The reduction of out of focus signal is a general task in fluorescence microscopy and is especially important in the recently developed super-resolution techniques because of the degradation of the final image. Several illumination methods have been developed to provide decreased out of focus signal level relative to the common epifluorescent illumination. In this paper we examine the highly inclined and the total internal reflection illumination techniques using the ray tracing method. Two merit functions were introduced for the quantitative description of the excitation of the selected region. We studied the feasibility of illumination methods, and the required corrections arising from the imperfections of the optical elements.

Optics Express, Vol. 22, Issue 16, pp. 18940-18948 (2014)

Localization-based super-resolution microscopy image quality depends on several factors such as dye choice and labeling strategy, microscope quality and user-defined parameters such as frame rate and number as well as the image processing algorithm. Experimental optimization of these parameters can be time-consuming and expensive so we present TestSTORM, a simulator that can be used to optimize these steps. TestSTORM users can select from among four different structures with specific patterns, dye and acquisition parameters. Example results are shown and the results of the vesicle pattern are compared with experimental data. Moreover, image stacks can be generated for further evaluation using localization algorithms, offering a tool for further software developments.

Biomedical Optics Express, Vol. 5, Issue 3, pp. 778-787 (2014)

Localization microscopy software generally contains three elements: a localization algorithm to determine fluorophore positions on a specimen, a quality control method to exclude imprecise localizations, and a visualization technique to reconstruct an image of the specimen. Such algorithms may be designed for either sparse or partially overlapping (dense) fluorescence image data, and making a suitable choice of software depends on whether an experiment calls for simplicity and resolution (favouring sparse methods), or for rapid data acquisition and time resolution (requiring dense methods). We discuss the factors involved in this choice. We provide a full set of MATLAB routines as a guide to localization image processing, and demonstrate the usefulness of image simulations as a guide to the potential artefacts that can arise when processing.

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