A software tool for tomographic axial superresolution in STED microscopy

Authors:

Koho, S., Deguchi, T., & Hänninen, P. E.

Keywords:

Axial tomography, image fusion, image processing, image registration, open source software, reconstruction algorithms, superresolution microscopy

Abstract:

A method for generating three-dimensional tomograms from multiple three-dimensional axial projections in STimulated Emission Depletion (STED) superresolution microscopy is introduced. Our STED< method, based on the use of a micromirror placed on top of a standard microscopic sample, is used to record a three-dimensional projection at an oblique angle in relation to the main optical axis. Combining the STED< projection with the regular STED image into a single view by tomographic reconstruction, is shown to result in a tomogram with three-to-four-fold improved apparent axial resolution. Registration of the different projections is based on the use of a mutual-information histogram similarity metric. Fusion of the projections into a single view is based on Richardson-Lucy iterative deconvolution algorithm, modified to work with multiple projections. Our tomographic reconstruction method is demonstrated to work with real biological STED superresolution images, including a data set with a limited signal-to-noise ratio (SNR); the reconstruction software (SuperTomo) and its source code will be released under BSD open-source license.

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