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.

For at least a decade, quantitative phase imaging (QPI) has been a rapidly developing field of research, in which the metrological approach is introduced into biology¹ as a new standard in cell studies. When it comes to setting standards, the fact that phase information has become quantitative instead of qualitative transforms observation into measurements. However, the nature of the information obtained is not the only reason to use the QPI techniques. It is the label-free, noninvasive, rapid, and high-resolution measurement capability provided by these methods² that significantly contributes to the growing popularity of QPI in biology. The hot topics and challenges in modern cell biology cover such areas as cell life cycle, three-dimensional (3-D) position tracking, cell morphology and pathology, or even drug treatment of cancer cells. Each of the aforementioned research areas can benefit greatly from QPI and measurements, especially if the result was a 3-D refractive index distribution. Of course, the possible areas of interest for QPI can be further extended for example to cell pulsation³ and the more complete information on the phase of cells could also allow exploring cell adhesion and motility.^4,5

Holographic tomography (HT) is a measurement technique that generates phase images, often containing high noise levels and irregularities. Due to the nature of phase retrieval algorithms within the HT data processing, the phase has to be unwrapped before tomographic reconstruction. Conventional algorithms lack noise robustness, reliability, speed, and possible automation. In order to address these problems, this work proposes a convolutional neural network based pipeline consisting of two steps: denoising and unwrapping. Both steps are carried out under the umbrella of a U-Net architecture; however, unwrapping is aided by introducing Attention Gates (AG) and Residual Blocks (RB) to the architecture. Through the experiments, the proposed pipeline makes possible the phase unwrapping of highly irregular, noisy, and complex experimental phase images captured in HT. This work proposes phase unwrapping carried out by segmentation with a U-Net network, that is aided by a pre-processing denoising step. It also discusses the implementation of the AGs and RBs in an ablation study. What is more, this is the first deep learning based solution that is trained solely on real images acquired with HT.

Limited-angle optical diffraction tomography suffers from strong artifacts in tomographic reconstructions. Numerous algorithms, mainly based on regularization methods, have been developed recently to overcome this limitation. However, the quality of results still needs further improvement. Here I present a simple yet extremely effective method of increasing the reconstruction quality in limited angle optical diffraction tomography that can be combined with known tomographic algorithms. In the method a finite object support is generated from the object data and utilized in the reconstruction procedure as an additional strong regularizer. Practical aspects of this method are given together with examples of application.

Optical Diffraction Tomography is a technique for retrieving a 3-dimensional refractive index distribution from phase objects without destroying the structure of the samples. In the article we discuss the selection and implementation of full and limited angle version of tomographic reconstruction processes together with the analysis of different methods for gathering projections. We present two efficient implementations of full and limited angle tomographic systems including total processing paths and providing the examplary results of 3-D refractive index determination measurements of biological samples.