Holographic tomography (HT) is an advanced label-free optical
microscopic imaging method used for biological studies. HT uses
digital holographic microscopy to record the complex amplitudes of a
biological sample as digital holograms and then numerically
reconstruct the sample’s refractive index (RI) distribution in three
dimensions. The RI values are a key parameter for label-free
bio-examination, which correlate with metabolic activities and
spatiotemporal distribution of biophysical parameters of cells and
their internal organelles, tissues, and small-scale biological
objects. This article provides insight on this rapidly growing HT
field of research and its applications in biology. We present a review
summary of the HT principle and highlight recent technical advancement
in HT and its applications.
Due to incompleteness of input data inherent to Limited Angle Tomography (LAT), specific additional constraints are usually employed to suppress image artifacts. In this work we demonstrate a new two-stage regularization strategy, named Generalized Total Variation Iterative Constraint (GTVIC), dedicated to semi-piecewise-constant objects. It has been successfully applied as a supplementary module for two different reconstruction algorithms: an X-ray type solver and a diffraction-wise solver. Numerical tests performed on a detailed phantom of a biological cell under conical illumination pattern show significant reduction of axial blurring in the reconstructed refractive index distribution after GTVIC is added. Analogous results were obtained with experimental data.
In this paper, we demonstrate the current concepts in holographic tomography (HT) realized within limited angular range with illumination scanning. The presented solutions are based on the work performed at Warsaw University of Technology in Poland and put in context with the state of the art in HT. Along with the theoretical framework for HT, the optimum reconstruction process and data visualization are described in detail. The paper is concluded with the description of hardware configuration and the visualization of tomographic reconstruction, which is calculated using a provided processing path.
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