Evidence map›Paper›PMID 42778066›Full record

ArticleBiophysical reports2026

Quantifying the spatio-temporal image degradation under motion blur in fluorescence microscopy.

Serafim Korovin, Kutay Ugurlu, Dylan Kalisvaart, Manon Kok, Rainer Heintzmann, Kirti Prakash, Carlas S Smith

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Article in Biophysical reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Serafim KorovinDelft Center for Systems and Control, Department of Mechanical Engineering, Delft University of Technology, Delft 2628, the Netherlands.
Kutay UgurluDelft Center for Systems and Control, Department of Mechanical Engineering, Delft University of Technology, Delft 2628, the Netherlands.
Dylan KalisvaartDelft Center for Systems and Control, Department of Mechanical Engineering, Delft University of Technology, Delft 2628, the Netherlands.
Manon KokDelft Center for Systems and Control, Department of Mechanical Engineering, Delft University of Technology, Delft 2628, the Netherlands.
Rainer HeintzmannLeibniz Institute of Photonic Technology, Jena, Germany; Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany.
Kirti PrakashDelft Center for Systems and Control, Department of Mechanical Engineering, Delft University of Technology, Delft 2628, the Netherlands; School of Computing and Information Science, Anglia Ruskin University, East Road, Cambridge, UK.
Carlas S SmithDelft Center for Systems and Control, Department of Mechanical Engineering, Delft University of Technology, Delft 2628, the Netherlands. Electronic address: c.s.smith@tudelft.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spatial resolution of optical imaging systems is fundamentally restricted by the diffraction limit. However, in widefield live-cell microscopy, the achievable resolution is further constrained by specimen motion, which indicates the existence of a fundamental spatio-temporal resolution trade-off between signal accumulation during full frame integration and the resulting motion blur. To improve the fidelity with which moving objects can be imaged, a quantitative understanding of this spatio-temporal trade-off is necessary. Here, we present a systematic analysis of motion-induced resolution dynamics measured with the spectral signal-to-noise ratio (SSNR). We developed a simulation framework that models the image formation of objects undergoing arbitrary motion to evaluate the degradation of the spatial resolution under translational, diffusive, and rotational dynamics. Our results demonstrate that for translating objects, the spatial resolution is anisotropically reduced as a function of the orientation of the object relative to the motion vector, leading to the SSNR degrading by up to 50% and the resolution by up to 40% for a 90° change in the motion direction. This motion-blur-induced spectral degradation was further observed on simulated points under Brownian motion and dynamic Drosophila data. Moreover, we show that for rotational motion, conventional radially averaged metrics such as the Fourier ring correlation cannot quantify the effects of angular blur, which the SSNR quantifies accurately. These findings underscore the necessity of an object-oriented imaging approach, in which acquisition parameters such as exposure time are tuned to specific biological spatio-temporal characteristics to optimize the trade-off between motion blur and spatial fidelity.

Identifiers

PMID42778066
PMCPMC13677949

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.