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Cited by 384

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Optimal Point Spread Function Design for 3D Imaging

Open access
Shechtman, Yoav, Sahl, Steffen J., Backer, Adam S., Moerner, W. E.·2014·Physical Review Letters

Abstract

To extract from an image of a single nanoscale object maximum physical information about its position, we propose and demonstrate a framework for pupil-plane modulation for 3D imaging applications requiring precise localization, including single-particle tracking and superresolution microscopy. The method is based on maximizing the information content of the system, by formulating and solving the appropriate optimization problem--finding the pupil-plane phase pattern that would yield a point spread function (PSF) with optimal Fisher information properties. We use our method to generate and experimentally demonstrate two example PSFs: one optimized for 3D localization precision over a 3 μm depth of field, and another with an unprecedented 5 μm depth of field, both designed to perform under physically common conditions of high background signals.

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Metadata

Authors
Shechtman, Y., Sahl, S. J., Backer, A. S., & Moerner, W. E.
Venue
Physical Review Letters
Published
2014-09-26
Volume
113
Issue
13
Pages
133902-133902
ISSN
  • 0031-9007 (ISSN-L)
  • 1079-7114
OpenAlex
W2330285287
Cited by
384
References
33
FWCI
9.69
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Optimal Point Spread Function Design for 3D Imaging. https://app.cochat.ai/papers/10.1103/physrevlett.113.133902
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Shechtman, Y., Sahl, S. J., Backer, A. S., & Moerner, W. E. (2014). Optimal Point Spread Function Design for 3D Imaging. Physical Review Letters. https://doi.org/10.1103/physrevlett.113.133902

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.