arXiv subjects

Jonas Ries

Publications and source records attributed to Jonas Ries.

4 recordsLinked to original sources

arXiv2024

MINFLUX -- molecular resolution with minimal photons

Optical super-resolution microscopy is a key technology for structural biology that offers high imaging contrast and live-cell compatibility. Minimal (fluorescence) photons flux microscopy, or MINFLUX, is an emerging super-resolution technique that localizes single fluorophores with high spatiotemporal precision by targeted scanning of a patterned excitation beam featuring a minimum. MINFLUX offers super-resolution imaging with nanometer resolution. When tracking single fluorophores, MINFLUX can achieve nanometer spatial and sub-millisecond temporal resolution over long tracks, greatly outperforming camera-based techniques. In this review, we present the basic working principle of MINFLUX and explain how it can reach high photon efficiencies. We then outline the advantages and limitations of MINFLUX, describe recent extensions and variations of MINFLUX and, finally, provide an outlook for future developments.

physics.optics↗

arXiv2024

Approximations of MINFLUX Localization Precision with Background

MINFLUX is an emerging super-resolution technology that measures the position of single fluorophores with nanometer precision using fewer photons than any other fluorescence imaging or tracking technique. Here, we derive simple and instructive analytical equations for MINFLUX localization precision with a special focus on background photons. A fluorescence background, either arising from an imperfect zero of the MINFLUX excitation point spread function (PSF) or from auto- or out-of-focus fluorescence, ultimately limits the resolution achievable with MINFLUX. Building on previous work, we try to improve our understanding of the influence of background, especially when it is unknown, through a new set of expressions for the localization precision, based on an explicit background term instead of signal-to-background ratio. We use these equations to generate an intuitive understanding of how fluorescence background affects MINFLUX measurements and illustrate that: - The precision of an emitter position estimate depends on the gradient of its excitation profile. - Knowledge of the fluorescence background, obtained during post-processing of MINFLUX traces or through separate measurements, provides a better localization precision than in the case of unknown background. - In diffraction-limited systems, localization with a PSF that features a near-zero minimum outperforms localization with a maximum. We also present an analytical expression for the localization precision in orbital tracking, which we use for comparison to MINFLUX.

physics.optics↗

arXiv2022

Better than a lens -- Increasing the signal-to-noise ratio through pupil splitting

Lenses are designed to fulfill Fermats principle such that all light interferes constructively in its focus, guaranteeing its maximum concentration. It can be shown that imaging via an unmodified full pupil yields the maximum transfer strength for all spatial frequencies transferable by the system. Seemingly also the signal-to-noise ratio (SNR) is optimal. The achievable SNR at a given photon budget is critical especially if that budget is strictly limited as in the case of fluorescence microscopy. In this work we propose a general method which achieves a better SNR for high spatial frequency information of an optical imaging system, without the need to capture more photons. This is achieved by splitting the pupil of an incoherent imaging system such that two (or more) sub-images are simultaneously acquired and computationally recombined. We compare the theoretical performance of split pupil imaging to the non-split scenario and implement the splitting using a tilted elliptical mirror placed at the back-focal-plane (BFP) of a fluorescence widefield microscope.

physics.optics↗

arXiv2020

Teaching deep neural networks to localize single molecules for super-resolution microscopy

Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.

eess.IV↗