Stefan Geissbühler

Super-resolution orientation estimation and localization of fluorescent dipoles using 3-D steerable filters

Fluorophores that are fixed during image acquisition produce a diffraction pattern that is characteristic of the orientation of the fluorophores underlying dipole. Fluorescence localization microscopy techniques such as PALM and STORM achieve super-resolution by applying Gaussian-based fitting algorithms to in-focus images of individual fluorophores; when applied to fixed dipoles, this can lead to a bias in the range of 5-20 nm.We introduce a method for the joint estimation of position and orientation of dipoles, based on the representation of a physically realistic image formation model as a 3-D steerable filter. Our approach relies on a single, defocused acquisition. We establish theoretical, localization-based resolution limits on estimation accuracy using Cramér-Rao bounds, and experimentally show that estimation accuracies of at least 5 nm for position and of at least 2 degrees for orientation can be achieved. Patterns generated by applying the image formation model to estimated position/orientation pairs closely match experimental observations.

Steerable filters for orientation estimation and localization of fluorescent dipoles

Fluorescence localization microscopy (i.e., PALM, STORM) has enabled optical imaging at nanometer-scale resolutions. The localization algorithms used in these techniques rely on fitting a 2-D Gaussian to the in-focus image of individual fluorophores. For fixed fluorophores, however, the observed diffraction pattern depends on the orientation of the underlying molecular dipole and does not necessarily correspond to a section of the systems point spread function. By using a physically realistic image formation model for dipoles to perform the fit, both the position and orientation of the dipole can be estimated with high accuracy, improving upon Gaussian localization. In this paper, we present an algorithm for joint position and orientation estimation based on a 3-D steerable filter, and show that the results are near-optimal with respect to the Cramér-Rao bounds. We show that patterns generated using estimated positions and orientations closely fit experimental measurements.

Three-Dimensional Localization of Nano-Emitters with Nanometer-Level Precision

We show nanometer-level localization accuracy of a single quantum-dot in three dimensions by self-interference and diffraction-pattern analysis. We believe that this approach has the capacity to push optical microscopy to the molecular level.

Super-resolved position and orientation of fluorescent dipoles

We introduce an efficient, image formation model-based algorithm that extends super-resolution fluorescence localization to include orientation estimation, and report experimental accuracies of 5 nanometers for position estimation and 2 degrees for dipole orientation estimation.