Learning an optimal PSF-pair for ultra-dense 3D localization microscopy
This work addresses a bottleneck in high-density 3D localization microscopy for biological imaging, representing an incremental improvement over existing PSF engineering methods.
The paper tackles the challenge of accurately localizing closely spaced particles in 3D microscopy by proposing the use of multiple engineered point-spread-functions (PSFs) to reduce lateral overlaps, and demonstrates this approach experimentally with fluorescently labelled telomeres in cells.
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.