Thank you Conrad! Your opinion and feedback is very much appreciated!

We hope this guide will make DDM as common as DLS in soft-matter and biological labs.

Give it a try, share your results, and let’s make DDM part of every microscope’s toolkit!

Link to the paper:
pubs.aip.org/aip/jcp/arti...

fastDDM comes with tutorial on using DDM to study:

- Colloidal suspensions
- Bacteria and active swimmers
- Microrheology of complex fluids
- Cells and tissues

The tutorial includes examples and notebooks for all of these.

We also introduce fastDDM — a new, open-source, high-performance implementation that makes DDM thousands of times faster and easy to use on large datasets.

🧰 Free, transparent, and ready for your next project.

We walk you through the complete pipeline ⬇️

🎥 Video acquisition → ⚡ Fourier analysis → 📊 Fitting → 🔍 Physical parameters

The same logic works for bright-field, phase-contrast, fluorescence microscopy, and beyond…

DDM lets you turn any microscopy video into a scattering experiment, revealing the dynamics of colloids, proteins, liquid crystals, foams, emulsions, cells, bacteria, or active matter directly from image sequences.

It’s like DLS, but with a microscope and a movie.

thanks for the detailed explanation. now I understand the meaning of wrong in this context. and I find it an interesting problem. I look forward to reading the full paper!

And I forgot a very important thing. This project was conducted at @univie-physik.bsky.social and made possible by funding from @fwf-at.bsky.social

Interesting paper! Can you elaborate on what you mean by wrong?

7/ 👩‍🔬 Whether you’re a soft matter physicist, biophysicist, or microscopy enthusiast—if you’re looking to measure dynamics without tracking, this guide is for you.

🧵 Read it, and use it!

💡 And reach out with feedback—fastDDM is community-driven!

📄 arxiv.org/abs/2507.05058

6/ 🧪 What can you do with this tutorial?
We guide you through:
✅ Particle sizing
✅ Merging fast & slow acquisitions
✅ Handling 2D vs. 3D dynamics
✅ Detecting axial drift & sedimentation
✅ Quantifying uncertainty
✅ Using image windowing
✅ Optimizing experimental design
…and more!

5/ 📊 Reproducibility first!
Each section is paired with Jupyter notebooks using real datasets.
🔄 You can follow every step, run the code, and adapt it to your system.
✅ Educational
✅ Extensible
✅ Transparent

4/ ⚙️ fastDDM
We introduce fastDDM—an open-source, high-performance Python package that speeds up DDM analysis by up to 4 orders of magnitude.
GPU + FFT + smart averaging = minutes instead of hours.

🔗 GitHub: github.com/somexlab/fas...
🔗 GitHub: github.com/somexlab/fas...

3/ 🎓 This is a comprehensive tutorial, not just a method paper.
We cover:
✔️ Physics of DDM
✔️ Mathematical foundations
✔️ Image formation
✔️ Practical data analysis
✔️ Common pitfalls
✔️ Real datasets & examples

2/ 🔍 What is DDM?
DDM analyzes time-lapse microscopy videos to extract dynamical information—without tracking particles. Think DLS + imaging.
✅ Works in bright-field, fluorescence, phase contrast, and more
✅ Resolves dynamics in space and time