Fast 3D localization of nano-objects in wide-field interferometric scattering microscopy via vectorial diffraction model-derived analytical fitting
Abstract Interferometric scattering microscopy (iSCAT) enables tracking single nano-objects in three dimensions (3D). Conventional image processing methods for 3D localization in iSCAT typically rely on template matching, which involves finding the maximum cross-correlation with modeled interferomet...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | npj Nanophotonics |
| Online Access: | https://doi.org/10.1038/s44310-025-00068-3 |
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| Summary: | Abstract Interferometric scattering microscopy (iSCAT) enables tracking single nano-objects in three dimensions (3D). Conventional image processing methods for 3D localization in iSCAT typically rely on template matching, which involves finding the maximum cross-correlation with modeled interferometric point spread functions (iPSFs). However, this approach can be computationally intensive and hinders the processing of nano-object movements on a large scale. In this study, we introduce an efficient analytical fitting approach for retrieving the 3D positions of nano-objects in wide-field iSCAT. We derive an approximate analytic iPSF model based on the Richards-Wolf vectorial diffraction model. The simplified analytic function includes a quadratically scaling amplitude term and a linearly scaling phase term, both of which change with the nano-object’s axial position. After using the Bayesian estimation method to obtain initial parameters, we can retrieve the axial location of the nano-object through univariate least squares fitting, achieving a 60- to 200-fold increase in processing speed compared with template matching. Intriguingly, we also show that without approximation, least squares fitting can yield higher precision than cross-correlation. We validate the proposed method by measuring the movements of static and moving nanoparticles in multiple experiments. In particular, we record the movements of nanoparticles on the order of tens of nanometers accompanying the thermal expansion of a polydimethylsiloxane (PDMS) substrate. The retrieved nanoparticle displacement matches the estimated expansion from finite element modeling. By combining precise Bayesian estimation of the fixed parameters and analytical fitting in which the only variable is the nano-object’s axial position, our method enables high-throughput 3D tracking of nano-objects in a wide field of view. This approach may benefit label-free monitoring of nano-objects (such as nanoparticles, exosomes, and viruses) on a large scale. |
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| ISSN: | 2948-216X |