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.
An experimental study--involving measurements with an optical microscope, a profilometer, and a scanning electron microscope--for determination of the surface profile of x-ray tube anodes is presented. The islands on the "mud-flatting" surface are separated by approximately 8 microm deep cracks. The surface roughness on the island is typically below 1 microm, and the area ratio of cracks to the total surface is higher on the more extensively used regions (anode aging). A simple model was proposed to calculate the spectrum modification introduced by the rough surface. Loss of x-ray intensity of 4% was predicted using the roughest surface at a small emission angle.
The effects of anode surface roughness on x-ray spectra were successfully simulated by a Monte Carlo method. It was proved that the effect of the anode surface roughness could not be modeled by simple filters made from the anode material. The surface roughness (Ra) was found to be an inadequate quantity to describe the effect of anode surface roughness on x-ray spectra.
In the case of inferior signal errors, the proposed method gives the same results as the dual-energy variant. Although the x-ray path length estimation method with SEMD is more complex, the dose is considerably lower.
Interpretation of high resolution images provided by localization-based microscopy techniques is a challenge due to imaging artefacts that can be categorized by their origin. They can be introduced by the optical system, by the studied sample or by the applied algorithms. Some artefacts can be eliminated via precise calibration procedures, others can be reduced only below a certain value. Images studied both theoretically and experimentally are qualified either by pattern specific metrics or by a more general metric based on fluorescence correlation spectroscopy.
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