Super-Resolution Fluorescence Microscopy Based on Physical ...

Abstract
is thesis introduces a collection of physics-based methods for superresolution
in optical microscopy. e core of these methods constitute a
framework for 3-D localization of single uorescent molecules. Localization
is formulated as a parameter estimation problem relying on a physically
accurate model of the system’s point spread function (PSF). In a similar approach,
methods for tting PSF models to experimental observations and for
extended-depth-of-eld imaging are proposed.
Imaging of individual uorophores within densely labeled samples has
become possible with the discovery of dyes that can be photo-activated or
switched between uorescent and dark states. A uorophore can be localized
from its image with nanometer-scale accuracy, through tting with
an appropriate image function. is concept forms the basis of uorescence
localization microscopy (FLM) techniques such as photo-activated
localization microscopy (PALM) and stochastic optical reconstruction microscopy
(STORM), which rely on Gaussian tting to perform the localization.
Whereas the image generated by a single uorophore corresponds to a
section of the microscope’s point spread function, only the in-focus section
of the latter is well approximated by a Gaussian. Consequently, applications
of FLM have for the most part been limited to 2-D imaging of thin specimen
layers.
In the rst section of the thesis, it is shown that localization can be extended
to 3-D without loss in accuracy by relying on a physically accurate
image formation model in place of a Gaussian approximation. A key aspect
of physically realistic models lies in their incorporation of aberrations that
arise either as a consequence of mismatched refractive indices between the
layers of the sample setup, or as an effect of experimental settings that deviate
from the design conditions of the system. Under typical experimental
conditions, these aberrations change as a function of sample depth, inducing
axial shift-variance in the PSF. is property is exploited in a maximumlikelihood
framework for 3-D localization of single uorophores. Due to the
shift-variance of the PSF, the axial position of a uorophore is uniquely encoded
in its diffraction pattern, and can be estimated from a single acquisi-
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