclusions:

Machine learning-enhanced DDM provides a robust solution for analyzing complex dynamics in soft matter systems.
This method expands the applicability of DDM to scenarios with limited data or non-equilibrium conditions.
The approach holds promise for high-throughput screening and studying dynamic processes.