Learning an optimal PSF-pair for ultra-dense 3D localization microscopy
- URL: http://arxiv.org/abs/2009.14303v1
- Date: Tue, 29 Sep 2020 20:54:52 GMT
- Title: Learning an optimal PSF-pair for ultra-dense 3D localization microscopy
- Authors: Elias Nehme, Boris Ferdman, Lucien E. Weiss, Tal Naor, Daniel
Freedman, Tomer Michaeli, Yoav Shechtman
- Abstract summary: A long-standing challenge in multiple-particle-tracking is the accurate and precise 3D localization of individual particles at close proximity.
One established approach for snapshot 3D imaging is point-spread-function (PSF) engineering, in which the PSF is modified to encode the axial information.
Here we suggest using multiple PSFs simultaneously to help overcome this challenge, and investigate the problem of engineering multiple PSFs for dense 3D localization.
- Score: 33.20228745456316
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A long-standing challenge in multiple-particle-tracking is the accurate and
precise 3D localization of individual particles at close proximity. One
established approach for snapshot 3D imaging is point-spread-function (PSF)
engineering, in which the PSF is modified to encode the axial information.
However, engineered PSFs are challenging to localize at high densities due to
lateral PSF overlaps. Here we suggest using multiple PSFs simultaneously to
help overcome this challenge, and investigate the problem of engineering
multiple PSFs for dense 3D localization. We implement our approach using a
bifurcated optical system that modifies two separate PSFs, and design the PSFs
using three different approaches including end-to-end learning. We demonstrate
our approach experimentally by volumetric imaging of fluorescently labelled
telomeres in cells.
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