A Bayesian reconstruction method for micro-rotation imaging in light microscopy.

ABSTRACT

The authors present a three-dimensional (3D) reconstruction algorithm and reconstruction-based deblurring method for light microscopy using a micro-rotation device. In contrast to conventional 3D optical imaging where the focal plane is shifted along the optical axis, micro-rotation imaging employs dielectric fields to rotate the object inside a fixed optical set-up. To address this entirely new 3D-imaging modality, the authors present a reconstruction algorithm based on Bayesian inversion theory and use the total variation function as a structure prior. The spectral properties of the reconstruction by simulations that illustrate the strengths and the weaknesses of the micro-rotation approach, compared with conventional 3D optical imaging, were studied. The reconstruction from real data sets shows that this method is promising for 3D reconstruction and offers itself as a deblurring method using a reconstruction-based procedure for removing out-of-focus light from the micro-rotation image series.