Point-spread-function engineering in MINFLUX: optimality of donut and half-moon excitation patterns.

Area of Science:

• Optics and Photonics
• Biophysics
• Microscopy

Background:

• Localization microscopy breaks the optical diffraction limit for super-resolution imaging.
• Maximally INFormative LUminescence eXcitation (MINFLUX) enhances resolution by optimizing excitation point spread function (PSF) and minimizing photon flux.
• The optimality of various proposed MINFLUX beam shapes for localization efficiency is not well-established.

Purpose of the Study:

• To determine optimal excitation patterns for MINFLUX super-resolution microscopy using a numerical and theoretical framework.
• To computationally search for novel beam patterns, reducing the need for costly experimental exploration.
• To evaluate the performance of new beam patterns against conventional methods.

Main Methods:

• Development of a numerical and theoretical framework for analyzing MINFLUX excitation patterns.
• Computational search for optimal beam shapes to maximize localization precision.
• Comparison of theoretical localization precision for different beam patterns, including donut and novel half-moon shapes.

Main Results:

• The conventional donut beam is identified as a robust optimum when all excitation beams share the same shape.
• A novel PSF engineering framework identified two pairs of orthogonal half-moon beams.
• These new half-moon beams theoretically improve localization precision by approximately a factor of two.

Conclusions:

• The study provides a computational approach for discovering optimal MINFLUX excitation patterns.
• Novel half-moon beams offer a significant advancement in MINFLUX super-resolution imaging, nearly doubling localization precision.
• This work paves the way for more efficient and precise super-resolution microscopy techniques.