Single particle raster image analysis of diffusion
Artikel i vetenskaplig tidskrift, 2017

As a complement to the standard RICS method of analysing Raster Image Correlation Spectroscopy images with estimation of the image correlation function, we introduce the method SPRIA, Single Particle Raster Image Analysis. Here, we start by identifying individual particles and estimate the diffusion coefficient for each particle by a maximum likelihood method. Averaging over the particles gives a diffusion coefficient estimate for the whole image. In examples both with simulated and experimental data, we show that the new method gives accurate estimates. It also gives directly standard error estimates. The method should be possible to extend to study heterogeneous materials and systems of particles with varying diffusion coefficient, as demonstrated in a simple simulation example. A requirement for applying the SPRIA method is that the particle concentration is low enough so that we can identify the individual particles. We also describe a bootstrap method for estimating the standard error of standard RICS.

Confocal laser scanning microscopy

Bias correction



Fluorescent beads

Maximum likelihood


Marco Longfils

Chalmers, Matematiska vetenskaper, Matematisk statistik

Göteborgs universitet

Erich Schuster

SIK – Institutet för Livsmedel och Bioteknik AB

Niklas Lorén

SIK – Institutet för Livsmedel och Bioteknik AB

SuMo Biomaterials

Aila Särkkä

Göteborgs universitet

SuMo Biomaterials

Chalmers, Matematiska vetenskaper, Matematisk statistik

Mats Rudemo

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Matematisk statistik

SuMo Biomaterials

Journal of Microscopy

0022-2720 (ISSN) 1365-2818 (eISSN)

Vol. 266 1 3-14


Annan fysik

Sannolikhetsteori och statistik





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