Auto-correlation for multi-view deconvolved reconstruction in light sheet microscopy

摘要: Tomographic inspection of fluorescent labels distributed within a specimen is an important aspect in biology. Light sheet microscopy (LSFM) offers powerful and simple tool to selectively slice the sample let us directly obtain tomographic view specimen. However, due non-isotropic resolution technique along axial scanning, one may want combine different views object add deconvolution process order achieve higher resolution. Typically, multi-view Bayesian methods based on Richardson-Lucy are used for this task once datasets exactly registered against each other. In work, instead, we begin investigate how avoid alignment procedure use direct algorithm form reconstruction. To do this, developed new framework auto-correlation analysis that deconvolved reconstructions starting from blurred auto-correlations. Since latter insensitive shifts, can auto-correlations coming acquisitions without taking care registration procedure.